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ISBN 978-951-42-9354-2 (Paperback)ISBN 978-951-42-9355-9 (PDF)ISSN 0355-3213 (Print)ISSN 1796-2226 (Online)

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OULU 2011

C 378

Dayou Yang

OPTIMISATION OF PRODUCT CHANGE PROCESS AND DEMAND-SUPPLY CHAIN IN HIGH TECH ENVIRONMENT

UNIVERSITY OF OULU,DEPARTMENT OF MECHANICAL ENGINEERING;DEPARTMENT OF INDUSTRIAL ENGINEERING AND MANAGEMENT

C 378

ACTA

Dayou Yang

C378etukansi.kesken.fm Page 1 Tuesday, December 21, 2010 3:43 PM

A C T A U N I V E R S I T A T I S O U L U E N S I SC Te c h n i c a 3 7 8

DAYOU YANG

OPTIMISATION OF PRODUCT CHANGE PROCESS AND DEMAND-SUPPLY CHAIN IN HIGH TECH ENVIRONMENT

Academic dissertation to be presented, with the assent ofthe Faculty of Technology of the University of Oulu, forpublic defence in Auditorium IT115, Linnanmaa, on 28January 2011, at 12 noon

UNIVERSITY OF OULU, OULU 2011

Copyright © 2011Acta Univ. Oul. C 378, 2011

Supervised byProfessor Kauko LappalainenProfessor Harri Haapasalo

Reviewed byProfessor Petri HeloDoctor Lasse Pesonen

ISBN 978-951-42-9354-2 (Paperback)ISBN 978-951-42-9355-9 (PDF)http://herkules.oulu.fi/isbn9789514293559/ISSN 0355-3213 (Printed)ISSN 1796-2226 (Online)http://herkules.oulu.fi/issn03553213/

Cover DesignRaimo Ahonen

JUVENES PRINTTAMPERE 2011

Yang, Dayou, Optimisation of product change process and demand-supply chain inhigh tech environmentUniversity of Oulu, Faculty of Technology, Department of Mechanical Engineering, P.O.Box4200, FI-90014 University of Oulu, Finland; University of Oulu, Faculty of Technology,Department of Industrial Engineering and Management, P.O.Box 4610, FI-90014 University ofOulu, FinlandActa Univ. Oul. C 378, 2011Oulu, Finland

Abstract

Information and communications technology (ICT) companies face challenges in an unpredictablebusiness environment, where demand-supply forecasting is not accurate enough. How tooptimally manage product change process and demand-supply chain in this type of environment?Companies face pressures to simultaneously be efficient, responsive and innovative, i.e. tominimise costs, and shorten order delivery and product change periods.

This thesis included three action research cycles within a real demand-supply chain of asignificant international actor. Each action research cycle sought answers by going into oneextreme of minimising costs, diminishing order delivery period, or shortening product changeperiods. In practice, these research cycles included the case company changing their businessaccordingly for each of these cases. Conducting required changes in the case company wereeconomically significant trials.

The results of this doctoral dissertation provide tips for global high tech companies. Largeinternational companies typically have manufacturing sites in different parts of the world.According to the results, mental shift from local optimisation to a global one is required forefficient manufacturing operations.

Companies have traditionally considered their strategy as a choice between minimising costs,quick delivery, and rapid product change. Also, companies have believed that one single strategyis adequate and applicable to all of their products. According to this thesis, different products mayhave a different strategy. This would allow companies to flexibly react to the needs of differentcustomer groups, business environments, and different competitors. In addition, strategy can bechanged relatively often, monthly, weekly, or even daily.

Based on the results of this doctoral thesis, companies must harmonise their product portfolioglobally, including all their sites. Once the same product version is at all sites, they can help eachother from components supply viewpoint. Consequently, product changes can be taken throughquicker.

Keywords: action research, agile, demand supply, innovativeness, lean, optimisation,synchronization

Yang, Dayou, Tuotemuutosprosessin optimointi ja kysyntä-tarjontaketju korkeanteknologian yrityksissäOulun yliopisto, Teknillinen tiedekunta, Konetekniikan osasto, PL 4200, 90014 Oulun yliopisto;Oulun yliopisto, Teknillinen tiedekunta, Tuotantotalouden osasto, PL 4610, 90014 OulunyliopistoActa Univ. Oul. C 378, 2011Oulu

Tiivistelmä

Informaatio- ja kommunikaatioalan yritykset kohtaavat haasteita toimiessaan vaikeasti ennustet-tavassa liiketoimintaympäristössä, jossa tilaus-toimitusennusteet ovat epätarkkoja. Miten tällai-sessa ympäristössä hallitaan optimaalisesti tuotemuutosprosessi ja tilaustoimitusketju? Yrityksil-lä on paineita olla samanaikaisesti tehokkaita ja innovatiivisia: miten minimoida sekä kustan-nuksia että lyhentää toimitus- ja tuotemuutosaikoja.

Tämä väitöskirja tehtiin toimintatutkimuksena merkittävän kansainvälisen yrityksen todelli-sessa tilaus-toimitusketjussa. Toimintatutkimus eteni vaiheittain kokeilemalla kolmea eri ääri-päätä minimoimalla 1) kustannuksia, 2) toimitusaikoja ja 3) tuotemuutosaikoja. Käytännössänämä ääripäät sisälsivät case-yrityksen liiketoiminnan muuttamista vastaavasti sisältäen talou-dellisesti merkittäviä kokeiluja.

Tämän väitöskirjan tulokset tarjoavat käytännön esimerkkejä globaaleille korkeanteknologi-an yrityksille. Suurilla kansainvälisillä yrityksillä on tyypillisesti valmistusyksiköitä eripuolillamaailmaa. Tämän tutkimuksen tulosten mukaan yritykset tarvitsevat asennemuutoksen paikalli-sesta optimoinnista globaaliin, jotta tuotanto toimisi tehokkaasti.

Perinteisesti yritykset ovat ymmärtäneet strategian tarkoittavan valinnan tekemistä kustan-nusten minimoinnin, nopeiden toimitusaikojen tai nopeiden tuotemuutosten välillä. Yrityksetovat myös uskoneet, että yksi yrityskohtainen strategia kattaa kaikki yrityksen tuotteet. Tämänväitöskirjan tulosten mukaan yrityksen eri tuotteilla voi olla erilainen strategia. Tällainen ratkai-su mahdollistaa nopean reagoinnin muutoksiin asiakasryhmien tarpeissa, liiketoimintaympäris-tössä ja kilpailutilanteissa. Strategiaa voidaan myös muuttaa usein, kuukausittain, viikoittain taijopa päivittäin.

Tämän väitöskirjatutkimuksen tulosten mukaan, yritysten tulisi harmonisoida tuoteportfo-lionsa globaalisti kattaen kaikki tuotantolaitokset. Silloin kun yrityksen kaikissa valmistusyksi-köissä valmistetaan samaa tuoteversiota, yksiköt voivat auttaa toisiaan komponenttien hankin-nassa. Tuotemuutokset voidaan tällöin toteuttaa nopeammin.

Asiasanat: innovatiivisuus, ketteryys, kysyntä, optimointi, synkronointi, tarjonta,toimintatutkimus

7

Acknowledgements

This dissertation is about a research initiated in a tough situation of high-tech

manufacturing back in 2002. It was economic hard time with lean strategy as a

must in the company where the researcher was employed. However, the company

had to struggle with inaccurate forecasts in their daily work making product

change management more challenging. Earlier planning, or even comprehensive

knowledge over JIT (Just-In-Time), was not enough due to big lead-time gaps in

demand-supply. Thus this learning journey was initiated to develop new solutions.

The research was conducted cycle-by-cycle, and the outcomes were gradually

implemented to IT over the years. During this process, many people provided

their valuable assistance.

I am very grateful to my supervising professors - Harri Haapasalo and Kauko

Lappalainen for their professional guidance through the whole research process.

Their strong commitment always inspired me to overcome any difficulties.

Constructive advice from Dr. Janne Härkönen, Dr. Pekka Belt and Dr. Matti

Möttönen of the University of Oulu were especially helpful. They helped to

broaden my way of thinking about my research and the dissertation and helped

me to see things from multiple viewpoints. Also I wish to thank Professor Juha-

Matti Lehtonen being so supportive and patient when I was struggling while

aiming to a breakthrough. Deep in my heart, great thanks belong to Mr. Ari

Kurikka who has remarkably coached me from the very beginning until all the

action research cycles were finished. The insight of focusing on the whole

demand-supply network kept the research aiming for a win-win solution to all

network parties. Special acknowledgement goes to Mr. Arto Tolonen for many of

his valuable advices. Especially with his “Design for Excellence” contribution

implemented in the company, it made it easier for this research to operate with

less product variants. I want to present my sincere thanks to Mr. Jukka Kukkonen,

Mr. Ville Jokelainen, Mr. Kaj Sundberg and Mr. Jussi Parviainen for supporting

me when conducting this research besides my daily work. I very much appreciate

the help and interest of my other colleagues for their insightful inputs. My warm

thanks belong to AAC Global Oyj and other native English-speaking friends for

their language assistance. I also need to acknowledge the financial aid from

Finnish Foundation for Economic Education.

In addition, I would like to thank the pre-examiners of this study - Professor

Petri Helo and Dr. Lasse Pesonen for their valuable comments and

recommendations.

8

Finally, my deepest gratitude belongs to my wife Weilin and my children

Yuchen & Tina. I value their support and care to tolerate my mental absence due

to this work. Their patience makes my learning journey possible and rewardable.

Oulu, December 2010 Dayou Yang

9

Abbreviations and key terminology

3C

3D CE

ABM

AR

ATO

BAM

BOM

BPR

BTO

CAD

CIB

CIM

CLM

CLCP

CM

CMMI

CPM

CRB

CRM

DA

DNA

ECN

ECR

EMS

ESP

ERP

EVDB

FAT

FMS

GIT

i2

ICH

IQ

IT

JIT

Capacity, Commonality, Consumption (management system)

Three Dimensional Concurrent Engineering

Agent-Based Manufacturing

Action Research

Assemble-To-Order

Business Activity Monitoring

Bill Of Material

Business Process Re-engineering

Build-To-Order

Computer Aided Design

Change Implementation Board

Computer Integrated Manufacturing

Council of Logistics Management

Closed Loop Change Process

Configuration Management

Capability Maturity Model Integration

Corporate Performance Management

Change Review Board

Customer Relationship Management

Delivery Accuracy

Deoxyribonucleic Acid

Enterprise Change Notice

Enterprise Change Request

Electronics Manufacturing Services

Equalised and Synchronised Production

Enterprise Resource Planning

Events and Venues Database

Focus, Architecture, and Technology

Flexible Manufacturing System

Goods In Transit

A management application supplier

Inventory Collaboration Hub

Intelligence Quotient

Information Technology

Just-In-Time

10

MAS

MICE

MRP

MTO

MTS

NMS

NPI

OEM

OPP

OPT

OSS

PASSI

PC

PMBOK

PS

PTK

PTO

PWB

R&D

RIA

RDBMS

ROI

SAP

SCM

SCOR

SOA

STO

TOC

TPI

TQM

TTC

TTM

UML

VMI

VOP

WIP

Multi-Agent System

Multimedia, information, communications, and electronics

Material Requirements Planning

Make-To-Order

Make-To-Stock

Network Managed Supply

New Product Introduction

Original Equipment Manufacturer

Order Penetration Point

Optimised Production Technology

Operation and Support Subsystem

Process for Agent Societies Specification and Implementation

Personal Computer

Project Management Body of Knowledge

Physical Stock

PASSI Tool Kit

Pack-To-Order

Printed Wiring Board

Research and Development

Rich Internet Application

Relational Database Management System

Return on Investment

A management application supplier

Supply Chain Management

Supply-Chain Operations Reference

Service Oriented Architecture

Ship-To-Order

Theory of Constraints

Trading Partner Integration

Total Quality Management

Time to Customer

Time to Market

Unified Modelling Language

Vender Managed Inventory

Value Offering Point

Work In Process

11

Please note that following list describes the terminology for the purpose of this

dissertation rather than giving official definitions.

Minimise costs (Lean) = Creating value with as little work and waste as possible.

Quick delivery (Agility) = Responsiveness in demand fulfilment

Fast product change (Innovativeness) = Making product changes as quick as

possible

Zero-series = series after proto type in product development, before actual

volume production

Component equalisation = In a large organisation there are different persons

responsible for buying different components, causing differences in the levels of

different components as buyers buy in different pace and their activities are not

adequately coordinated. In a situation with too many components, the component

you have least determines the equalised level. If you have any components more

than the equalised level, those can be considered as waste. The difference

between the equalised level and the original forecasted level can be considered as

tolerance margin increasing agility. However, if the company prefers lean over

agility, this type of tolerance should be avoided.

Time based optimisation (Synchronisation) = In modern business, when new

product versions are introduced, there are a large number of tasks that must be

conducted. As time has become increasingly important aspect for business

success, time-based coordination of activities is important for total optimisation.

In this dissertation this coordination is also called synchronisation. Also, the

handling of component supply change, including component equalisation on time

basis, must be included in this synchronisation.

Liability = Company has contractual obligations for a certain period of a forecast

before they can stop buying certain components from a supplier. From a

supplier’s viewpoint, this gives a level of security for a certain period of time,

such as two months, allowing it to cut costs and adjust to changes. This liability

only applies to buyer company specific components.

12

Dynamic cut-off window = Buyer company has a natural goal of minimising the

liability of the amount of components it is obliged to buy. In order to optimise the

operations of buyer-seller cooperation, the information on critical issues must be

transferred as early as possible, for instance updating forecasts on a weekly basis.

This way of dynamically informing a supplier allows it to have time to react

accordingly. This in turn makes it possible to reduce the liability of the buyer.

Fixed cut-off window = Before starting a zero-series, product new version

changeover date is selected and fixed. This type of fixed cut-off window enables

suppliers to deliver the existing order plus liability. No further orders are placed

for the old material.

13

Contents

Abstract

Tiivistelmä

Acknowledgements 7 

Abbreviations and key terminology 9 

Contents 13 

Introduction 15 

1.1  Research background & motivation ........................................................ 15 

1.2  Objectives and scope ............................................................................... 18 

1.3  Research process ..................................................................................... 19 

1.3.1  Action research ............................................................................. 19 

1.3.2  Research context ........................................................................... 20 

1.3.3  Practical realisation ...................................................................... 22 

1.4  Structure of the thesis .............................................................................. 23 

2  Literature review 25 

2.1  Manufacturing philosophies .................................................................... 25 

2.1.1  Lean manufacturing and JIT philosophy ...................................... 25 

2.1.2  ESP concept beyond JIT philosophy ............................................ 26 

2.1.3  Agile manufacturing and leagility concepts ................................. 27 

2.1.4  Manufacturing strategies and product life cycle ........................... 28 

2.1.5  The innovator’s strategy ............................................................... 29 

2.1.6  Summary of manufacturing philosophies ..................................... 30 

2.2  Developing demand-supply network ...................................................... 32 

2.2.1  Value oriented development for demand-supply network ............ 32 

2.2.2  Manufacturing strategies affect demand-supply network ............. 36 

2.2.3  The role of collaboration in demand-supply ................................. 40 

2.2.4  Measuring demand-supply performance ...................................... 44 

2.2.5  Purchasing automation challenge in product life cycle ................ 46 

2.2.6  Optimisation of demand-supply with thinking of BI

automation .................................................................................... 48 

2.3  Product change management ................................................................... 52 

2.4  Special characteristics of high-tech industries ........................................ 54 

2.4.1  Challenges in forecasting ............................................................. 54 

2.4.2  Telecom supply chain of case company ....................................... 55 

2.4.3  Case Ericsson (analysed in 2002–2003) ....................................... 56 

14

2.4.4  Case Dell Corporation/Lucent Technologies (analysed in

2002–2003) ................................................................................... 58 

2.4.5  Case Huawei Technologies (the new competition reality) ............ 60 

2.4.6  Other studies oriented by value differentiation or unique

advantage ...................................................................................... 61 

2.5  Theory synthesis...................................................................................... 69 

3  Results of the three action research cycles 73 

3.1  Research Cycle 1 – minimising costs ...................................................... 75 

3.1.1  Pre-Step ........................................................................................ 76 

3.1.2  Diagnosis ...................................................................................... 77 

3.1.3  Planning ........................................................................................ 77 

3.1.4  Taking action ................................................................................ 80 

3.1.5  Evaluation ..................................................................................... 81 

3.2  Research Cycle 2 - shortening order delivery time ................................. 84 

3.2.1  Pre-Step ........................................................................................ 84 

3.2.2  Diagnosis ...................................................................................... 85 

3.2.3  Planning ........................................................................................ 86 

3.2.4  Taking action ................................................................................ 88 

3.2.5  Evaluation ..................................................................................... 89 

3.3  Research Cycle 3 - shortening product change time ............................... 91 

3.3.1  Pre-Step ........................................................................................ 93 

3.3.2  Diagnosis ...................................................................................... 94 

3.3.3  Planning ........................................................................................ 94 

3.3.4  Taking action ................................................................................ 95 

3.3.5  Evaluation ..................................................................................... 96 

4  Discussion 99 

4.1  Answering research questions ................................................................. 99 

4.1.1  Research question 1 ...................................................................... 99 

4.1.2  Research question 2 .................................................................... 100 

4.1.3  Research question 3 .................................................................... 102 

4.2  Managerial implications ........................................................................ 103 

4.3  Scientific implications ........................................................................... 105 

4.4  Reliability and validity .......................................................................... 107 

4.5  Research contribution & discussion ...................................................... 110 

4.6  Future research ...................................................................................... 112 

5  Summary 115 

References 117 

15

Introduction

1.1 Research background & motivation

Industrial globalisation has greatly changed high-tech companies while they have

created significant operations in multiple countries. Because poor visibility and

massive uncertainty are part of the operational nature, new challenges arise

continuously for companies who want to internationalise their demand-supply

network. The struggle to survive has become an integral part of each giant

company’s way of life (Hill, 2000). As the operations become more dynamic

(Wazed et al. 2009), the problems of the famous JIT (Just-In-Time) concept (Voss,

1987) are increasingly reported with the facts, even in Japan: zero-inventory

management is just a fiction (Hann et al., 1999), and JIT is not necessarily useful

for part suppliers (Naruse, 2003). Even Toyota Motor Corporation as a model of

operational efficiency within the auto industry, it also got its first annual operating

loss in 2009 after 70 years of enjoying healthy profits. Not as a symbol of

operational excellence, Toyota recall crisis of 2010 has prompted much criticism

in media circles, national business forums and automotive trade publications

(Piotrowski and Guyette 2010). Consequently, it is now time for new thinking.

For example, it needs to go against the mainstream and take current strategy to a

more extreme version of itself, before scaling back just a little bit (Schmitt 2007).

The research was initiated in 2002 during last economic downtime by

solution-finding for product change management in a famous international

company, the case company of this research, who operates as one the world’s

largest telecommunications infrastructure suppliers, and which continuously

suffers from inaccurate forecasting and dynamic demand in its innovative

manufacturing. As the nature of mobile infrastructure industry (Collin et al., 2005;

Heikkilä 2002), the system vendors have to be able to quickly respond to short-

term changes in demand. On the one hand, they are forced to have an in-built

ability to constantly adapt their supply chains to rapid and unexpected changes in

the markets or technologies (Raisinghani et al. 2002; Webster 2002). On the other

hand, the vendors are also expected to be fast and flexible while delivering

customised products and services with a high standard of delivery accuracy

(Alfnes and Strandhagen 2000; Småros et al. 2003; Knowles et al. 2005).

In the case company, the old way of doing things was to make a perfect

production plan based on a perfect forecast, at some point this did not work

16

anymore. In reality there was always some components missing, and production

stopped. As a consequence, scrapping costs became very high. There were

different product versions in different sites, with up to one year’s difference

resulting in sites being unable to help each other. In addition, more R&D people

were required to support the supply-chain and product changes became very slow,

almost out of control.

Figure 1 presents an example of the problem situation, relating to demand

fulfilment, during a one-year period in the case company. It shows how the

forecasts of one or two months were so different from the true demand fulfilled.

The example records a hopeless situation, in which such uncertainties make

product innovation through engineering changes as well as normal delivery of

customer order fulfilment extremely problematic. In other words, and to state the

problem for academic purpose, the intangible information flow in demand-supply

network cannot ensure physical product flow just-in-time at each step of the

manufacturing operation. Due to the bullwhip effect (Lee et al., 1997; Lee, 2002)

in material forecast and product delivery, it is even more frustrating when

utilising traditional purchase orders or long distance transportation. The tough

choice of a trade-off (such as inventory increase, change slow-down, delivery

delay, lost sales) has to be made due to such lead-time gaps in global operation

(Shahbazpour and Seidel 2006; Bozarth et al. 2009). It can be even worse when

product changes are included as extra uncertainties in this unsynchronised status

(Salmi and Holmström 2004).

Fig. 1. Challenge with monthly forecast and true demand.

17

In innovative businesses, the changes occur for most of a product’s life with great

impact to whole demand-supply network (Aitken et al. 2003; Dreyer et al. 2007).

It is unique to utilise the details about cases of product change management

constantly in the research of manufacturing operation, which was not seen in

previous attempts by others. It can include more factors than those studies only

dealing with product development (Knight, 2003; Guess, 2002) and demand-

supply operation (Bengtsson, 2002; Christopher and Peck 2004) alone, or mainly

at a conceptual and simulation-oriented level (Subramoniam et al. 2008; Falasca

and Zobel 2008; Koh and Gunasekaran 2006; Zhou 2006; Kemppainen and

Vepsäläinen 2004; Saab and Correa 2004). Under a complex business

environment as in Figure 2, the research was based on a simple clue from product

change implementation. It is then expected to equalise the amount of all material

in the whole supply operation at anytime and anywhere.

Fig. 2. Business operational environment of the research.

18

In the case company, there were simultaneous pressures to minimise costs,

shorten the product change period and quicken order delivery processes. In

addition, the case company had an aim to minimise scrapping costs in all

situations.

1.2 Objectives and scope

The research problem arises from the case company’s challenges in an

unpredictable business environment, where demand-supply forecasting is not

accurate enough. How to optimally manage product change process and demand-

supply chain in this type of environment? Companies phase pressures to

simultaneously be efficient, responsive and innovative, i.e. to minimise costs, and

shorten order delivery and product change periods. The research problem of this

dissertation is formulated:

How should companies optimise the product change process strategy in a

situation where there are simultaneous and variable pressures to be lean,

agile and innovative.

This research problem is addressed by focusing on product change process and

demand-supply chain optimisation of large global ICT companies operating in

business-to-business environment.

First, literature was reviewed to gain understanding on lean philosophy,

agility, and innovativeness and consequently to find potential solutions for the

research problem.

In order to obtain information for deeper analyses and conclusions, the

following research question were formulated.

RQ1 What are the effects for the product change process when costs are

minimised (Cycle 1)?

RQ2 What are the effects for the product change process when order delivery

period is minimised (Cycle 2)?

RQ3 What are the effects for the product change process when product

change time is minimised (Cycle 3)?

Action research method was utilised in the case company to find answers to these

above mentioned research questions. Each action research cycle, representing a

separate trial, seeks answers for one research question by going into one extreme

19

of minimising costs, diminishing order delivery time, or shortening product

change periods.

1.3 Research process

The aim of this study was to conduct practical analyses on the effects of changes

in essential parameters, namely inventory level, order delivery period, and

product change time. The effects were studied for a real demand-supply chain of a

significant international actor. Secondly, based on these analyses, this study

attempted to find new means of dealing with complex issues in the described

environment.

1.3.1 Action research

According to O’Brien (1998) action research can be used in practical situations

where the primary focus is on solving real problems. In addition, the researcher

was employed by a company to whom the studied aspects were of great

importance. Action research was chosen as a research method as it enables

combining research and ordinary business work within the studied organisation.

Action research is concerned with the resolution of organisational issues,

such as the implications of change together with those who experience the issues

directly. In action research the practitioners are involved in the research, and there

is a collaborative partnership between practitioners and researchers. In simple

terms, the researcher is a part of the research subject. Often action research is an

iterative process, often depicted as a spiral, of diagnosing, planning, taking

actions and evaluating. (Saunders et al. 2007).

Action Research is the process of systematically collecting research data

about an ongoing system relative to some objective, goal, or need of that system;

feeding these data back into the system; taking actions by altering selected

variables within the system based on the data and on the hypothesis; and

evaluating the results of actions by collecting more data (French et al., 1973).

Action research enables simultaneous utilisation of different research

methods and techniques (O’Brien 1998). According to Coughlan (2002) action

research requires that the researcher enters the culture, understands the common

values, and uses its language. This research method was chosen, even though

action research does not meet the verification criteria of positivitism, meaning

objective study as in natural sciences (Susman and Evered, 1978; Saunders 2007).

20

1.3.2 Research context

Selected case company is a significant global actor in the ICT system business.

The researcher was employed by the company, thus having a good access and the

research was related to his everyday work. The global demand-supply chain of

the case company is studied in this thesis from the perspective of product change

process.

The research can be described to simultaneously include aspects of

worldwide business impact, rapid innovative pace, and high volume in operation.

There are many engineering changes during a product’s lifetime without a period

when new and old versions overlap as execution principle. Component changes in

products often happen at any time adding extra complexity for manufacturing

besides original demand uncertainty. Product versions were different more than

one year at some manufacturing sites before the research was launched.

The component logistics, as in the electronics industry in general, is

extremely complex due to a vast number of required components with long

production or delivery lead-times. For example, the lead-times may differ by days,

weeks (such as PWB and own specific integrated circuits), or even months due to

sea transportation (such as the cabinet). This causes bottlenecks or big inventories

in the supply network due to those time variances and real demand often not

matching with earlier forecasts. The case company had to combine push-based

supply chain and pull-based demand chain together as a mix to synchronise

production and delivery of all product parts with big lead-time gaps. Pull

principle was applied at internal steps of the production, as well as the delivery

end. Push principle had to apply for the supply end and keep the inventories to

absorb the impact of inaccurate forecast. Demand-supply network had to thus

have enough tolerance to avoid undesirable conditions, such as production stop

due to lack of key components.

Below list describes the challenges faced by the case company:

1. Both strategies of lean or agile thinking were not good enough as there were

some obvious drawbacks. For example, production lead time was at a level of

counting hours or days, which was not a critical step if comparing to months

or weeks for material supply. The wish of zero inventory or fast response is

hard to achieve constantly in dynamic demand situation. With whole demand-

supply network in consideration, not just the case company itself, lead time

gaps could not be solved by lean or agile principles alone. It was the

21

playground reality when product changes were to be also added into the

complexity.

2. Production can be described as a multiproduct / multistage stochastic pull

system (Askin and Krishnan 2009). Pull principle was applied from product

delivery till production start, in order to balance the pace and the flow of

manufacturing operations. When the gap of material supply occurred, such a

balance would be destroyed in a fire-fighting manner to take time for its

recovery. As an example, principles of popular theories were all checked but

with the product flow in FIFO (First-In-First-Out) mode at each step of

manufacturing, meant that not a same product was initiated, moved and

delivered in the operation to fulfil the demand at customer end. Observing in

various ways, the effects of different theories could be seen “virtually”, e.g.

MTO (Make-To-Order), ATO (Assemble-To-Order), DTO (Deliver-To-Order),

and even MTS (Make-To-Stock).

3. The main difficulty related to material supply and its liability for key

components due to long lead times. It could not be avoided and was a reality

for the case company if lead times were not possible to be shortened. For

example, new and old material in product change should be controlled well in

such a synchronisation. Especially, old components with lead time as weeks

could cause the liability as the amount for months to consume. Otherwise, it

could result in enormous scraping costs. It was the limitation to product

change and normal operations lean effect in mind. The liability was invisible

in MRP systems because of inaccurate forecast in the past, which was seldom

to be studied to reduce its effect.

The bottom-line was to deliver products to customers’ requirements (especially

having the changes of delivery amount or product configuration) at a high speed,

without means to develop efficient forecasting processes to manage demand

uncertainty. Whenever the volume of pull at delivery side was larger than the

amount of push at supply side, production had to be stopped due to missing

components. The case company had to find an alternative way to survive better in

the competition as everyone in the industry suffered by those same challenges.

In addition, multiple tiers of many companies were involved in the demand-

supply chain with international manufacturing operation. Faster transfer of

demand information or a more reactive planning was not enough to save

manufacturing companies as a physical process is inflexible in responding to

frequent plan changes in normal operation. When product changes added on this,

22

demand-supply planning practices became even more fragmented and frustrated.

There were no existing solutions available, academic or industrial, at the time.

1.3.3 Practical realisation

The research was mainly realised during the period of 2003–2006. The research

included three action research cycles. Each action research cycle sought answers

by going into one extreme of minimising costs, diminishing order delivery period,

or shortening product change periods. In practice, these research cycles included

the case company changing their business accordingly for each of these cases.

Conducting required changes in the case company were economically significant

trials. Figure 3 describes the research process.

Fig. 3. The research process.

Research Cycle 1 included the case company aiming all of its actions to

minimising costs. The case company executed a strategy of cost effectiveness.

Minimising inventory and scrapping costs required swift component control in the

whole demand-supply chain.

In research Cycle 2 the case company aimed at diminishing order delivery

period. In this trial, the case company aimed at strong concurrency in engineering

to get order delivery period as short as possible.

Research Cycle 3 concentrated on shortening product change period. The

case company executed a strategy of innovativeness making product changes as

fast as possible. The trial clarified whether a ready-product inventory could be

used to speed up product change.

During research cycles, every change case was recorded using change notes

(CN). Change notes compare the old and the new product versions, indicating all

changes in used components. CN also indicated the expectation when the changes

23

will be conducted. CN was common for all sites enabling to tell which site is

influenced.

Site specific implementation reports were utilised to record changes, the

implementation time and scrapping costs. Implementation report described all the

results from different sites. Both, implementation reports and change notes were

stored into a database.

There were over one hundred product change cases available within the

company at the time of research. The researcher selected three cases out of all

product changes, one for each cycle. The cases were important for business and

there was a significant change in the product.

Process improvements were made based on the three selected product change

cases individually. After the process improvements, it was checked whether the

targets set for that particular cycle was reached or not.

The researcher worked as the project manager for all the studied product

change cases. He was responsible for product change implementations, including

planning & informing all the sites, and cooperation between these sites, collecting

results, analysing and making conclusions.

1.4 Structure of the thesis

Chapter 1 describes the background information of this research straightforwardly

by using a true problem from industrial practices. The goal is to survive better

than others in the industry under inaccurate forecast. Because modern

manufacturing in global scale is more sophisticated than ever, it is essential to

define the scope and the limitation of this research precisely. It is aiming to be

beyond lean or agile manufacturing, as well as any improved versions currently in

use. The research approach is selected briefly from reviewing different

methodologies in order to obtain the advantages of the action research method.

This method enables developing modular solutions piece by piece in an

innovative way.

In Chapter 2, the literature review is conducted to collect applicable elements

from existing management science for further development. They are mainly

from the fields of manufacturing philosophies, operational performance of

demand-supply, product change management, and industrial case study.

The empirical research is stated in Chapter 3, and the results accomplished in

3 cycles of action research are presented. The key thoughts of each research cycle

are verified in order to ensure the research questions studied by sufficient details.

24

In Chapter 4, research questions are answered to summarise the thoughts on

flexible optimisation rather than choosing only one option and being stuck in the

middle. The key is applying multi-strategies in business environment as a

multidimensional playground. The validation and reliability of the research are

checked. The implications of research with its constructive contributions are

discussed for practical and academic evaluation. After summarising new

contributions of the research, the recommendations for future development are

also presented in order to continue the learning journey further for great success.

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2 Literature review

2.1 Manufacturing philosophies

Different manufacturing philosophies include, lean thinking, JIT (Just-In-Time),

agile manufacturing, and their derivates.

2.1.1 Lean manufacturing and JIT philosophy

Lean manufacturing, as practiced in the Toyota production system, was a

revolutionary change of just-in-time (JIT) philosophy to mass production

practices in the automotive industry (Haan et al., 1999). The conceptual model

can be like a continuously moving conveyor belt from the beginning of

production to the delivery of finished products. It aimed to provide cost-effective

production as its delivery of only the necessary quantity of parts at the right

quality, at the right time and place, while using a minimum amount of facilities,

equipment, materials and human resources. A time line from 1930 to 2006 about

its development within Toyota to form an overview of JIT can be found in

Holweg (2006).

However, the problems have been widely reported more and more as the

disadvantages of JIT in the dynamic business of global manufacturing nowadays:

– Limited to repetitive manufacturing

– Requires stable production level

– Does not allow much flexibility in the products produced

Seeking for the improvements, one example is the most efficient type of JIT

operation – Synchronous Manufacturing (Umble et al., 1996; Srikanth et al., 1997;

Doran, 2002), which can be a direction towards new JIT to solve the above

drawbacks. Synchronous manufacturing embodies many concepts related to

focusing and synchronising production control around bottleneck resources

(Frazier et al., 2000). Other common names for these concepts are the theory of

constraints (or simply TOC) and Drum-Buffer-Rope, which was introduced in

1984 by Eliyahu Goldratt in The Goal (Walker, 2002).

The Theory of Constraints (TOC) is an overall management philosophy that

aims to continually achieve more of the goal of a system. The key is to improve

schedule attainment performance and reduce inventories, as well as lead times

26

(Frazier et al., 2000). Drum-Buffer-Rope is a manufacturing execution

methodology, named for its three components.

– The drum is the physical constraint of the plant: the work centre or machine

or operation that limits the ability of the entire system to produce more.

– The buffer protects the drum, so that it always has work flowing to it. Buffers

in DBR have time as their unit of measure, rather than quantity of material.

– The rope is the work release mechanism for the plant. Pulling work into the

system earlier than a buffer time guarantees high work-in-process and slows

down the entire system.

It was also reported Drum-Buffer-Rope as the synchronisation for agility purpose

(Walker, 2002). This can support optimisation, possibly for both lean and agile

manufacturing as two different balancing points for the synchronisation.

However, few companies can keep the focus on bottlenecks (as they are hard

to identify or too often keep changing) to plan and control production. It cannot

become a popular way due to such a limitation from the Theory of Constraints

(TOC) as the base of synchronous manufacturing. In fact, the synchronisation

should not be related only to the constraints – it is more reasonable to act above

the business bottom-line if the tolerance is needed as a must from the view of

synchronisation.

2.1.2 ESP concept beyond JIT philosophy

In high-mix manufacturing, a new concept of Equalised and Synchronised

Production (ESP) has been researched by Toshiki Naruse for a revolution beyond

the Japanese Just-In-Time (JIT) system (Naruse, 2003).

According to Naruse (2003), the new system of ESP has the following

features in the development:

– ESP original concept one: Production guard strictly to customer needs is

inefficient.

– Hint: Need product inventory to separate production schedule from direct

link to the buyer’s orders.

– ESP original concept two: To fulfil the production division’s mission, daily

production output and production sequences must be stabilised, with

production output equalised among the various item numbers.

27

– The production Division’s mission:

– To maximise production efficiency by making and maintaining

improvements toward that end.

– To minimise inventory by working toward the goal of zero inventory.

For the JIT concept, the supplier or its warehouse must physically locate its plants

either within the manufacturer’s site or nearby. If located far away, it is hard for

them to make synchronisation well enough to meet the requirements of demand-

supply (specific volumes and delivery deadlines for specific product items).

However, Naruse (2003) claimed the ESP approach is the best way for suppliers

in various industries.

As a feature or a limitation from view of Naruse (2003), the system of ESP is

more for a parts supplier to deliver products made on its production lines to

multiple buyers / locations. JIT is more for a company to purchase material from

a parts supplier and assemble them to finished products, or a parts supplier to

built dedicated production lines synchronised with the production of

corresponding buyers. The ESP production system basically uses the periodic

reordering of variable amounts method. Both production and purchasing can use

the multiples of these equalised units. It also needs to ensure the supplier

implements synchronisation with the buyer’s delivery deadline. Shortening lead

time, using smaller lots and raising in-house production efficiency are all key

activities under ESP. Comparing with JIT of 100 percent response to orders from

customers, ESP emphasises maximising in-house production efficiency and

minimising inventory as its focus.

2.1.3 Agile manufacturing and leagility concepts

Because of the complexity of today’s supply chains, another direction of

operational improvements leading to agile manufacturing has been discussed

widely (more radical than the above lean-alternatives of synchronous

manufacturing or ESP). Other names include responsive manufacturing and

supply chain flexibility. The 1990s is associated with two important

considerations of agility and supply chain in a history review by Sharifi et al.

(2006). A summary of the literature on supply chain flexibility can be found from

Stevenson et al. (2007). There is also a list of the contributors relating to

flexibility / responsiveness / agility in Reichart et al. (2007).

28

Agile manufacturing is a vision of manufacturing that is a natural

development from the original concept of lean manufacturing (Gunasekaran,

1999). Yusuf et al. (1999) indicates the main driving force behind agility is

change. It is recognised as a necessary condition for competitiveness. The

comparison of lean supply with agile supply can be seen in the following Table 1

(Mason-Jones et al., 2000):

Table 1. The comparison of lean supply with agile supply.

Distinguishing attributes Lean supply Agile supply

Typical products Commodities Fashion goods

Marketplace demand Predictable Volatile

Product variety Low High

Product life cycle Long Short

Customer drivers Cost Availability

Profit margin Low High

Dominant costs Physical costs Marketability costs

Stockout penalties Long-term contractual Immediate and volatile

Purchasing policy Buy goods Assign capacity

Information enrichment Highly desirable Obligatory

Forecasting mechanism Algorithmic Consultative

However, it is very rare to see benchmark cases from famous companies for agile

supply operation as well as IT applications (Helo et al., 2006). More and more,

researchers are adjusting the concept backwards and forwards, using with a new

word, “leagility” – better to keep efficiency and flexibility always together. It is a

more balanced thinking to compare or combine both factors properly in business.

According to Mason-Jones et al. (2000) leagility is the combination of the

lean and agile paradigm within a total supply chain strategy by positioning the

decoupling point so as to best suit the need to respond to volatile demand.

2.1.4 Manufacturing strategies and product life cycle

Scholarly research in the manufacturing strategy field has moved its focus more

and more to the total impact on product life cycle, as well as to the trend to whole

supply chain in a global scale (Aitken et al., 2003). Aitken (2003) identified the

operational differences of demand-supply network needed in each phase of

product life cycle (PLC) as an interesting example of those multiple choices at

29

strategy level. The strategic effect from a higher level can provide a larger

tolerance to supply operation.

Holmström et al. (2006) reported external collaboration initiatives such as

Vendor Managed Inventory (VMI) and Collaborative Planning Forecasting and

Replenishment (CPFR) not being sufficient on their own to produce improved

efficiency and responsiveness. Firms need to actively co-ordinate internal

collaborative practices between functions to benefit from their development

projects with customers and suppliers.

As the view of Hilletofth et al. (2010), it has been always a big challenge

how to bring new product to the market faster as a competitive advantage, which

remains to be an essential need in high-tech industries discussed. In markets

where short product life cycles are the norm, delays in bringing products to the

market can have detrimental consequences to sales and profit. To remain

competitive in these environments, companies need to produce innovative, high

quality, highly value-added products and services and bring them quickly and

effectively to the market.

Hilletofth et al. (2010) emphasise two major issues need to be addressed:

– The need to develop innovative, value-adding products

– The necessity of bringing them quickly to the market.

2.1.5 The innovator’s strategy

With the additional interest of radical innovation in industries, a further review

was conducted of the innovator’s strategy (Christensen, 2003) about an

extraordinary way of competing by disruption in business, as well as its great

impact especially on the manufacturing operation. There are two kinds of

industrial innovation: Sustaining or disruptive innovation.

A sustaining innovation targets satisfying highly demanding customers by

incremental improvements in products with better performance, rather than what

was previously available. A disruptive innovation model shapes the strategies for

those new growth builders to win the fights.

To create a new value network on the third axis is called new-market

disruptions. According to Christensen (2003), it brings an opportunity for the

company to satisfy the customer well enough by squeezing the bubble out of

disruptive innovation. The innovation is thus leveraged by the value as business

driver focused clearly on the customers.

30

With its big impact, disruptive innovation can act as a force also in

manufacturing, for example, going to market as soon as possible to take more

risks than in normal time. This is the reason to use the innovation in

manufacturing strategies along with product life cycle changes as a new thinking,

which actually also happened in one of the cases in the action research.

2.1.6 Summary of manufacturing philosophies

This thesis utilises the following concepts from earlier research as theoretical

foundation:

1. New JIT to adopt postponement for a leaner efficiency as synchronous

manufacturing

Originally JIT was oriented for a repetitive manufacturing environment.

Synchronous manufacturing was developed for low-volume/high-mix

production. Concepts related to JIT operational strategy include lean and

postponement principles together with flexibility in the manufacturing

process. (Cusumano, 1992; Gunasekaran, 1999; Haan et al., 1999; Frazier et

al., 2000; Vokurka et al., 2000; Amasaka, 2002; Coronado M. et al., 2002;

Doran, 2002; Papadopoulou et al., 2005; Bhasin et al., 2006; Graman et al.,

2006; Holweg, 2006; Ruffa, 2008).

2. Agile manufacturing to achieve flexible and responsive operation

The concepts related to agile manufacturing are claimed to be the next steps

after the lean philosophy in production management evolution. Their focus is

to respond to customer needs and market changes faster while still controlling

costs and quality. These agile concepts are suitable for product-based

industries with unstable markets and volatile demand, as well as products

with short life cycles. (Brennan et al., 1999; Gunasekaran, 1999; Yusuf et al.,

1999; Rigby et al., 2000; Hoek et al., 2001; Little et al., 2001; Prater et al.,

2001; Welker et al., 2005; Sharifi et al., 2006; Swafford et al., 2006;

Reichhart et al., 2007; Stevenson et al., 2007).

3. The leagility to combine lean and agile characteristics

The definition of leagility, i.e. combining leanness and agility, was originally

developed to describe manufacturing supply chains. The basic idea behind

leagility is the existence of a decoupling point, which separates the lean

31

processes from the agile processes in the supply chain. Lean processes are

seen to be on the upstream side of the decoupling point, and agile processes

on downstream. A similar concept is applicable also within a company. Lean

and agile concepts can be applied at different stages of the same

manufacturing process, for different machines and parts, etc. In this case, a

level of buffer stock is maintained between lean and agile manufacturing

strategies. (Bonney et al., 1999; Naylor et al., 1999; Robertson et al., 1999;

Bolander et al., 2000; Hoek, 2000; Mason-Jones et al., 2000; Pagell et al.,

2000; Sahin, 2000; Takahashi et al., 2000; McCullen et al., 2001; Prince et al.,

2003; Christopher et al., 2002; Stratton et al., 2003; Corti et al., 2006; Hoque

et al., 2006; Stratton et al., 2006; Krishnamurthy et al., 2007; Mohebbi et al.,

2007).

4. Manufacturing strategy management focused for superior demand-supply

performance

Demand-supply performance is further studied for optimising, not only a

company, but also its ecosystem. Competitive advantages of global

manufacturing can be achieved if the supply chain has less organisational

boundaries. The key is to simultaneously aim for operational efficiency and

market responsiveness, including all parties. (Lummus et al., 1998; Banerjee,

2000; Golder, 2000; Sahin, 2000; Brassler et al., 2001; Olhager et al., 2001;

Christopher et al., 2002; Hinterhuber et al., 2002; Loch et al., 2002; Brown et

al., 2003; Stratton et al., 2003; Hui, 2004; Hallgren et al., 2006; Brown et al.,

2007).

5. Others: product innovation, agent-based modelling, IT implementation

proposal, research methodology

This group of concepts ensures the research supporting a wider knowledge

base. For example, the innovation through product changes is in the focus of

this research. The development of IT tools for optimising manufacturing

execution can be also important, as well as right methodology. (Papandreou et

al., 1998; Bajgoric, 2000; Davidrajuh et al., 2000; Thomke et al., 2000;

Corbett et al., 2001; Coronado M. et al. 2002; Coughlan et al., 2002; Forza,

2002; Mandal et al., 2002; Walker, 2002; Dooley et al., 2003; Jalote et al.,

2004; Ottosson, 2004; Ashayeri et al., 2005; Buxey, 2006; Helo et al., 2006;

Nilsson et al., 2006).

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In order to ensure the literature review focusing on manufacturing optimisation,

the discussion includes synchronous manufacturing, Equalised and Synchronised

Production (ESP), the Leagility, Manufacturing Strategies in Product Life Cycle,

and the Innovator’s Strategy.

2.2 Developing demand-supply network

It has been many years as a popular thought that DCM (Demand Chain

Management) and SCM (Supply Chain Management) are not separate but

inextricably intertwined (Min and Mentzer 2000) The demand-supply network

management concept of Holmström et al. (1999) proved to be a useful tool in

analysing the demand and supply balancing mechanisms (Auramo and Ala-Risku

2005). Combining push-based supply chain and pull-based demand chain together,

the study is better focused directly on demand-supply network theory more

applicable to case company in the research. The reason is no major difference

between the demand and supply chain with respect to the network of

organizations involved, which are all to create, produce, and deliver customer

value. (Hilletofth 2010).

2.2.1 Value oriented development for demand-supply network

The target of developing demand-supply network is to maximise the overall value

generated.

Value as a key of winning in competition

According to the analysis by Chopra & Meindl (2001), the value is the difference

between what the final product is worth to the customer and effort the supply

chain expends in filling the customer’s request. The success key is the appropriate

management of all flows of information, and product, generating costs within the

supply chain. Monczka and Morgan (2000) identified those “critical six” as

follows to be the trend of developing demand-supply network:

– Increasing efficiency requirements

– Making use of information technology

– Integration and consolidation

– Insourcing and outsourcing

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– Strategic cost management

– “Network” management.

For example, PC (Personal Computer) industry has many ways to organize the

value chain in a network manner. Curry and Kenney (1999) illustrated that the

traditional production-distribution channel (such as IBM and Compaq) co-existed

with new emerging structures represented by “local assemblers” and “direct

marketers” such as Dell. Such a complexity as global operation scale has been

also seen nowadays widely in other high-tech industries.

Ketchen et al. (2008) presented a tool as the best value supply chains

designed to deliver superior total value to the customer in terms of speed, cost,

quality, and flexibility. It is not just simply to create low costs, but also to

maximise the total value added to the customer. Relative to traditional supply

chains, best value supply chains also take much different approaches to key

functions such as strategic sourcing, logistics, information systems, and

relationship management.

Thinking as a networked way

Wu and Zhang (2009) introduced the value network perspective into the field of

business model study and discussed basic issues about business model such as

definition, elements and classification through the lens of value network. From

the perspective of value network, the definition of its business module is the

system connecting internal and external actors by value flows to create, deliver

and capture value:

– Value actors as the network nodes

– Value flows as the network relation

– Part of or the whole value network as the network structure.

In comparison with real business cases, Wu and Zhang (2009) summarised

business model innovations of value network as follows:

– Business model innovation based on actor change

– Business model innovation based on relation change

– Business model innovation based on network subdivision

– Business model innovation based on network extension

– Business model innovation based on network integration.

34

Gadde and Håkansson (2001) studied activity co-operation of JIT (Just-In-Time)

deliveries with numerous activities conducted by a large number of actors as a

network view. The complexity of strategising in networks is related to their

multidimensionality. Any change has some direct effects but also a number of

indirect effects, on other firms, impact on the actor’s performance. The focus is

emphasised on the interdependence among the activities conducted by customer

and supplier and call for more co-ordination than is needed when inventories

serve as buffers. The main issue in all network thinking is that “others” need to be

included. The second key aspect is related to time. The importance of others and

the crucial time dimension indicate that boundaries are key issues in all network

thinking.

Focus on demand or supply?

Esper et al. (2010) emphasised two primary sets of processes through which the

firm creates value for its customers by moving goods and information through

marketing channels: demand-focused and supply-focused processes. Historically,

firms have invested resources to develop a core differential advantage in one or

other of these areas—but rarely in both—often resulting in mismatches between

demand (what customers want) and supply (what is available in the marketplace).

Yusuf et al. (2004) also found supply chains (or demand-supply network) were

understood mainly in terms of long-term upstream collaboration with suppliers.

However, an equal amount of emphasis is then paid to downstream collaboration

with customers and even collaboration with competitors as a means of integrating

the total value creation process.

Hilletofth and Hilmola (2010) indicated management of the demand side

(DCM – Demand Chain Management) being revenue driven and focused on

effectiveness whilst the management of the supply side (SCM – Supply Chain

Management) having a tendency to be cost oriented and focus on efficiency.

Together these management directions determine a company’s profitability and

thus need to be coordinated, requiring a demand�supply oriented management

approach. As the finding of Hilletofth (2010), it is important to promote the

coordination of DCM and SCM, which can occur within a particular company

and across the demand�supply chain at different planning levels (strategic,

tactical, and operational).

From a survey result by Boonyathan and Power (2007), following outcomes

were found:

35

– Supply uncertainty is a more significant determinant of performance than

demand uncertainty.

– Closer relationships with trading partners are associated with higher levels of

performance.

– Uncertainty can be reduced by being more closely aligned with both suppliers

and customers.

Mason-Jones et al. (2000) emphasised that the success and failure of supply

chains are ultimately determined in the marketplace by the end consumer. Getting

the right product, at the right price, at the right time to the consumer is not only

the lynchpin to competitive success but also the key to survival. According to the

report from Ervolina et al. (2006), availability management process called

Available-to-Sell (ATS) is an example that incorporates demand shaping and

profitable demand response to drive better operational efficiency through

improved synchronisation of supply and demand. IBM has implemented an ATS

process in its complex-configured server supply chain in 2002. The realized

savings include $100M of inventory reduction in the first year of implementation

and over $20M reduction annually in the subsequent years.

New trend of operations management

As a strong trend, demand management should be more integrated in supply

operation to increase customer satisfaction and life cycle profit (Reiner et al.

2009). As the view of Frohlich and Westbrook (2002), the DCM strategy

appeared to be the best overall approach for manufacturers to follow and the

relatively few manufacturers that are already following this approach. As Ettl et al.

(2006) described, a demand-driven supply network (DDSN) is a system of

technologies and business processes that senses and responds to real time demand

across a network of customers, suppliers, and employees. DDSN principles

require that companies shift from a traditional push-based supply chain to a pull-

based, customer-centric approach.

Waters and Rainbird (2008) even claimed the demand chain and response

management is new direction for operations management. Supply chain

management would appear to be at the end of its lifecycle. Customers of all types

are expressing preferences based upon some degree of product-service

differentiation and not simply on cost. They suggested the supply chain is

obsolescent and should be replaced by a more proactive response system.

36

2.2.2 Manufacturing strategies affect demand-supply network

Mason-Jones et al. (2000) presented that classifying supply chain design and

operations according to the Lean, Agile and Leagile paradigms enables the

companies to match the demand-supply type according to marketplace need. For

example, they could be mechanical precision products (lean); carpet manufacture

(agile); and electronics products (leagile).

Multiple strategy choices

Christopher and Towill (2000) summarised the differences on how to apply lean

or agile thinking for demand-supply network affected by manufacturing strategies.

The lean paradigm requires that ``fat'' is eliminated. However, the agile paradigm

must be ``nimble'' since sales lost are missed forever. An important difference is

that lean supply is associated with level scheduling, whereas agile supply means

reserving capacity to cope with volatile demand.

Lack of agile benchmark cases brings the difficulty to understand such a

concept clearly. As the view of Yusuf et al. (2004), the agility of a supply chain is

a measure of how well the relationships involved in the processes of design,

manufacturing and delivery of products and services. Monroe and Martin (2009)

described that agility in the supply chain is described as being able to “respond to

sudden and unexpected changes in markets. Agility is critical, because in most

industries, both demand and supply fluctuate more rapidly and widely than they

used to.

According to the explanation of Mason-Jones et al. (2000), leagile supply

chains already exist in the real world. Just as case company due to big differences

of material supply lead-time, there is decoupling point in demand fulfilment

process where order-driven way changed to forecast-driven way.

Design of demand-supply network to support strategies

Vonderembse et al. (2006) defined the characteristics for standard, innovative,

and hybrid products, and provided a framework for understanding lean and agile

supply chains. Lean supply chains (LSCs) employ continuous improvement

efforts and focus on the elimination of nonvalue added steps across the supply

chain. Agile supply chains (ASCs) respond to rapidly changing, continually

fragmenting global markets by being dynamic, context-specific, growth-oriented,

37

and customer focused. Hybrid supply chains (HSCs) combine the capabilities of

lean and agile supply chains to create a supply network that meets the needs of

complex products.

As the view of Vonderembse et al. (2006), early in their product life cycle,

innovative products, which may employ new and complex technology, require

ASC. As the product enters the maturity and decline phases of the product life

cycle, a LSC could be more appropriate. Hybrid products, which are complex,

have many components and participating companies in the supply chain. Some

components may be commodities while others may be new and innovative.

Hilletofth (2009) suggested that companies need to use several SC (Supply

Chain) solutions concurrently (i.e. develop a differentiated SC strategy) to stay

competitive in today’s fragmented and complex markets. The arguments in favour

is that there are no SC strategies that are applicable to all types of products and

markets and since companies usually offer a wide range of products and services

in various types of non-coherent business environments. In particular, Hilletofth

and Hilmola (2010) also emphasised a need for real life based industrial case

studies addressing how the various demand and supply processes influence each

other and how they can be coordinated across intra- and inter-organizational

boundaries. Thus, benefits to all parties should be aimed for developing win-win

solution in demand-supply network co-operation.

The differences in supplier selection were further studied by Chopra and

Sodhi (2004) how to plan the manufacturing in demand-supply network smarter:

When planning capacity, companies should select an efficient, low-cost supplier

for fast-moving (low-risk) items. In contrast a more responsive supplier better

suits slow-moving (high-risk and high-value) items. For example, Cisco tailors its

response by manufacturing fast-moving products in specialised, inexpensive but

not-so-responsive Chinese plants. High-value, slow-moving items are assembled

in responsive, flexible (and more expensive) U.S. plants.

Santoso et al. (2005) reported a stochastic programming model and solution

algorithm for solving supply chain network design problems of a realistic scale.

Existing approaches for these problems are either restricted to deterministic

environments or can only address a modest number of scenarios for the uncertain

problem parameters. Santoso et al. (2005) proposed a methodology to quickly

compute high quality solutions to large-scale stochastic supply chain design

problems with a huge (potentially infinite) number of scenarios.

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Lead time reduction as strategic effect

Amoako-Gyampah (2003) indicated that manufacturing strategy represents the

way a company plans to deploy its manufacturing resources and to use its

manufacturing capability to achieve its goals. Lead time has been recognised as a

very important issue in almost all strategy theories. It is one of the root-causes to

determine the choice of manufacturing strategies in many cases. From the view of

Sapkauskiene and Leitoniene (2010), speed as a competitive factor is gaining

more and more importance for companies involved in global market competition.

The company tends to compete for rapid response to consumer demand and new

products and technologies introduced to the market. This type of competition in

terms of reaction time is described as time based competition (TBC).

Comparing to lead time reduction in production, such an effort in demand-

supply network is often limited so as to bring big operational uncertainty and the

bullwhip effect significantly. The time gains so greater importance, as speed,

which is required by business and consumer expectations, continues to increase

even more (Sapkauskiene and Leitoniene 2010). Lyu and Su (2009) described the

challenges in demand-supply including uncertainty of customers’ demands, high

inventory levels and cost, inaccurate due date estimation, and slow response to

customer inquires. Lead time reduction is a critical issue which enables

manufactures to solve problems. They proposed extended master production

scheduling (MPS) system, developed using Internet technology, can be deployed

in a supply chain environment.

As similar philosophy focused for reducing lead time, Quick Response

Manufacturing (QRM) developed by Rajan Suri is a strategy that enables

companies to significantly improve their productivity and their competitive edge.

Suri (1998) presented the way how QRM has refined time based competition by:

– Focusing only on manufacturing.

– Taking advantage of basic principles of system dynamics to provide insight

into how to best reorganise an enterprise to achieve quick response.

– Clarifying the misunderstandings and misconceptions managers have about

how to apply time-based strategies.

– Providing specific QRM principles on how to rethink manufacturing process

and equipment decisions.

– Developing a whole new material planning and control approach.

– Developing a novel performance measure.

39

– Understanding what it takes to implement QRM to ensure lasting success.

Suri (2002) claimed that JIT (Just-In-Time) was perfected by Toyota over 30

years ago. For certain markets, lean manufacturing has several drawbacks. Quick

Response Manufacturing (QRM) can be a more effective competitive strategy for

companies targeting such markets. Specifically, QRM is more effective for

companies making a large variety of products with variable demand, as well as

for companies making highly engineered products.

Suri (2003) explained why QRM has greater competitive potential and

described POLCA (Paired-cell Overlapping Loops of Cards with Authorization), a

material control system to be used as part of QRM. The combination of QRM and

POLCA will provide companies with significant competitive advantage through

their ability to deliver customised products with short lead times.

Suri and Krishnamurthy (2003) explained that POLCA is a hybrid push-pull

system that combines the best features of push/MRP systems and Kanban/pull

control, while at the same time avoiding their disadvantages. The flow of orders

through the different production cells is controlled through a combination of

release authorizations (High Level Materials Requirements Planning system or

HL/MRP) and production control cards known as POLCA cards (not part-specific

like a Kanban card). The release authorization times only authorize the beginning

of the work, but the cell cannot start unless the corresponding POLCA card is also

available. A POLCA card is a capacity signal, while a pull/Kanban signal is an

inventory signal. If there is no authorized job, then no job is started, even though

a POLCA card is available. It should be designed available capacities are not

significantly below the required levels.

From the description by Suri and Krishnamurthy (2003), there are Safety

Cards, which are only used to release POLCA cards that get stuck in the loop due

to occasional component part shortages. After a period of time, statistics from

these incidents will provide concrete insight into root causes of the shortages.

As their suggestions, the key metrics are measured as follows:

– The lead times for the products

– The throughputs of the cells

– The reliability of delivery between cells

– WIP inventories at various points in the system

– The on-time delivery performance of upstream and downstream cells in the

POLCA loops.

40

Vandaele et al. (2005) also reported the implementation of an E-POLCA system

in a paperless – cardless – environment. It is a load based version for a multi-

product, multi-machine queuing network to determine release authorisations and

allowed workloads.

2.2.3 The role of collaboration in demand-supply

According to the explanation of Kaipia and Hartiala H (2006), manufacturing

companies need the collaboration with customers and suppliers to improve the

performance of demand-supply network. Better information-sharing can reduce

both the bullwhip effect and the operational risk (such as the level of safety

stocks).

Networked collaboration for better performance

Holweg et al. (2005) discussed that collaboration in the demand-supply network

comes in a wide range of forms, but in general have a common goal: to create a

transparent, visible demand pattern that paces the entire supply chain. Such

collaboration is for jointly creating the common pace of information sharing,

replenishment, and supply synchronisation in the system to reduce both excess

inventory and the costly bullwhip effect.

For example, Ryu et al. (2009) can identify types of demand information

according to their timestamp. There are three types of demand information

classified according to where they are located along the time-axis. These are

realised demand information, planned demand information, and forecasted

demand information. Two different information-sharing methods are defined

according to types of shared information and sharing procedures. One is the

‘planned demand transferring method (PDTM)’ and the other is the ‘forecasted

demand distributing method (FDDM)’.

Udin et al. (2006) proposed a collaborative supply chain management

framework. Normally, supply chain management (SCM) is a system that contains

multiple entities, processes and activities from suppliers to customers.

– The basic concept behind SCM is how the raw materials and information

flow from the supplier to the manufacturer, before final distributions to

customers as finished products or services.

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– In addition, functional areas within the organisation also need information

that flows through the SCM in order for them to make a decision to produce

products.

– The capability of sharing and exchanging information is essential to improve

the effectiveness of the SCM.

Udin et al. (2006) provided a collaborative framework how to analyse the gap

between the current and the desirable position (benchmark) for its effective

implementation in organisation.

Heikkilä (2002) described about the collaboration oriented more by changing

from SCM (Supply China Management) to DCM (Demand Supply Management)

with following propositions:

1. Good relationship characteristics contribute to reliable information flows.

2. Reliable information flows contribute to high efficiency.

3. Understanding the customer situation and need and good relationship

characteristics contribute to co-operation between the customer and supplier.

4. Good co-operation in implementing demand chain improvement contributes

to high efficiency and high customer satisfaction.

5. High customer satisfaction contributes to good relationship characteristics.

Collaboration to reduce bullwhip effect

As explained by Ismail (2009), bullwhip effect is a major problem in supply

chains. It means the amplification of orders as you go up along the supply chain.

The bullwhip effect is a phenomenon that was discovered by Forrester (1958)

who realized that variations of demand increase up the supply chain from

customer to supplier, what was called the Bullwhip Effect or known as the

Forrester Effect. Holweg et al. (2005) also described that unpredictable or non-

transparent demand patterns have been found to cause artificial demand

amplification in a range of settings, which is also referred to as the ‘bullwhip’

effect’ (Lee et al., 1997; Lee, 2002). This leads to poor service levels, high

inventories and frequent stock-outs.

After studying three proposed scenarios, Bolarin et al. (2008) concluded that

collaborative structures improve the Bullwhip effect and reduce the total costs of

the supply chain in which these structures applied. Those are 3 scenarios in the

simulation: Traditional Supply Chain, VMI (Vendor Management Inventory)

(based on collaborative structures among the members that make up the Supply

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Chain), and EPOS (Electronic Point of Sales). In the collaborative EPOS scenario,

the end consumer sales are sent to all members of the supply chain. Specifically,

in this strategy the end consumer sales may be used by each echelon for their own

planning purposes, but each echelon still has to deliver (if possible) what was

ordered by their customer (Disney et al 2004). The EPOS chain has proved to be

more efficient than the VMI and the traditional ones in reducing the Bullwhip

effect and in holding costs.

Susarla et al. (2004) argued that advances in information technology (IT) that

improve coordinated information exchange between firms result in a significant

impact on measures of operational efficiency such as time to market, inventory

turnover, and order delivery cycle time. To reduce bullwhip effect, IT can also

make it possible by exchanging information on a variety of parameters such as

demand and inventory related information, process quality information, feedback

from customers etc.

Collaborative risk management

Christopher and Lee (2004) noticed that many companies have experienced a

change in their supply chain risk profile as a result of changes in their business

models, for example the adoption of ‘lean’ practices, the move to outsourcing and

a general tendency to reduce the size of the supplier base. As their view, the

improvements in confidence can have a significant effect on mitigating supply

chain risk.

Snyder et al. (2006) researched about supply chain disruptions. It needs to

consider the risk of disruptions when designing supply chain networks. Supply

chain disruptions have a number of causes and may take a number of forms. They

presented a broad range of models for designing supply chains resilient to

disruptions. For example, these models can be categorised by the status of the

existing network: A network may be designed from scratch, or an existing

network may be modified to prevent disruptions at some facilities. Snyder et al.

(2006) emphasised that the companies may face costs associated with destroyed

inventory, reconstruction of disrupted facilities, and customer attrition (if the

disruption does not affect the firm’s competitors). In addition, the competitive

environment in which a firm operates may significantly affect the decisions for

risk mitigation. The key objective may be to ensure that their post-disruption

situation is no worse than that of their competitors.

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Goh et al. (2007) presented a stochastic model of the multi-stage global

supply chain network problem, incorporating a set of related risks: supply,

demand, exchange, and disruption. With the increasing emphasis on supply chain

vulnerabilities, effective mathematical tools for analysing and understanding

appropriate supply chain risk management are now attracting much attention.

They provided an optimal solution with profit maximisation and risk

minimisation objectives.

Thomas and Tyworth (2006) discussed about pooling lead-time risk by order

splitting. The policy of pooling lead-time risk by simultaneously splitting

replenishment orders among several suppliers continues to attract the attention of

researchers even after more than 20 years of extensive study. The research has

following major tracks:

– Modelling effective lead-time demand under a variety of stochastic

assumptions and enabling an assessment of the impact of pooling on reorder

points, stockout risk, safety stock, and shortages.

– Modelling cost tradeoffs on a comparison of the long run average total costs

for single-source versus dual- or multiple-source models under identical

conditions.

Thomas and Tyworth (2006) revealed two important and persistent limitations:

– The models do not give appropriate attention to transportation economies of

scale. Specifically, there are important gaps with respect to the true

magnitude of transportation cost, as well as the impact of order quantity

(weight), supply lines (distance), and mode (especially air versus ocean

shipments in a global setting) on transportation and incremental ordering

costs.

– The current theory that a reduction in average cycle stock is the key benefit of

splitting orders simultaneously considers only the buyer’s on-hand inventory

in the supply chain. The absence of in-transit inventory is an important

limitation, because simultaneously splitting an order among suppliers does

not reduce the combined amount of in-transit stock and cycle stock in the

system. Consequently, the only meaningful benefit of pooling lead times is to

safety stock from a total system-cost perspective.

Thomas and Tyworth (2006) also introduced other options such as a single

supplier to receive an order and then split it into smaller shipments released

44

sequentially. The long-term transportation commitments can also absorb some of

the demand variability at the consumer-facing point in the supply chain.

2.2.4 Measuring demand-supply performance

As the view of Jammernegg and Reiner (2007), supply chain performance

improvement should be measured by reduced total costs (transport, inventory

carrying and resources), as well as improved customer service (delivery

performance). For MTO (Make-To-Order) and ATO (Assemble-To-Order)

production, delivery performance (percentage of orders fulfilled within the

promised delivery time (or due date)) is used as measure of delivery reliability.

However, the trade-off between inventory cost and capacity cost has to be

considered. Reiner (2005) also discussed how performance measures derived

from total quality management (TQM) models could help to overcome the

limitations of financial measures. In such a context, process management and

customer orientation occupy a central position.

The performance of demand-supply network should be measured so as to

ensure its improvement accountable or at least visible. As one of other more

comparable options, it is also better to use existing key performance indicators for

a SCOR (Supply Chain Operations Reference) model, which can compare other

cases in this field. Here is an overview of SCOR model (Supply Chain Council,

2005):

SCOR-model key performance indicators

1. Customer focus

– Delivery performance

– Fill rate

– Order fulfilment lead time

– Perfect order fulfilment

– Supply chain response time

– Production flexibility

2. Internal cost focus

– Total supply chain management cost

– Cost of goods sold

– Value-added productivity

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– Warranty cost or returns

– Processing cost

– Cash-to-cash cycle time

– Inventory days of supply

– Asset turns.

Ho et al. (2005) emphasised the SCOR model is to help companies in managing

their supply chain. Process reference models integrate the mechanisms of

business process reengineering, benchmarking, and process measurement in a

cross-functional framework to helping companies to capture the “as-is” state of a

process and derive the desired “to-be” future status. However, Ho et al. (2005)

also indicated that SCOR does not provide a mechanism for measuring

uncertainty to enable a company to understand clearly the problems related to

uncertainty before the setting strategy.

Besides, Drzymalski and Odrey (2006) summarise a list of performance

metrics options from literature review, as well as ISO9001 and FEA (Federal

Enterprise Architecture) Consolidated Reference Model Document v2.0. Chan

(2003) presents following performance measurements as the suggestion. Apart

from the common criteria such as cost and quality, five other performance

measurements can be defined: resource utilisation; flexibility; visibility; trust; and

innovativeness.

Kaipia et al. (2007) introduced another option as the time benefit method,

which compares two potential collaboration modes as the following steps:

1. Describe the existing mode of replenishment process – the base case – and

one alternative mode.

2. Collect demand data for both alternatives to be examined.

3. Calculate the following for each item in the product range, and for both the

base case and the alternative solution.

4. Calculate for each item in the product range.

5. Graph for each product item in the product range the time benefit and

reordering amplification of demand.

For applying the thought from Kaipia et al. (2007) to product change

implementation, the most of key components (such as material supply normally)

belong to the base case and others belong to attentive case (such as VMI).

Furthermore, the trend of leading companies in high-tech industry has been

changed to using IT (Information Technology) solutions as a must in demand-

46

supply performance (Kauremaa et al. 2004). Auramo et al. (2005) found the IT

solutions to be divided into three categories, 1) transaction processing, 2) supply

chain planning and collaboration, and 3) order tracking and delivery coordination.

The role of information technology is shifting from a passive management

enabler through databases to a highly advanced process controller that can

monitor each activity (Gunasekaran et al. 2001). New idea or theory how to

measure the performance should be embedded in information technology tools as

IT-enabled research and development (Dong 2010).

It could improve real business in global scale and also bring reliable

academic value, which is a trend focused on how to leverage knowledge faster

and better than competitors (Thite 2003). In order to discuss such a trend, Auramo

et al. (2005) presented an explorative study about the benefits and their

observations of IT involvement in performance measurement. To gain strategic

benefits, the use of IT has to be also coupled with business process re-design. It is

a new normal of playground for business and a new interesting field for academic

research, which is so called IT enabled innovation (Watad 2009).

2.2.5 Purchasing automation challenge in product life cycle

Purchasing is a key activity in demand-supply operation especially hard in

dynamic product changes. Hilmola et al. (2008) suggested why a portfolio

approach of using different purchasing policies may be central to new intelligent

purchasing systems. A portfolio approach means lot for lot policy (L4L - The

order or run size is set equal to the demand for that period) may be useful in an

early phase of the product life-cycle, and later it may be an advantage to change

over to economic order quantity (EOQ) based ordering. Jammernegg and Reinera

(2007) described about the trade-off of inventory level in purchasing operation.

On the one hand, different types of inventory are necessary to buffer against

market and operational uncertainties but, on the other hand, inventory is

sometimes the result of inefficient management of the supply chain processes.

Therefore, inventory management has been a focal point of managing supply

chain processes.

As emphasised by Hilmola et al. (2008), accuracy of demand forecasting is

vital to switching point estimation. One potential method for tracking these

signals of that switching point was mentioned as the development of the GARCH

technique (proven useful in financial risk management and awarded the 2003

Nobel Prize in Economics). GARCH stands for Generalized Auto Regressive

47

Conditional Heteroscedasticity, which is an econometric model used for

modelling and forecasting time-dependent variance.

Lin (2010) proposed a GARCH based collaborative planning, forecasting,

and replenishment (CPFR) model. Through numerical analysis, a GARCH based

collaborative forecasting model is much suitable than the other time series

forecasting model. From the view of Lin (2010), ability to evaluate and qualify

risk associated with volatility by GARCH will enable businesses to

collaboratively manage inventory risks better and benefit both parties. Meanwhile,

through setting an optimal safety multiplier in exception policy, an exception

demand also can be efficiently and effectively controlled to maximise the net

present value.

According to the view from Rantala and Hilmola (2005), business conditions

of electronics manufacturers are demanding due to ever shortening product life

cycles, higher variety and increased outsourcing activity. Even though companies

could manage the increasing amount of purchased items with modularity,

software-based customization and well designed product platforms; the case is

often so that item count in purchasing is increasing with high rates. Rantala and

Hilmola (2005) proposed about purchasing automation to solve it as a

combination of ERP system integration as well as supply chain solutions, which

was measured by inventory turns.

Based on the case study of a middle-sized telecom electronics manufacturer,

Rantala and Hilmola (2010) further reported that an entirely automated order

enables the full use of ‘economic order quantities’ and its derivatives with

following factors in the conditions:

– Lead time for components is set to be five working days

– MOQ (Minimum Order Quantities) is the manufacturer package size and its

coefficients

– Safety stock for parts is 20 days demand, estimated based on six months’

historical demand

– Period of Supply (POS) for needs is 15 working days.

As the research finding of Rantala and Hilmola (2010), the inventory turns tend to

move towards a common inventory turn level that is around ten times a year and

component level variance declined a great deal by purchasing automation.

However, it was worried MRP nervousness and component buffering services

represent caveats for future APO implementations and use.

48

Dreyer et al. 2007 discussed the concept of Global Control Centre (GCC) for

manufacturing activity. The main elements of the GCC is found to be the global

control model, performance measurement system, ICT solutions and the

organization and the physical environment. The GCC should decrease the level of

complexity and improve control of operating environment for those main benefits:

– The access to true-time monitoring facilities at a high level

– A true SC (Supply Chain) perspective (different from a single actor

perspective)

– Speeding up recognition and decision making

– Integrated decision making (for instance purchasing and production control).

2.2.6 Optimisation of demand-supply with thinking of BI automation

Similar as purchasing automation, demand-supply network is mostly supported

by Business Intelligence (BI) solutions with information technology to ensure its

performance management (Blankenship 2004). BI is a field of the investigation of

the application of human cognitive faculties and artificial intelligence

technologies to the management and decision support in different business

problems (Ranjan 2009). It also needs the thinking of automation to enlarge

business value and create higher differentiation effect (Kaipia and Laiho 2009) for

the companies to win in global competition.

According to the view of Ranjan (2009, companies have understood the

importance of enforcing achievements of the goals defined by their business

strategies through business intelligence concepts. However, it is a challenge in

leading companies how to utilise huge amount of operational data for monitoring

and reporting to achieve business excellence (Zicojinovic and Stanimirovic 2009).

As the finding of Popovic et al. (2010), measuring the business value of business

intelligence in practice is often not or hard carried out due to the lack of

measurement methods and resources. Organisational or enterprise boundaries

(Nightingale 2009) often cause the development of such competitive advantage

extremely hard, which can be seen as lower priority if the company is always

stuck in business fire-fighting issues.

With own end-to-end insight, business intelligence automation, is thought as

a journey of innovation how to visualise, connect, simplify and optimise the

intelligences. The available knowledge can be found mostly about visualisation

and optimisation of demand-supply network:

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Visibility of Demand-supply Network

Demand-supply network needs good enough visibility as a condition for

optimising business operations. It has been one of top priorities in the most of

companies for high-tech industries. Otherwise, it is very hard in daily work to

match supply and demand with least inventory (Joshi 2000; Kaipia and Hartiala

2006). As observed by Falck et al. (2003), the challenge in developing an

information management approach is to find solutions that enable information

management across many different organizations. The issue of how to integrate

external collaboration with internal processes is identified as a gap by Holmstrőm

et al. (2003).

As the view of Holweg et al. (2005), collaboration in the supply chain comes

in a wide range of forms, but in general has a common goal: to create a

transparent, visible demand pattern that paces the entire supply chain. Reducing

uncertainty via transparency of information flow is a major objective in external

supply chain collaboration.

Kaipia and Hartiala (2006) have reported that there are several sources of

information along the supply chain, differing in data quality, information delays,

and usability. There is a challenge in choosing the most beneficial data sources

and in making the best use of the data. Information-sharing can take place across

various numbers of levels in the supply chain, the most typical being sharing

information between two levels. The information needs also varies according to

the role of each supply chain player and the location they have in the chain. Also,

according to the finding from Lehtonen et al. (2005), replenishment collaboration,

such as VMI, between manufacturers and distributors may be of little value in

speeding up demand synchronisation in product introductions.

As the view from Kaipia (2009), specific supply chain characteristics need to

be balanced by selecting a coordination mechanism that uses information

optimally to support the material flow. Flexible material flow needs frequent

updates of the plan based on accurate information:

– If frequent information sharing and planning practices are used to support

inflexible material flow, the result may be volatility in plans, and planning

resources are wasted.

– If a flexible material flow is supported by inadequate information, waste may

be produced in the material flow, in the form of excess inventories or capacity.

50

Obviously, the influence of demand-supply network integration on product

innovation is greater than other variables (Baharanchi 2009). It is important that

rapidly responding demand-supply requires more integrated planning and

frequent information sharing (Kaipia 2009).

As studied by Christopher and Towill (2000), lean or agile strategy needs also

to emphasise information visibility in demand-supply network. Whereas

information transparency is desirable in a lean regime, it is obligatory for agility.

Lean forecasting is algorithmic, but agile forecasting requires shared information

on current demand captured as close to the marketplace as possible.

As the observation from Auramo (2006), visibility can be thus approached

from both a tactical and strategic perspective. The tactical perspective focuses on

transactions as it offers visibility to the flow of materials, available capacity and

resources. From a strategic perspective, visibility enables evaluation and

reshaping of such operational network more in line with changing business

environments.

Optimisation of Demand-Supply Network

Thinking of business intelligence automation is not just for traditional automation

of tasks that were previously performed by humans (Stohr and Zhao 1997). With

visibility development focusing heavily on individual results, there are many

opportunities to connect and simplify them further for new offers in business

intelligence field. They are the steps leading to optimisation of demand-supply

network, which can form a journey of business intelligence automation to develop

great value. But, capturing the business value of business intelligence (BI) is a

strategic challenge (Williams et al. 2003). It has bee hard to find those practical

cases of optimisation reported in industrial or academic world. Especially,

available information of the research is found more often as the simulation or

mathematical models (Reiner et al. 2009, Sepehri et al. 2010). A focused review

of literature is mostly to study the outcome (such as simulation result or

conclusion) if applicable for its trial and implementation later in real business

environment. The thoughts can be useful to support action research at least before

own thinking of business intelligence automation will be continued.

Reiner (2005) described how process improvements can be dynamically

evaluated under consideration of customer orientation and supported by an

integrated usage of discrete-event simulations models and system dynamics

models. It was the use of selected performance measures as well as indicators by

51

a specific process improvement (postponement), which was conducted by an

electronic manufacturer in the telecom industry.

Using process simulation by Jammernegg and Reiner (2007), they

demonstrated how the coordinated application of methods from inventory

management and capacity management result in improved performance measures

of both intra-organizational (costs) and inter-organizational (service level)

objectives. It was conducted to a quantitative model-oriented research, based on

empirical data. The results had shown that a change from MTS (Make-To-Stock)

to ATO (Assemble-To-Order) production leads to reduction of total costs

(shipping and inventory carrying) of 11% on average.

Govindu and Chinnam (2007) described a generic process-centred

methodological framework for analysis and design of multi-agent supply chain

systems with following contributions:

– Development and validation of generic methodological support for analysis

and design of multi-agent supply chain systems.

– Creative adoption of SCOR (Supply Chain Operations Reference) to generic

MAS (Multi-Agent Systems) development methodology.

– Introduction of the notion “process-centred organisation metaphor” for multi-

agent systems.

Amer et al. (2008) provided a method optimising order fulfilment by six sigma

and fuzzy logic, which is as an effective methodology for monitoring and

controlling supply chain variables, optimizing supply chain processes and

meeting customer’s requirements. Unlike product design where the final

deliverable is a tangible product, the supply chain can be presented as an

intangible component of service design (i.e. covering a work plan to meet supply

targets, management of information flow, decision making, etc) with the tangible

component being the practical implementation of the service design (actual

hardware like logistics, transportation, information infrastructure, etc).

Raj and Lakshminarayanan (2008) proposed that entropy based complexity

minimization method is able to improve the performance of the distribution

system significantly compared to the initial performance of the supply chain. This

complexity management strategy can be extended to the overall network and for

systems with more states of interest. The work aims to improve supply chain

performance by quantifying and minimizing the complexity associated with the

distribution system through entropy calculations in accordance with the business

goal and demand pattern faced by the network.

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Reiner and Fichtinger (2009) developed a dynamic model that can be used to

evaluate supply chain process improvements, e.g. different forecast methods. In

particular they used for evaluation a bullwhip effect measure, the service level

(fill-rate) and the average on hold inventory. It was found that the bullwhip effect

is an important but not the only performance measure that should be used to

evaluate process improvements

Rabta et al. (2009) discussed about queuing networks modelling software for

manufacturing. In order to improve performance of a complex manufacturing

system, the dynamic dependencies need to be understood well (e.g., utilization,

variability, lead time, throughput, WIP, operating expenses, quality, etc). In this

manner rapid modelling techniques like queuing theory, can be applied to

improve such an understanding. Queuing networks are useful to model and

measure the performance of manufacturing systems and also complex service

processes.

Radhakrishnan et al. (2009) studied inventory optimisation in supply chain

management using genetic algorithm. It is a innovative and efficient methodology

that works with the aid of Genetic Algorithms in order to facilitate the precise

determination of the most probable excess stock level and shortage level required

for inventory optimization in the supply chain so that minimal total supply chain

cost is ensured.

Sepehri et al. (2010) suggested a Corporate Supply Optimizer (CSO), as a

central entity taking advantage of the notion of flow networks, gathers necessary

operational information from members of the corporate supply chain. The CSO

then guides supply chain members on ordering decisions for a minimum overall

cost for the entire supply chain. The CSO seeks a solution with minimum total

costs, unlike non-cooperative supply chains where individual members compete

to optimize their local costs.

2.3 Product change management

As Christopher (1998) explained, time has become a critical issue in management

as the most visible feature in industries. Product life cycles are shorter than ever,

industrial customers and distributors require just-in-time deliveries, and end users

are ever more willing to accept a substitute product if their first choice is not

instantly available. Product change management has to be applied as a key role in

enterprise operation, which has been able to establish a differential advantage in

high-tech industry. It can bring an end-to-end impact through the supply network

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to other partners. According to Knight (2003), Product change management had

been standardised to a popular process - enterprise change request and notice in

its version control. In detail, it was mainly based on CM II principles of Closed

Loop Change Process (CLCP), which was developed and marketed by the

Institute of Configuration Management in co-operation with Arizona State

University and the University of Tennessee.

All results of product version changeover were recorded as quantified data no

matter if the case was belonging to 75–85% of the fast track or 15–25% of large

changes. For high-quality of product change management, a balance is needed

between implementation speed at each manufacturing site and scraping cost in the

whole supply network. Scraping cost was normally caused by those non-

standardised components in material supply not usable anymore after product

changes. The targets of faster implementation and lower scraping cost should be

controlled carefully in all changes.

Particularly, every ECN was generated with change description detail, as well

as Bill of Material (BOM) for product current and new versions. It included all

affected sites and their implementation results. The analysis to indentify change

drivers (key components) was essential for updating demand-supply status at

weekly level and selecting changeover date with scraping cost known in advance

as a quantitative manner. The amount of new, existing and closed ECN was

followed monthly with implementation time as main focus. The scraping cost

trend was also studied regularly according to product or manufacturing site.

Quantified data in such a change management practice was traceable along with

whole product life.

As a research, the selection of those cases was oriented by different

manufacturing strategies in order to present the results from ECN database in a

comparable way but also with meaningful diversities. Christopher (1998)

suggested that successful companies should have a productivity advantage (lower

cost profile) or a “value” advantage (offering a differential “plus” – such as quick

delivery), or a combination of the two. The research was aimed to develop a

unique breakthrough that goes beyond either traditional lean or agile benchmarks

(Krishnamurthy et al., 2007 or Mohebbi et al., 2007).

As the research shown by Reiner et al., (2009), technology advances and

competitive pressure have shortened the life cycles for many products and

drastically increased the penalty of holding inventories. A major problem is that

forecasting the volume of products with short life cycles is difficult. Therefore,

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many supply chains rely on large inventory holding to reduce the risk of product

unavailability, which is a costly way very slow for implementing product changes.

The research of Reiner et al. (2009) has been targeted to mobile phone

industry with simultaneous inventory and pricing decision in the consideration,

which utilise the software tool Ithink to generate and analyse the mathematical

formulation. However, it is a different challenge to study mobile infrastructure

companies due to no product version overlapping there. For example, it is hardly

to see any research about reducing material supply liability and obsolete cost in

such product changes. With product innovation as a creative force proposed by

Utterback (1996), new search can be thus as the first study in this field, including

adequate details, covering new thinking to utilise product change cases to

understand the nature of global manufacturing.

2.4 Special characteristics of high-tech industries

2.4.1 Challenges in forecasting

Similar analyses of typical disturbances in industrial environments can be easily

found from others researching the uncertainties of supply networks. According to

Mascada (1998), they can be grouped to two main types: internal and external

disturbances. The internal disturbances can include equipment failures, quality

miss, lack of co-ordination, and workforce unavailability. The rest of the others

are external disturbances caused by customers or suppliers. All of those factors

affect the forecast, which is thus difficult to make it accurately.

Another sample is from the research on different planning deviations and

disruptions in the risk management of supply chains (Roshan et al., 2004) shown

as Table 2:

Table 2. Types of deviations

Planning Level Type of Event Example

Strategic Deviation Logistics/Manufacturing Capacity Reduction

Disruption Supplier bankruptcy

Tactical Deviation Order forecast

Disruption Port strike

Operational Deviation Lead-time variation

Disruption Machine/Truck breakdown

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The events at each level of corporate planning can bring challenges with different

scales. As Roshan et al. (2004) states, both factors of regular deviations and major

disruptions should be considered. Especially, the impact from the higher level can

create a disaster with a system-wide effect.

From all of the above analysis, it is reasonable to consider that the business

forecast is unlikely to be right most of time in light of these uncertainties. People

have to live with and survive business uncertainty by seeking other ways if not

just to improve forecasting accuracy alone. As global markets are becoming more

turbulent and volatile, it reveals such a common challenge in the industry

affecting truly to everyone. Thus, it can be also as a great opportunity for research

purpose.

2.4.2 Telecom supply chain of case company

Manufacturing operation in telecommunications infrastructure industry is closely

related to product life cycles. Such a feature of innovative business model has

been studied by Bengtsson and Berggren (2002), which can be briefly as a basic

introduction of the industry.

As the operational type of case company, both product development and

manufacturing functions are well combined in a project business way to fulfil

customer demand (Collin 2003). For example, it includes prototype fabrication

and pilot capability (zero-series production), departments for product

industrialisation and high volume production (Bengtsson and Berggren 2002).

Volume production can be also called flow production, repetitive flow production,

or other names. Indicated by Terwiesch et al. (1999) for achieving a fast pay-back

of investments in new product designs and production facilities, companies in

high-tech industry must reduce their development time (time-to-market) as well

as the time it takes them to achieve acceptable manufacturing volume, cost, and

quality (time-to-volume). The reason for keeping volume production in-house,

apart from cost and revenue considerations, is the importance of maintaining a

high skill level in manufacturing from the view of Bengtsson and Berggren

(2002). As Flynn (1994) emphasised, fast product innovation can be considered to

be an element of world class manufacturing. Such a way can thus provide a rapid

feedback from mass production to product design and engineering directly.

As “product focused”, the manufacturing and sourcing operation is a

“component oriented” manner, which means the company maintained strategic

components and processes in-house, together with the majority of final assembly

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and testing of ready modular products (Bengtsson and Berggren 2002). Of course,

non-strategic components are sourced from selected suppliers or sub-contracted

manufacturers. However, a reliable forecasting for telecom industry is difficult in

such an increasingly deregulated and competitive market place (Fildes and Kumar

2002). It can be very hard to analyse future trends and adapt the capacity levels

accordingly for all parties in demand-supply network.

Heikkilä (2002) discussed about three demand chain processes as variations

of generic demand chain architecture with following features:

1. Supporting the customer’s network building process by sufficiently fast

deliveries.

2. Building a product structure to enable decisions on the order-penetration

point for a base station according to the customer need.

3. Flexibility in the assembly capacity to meet the market uncertainty.

4. Inventory optimisation within the constraints resulting from the above criteria.

Heikkilä (2002) proposed that supply chain improvement should start from the

customer end, and the concept of SCM should be changed into demand chain

management. Demand chain management understands the need for good

customer–supplier relationships and reliable information flows as contributors to

high efficiency.

Berggren and Bengtsson (2004) have described this horizontal model as

superior option, which includes the advantages of speed to market, and revenue.

The used horizontal model can facilitate intensive interaction and reduces inter-

organisational interfaces. It is seen as more responsive and conducive to rapid

industrialisation of new products than a vertically sliced model, where volume

production is externalised.

2.4.3 Case Ericsson (analysed in 2002–2003)

From the study to one of leading players in this industry, Gustafsson and Norrman

(2001) reported a detail description about TTC (Time to Customer) flow and

TTM (Time to Market) flow in Ericsson’s demand-supply network. An obvious

feature of the demand-supply network is to use a common forecast to all parties

and call-off as the feedback to form a close loop. Due to its manufacturing mainly

outsourced, the information flow interacting with the customers and suppliers is

very essential to Ericsson. With such a set-up, it could help the speed of

introducing new products and also normal time of its global operation.

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Ericsson Radio System’s demand-supply chain was proud of its following

features (Gustafsson et al., 2001):

– Able to track and manage customer orders from receipt to fourth-tier supplier

authorization.

– A response to a customer inquiry about a delivery promise can be determined

within 10 seconds based on a current view of value-chain capabilities.

– The order information is then sent throughout the enterprise, which includes

the currently connected 25 first-tier suppliers, 10 second-tier suppliers, one

third-tier supplier, and one fourth-tier supplier.

– The resulting improvements include order lead-time reductions from 15 to 1–

2 days, inventory-turn increases from five to 80, and on-time delivery

improvement from 20% to 99.9%.

But, Ericsson’s bad situation (at the end of 2003) came back later again. It

was mainly because the difficulty was not just to measure one company itself but

to synchronize all parties in demand-supply network. Here was the information

from internet (Contact no.20) found at that time:

– Purchasing amount is near 2/3 of Ericsson’s total costs.

– Market is unpredictable in challenging to require better forecasts.

– Product volumes are smaller but the level of complexity is greater.

– Fire-fighting to get components.

– Delivery problems in Ericsson and its suppliers.

– Existing lead-time too long and uncertain forecasts are causing production

plan out of synch with actual demand for last-minute changes.

– Sales organization will add a safety margin and order more than needs

resulting greater variations in volumes with long or increasing lead times.

– Material shortage causes the plant and subcontractors with more stress,

money, quality inspection … All putting Ericsson back where it started – long

lead times.

– Customer satisfaction is only about 70 percent.

– “Santa Claus always delivers on time, but only once a year.”

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2.4.4 Case Dell Corporation/Lucent Technologies (analysed in 2002–

2003)

As one of the most commonly cited success stories of business operational

excellence, Dell Corporation represents the out-box-thinking model in computer

industry with remarkable achievements (Bilbrey 2000). Karemer et al. (2000)

described the exceptional performance was achieved by innovative response to a

fundamental competitive factor in the personal computer industry—the value of

time. It included Dell’s strategies of direct sales and build-to-order production

have proven successful in minimizing inventory and bringing new products to

market quickly, enabling it to increase market share and achieve high returns on

investment. The detailed features of Dell’s model are stated as follows (Dell

Corporation 2003):

– Dell computers are made with the latest available technology.

– Materials costs account for about 74% of the revenues.

– The suppliers are actually located all over the world (such as its Ireland plant

with the suppliers 65% in Asia, 25% in Europe and 10 % in USA). Many of

the suppliers have plants within 20 minutes of Dell's manufacturing plants.

Dell replenishes inventory levels as often as hourly with some vendors; it

turns over 52 inventory cycles each year, or once a week.

– Share information by real time communication with suppliers for rapid order

fulfilment (such as 10,000-plus customers every day in USA to change

demands unpredictably).

– Five day average Dell’s inventory in 2001 with target of 2.5 days (the

competitors carry 30, 45, or even 90 days' worth) & the third-party logistics

providers storing supplier-owned products with ten extra days or one week in

HUB near Dell’s factory.

– Dell Company gets billed after the components leaving supplier’s HUB. The

inventory-carrying costs are transferred to its suppliers to decreases the level

of inventory on Dell’s balance sheet.

– Demand-pull rather than supply-push. It never builds a computer without a

customer order. Most Dell systems are built in five hours or less.

– Excess and obsolete inventory (about $21 million / year) between 0.05% and

0.1% of total material costs (the competitors probably 2% to 3% worth of

excess and obsolete inventory).

– 84% of orders are built, customized, and shipped within 8 hours.

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– Dell sells 90% product directly to the customer.

– Market share +170% in 5 years (1997–2002) with profitable growth even in a

global economic hard time.

As indicated by Karemer et al. (2000), the key to Dell’s success has been its

direct sales and build-to-order business model. This model is simple in concept

but highly complex in its execution, especially under conditions of rapid growth

and change. Dell has continually renewed and extended its business model while

striking a balance between control and flexibility. However, the customer

feedbacks show the results of its delivery still with big challenges (from web of

HardwareCentral accessed in 2003):

– Good feedback: “The delivery was 12 business days after ordering”, “PC

delivery was within 6 business days”.

– Bad feedback: “Computer was not received more than 3 weeks after

ordering”, “Delaying the delivery by 3 weeks”, “Delayed shipment up to 30

days”

– Customer satisfaction indicator = 2.9/5 (58% - quite low).

The challenge aiming for delivery properly on time seems hard in real life to Dell

Corporation due to its demand-supply chain sometime not matching with the ideal

requirement of responsiveness if bottlenecks does exist in suppliers!

As explained by Hoover et al. (2001), a new approach was developed in

Lucent Technologies as 3C (capacity, commonality, consumption) materials

management system with the following principles:

– Plan the business (sales) based on capacity.

– Leverage commonality to reduce inventory.

– Produce according to consumption (actual demand).

Its success key factor is because the 3C approach links sales planning seamlessly

to component suppliers using a collaboration process based on ranking maximum

usage rates of individual components (Holmström et al. 2002). Hoover et al.

(2001) stated the details about the 3C approach: The first step is to define a

maximum sales rate of each end product that the factory will support. Second, the

factory capacity to produce the end product (units of output per day) is

determined. And finally, the component level maximum daily usage rate is

defined.

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Kumar and Meade (2002) described the system allows a manufacturer to be

prepared to produce anything they manufacture up to the maximum production

capacity for that item at any time. Instead of being driven by a finished goods

forecast that is turned into a daily or weekly production schedule, the 3C system

is driven by component-level maximum daily usage rates, which are set quarterly

or annually.

According to further explanation by Hoover et al. (2001), the only thing that

is needed daily is to check the on-hand inventory, what is on the way from

suppliers, and make sure that the sum is better than the maximum usage rage for

the number of days it takes the supplier to replenish. The supplier replenishes to

consumption. As a result at the Lucent Technologies Tres Cantos, Spain, plant, the

application of 3C led to an increase in fill rate from 75% to 95% (Kopczak et al.,

1998), nearly double the industry average.

2.4.5 Case Huawei Technologies (the new competition reality)

As indicated by Pisano and Shih (2009), outsourcing manufacturing has left U.S.

industry without the means to invent the next generation of high-tech products.

Nearly every U.S. brand of laptop and cell phone is not only manufactured but

designed in Asia. A new original equipment manufacturer (OEM) should be

studied from those rapid growth companies or countries, in which Huawei

Technologies can be such a leading Chinese player with remarkable impact in

international telecommunications markets. Its aggressive strategy has resulted in

the acquisition and merger of several international telecommunication device

suppliers (Dickson and Fang, 2008).

In 1988, Huawei was establishes in Shenzhen China as sales agent for Hong

Kong company producing Private Branch Exchange (PBX) switches. It was

ranked as No. 3 in terms of worldwide market share in mobile network equipment

in 2008. Then, it became No.2 in global market share of radio access equipment

in 2009 (Huawei 2010). According to the view of Nishimura (2008), Huawei

should be able to attain its full growth potential as one of the strongest

multinational companies. With its strong capabilities in development and design,

it can combine with the most advanced technology and parts, meanwhile utilising

cheap domestic labour and other resources.

As reported by Wu and Zhao (2007), Huawei applied different market entry

mode in different markets (different geographical markets and different products

markets). It had to enter the developing countries’ market first before it enters

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developed countries’ market. Similar as its business model in domestic market,

the method was to set up the R&D department or register subsidiary companies in

developed countries to develop an international market share.

Zhang (2009) studied also following reasons why Huawei has been

recognized by Business Week as the 3rd World’s Most Influential Company

(following after Apply and Google):

– In order to develop management skills and structure, Huawei invested in

collaboration with IBM Consultant.

– Besides catching up in management, Huawei invested heavily in Research

and Development. Averagely each year, at least 10 % of annual sales were put

into R&D for developing absorptive capacity. For example, Huawei so far has

established 14 R&D centres around the world.

– Its alliance-based network is characterized by multidiscipline, multi-level,

and multi-regions, being embedded in the collaboration with suppliers,

customers, universities, and leading players.

To support motivating Huawei people, it adopted a bonus and stock-option system

to reward good technology (Lau et al. 2002). As the observation of Liu (2005),

Huawei can thus grow faster based on a market-oriented innovation strategy.

In contrast with current No. 1 leader in same industry, the battlefield of

leagility in demand-supply operation can be no longer to protect its leading

position or even ensure its better survival. The key is because Huawei has more

relative advantages as the compensation to win the battle: lower break-even and

lower revenue expectation in cost-profit analysis. Same competitive effect could

be achieved easily if product value is as good as other competitors. It will become

an unstoppable journey for Huawei to re-write the history if other leading

companies would ignore the radical innovation as a new must nowadays. Similar

in many circumstances, the No. 1 leader should bring its value differentiation to

the customers or keep its unique advantage in the industry.

2.4.6 Other studies oriented by value differentiation or unique

advantage

Kim and Mauborgne (2005) indicated that head-to-head competition results in

nothing but a bloody red ocean as rivals fight over shrinking profits. Similar as

their proposal of blue ocean strategy focused on creating unknown market space,

value differentiation can have a same effect in any period of the lifecycle for

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industrial innovation by making the competition irrelevant, as well as leading the

trend in Information and Communication Technologies (ICTs).

Industrial lifecycle analysis as a tool

According to the research of Gottfredson et al. (2008), experience curves can be

used to show how much industry prices and company costs have fallen each time

the industry’s cumulative experience (total units produced or services delivered)

has doubled. It is possible to allow the companies to predict how much inflation

adjusted prices and costs are likely to decline in the future.

Tan and Mathews (2010) made a similar research how to utilise the view of

business cycle, industry / technology lifecycle, and industry cycle for the

companies to win in the competition. They also indicated that cyclical behaviour

in the economic system is one of the great themes in economic forecasting and

innovation study. Firms such as Intel have made a major discovery in their ability

to profit from industry cyclical downturns. Intel has consistently acted as a

‘counter-cyclical investor’ over the past two industry cycle downturns. These

business successes now call for complementary innovations in the fields of

business policy and strategy to generalize the findings and account for their

success in terms of the field's theoretical frameworks.

Tan and Mathews (2010) executed the time series analysis in the time domain

and in the frequency domain. It was not only to understand more precisely the

cyclical movement of the industry, but also new insights about potential sources

of the cyclicality and the implications of industry cycles to innovation strategies

and behaviour in the industry.

Business growth by blue ocean strategy thinking

The Blue Ocean Strategy was introduced by W. C. Kim and R Mauborgne with

following six principles (Kim and Mauborgne 2005):

1. Reconstruct Market Boundaries

2. Focus on the Big Picture, not the Numbers

3. Reach beyond existing demand

4. Get the Strategic Sequence Right

5. Overcome Key Organizational Hurdles

6. Build Execution in the Strategy.

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Kim and Mauborgne (2005) emphasise the strategic move is the right unit of

analysis for explaining the root of profitable growth, and not the company or the

industry. As explained by Kim and Mauborgne (2005), the strategic move is the

set of managerial actions and decisions involved in making a major market-

creating business offering. The definition of the red or blue ocean can be seen as

follows:

– In the red oceans, industry boundaries are defined and accepted, and the

competitive rules of the game are known. As the market space gets more

crowded, prospects for profits and growth are reduced. Products become

commodities, and cut-throat competition turns the red ocean bloody.

– Blue oceans, in contrast, are defined by untapped market space, demand

creation, and the opportunity for highly profitable growth. Although some

blue oceans are created well beyond existing industry boundaries, most are

created from within red oceans by expanding existing industry boundaries. In

blue oceans, competition is irrelevant because the rules of the game are

waiting to be set.

From the view of Kim and Mauborgne (2005), the market universe has never

been constant; rather, blue oceans have continuously been created over time. To

focus on the red ocean is therefore to accept the key constraining factors of

competition— limited market space and the need to beat the enemy in order to

succeed. However, companies need to go beyond competing in established

industries. To seize new profit and growth opportunities, they also need to create

blue oceans.

Leading industrial innovation as Apple

In order to create value differentiation via platform leadership similar as Intel,

Ghazawneh (2010) emphasised the Apple’s iPhone as another one of the projects

adopting the open innovation paradigm since it does not only depend on internal

but external and distributed sources for the developments of its applications and

services. The adoption of this open innovation model is mainly fulfilled by the

implementation of a product platform that enables almost anyone to innovate

upon its evolving system in an interdependent way.

From the view of Braithwaite (2007), the benefit of using the iTunes platform

is that Apple can maintain a direct and ongoing relationship with customers not

feasible for other handset manufacturers. Apple uses the iTunes ecosystem as the

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means for upgrading the phone’s capabilities through software upgrades as well

as an e-commerce web site for the sale of music and video content. Braithwaite

(2007) argued the revolutionary “user interface” and enhanced “user experience”

as the function of new technology as well as software designed to simplify the

operations of the phone. The iPhone proves to be as revolutionary as widely

predicted other cell phone manufacturers would need to respond.

As reported by Mohr et al. (2010), Apple does product design of all its

products in-house in California with its own designers and engineers. Design is a

core, proprietary skill set for Apple which gives it competitive advantage in the

marketplace. For example, Rixner (2007) indicated the Apple’s iPod wildly

successful relative to MP3 offerings is a business design that provides a complete

digital music experience. While its competitors pursued either a device approach

focused on MP3 players or a music-store approach focused on downloadable

songs, Apple provided an integrated offer of hardware (iPod), software (iTunes

music library), and content (iTunes music store).

For Apple’s iPod, its manufacturing and even core software are outsourced

(Lo 2008). The subcontracting manufacturer likes to work with Apple more than

with other firms because “the iPod’s popularity ensures that orders keep coming

in” largely due to customers’ loyalty to Apple’s notable R&D capabilities. As

mentioned by Spink and Krudewagen (2009), Apple sells a $299 iPod (designed

in California, assembled in China), for instance, it makes an $80 profit, while the

Chinese assembly plant makes $4. Known from the analysis of Mulrennan (2010),

Apple’s share price rise from $9.43 to $203.00 per share in the following eight

years after the iPod was launched in 2001. By late 2009, the unique position that

the iPod held within the market was validated by the announcement that 225

million units had been sold worldwide. The iPod currently holds a market share of

78% among digital media players.

By contrast, Copeland and Shapiro (2010) mentioned that Apple is slower at

technological adoption than the other PC (Personal Computer) manufacturers. PC

manufacturers are introducing significantly more products with shorter life spans

relative to Apple. Apple keeps its computers on the market about twice as long as

the other PC manufacturers. Apple's prices fall relatively slowly and less

extensively than do the prices of the PC manufacturers. Prater et al. (2001) also

mentioned Apple's supply chain was not complex, the uncertainty involved in sea

transportation made Apple's supply chain vulnerable. At the same time, Apple's

supply chain agility was low because of the low speed and flexibility with which

product could be brought to market.

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Obviously, Apple has learned the tricks from its practices in PC industry and

brought open innovation into smart-phone industry better than other competitors

(such as Nokia or RIM’s Blackberry acting still so similar as Apple’s vertical

platform in PC industry), as well as keeping some advantages on product lifecycle

control. Sako (2009) claimed Apple Computer was not successful as an integrated

PC firm, but emerged successful as Apple Inc. with its iPod and iPhone, when it

bundled entertainment and mobile telephony.

Competing by new product or business design (case RIM’s Smartphone)?

In comparison with unique advantage of Apple in a same market, it is interesting

to check other competitors such as RIM (Research In Motion) with its BlackBerry

product as an example. As Hahn and Singer (2009) described, it was Research in

Motion (RIM) and not Nokia that developed the smart-phone segment. Although

RIM’s BlackBerry was not the first wireless device with reliable e-mail access, it

popularized mobile e-mail among business professionals because of its

integration with Microsoft Exchange servers and strong encryption. Through the

introduction of the iconic BlackBerry, RIM has proven itself to be a leader in the

handset industry. Expectations were high in November 2008 when RIM

introduced a touch-screen smart-phone, the BlackBerry Storm, to compete with

the iPhone. But the Storm has proven to be somewhat of a disappointment.

However, innovation is a continuous process.

Hahn and Singer (2009) believed that BlackBerry will likely learn from its

successes and failures. Given the pace of technology development in the mobile

handset market, the iPhone’s position is hardly guaranteed. A new device could

render the iPhone obsolete quickly. As indicated by Rixner (2007), the key to

each of these successes (such as Intel, Apple, or RIM) goes far beyond the

company’s products and lies in the business designs surrounding their

technologies. If the Apple’s iPhone can be seen as a strategic move to the blue

ocean of Smartphone market, what is the next big thing to beat it or re-create a

new successful story by another unique way?

“Shanzhai” to be a bad copycat manner or as an open innovation

As Lee et al. (2010) explained, “Shanzhai” is a Chinese term referring to

companies that operate outside traditional rules and practices. One product that

has been particularly impacted by Shanzhai manufacturers is the mobile phone.

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According to the study of Li (2010), the first Shanzhai mobile phone appeared in

2004. They were fake goods of famous brands such as Nokia or Samsung. With

very cheap chips in bad quality, they were not accepted by consumers. Since 2006,

the MTK mobile phone chip was developed by MediaTek (headquartered in

Taiwan). Due to more integration of multimedia features and lower prices, the

MTK chip was utilized by mobile phone companies and mobile phone design

companies in a wide range. Lee et al. (2010) reported an impressive growth status

of Shanzhai phones. In 2008, more than 750 million cell phones were produced in

China. A significant portion (20 percent, or about 150 million units) of these

phones were produced by Shanzhai companies. These companies had rapidly

taken a significant share (about 10 percent) of the worldwide market. As the

comparison from International Data Corporation (IDC) about market share of

main business players in the fourth quarter of 2008, Nokia is 39.1%, Samsung is

18.3%, LG is 8.9%, Sony Ericsson is 8.4% and Motorola is 6.6%.

Li (2010) stated Shanzhai mobile phones can attract many customers who

focused on the cost performance of products. There are many advantages to

Shanzhai products: no 17 percent added-value tax, no network license fee, no

sales tax, and no 3–4 Euros checking fee to the government. Shanzhai running

costs are further minimized by the absence of marketing and after-sales service.

However, sales were not only high in the domestic mobile phone market; its

export volume was considerable as well, including India, Brazil, Russia, and even

the European market.

Wu and Zhang (2009) indicated “Shanzhai” is actually not simply to be a

copycat, which was thought as a bad manner in the competition with its threat

often ignored by mainstream companies. In fact, "Shanzhai mobile phone" can

also offer numerous innovative functions such as emergency light, telephoto lens

and even counterfeit currency detector. "Shanzhai mobile phone " represents not

only product innovations, but also business model innovations. Lee et al. (2010)

emphasised this phenomenal growth of “Shanzhai” was primarily due to

nonconventional approaches to the global market in market positioning, rapid

product development, and tightly coupled, responsive and efficient supply chain

management. Known from the explanation of Wu and Zhang (2009), "Shanzhai

mobile phone" companies needn’t to invest on R&D because of using chips from

Taiwanese company MTK as “turn-key” solution also with SDK (software

development toolkit) and application software ready. Besides, there are thousands

of design houses in Shenzhen, the capital of "Shanzhai mobile phone" providing

total solution of mobile phone design and thousands of dealers providing all kinds

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of components like supermarkets. Such an open innovation way applied in

manufacturing industry is so well combined with the innovator’s strategy when its

disruptive effect in market competition is often noticed too late by those leading

companies.

As the view of Li (2010), in the low-end consumption market in China, the

foreign products tend to be over-designed. Thus, a large number of domestic

supplier and demands emerge which results in a heated competition on prices.

Shanzhai manufacturers start to produce recreation products through imitation,

and then undergo the rapid change from imitation to innovation. The Shanzhai

industrial development began with imitation, which can be traced back to the

examples in Korea (such as Samsung) and Japan (such as Toyota). They are all as

leading companies nowadays in the industry - not anymore just based on offering

cheaper cost or lower-end product. As Quad-Band-Phones.com (‘QBP’) to be

another example (accessed in December 2010), it can offer some really cool non

brand mobile phones that are for sale at ridiculously low prices, which is owned

by US citizen even the company is located in China. Obviously, “Shanzhai” has

been becoming more neutral with many complex effects as a business model for

new comers in the industries.

Lee et al. (2010) argued it would be unfair and inaccurate to classify all

Shanzhai mobile phones as “pirated” or “illegal” products. Whether legal or

illegal, whether they imitate or innovate, they have demonstrated amazing

flexibility and tenacity. With the determination, a company can be successfully

transitioned from a Shanzhai culture to become a major mainstream force in the

industry. “Shanzhai” just indicates that it has been gradually organized and

enlarged in an unauthorized fashion during company’s earlier life. The innovation

can be as one of the driving forces of Shanzhai manufacturer’s competitive

strategy. Understanding the product development process and supply chains used

by Shanzhai mobile phone makers may stimulate new ideas for design and

manufacturing by mainstream companies.

The complexity of high-tech innovation studied by case Nokia

Naturally, the review of mobile phone industry should be continued with case

Nokia as the next step after a wider study was mostly concentrated on those with

business model impacts (such as Apple and Shanzhai). Although many of new

challenges have turned the competition as a red bloody ocean, how Nokia can

remain its No. 1 leading position better than other competitions? As analysed by

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Chang and Horng (2010), Nokia operates successfully not only on high-end

market but also on low-end mobile phones. For example, Nokia ranked number

one in China’s branding market in 2008. Its high-quality low-price business

strategies include many creative changes actually as a new business model in the

past if comparing to other competitors:

– Manufacturing strategy on integrating supply chain

– Technology strategy on establishing R&D centres in China

– Channelling strategy of consumers in small towns and villages

– Pricing strategy in response to low-end market.

The capability to bring radical changes in the innovation based on people spirit of

motivation to win business growth is the key of Nokia success in the past or in the

future, no matter facing which traditional competitors as Motorola or Samsung

lack of such impacts. However, Nielsen and Hanseth (2010) compared Nokia with

the iPhone approach from a free and open innovation perspective. Apple has at

the same time shown as a fairly successful model in serving users and innovators.

For example, buying an iPhone is also buying into a value network where new

services can easily be bought and installed from an application store (App Store).

Even if the application store has been criticized for challenging some of the core

values of the Internet since all applications have to be signed by Apple, this has

really made a difference for the users, and other mobile phone manufacturers are

following (like Nokia’s Ovi). As reported by Halonen et al. (2010), Nokia hasn’t

been as successful as Apple in building its application store. Nokia launched Ovi

Store in May 2009, almost one year behind Apple. During the first three months,

Ovi Store had only 10 million downloads; whereas Apple App Store had 100

million downloads during the first two months only. The weakness of open

innovation comparing with Apple makes Nokia to introduce radical changes so

slowly, which used to be Nokia strength but now as a sign of dangerous losses in

high-end market.

Similar as Nokia Siemens Networks in mobile infrastructure industry

struggling with Ericsson and Huawei, another threat to Nokia can come from low-

end competition, in which the advantages of Shanzhai can be mostly utilised by a

much more powerful competitor. As an example reported by Foster (2010),

Huawei Technologies shot past Alcatel-Lucent and Nokia Siemens in 2009 to

become the world's No. 2 telecom-equipment provider, powered by quality and

product upgrades on top of its long-standing low prices. For leading companies

(as Nokia or Ericsson), the winner at the end of battle in red ocean will be not

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determined by lean or agile improvement efforts, but the No. 1 position in the

industry by the capability to create radical innovation and bring blue ocean

opportunities.

If the urgency is misplaced somewhere else, same wrong focus can affect

Nokia’s success in mobile phone industry again even Apple is just a new comer at

high-end market now. From the view of Braithwaite (2007), the iPhone has the

capacity to impact all players in the cell phone network: consumers, rival wireless

carriers, Apple’s wireless partner, and rival handset manufacturers. Issues of

usability and enhanced ‘user’ experience are also likely to influence rivals’ phone

operating systems software. From the perspective of the ‘user experience’, the

multi-touch screen and enhanced functionality, the iPhone introduces a radically

innovative and simplified user interface. Due to the complexity of high-tech

business, Nokia should be alerted and focused how to regain the competitive

advantage beyond Apple and lead new radical innovation in the industry. Nokia is

still as the No. 1 leader for market share even now also in smart-phone field.

Great opportunity to win the competition exists if Nokia will not repeat the path

of Nokia Siemens Networks and keep top priority to its right battlefield.

Outcome of value differentiation studies

All in all, the innovation for value differentiation should be emphasised not only

as the element in lean or agile improvements, but also more importantly as its

own portion in the research. The difficulty of radical innovation must be not

underestimated in business with the risk ahead. Besides, same thought can be also

applied to optimise company’s manufacturing operation, as well as new product

industrialisation in change implementation research. It should not be forgotten

about great opportunities in leading companies how to synchronise with industry

lifecycles – always aiming for value differentiation or unique advantage by the

innovation!

2.5 Theory synthesis

There is a growing concern to emphasise global manufacturing in a strategy-

driven way. The above review of existing theories indicates that there is a gap

requiring further research. For example, there have been a number of valuable

studies emphasising lean and agile in global manufacturing, separately as well as

their combination. However, business reality is much more complex similar as a

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dynamic world with three-dimensions, which can not be looked sufficiently as a

static picture of two-dimensions. Lean and agile strategies have not been studied

well in an environment of global manufacturing where the third dimension is

innovativeness: interacting dramatically with each other influencing product

change. Such an environment can be shown as following Figure 4:

Fig. 4. Thinking three dimensions for product change.

Theory findings that act as a basis for this research:

1. Strategy is not static but needs adapt swiftly

In modern high tech business, the competitive situation is turbulent, resulting

in pressures for changing manufacturing strategy even separately for different

products or product groups. Previously it was thought the strategy can be

generated and maintained for years, even the shortest update period could be

as long as a half year.

2. Lean and agile aspects are both needed in manufacturing strategy

Traditionally, literature indicates that a company must make a choice between

lean or agile. This can be misleading and result in an unbalanced situation in

modern high tech business. On the contrary, lean and agile ingredients should

be embedded. The literature uses the term leagility to describe the

simultaneous combination of these two. Only in few extreme cases, extreme

choice along lean-agile axis is sensible.

3. Rapid product change as a driver for manufacturing strategy

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Rapid product change is not an element of lean or agile, but an element of its

own influencing the choice of strategy. It is not enough to work on the two

dimensions of lean and agile, but rather to introduce a third dimension of

rapid product change. Rapid product change can be seen as a part or an

example of the innovation. Same principle can be applied to any of other

similar innovative changes (such as new breakthrough technologies,

disruptive business model, or even revolutionary “user interface” and

enhanced “user experience” as iPhone).

4. Demand-supply chain as a competitive factor

Industrial competition in this current period of globalisation is becoming a

battle between demand-supply networks, not just single companies. However,

it is a great challenge to find ways to tackle operational bottlenecks, and to

overcome organisational boundaries, both within the company and between

its suppliers.

5. Accept inaccurate forecasts

As seen from Ericsson’s model, it is challenging to ensure the reliability of a

company’s operational performance. A company has to make a choice in an

environment with uncertainties on whether to accept inaccurate forecasts or

seek for other ways to overcome this problem.

6. Emphasise radical innovation always in new high-tech business reality

With many high-tech companies analysed in the literature review, it shows

the innovation should be emphasised as an independent strategy, not just as a

component in the legality thinking. For leading companies or new comers

aimed for the No. 1 position in industry, radical innovation needs to be as a

must to achieve or keep winning in the competition. Although it increases the

complexity to describe, business reality should be thought as a 3D world no

matter how easier from lean or agile view only.

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3 Results of the three action research cycles

Selected case company is a significant global actor in the mobile infrastructure

industry. The research environment can be described in line with product change

implementation, which is mostly focused how to optimise manufacturing

operation and global demand-supply chain.

There are many engineering changes during a product’s lifetime without a

period when new and old versions overlap as execution principle. Component

changes in products often happen at any time adding extra complexity for

manufacturing besides original demand uncertainty. Product change management

scope includes planning & informing all the sites (own primary and substitute

factories, as well as its subcontract manufacturing partners), and cooperation

between these sites, collecting results, analysing and making conclusions. The

research can be characterised to simultaneously include aspects of worldwide

business impact, rapid innovative pace, and high volume in operation.

The case company combines push-based supply chain and pull-based demand

chain together as a mix to synchronise production and delivery of all product

parts with big lead-time gaps (mostly unavoidable from material supply). Pull

principle is applied at internal steps of the production, as well as the delivery end.

The product flow is in FIFO (First-In-First-Out) mode at each step of

manufacturing, meant that not a same product is initiated, moved and delivered in

the operation to fulfil the demand at customer end. With it, short lead time can be

achieved in production to balance the pace and the flow of manufacturing

operations. Push principle has to apply for the supply end and keep the

inventories to absorb the impact of inaccurate forecast. Demand-supply network

has to thus have enough tolerance to avoid undesirable conditions, such as

production stop due to lack of key components. Observing in such various ways,

the effects of different theories could be seen “virtually”, e.g. MTO (Make-To-

Order), ATO (Assemble-To-Order), DTO (Deliver-To-Order), and even MTS

(Make-To-Stock).

Obviously, the challenge is caused from component logistics in the

electronics industry, which is extremely complex due to a vast number of required

components with long production or delivery lead-times. For example, the lead-

times may differ by days (such as VMI – Vendor Managed Inventory or two-bin

system), weeks (such as PWB and own specific integrated circuits), or even

months due to sea transportation (such as the cabinet). This creates bottlenecks or

big inventories in the supply network due to those time variances and real demand

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often not matching with earlier forecasts. When the gap of material supply

occurred by the changes of product customisation or delivery requirements,

demand-supply network balance would be easily destroyed in a fire-fighting

manner to take time for its recovery. It affects also the speed of product

development in its change implementation phase. For example in case company,

the product versions were different more than one year at some manufacturing

sites before the research was launched.

The case company had to find an alternative way to survive better in the

competition as everyone in the industry suffered by those same challenges. The

bottom-line was to deliver products to customers’ requirements (especially having

the changes of delivery amount or product configuration) at a high speed, without

means to develop efficient forecasting processes to manage demand uncertainty.

Whenever the volume of pull at delivery side was larger than the amount of push

at supply side, production had to be stopped due to missing components. Such a

demand-supply network problematic area could not be simply solved by the

outsourcing of manufacturing operation or VMI (Vendor Managed Inventory) like

supply, which would just mean to move the headaches to other business partners.

Faster transfer of demand information or a more reactive planning was also not

enough to save manufacturing companies as a physical process is inflexible in

responding to frequent plan changes in normal operation. When product changes

added on this, demand-supply planning practices became even more fragmented

and frustrated. There were no existing solutions available, academic or industrial,

at the time.

As a competitive advantage of case company, both product development and

manufacturing functions are well combined. For example, it includes prototype

fabrication and pilot capability (zero-series production), departments for product

industrialisation (where the research existed) and high volume production. The

pilot of zero-series production uses same BOM (Bill-Of-Material) as the last

prototype-run but now with a bigger volume similar as normal production lot size.

If the result is failed, product development should be returned to prototype phase

to solve the problems found in the pilot and then back to zero-series production in

the future. If the result is successful, it is the approval for product development

entering new phase of change implementation. There are no other more-series of

the pilot (or normal production) needed as the way of one single gate to approve

product development before volume production phase. Volume production can be

also called flow production as manufacturing the products in a repetitive manner.

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Research Cycle 1 included the case company aiming all of its actions to

minimising costs, which was as a strategy of cost effectiveness. Minimising

inventory and scrapping costs required its effect into the whole demand-supply

chain. In research Cycle 2 the case company aimed at diminishing order delivery

period. Research Cycle 3 concentrated on shortening product change period. The

case company executed a strategy of innovativeness making product changes as

fast as possible. During research cycles, every change case was recorded using

change notes (CN). Change notes compare the old and the new product versions,

indicating all changes in used components. CN also indicated the expectation

when the changes will be conducted. Site specific implementation reports were

utilised to record changes, the implementation time and scrapping costs.

Implementation report described all the results from different sites. Both,

implementation reports and change notes were stored into a database. There were

over one hundred product change cases available within the company at the time

of research. The researcher selected three cases out of all product changes, one for

each cycle. The cases were important for business and there was a significant

change in the product.

Process improvements were made based on the three selected product change

cases individually. After the process improvements, it was checked whether the

targets set for that particular cycle was reached or not.

3.1 Research Cycle 1 – minimising costs

The action research was initiated by problem-solving in a tough situation: The

forecast was so inaccurate. The lead time of material supply varied from a few

hours to several months. When demand is this dynamic, it is not possible to react

quickly, often delaying of the implementation of product changes or causing a

production stop at some manufacturing sites. The old way of planning based on

the forecast cannot work well anymore when faced with such business uncertainty.

Although the learning journey actually started in a fire-fighting way, a systematic

approach was planned.

In the research, the focus was on the challenges of seeking a new solution for

surviving product changes. Even with the urgency of problem solving, it was

expected to be a part of the long-term development (as the above pre-step) of

global manufacturing’s adaptation to a dynamic business environment. With

product change implementation and action research combined together, it can

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ensure the quality in a systematic way with all strategic and operational factors

taken into account.

3.1.1 Pre-Step

Before the first cycle of action research was started, several cases of large product

changes were actually already done. The implementation of product change can

be seen as a black-box in context analysis. Its interactions with external factors

were very similar in all cases.

It was already introduced in an earlier chapter when explaining why product

changes can be used for operational improvement – both with the same variants

affecting inputs or outputs. There were many ideas from previous lessons for

further development. Such a procedure was repeatedly utilised also in later

research cycles because it can act in a target-driven manner to make the outcome

with a better quality.

Here was the review of research conditions in the company as a context

analysis:

– A worldwide economic hardship to most of the global companies at that time

naturally with cost-effectiveness as the strategy.

– Dynamic business environment with forecast accuracy extremely poor.

– The product or material variation has been controlled and reduced

continuously well by company-wide process of “Design for Excellence”.

– Lead time of own production has been shortened to a good level (not as a

main factor).

– Inventory should be kept as small as possible to be an operational condition.

– To avoid scraping cost in material supply as a big issue to product change

management.

– Earlier planning to reduce inventory for implementation of product changes

not working good enough.

– The production stop caused by actual demand and supply do not match each

other due to inaccurate forecast and material lead time gaps.

– It had to seek new solutions in the survival for product delivery and

engineering changes.

As expecting synchronisation for survival, it was aimed to accept inaccurate

forecast / dynamic demand, introduce a timely manner in balancing supply

operation, and even synchronise invisible liability. From a Change Notice

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database of product change results, the implementation time and scraping cost

were used as two measuring indicators comparable with all research cycles. The

thoughts from this round can be brought to the next cycle as its input, i.e., as the

pre-step of that cycle. It can thus make the cycle-by-cycle progress a continuous

learning journey.

3.1.2 Diagnosis

The diagnosis was done in a more practical sense to detail new improvement

ideas, which can be used further for action planning in the next step. Such a way

of analysis (at pre-step in general and at this step in practice) will be used for

every cycle of action research. It can thus ensure the quality of action research, as

well as product change implementation itself. The outcome of diagnosing in this

Cycle 1 can be shown as follows:

– Acceptance of inaccurate forecasts

– Clock-speed to be weekly as the material in-flow for demand-supply network

– Invisible liability in material supply – controllable or not?

All of the above elements acted as the targets or baselines for planning the actions

in this cycle and to prepare for the next step.

3.1.3 Planning

The breakthrough was intended in action research Cycle 1 to achieve the

following two solutions of weekly clock-speed and dynamic cut-off window.

Weekly Clock-speed

With PWB (Printed Wiring Board) purchasing as an example, a new attempt was

made to change the material order for small and frequent deliveries (such as on a

weekly basis) as the improvement in demand-supply operation. It will replace the

way originally with a big order just according to the message from MRP system

by inventory level control. The idea was to consider the time added to the amount

as both factors for forming a material supply flow.

The actions were planned in a procedure shown as following Figure 5:

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Fig. 5. A series of actions planned for the trial of weekly purchasing.

Even with the difficulties at the beginning, it was gradually becoming more and

more understandable by the buyers in practice. It was found that it was better to

consider not only the amount but also the time for all components in the supply. It

was needed to repeatedly equalise the available amount of each component

according to the product’s BOM (Bill Of Material) at every moment in line with

the weekly updating of the forecast or demand. Such a dynamic balance with the

postponement of manufacturing can be the key of true synchronisation. No matter

how the forecast would be right or wrong, it actually just changed the usage time

due to dynamic demand. The adaptive focus was clearly moved to the delivery

and the usage – even the supply operation was still in a business uncertain

situation.

Although material sourcing department had difficulties understanding the

idea at first, the attempt was finally succeed in moving the focus to

synchronisation for demand-supply as a weekly pace!

Dynamic Cut-off Window

The material liability to the company was caused by its responsible forecast to

other parties in the supply chain on a pre-agreed scale (such as weeks or months).

It is the duty of the platform company to take the liability amount for its

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consumption or pay for it as scraping cost. It can thus impact the implementation

of product change or the scraping cost dramatically as an unbalanced component

amount. Normally, the material liability amount cannot be seen directly in MRP

systems of any company when approaching the version changeover date. It was

because the forecast can already be changed so greatly month by month. It should

be negotiated case by case between the companies at each tier of the demand-

supply chain.

The design of a creative solution for this challenge can be stated as a four-

step process called dynamic cut-off window in Figure 6:

Fig. 6. Action plan for a dynamic cut-off window.

It was needed to set a product version change date in that MRP system that was

longer than the longest lead-time of any component in the current and new BOM.

This date can be changed in a weekly management meeting. The idea was to keep

a status just not to stop the ordering of old material but also not to start the

purchasing of new material during the trial period of a new product version.

The moving of version changeover date was done weekly to ensure such a

time-window (one week more than the longest lead-time of key component – the

change driver) just working at the “risky-edge” to cut off the forecast of current

version material. The attempt was meant to avoid the liability and not harm the

current supply operation. If the product trial period might take one month, the

forecast of that amount globally could be eliminated without harmful side-effects.

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It can thus reduce the maximum amount of material liability. Besides, it was

always planned to keep a difference of implementation time (such as one month)

between the platform site and the lean sites of the company. It can help the

situation further to consume the exceeding amount of the material by those lean

sites.

However, there were the disadvantages of confusing the whole demand-

supply network because of the trouble in moving the version change date weekly

in MRP. It was a type of manual planning – also a lack of effective

communication in advance. (Can it be improved in a process well-defined way

also with better IT supports if possible?) The forecast proved its power by

penetrating everywhere in the enterprise eco-system in a forced manner.

Obviously, as its benefit at the end, a good result was to cut all liabilities by that

one-month period before the change approval to a new product version.

3.1.4 Taking action

The actions were taken using the process shown in the following Figure 7.

Fig. 7. Actions in action research Cycle 1.

The figure describes the approval process how to promote product development

phase ready for entering volume production, as well as some improvement

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happened in that research cycle. The BOM (Bill-Of-Material) in the last prototype

run had to be tested by a bigger amount of volume manufacturing called “0 series”

run. It was mainly to prove the last proto BOM suitable as a product change

without a quality problem in volume production. The BOM of this version can be

utilised as “Planning BOM” for some earlier activities in product change

management, such as comparing with current BOM to identify “Product Change

Driver” (the component with the longest lead time or as the most expensive one).

If the result of the “0 series” run was OK, it can mean product change

approval ready for buying new material and stopping the purchase of old material

– a version change to the product. If the result of the “0 series” run is not OK, it

could mean another prototype run for big modification change and another 0

series after it (or to run it directly again for small modifications). The

implementation time of product change and scraping cost (caused by extra

material left in the company not possible for its consumption or transfer to other

sites) were the targets to control a change properly implemented.

3.1.5 Evaluation

The result of change implementation time with very low scrapping cost can be

seen in the following Figure 8 for platform site (Site 1 as primary site of case

company) and another main lean site (Site 2 as one of substitutive sites).

Fig. 8. Implementation result of action research Cycle 1 from change notice database.

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The analysis and explanation of the implementation result

The implementation result can be analysed also with some of more explanations.

A platform site (site 1) and another main lean site (site 2) of global manufacturing

are included in the figure of the implementation result. The target of

implementation time for site 1 was determined because the lead time for PWB

(Printed Wiring Board) was six weeks, so using seven weeks as a reasonable

shortest time due to the spare amount of safety inventory at a very limited level

still required at any time. It was planned to the change implementation better

always with one month difference between both sites to reduce the risk. The target

of the second site for implementation of product change can thus be 11 weeks (the

shortest possible and allowable time). It is also aiming to the implementation at

the first site as soon as possible with reasonable amount of exceeding material

transferable to the second site to be consumed there. All sites are independent of

the implementation (such as getting cost saving from product change due to

cheaper material in new BOM) and scraping cost. Due to material transfer from

the first site to the second site, the consumption at the second site can generally

take about 1–1.5 months depending on their capacity ratio.

In this case, both sites had done the implementation with a “normal” speed

but without the liability amount of old material for further consumption. Product

1 & 2 are main products with a larger volume normally also with common

components that product 3 & 4. For old material consumption, it can take a longer

time if only using product 3 & 4 in the manufacturing.

New findings in this action research cycle

Most of the new improvements had been achieved as expectation. They can be

utilised repeatedly in other cases of product changes or next cycle of action

research.

1. Effectiveness strategy as one extreme of the choices – The strategy of cost-

effectiveness means material supply is synchronised at a balance point of

minimum inventory. (This also affects implementation time, producing a

shorter one but with less tolerance – production stop occurred a few times).

2. Acceptance of inaccurate forecasts – It can be possible if the equalisation of

old material was focused.

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3. Clock-speed to be weekly base – It helped the material supply in a dynamic

situation with the focus on the factors of amount as well as time.

4. Dynamic cut-off window showing IT importance – It was done via MRP

system to the whole supply chain, but manual changes were weekly

adjustments.

5. Possible to control invisible liability of old material – The result

demonstrated a large improvement with the dynamic cut-off window, but

when done manually problems appeared.

Practical contributions

The contributions can be stated as follows so as to show practical business value:

1. Keeping a big picture view of the strategy in order to ensure that activities at

the operational level match the target of cost-effectiveness and that the

benefits are spread to all in the supply chain.

2. Moving the focus away from inaccurate forecasts to the equalisation of

material supply in a timely way as the core of synchronisation in global

manufacturing.

3. Implementing the supply balance on a weekly basis with factors of amount as

well as time clearly as a synchronisation trial.

4. Doing dynamic cut-off window as an example of synchronisation – as

manual changes in IT systems to achieve operational adjustment interactively

with global manufacturing.

5. Showing synchronisation in demand-supply operation is capable of

controlling old material liability.

Comparing to the targets of product change management, Table 3 summarises the

findings of the research Cycle 1:

Table 3. Targets & findings of the Cycle 1.

Strategic Targets in Product Change Management The Implications of New Findings from the Results

Executing corporate strategy: cost effectiveness.

Trial for operating from inventory level to clock-

speed control

Balancing between fast implementation & lower

scraping cost in whole demand-supply chain

Acceptance of inaccurate forecasts

Effectiveness strategy as one extreme of the choices

Clock-speed to be weekly base

Dynamic cut-off window showing IT importance

Possible for controlling invisible liability

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3.2 Research Cycle 2 - shortening order delivery time

The action research continued in another cycle using a new product change case

in Figure 9. The economic hard time was almost over and ready for agile thinking

as a new improvement focus. It was the period with a different strategy so called

“responsiveness” to the company and its demand-supply network. In this cycle,

the effort was targeted to a new operational model of faster responsiveness in

product delivery. It was a great opportunity to see how this strategy in an opposite

way to affect business operation. Besides, the argument of what as good or bad

happened in this case provided a valuable lesson that served to enhance the

understanding of synchronisation.

Fig. 9. The action research for new operational model.

With thinking for a new operational model, some small modifications were made

also to the action research process, though most of the steps were still similar. It

could be a great opportunity during a better economic period to make more

radical changes.

3.2.1 Pre-Step

Because the research framework was generally explained in earlier parts, the

information in this cycle can be stated immediately with the topics. As a target-

driven way, it was determined to verify existing knowledge such as ESP and

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Three Dimensional Concurrent Engineering (3D CE) – Product, Process, and

Supply Chain.

The Review of Research Conditions in Context Analysis:

– Worldwide economy was recovering with a different situation to the most of

global companies possible for using a new strategy.

– It was acceptable to deal with inaccurate forecast in business operation as a

given condition.

– Responsiveness as the strategy even to its extreme – keep the material

inventory level according to maximum production capacity.

– Weekly pace to demand-supply operation as a default.

– Planning BOM with dynamic cut-off window done in a manual way was

applied again at the beginning but cancelled later. It was mainly due to its

trouble and the confusion it caused others in the co-operation.

3.2.2 Diagnosis

It was noticed that the forecast function did not matter so much in the situation if

aiming to reserve spare capacity fully. It was a case after diagnosing and planning

for a trial of new strategy to its extreme – to keep inventory required by

maximum production capacity without the forecast needed in MRP system.

The outcome of diagnosing in this Cycle 1 can be shown as follows:

Key Points of New Improvement Thoughts

– The extreme responsiveness as the strategy (jut according to maximum

production capacity in operation without forecast)

– Concurrent engineering with R&D by 2 phase approval for product

change started earlier in demand-supply operation

It was a radical change to the forecast by a new thinking: without it at all in the

operation because it was wrong anyway the most of time. Synchronisation

between both processes of product creation and demand fulfilment was another

key issue in the trial with two-phase approval for product change. If the first

approval can pick an earlier status in product change process, it was expected to

see the benefit from applying 3D CE (Three Dimensional – Product, Process, and

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Supply Chain – Concurrent Engineering). Such extra time in the supply chain will

make product change implementation faster.

3.2.3 Planning

Planning work was done for the following two issues:

New Trial for an Extreme of Responsiveness Strategy

The responsiveness as the strategy means the balance point of synchronisation for

material supply was moved to a reasonably high level in the inventory. This was

based on a new operational model consisting of the following principles:

– Systematic Concept 1: Real-Demand-Pull for whole demand-supply chain

– Systematic Concept 2: Immediate delivery without extra cost at each tier

– Systematic Concept 3: “Financial Zero Inventory” by “Cash-To-Cash Time”

– Systematic Concept 4: Profitable by the volume and speed from the

innovation.

During the trial, it should keep checking any of bad influences due to no forecast

to both normal manufacturing operation and product change implementation. The

procedure how the actions were planned is shown in following Figure 10.

Fig. 10. Action plan for the extreme of responsiveness strategy.

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The four principles of the new operational model were verified if they can work

without the forecast. It depended just on demand pull at each tier of the supply

chain with the inventory as the buffer to compensate lead-time gaps. In this way,

it was targeted for a quick delivery everywhere in the operation of demand-supply.

Concurrent Engineering by Two-Phase Approval

The principles of two-phase approval in Figure 11 are simple. Aiming for earlier

implementation with controllable risks, it needs the planning of new and old

material supply to be quantified to a detailed level. If product version change

happened before the second approval, it can cause the confusion and disaster in

global manufacturing operation.

Fig. 11. Action plan for two-phase approval.

As a process modification to try 3D CE, the attempt at two-phase approval should

be communicated to all relevant personnel in product creation and demand

fulfilment. The time comparison and the amount of material supply should be all

based on the detail of quantified information.

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If affecting as an operation to multiple sites, the risk management should also

be in place. Of course, its implementation can be adjusted or stopped whenever to

find any issue out of the control. After the result is made available by the trial, it

should be analysed to see if it can be a solution for long-term usage or not.

3.2.4 Taking action

The actions were taken with the process shown in the following Figure 12:

Fig. 12. Actions in action research Cycle 2.

The process was similar as the research Cycle 1 but with some of new

improvement ideas as a trial. The BOM (Bill-of-Material) in the last prototype-

run was not used for the dynamic cut-off window in the 0 series period to avoid or

reduce old material liability problem as in Cycle 1, because it was cancelled

shortly at the beginning stage due to it being so hard to operate manually.

Besides, the concurrent engineering principle was used between R&D and

product change management with two approvals to product change. The first

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approval can be just after the testing was done without problem so as to allow

buying new material and stopping old material purchasing immediately. The

second approval can be after all R&D work was done to approve product change

finally. The important thing was to be sure the period between the first and the

second approval should be shorter than the implementation time of the first site.

3.2.5 Evaluation

The result of change implementation time with very low scraping cost can be seen

in the following Figure 13 for the platform site (Site 1) and another main site (Site

2):

Fig. 13. Implementation result of action research Cycle 2 from change notice database.

The implementation result provided many meaningful implications for further

analysis. It was the first time to have the results shorter than the target time at the

first site. It was due to two approvals by saving about one month time from the

concurrent engineering with R&D.

The lesson was also learned from the material liability problem caused by the

cancellation of the dynamic cut-off window. It was found a big amount of liability

after the implementation was done. It had to request that the second site return to

the old version in order to consume the old material so that the implementation

time shown in the figure on the right was 34–35 weeks at the second site.

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Without the information from the forecast, it was a lack of a future estimation.

Such a situation made it very hard to plan for the next product change coming

after this case.

New findings in this action research cycle

In action research Cycle 2, the attempt was actually focused on synchronisation to

ensure the maximum production capacity as a balance point. Due to the forecast

being mostly wrong, an attempt was made to live without it as an extension of

Cycle 1.

With the opportunity of the responsiveness strategy with a larger tolerance,

there were also other lessons learned in the analysis to understand the secrets of

global manufacturing.

As a summary, here are new findings in action research Cycle 2:

1. Possible to live without forecast – Just keeping inventory according to

maximum production capacity if business strategy can be the extreme of

responsiveness strategy.

2. Another balance point for synchronisation – However, it can be selected not

necessary always at the extreme (OK also with other possibilities).

3. Concurrent engineering as synchronisation extended to R&D – possible again

applying to other fields in business operation?

4. Invisible material liability as an X-ray picture of supply operation – It seems

no other options better than a dynamic cut-off window.

– It will be much better if information can be visible as a direction for

improvements.

– Is it possible to use IT solutions to obtain benefits for synchronisation

deep into demand-supply chain but without causing too much trouble in

the operation?

5. The forecast actually as a balance point - With similar principles, the forecast

can act as a reference to be a balance point for synchronisation around it

dynamically.

– The extreme of a strategy can be one of the statuses among a whole

operational range.

– How to “place” global manufacturing by corporate strategy for other

options in the middle?

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Practical contributions

All of those new findings were the elements to enhance synchronisation. The

contributions can be stated as follows so as to show practical business value:

1. It was a valuable experience to implement an extreme version of

synchronisation (operating even without the forecast). The range of adjusting

synchronisation can be thus pushed to the boundary approaching its limits.

2. It was a case of understanding the lean or agile thinking to notice its extremes

in synchronisation.

3. With synchronisation extended to R&D, the total operational range was

further thought as a better way.

4. Although invisible material liability was a lesson to learn in this case, it can

show that synchronisation made huge differences in the results for product

change management, and also provided meaningful insights to global

manufacturing in general.

5. It was the case of facing uncertain future to understand the importance of the

forecast - as a reference point in dynamic status.

Comparing to the targets of product change management, Table 4 summarises the

findings of the research Cycle 2:

Table 4. Targets and new findings in action research Cycle 2.

Strategic Targets in Product Change Management The Implications of New Findings from the Results

Executing corporate strategy: responsiveness.

Trial of four systematic concepts but actually to the

extreme of responsiveness

Trial of concurrent engineering

Possible to live without forecast as an extreme

Another balance point for synchronisation at the

opposite extreme

Concurrent engineering as synchronisation extended

to R&D

Invisible liability as an X-ray picture of supply

operation

The forecast actually as a balance reference point in

dynamic business

3.3 Research Cycle 3 - shortening product change time

In this strategy choice of action research Cycle 3 a technology leader with a new

product or a significant change of an existing product quickly goes to market to

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capitalise on a booming business. It was the focus moved to making product

change as fast as possible even to accept a much bigger scraping cost.

Obviously, it was not same as the strategy of cost-effectiveness in Cycle 1 or

responsiveness in Cycle 2. The Cycle 3 in Figure 14 was a case in product change

management including a big innovation. In this strategy, a revolutionary solution

was expected.

Fig. 14. The action research for faster innovativeness.

All three extreme strategy choices were for different business reasons. In order to

avoid the confusion, other formats of similar statement about those strategies are

listed in following Figure 15.

Fig. 15. A new choice of business strategy.

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Not as easily understandable as other two options, innovativeness can help a

technology leader to achieve technical advance, and then to use that advance as a

weapon in tough competition. It was not the same as effectiveness or

responsiveness, which can be for lean or agile manufacturers without the need for

more explanations. It was a strategy used in action research Cycle 3 to do

everything (even to accept much larger operational risk and scraping costs) just so

that product change was made quickly. For this case only, the target was to make

the innovation speed of new product to the market as fast as possible.

Choosing the extreme strategies can be a natural way when starting to seek

more alternatives. This research cycle became very exciting because it presented

the opportunity to try something different from the two previous cycles.

3.3.1 Pre-Step

By using innovativeness as the strategy, it should keep old material as little as

possible to get rid of the previous version quickly as a lean effect. But, it also

requires new material as much as possible to enter market faster with the new

version as an agile purpose.

The review of research conditions in context analysis:

– It was essential to achieve a fast product innovation with shorter time to the

market. In a certain situation, it was also extremely vital in global

competition for the company to do all possible efforts for it.

– Profitable innovation is an important competence for the company.

– Innovativeness as a strategy: a bigger scraping cost was acceptable for a

faster speed of product changes.

– The inventory of ready products in the outbound warehouse (HUB) was a

new condition to the demand-supply network with the possibility to watch

product version change at the HUB (but not version modification for small

changes).

– The forecast was used again in the MRP system, even though it could be still

wrong.

– Planning BOM of the last prototype run was applied again during the 0 series

period for material preparation but not as a dynamic cut-off window (actually

as a fixed cut-off window with material version changeover date selected

earlier but without any modification).

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– It was using the safety inventory of ready product in HUB to avoid the risk of

the 0 series run failing.

– The implementation result can be evaluated in a new way to consider the

product version change at both production and HUB inventory.

It was a valuable opportunity to study a new extreme strategy – the

innovativeness as another alternative outside the box of lean or agile

manufacturing!

3.3.2 Diagnosis

A new process was needed to develop what was achieved in the previous cycles

for an even faster implementation of product change. The complexity of the

dynamic cut-off window and the two approvals can be improved with more

disruptive intentions. A simpler way was tried during the 0 series period just by

starting implementation activities directly. With its bigger risk covered by ready

product inventory, it can go beyond the limits of normal operational process for

radical changes never done in the past. The outcome of diagnosing can be shown

as follows:

– “Actual” implementation of product change started before 0 series.

– Fixed cut-off window better than dynamic cut-off window?

It was an opportunity to go beyond normal limitations in manufacturing operation

for more creative improvements. The strategy of innovativeness was thus as the

third factor to affect the balance of synchronisation efforts.

3.3.3 Planning

The safety inventory of products and the bigger scraping cost were accepted.

Radical changes can be made to the product change process. Of course, the

evaluation after the trial or implementation should also be made carefully. Each

step of the action plan was aligned using the following procedure in Figure 16:

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Fig. 16. Action plan for faster innovativeness.

Even if the 0 series fails, product delivery to the customers can still be ensured by

a HUB inventory of ready products. The worst situation can be the scraping cost

from unusable new material and production stop due to lack of old material.

However, it can be recovered with only limited damage in a short period of the

trial.

3.3.4 Taking action

The actions were taken following the process shown in the Figure 17 below:

Fig. 17. Actions in action research Cycle 3.

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The radical change in business process was planned and verified in the research

Cycle 3 with the intention seeking a breakthrough effect in product innovation.

The BOM (Bill-of-Material) in the last prototype run was used as a planning

BOM to buy new material and stop old material purchasing even before the 0

series. The version change date was input into MRP without moving as a fixed

cut-off window. Due to the unknown result of the 0 series (to approve product

change or not), ready product inventory at the HUB was used as safety inventory.

Product in HUB inventory should be consumed by itself without any chance of

transferring it to other sites.

If the 0 series run fails, another prototype or/and 0 series run will be needed,

which, according to the modification, can be large or small. New material can be

scraped if it is not used in the next new BOM. However, product delivery will not

be delayed due to safety inventory in HUB, but production can be stopped due to

no material of old version.

It was thus beyond the limits at the material supply side involving the risks

almost at a not acceptable level.

3.3.5 Evaluation

The result of implementation time can be seen in the following Figure 18 for

platform site (Site 1) and another main lean site (Site 2):

Fig. 18. Implementation result of action research Cycle 3 from change notice database.

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The analysis of implementation result indicated the challenge of product

innovation due to its complexity in global manufacturing environment. The result

was different from the perspective of production or HUB at the first site. From a

production perspective, it was a very quick change done just after the approval –

never working at such a fast speed in any previous cases. But from the view of

HUB, product inventory was still consumed with a long period and showed

almost no difference to past cases. The business benefit of cost saving from this

product change can be achieved only after product version change done at HUB,

but not just in production. Besides, the version change was selected as a fixed

date, so the unbalanced material caused a higher scraping cost in this case (even

though it was expected). Actually later in the practice, it was forbidden to use

such an extreme way again without waiting for the 0 series result. But, this lesson

had provided an unusual experience on how to move beyond the limits of product

change management. The opportunity of innovation oriented manufacturing in a

revolutionary way was remained for further research.

New findings in this action research cycle

In this case of innovativeness strategy, the product change was happening quickly

in material inventory and production. The balance to equalise material supply for

the changeover moment was not achieved. It was an impressive scraping cost to

show such an unbalanced status of demand-supply: How big could the amounts of

related components be if synchronisation was not done properly. Besides, the

situation in product inventory reflected synchronisation equally important to all in

demand-supply chain. It can imply the value of synchronisation better if it is done

not just for a certain moment or a special part of enterprise ecosystem.

As a summary, here are the new findings of action research Cycle 3:

1. The strategy of innovativeness emphasised as an independent option - It is

not same as the innovation in lean or agile strategy to be its ingredient.

2. Actual implementation of product change not so faster – As seen from this

example, it can be very important how to evaluate the improvements properly

to get an accurate picture.

3. An unbalanced status in the supply without synchronisation – But, it can also

be a great opportunity if synchronisation can be properly done.

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4. Demand-supply chain always considered as a whole – As in many other

examples, the whole demand-supply chain should be re-engineered not just

for a company or a department.

Practical contributions

All of those new findings were valuable lessons for synchronisation. The

contributions can be stated as follows so as to show practical business value:

1. This cycle was a case to introduce the third factor into operational

synchronisation for business complexity shown in an innovative way.

Although the innovation would increase the uncertainties in strategic

planning, this third factor can describe the real challenges in global

manufacturing often truly driven by such a strategy.

2. The lesson learned from the evaluation of the final result can indicate that the

expectation of improvements should be ensured by a right way to do

synchronisation in a big picture of the total range – not just a part of it.

3. This cycle was also as a case to show an unbalanced status in the material

supply if synchronisation was not done properly. It was obviously better if

synchronisation can be done not just during a changing period but also during

a normal time of healthy global manufacturing.

4. It was increasingly noticed that as the range of synchronisation gets bigger

and bigger, it approaches a total range, which was one of the reasons that

finally led to synchronisation. It was learning by doing to find a new way in

line with those action research cycles for a breakthrough.

Comparing to the targets of product change management, Table 5 summarises the

findings of the research Cycle 3:

Table 5. Targets and new findings in action research Cycle 3.

Strategic Targets in Product Change Management The Implications of New Findings from the Results

Executing corporate strategy: innovativeness

(product change as fast as possible).

Disruptive changes in the process

Trial how to use ready-product inventory in product

change management

The strategy of innovativeness with possibility to be

an extreme

Actual implementation of product change not faster

An unbalanced status in the supply if there is no

synchronisation

Demand-supply chain always considered as a whole

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4 Discussion

4.1 Answering research questions

4.1.1 Research question 1

Research Cycle 1 included the case company aiming all of its actions to

minimising costs (Figure 19). The case company executed a strategy of cost

effectiveness. Minimising inventory and scrapping costs required swift

component control in the whole demand-supply chain. This chapter answers

research question one by describing the results obtained through research Cycle 1,

including both the positive and negative impacts of this trial.

Fig. 19. Focus of research Cycle 1.

As a starting point for this cycle, forecasts were not accurate and scrapping costs

were a challenge. Lean aims to minimise costs. This research identified that it is

beneficial to accept that forecasts are not always accurate and find ways to

navigate in this type of reality.

Before, in the case company, components had set minimum inventory levels.

New order was placed once going below this minimum. Order sizes were set

based on pre-calculated batch sizes. The company moved from this type of

solution to a weekly assessment. Inventory levels and forecasts were followed on

a weekly basis, and were further used for necessary orders. This resulted in

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smaller batch sizes, more swift reaction on changes in demand and product

variations, and less scrapping.

In the case company, the old way was that new product version’s introduction

to production was considered only after obtaining approval of zero-series. In

order to speed up new product versions entering production, and improve

transparency towards suppliers, ramp-up is now considered already during the

zero-series.

Changes in a product result in changes in the BOM. The company first

analyses the critical components of the new BOM version. Component is critical

if it is expensive, or it has a long lead time from order to delivery. In order to

minimise scrapping in product change situations, it is necessary to identify the

components with the longest lead times. This decides the earliest possible point

when one can move to a new product version. One week’s margin is utilised to

decide the product version change moment. The zero-series of the new version is

followed, and ramp-up is postponed weekly until zero-series approval. As

suppliers are now informed already in the beginning of zero-series, the suppliers

will have time to react accordingly. This in turn reduces the liability and possible

liability related costs. The case company uses the term of dynamic cut-off window

for the new way.

Negative aspects: People complained that dynamic cut-off window causes

confusion as it changes every week. Manual way of changing target dates for new

product versions was seen non-suitable new ways desired and IT tool would be

possibly of benefit.

4.1.2 Research question 2

In research Cycle 2 the case company aimed at diminishing order delivery period

(Figure 20). In this trial, the case company aimed at strong concurrency in

engineering to get order delivery period as short as possible. This chapter answers

research question two by describing the results obtained through research Cycle 2,

including both the positive and negative impacts of this trial.

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Fig. 20. Focus of research Cycle 2.

Quick delivery (agility) strategy was utilised and the case company considered

whether it would be possible in some situation to live without forecasts and

accept high inventory. Higher component inventory enabled greater tolerance.

The old way of doing things included new product versions going through

zero-series, production and testing, and if the results were ok, documentation and

approval process followed. Documentation and approval took about 3–4 weeks.

Only once R&D approved new production version, the company started buying

new material and stopped buying old material. Downside of the old way was that

this waiting time of 3–4 weeks can be considered as waste from the perspective of

product change management.

The new way, tried during this research cycle, aimed to speed up the process

by involving R&D to give earlier signal to product changes, so that the supply-

chain management people could start their work earlier. After Zero-series

production testing proved acceptable, buying new material was started and buying

old material finished. Documentation and approval process was conducted in

parallel by R&D.

Negative aspects: Using dynamic cut-off window was abandoned in this case.

As a consequence the liability of the case company increased as the company had

committed to buy components for the need of a certain period. This experiment

caused delays at other sites, even if the situation was ok at the main site. This type

of situation might cause other sites having to switch back to producing an old

product version.

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Without forecasts in the system, visibility over coming changes was lost.

Now it was understood that a forecast would act as a reference point for

equalising supply. This situation would also influence other companies, aside the

case company.

4.1.3 Research question 3

Research Cycle 3 concentrated on shortening product change period (Figure 21).

The case company executed a strategy of innovativeness making product changes

as fast as possible. The trial clarified whether a ready-product inventory could be

used to speed up product change. This chapter answers research question three by

describing the results obtained through research Cycle 3, including both the

positive and negative impacts of this trial.

Fig. 21. Focus of research Cycle 3.

Normally scrapping costs are minimised. In the research Cycle 3, scrapping costs

were accepted, while everything was arranged to make product changes as quick

as possible. This approach significantly differs from lean and agile.

Unit-level ready products were used as inventory. This way the company

could stop buying old material earlier and avoid problems in delivering to

customers.

Before starting zero-series, product new version changeover date was

selected and fixed. This fixed cut-off window enabled suppliers to deliver the

existing order plus liability. No further orders were placed for the old material,

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and at a certain point during zero-series, order for new material was placed. This

left the company with the possibility of zero-series failing, resulting in a second

zero-series and stopping production due to old material running out.

This experiment enabled the case company to understand the demand-supply

chain better from a wider perspective, thus providing beneficial learning.

Negative aspects: This experiment had more negative impacts than positive

ones, and consequently this approach was banned after the experiment. From the

business perspective, there was no improvement in the sense of cost savings.

In this case, at the point when purchasing of old components was stopped, the

level of different components was not equal, resulting in expensive scrapping

costs. The difference in the levels of different components is caused by different

buyers buying in the components they are responsible for in different pace and

their activities not being coordinated.

4.2 Managerial implications

The results of this study provide tips for global high tech companies. These large

international companies typically have manufacturing sites in different parts of

the world. Based on the results, mental shift from local optimisation to a global

one is required for efficient manufacturing operations.

Companies have traditionally considered their strategy as a choice between

minimising costs, quick delivery, and rapid product change. Also, companies have

believed that one single strategy is adequate and applicable to all of their products.

However, according to this study, different products may have a different strategy.

This allows companies to flexibly react to the needs of different customer groups,

business environments, and different competitors. Strategy can also be changed

relatively often, monthly, weekly, or even daily.

Companies must consider all the three elements of minimising costs, quick

delivery, and rapid product change and to find an adequate balance among these

in order to succeed (Figure 22). The arrows in the figure represent flexibility in

changing strategy. There can be different strategy for different products and

competitive situations. In addition, companies have multiple partners and

consequently a suitable balance is required for the entire demand-supply chain.

Forecasts are an important, powerful tool for influencing the supply operations, as

forecasts give information for suppliers. A company should try to make relevant

information, including product change management, visible for both the company

itself, and the entire supply chain. This would make it easier for the

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subcontractors to optimise the entire chain if it has adequate access to critical

information. Two-way communication is required to fully optimise the entire

demand-supply chain.

Fig. 22. Flexible optimisation on situation basis.

Once optimising the entire supply-chain, in modern business environment, time is

a vital competitive factor and companies must be swift in their moves. This

results in optimisation on time basis becoming a key. This type of time-based

optimisation means synchronisation of R&D, production, material handling, and

related planning. Special attention should be paid to bigger events, such as new

product launches, and significant engineering changes, as they have a wide

influence.

Based on the results of this study, companies must harmonise their product

portfolio globally, including all their sites. Once the same product version is at all

sites, they can help each other from components supply viewpoint, and

consequently product changes can be taken through quicker.

Companies must also equalise material status for supply, and follow it weekly.

This is as different components of a same product must be seen as dependent on

each other, not separately, meaning that if you cannot buy component A, there is

no point buying components B, and C either. In a situation with too many

components, the component you have least determines the equalised level. If

there are any components more than the equalised level, those can be considered

as waste. The difference between the equalised level and the original forecasted

level can be considered as tolerance margin increasing agility. However, if the

company prefers lean over agility, this type of tolerance should be avoided.

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Above described new kind of thinking require developing IT tools to support

global visibility and operations. These IT solutions would enable changing

strategy often, even on product basis, resulting in business model agility.

The fast industrialisations of R&D achievements constantly into a full scale

of own global manufacturing is a stronger competitive advantage in case company,

comparing to others in the industry with the production mostly in an outsourced

way. The big difference of the speed can bring the success or the failure as the

innovation in the industry. If such an advantage is not fully utilised or even gone

in the future, the lost of leading position could happen as one of the reasons

coming from this battle field.

4.3 Scientific implications

The systematic review of the literature identified a number of important research

gaps as the opportunities to make scientific contributions. It was lack of academic

studies as either or both outside-in and inside-out manners to develop new

thoughts along with product innovation in lean or agile manufacturing. The

innovation itself was emphasised later even as an independent strategy to affect

manufacturing operation beyond the lean or agile thinking box. No matter how

harder to show 3-dimensional world, business complexity should be considered

and handled in a fresh thinking of right way similar as the Figure 23 (not enough

with a 2D view to different 3D realities):

Fig. 23. Business complexity as 3-dimensional world out of lean or agile thinking box.

Those are the knowledge gaps indentified as an approach to describe business

optimisation studied by the research with sufficient scientific purposes:

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– Is one strategy only to avoid “stuck in the middle” still valid or just suitable

in some conditions?

– What can be a new thinking beyond traditional lean or agile manufacturing

theories?

– How to ensure a balance at strategy level to reduce the risk of business

failures?

– What should be the key of optimisation in high-tech manufacturing?

– What could be an alternative way with more details to the forecast research?

– How the radical innovation is emphasised and used as a must in high-tech

industry?

Traditionally, it was thought a company can have only one strategy and that

strategy is valid for a long period of time. Porter (1980 & 1998) emphasised that

to be successful over the long-term, a firm must select only one of the three

generic strategies. Otherwise, with more than one single generic strategy the firm

will be "stuck in the middle" and will not achieve a competitive advantage. He

argued that firms that are able to succeed at multiple strategies often do so by

creating separate business units for each strategy. Similar idea (Treacy et al. 1993)

also indicated that a company should have a clear position among the following

choices to avoid the stuck-in-the-middle situation due to a lack of focus:

– Operational Excellence

– Customer Intimacy

– Product Leadership.

This study indicates the contrary: a company must excel with flexible

optimisation choosing from multiple strategies on situation basis. A company

should not be stuck in the middle, or only good at one of these strategic choices.

For example, lean and agile ingredients should be simultaneously embedded. This

aims to break the boundaries even further, because some literature uses the term

leagility to describe the simultaneous combination of these two (Mason-Jones et

al. 2000).

In most literature, the rapid product change viewpoint is not as common as

lean or agile studies (Gunasekaran, 1999). This thesis demonstrates that it is not

enough to work on the two dimensions of lean and agile, but rather introduces a

third dimension of the innovation – rapid product change. Consequently, the

manufacturing strategy should be seen as a multidimensional playground, where

the optimum can be different in different situations.

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With above thoughts, this provides newness into scientific thinking. In

modern high tech business, the competitive situation is turbulent, resulting in

pressures for changing manufacturing strategy more often and even to have

separate strategies for different products or product groups. A single strategy for a

company or a business unit is not functioning well anymore. An unbalanced status

from one of the three elements at the strategy level can cause a disaster for

corporate business. For example, Toyota has been proud as the lean & TQC (Total

Quality Control) benchmarks in the industries, as well as its Prius Hybrid models

leading the innovation in the car industry. However, its business growth without

proven design quality to ensure a proper supply & delivery expansion (similar to

this study also as a bigger scale actor in another industry) brings Toyota into

tremendous troubles. It has made loss during the two last years, after 70 years of

outstanding financial results. Toyota is still struggling to recover from its recall

disaster and regain a reputation that has made it the biggest car company in the

world.

This dissertation is thus highlighting that the optimisation of enterprise

strategy within multidimensional playground should be conducted on time basis.

This view is in line with the fact that time has become an increasingly important

factor in high tech business (Christopher 1998). Flexible optimisation in a timely

way is thought as a total synchronisation concept, which has been researched

further in recent years as next big outcome.

This dissertation confirms the findings of Einhorn (1986) from decision

research about accepting error to make less error. In dynamic business nowadays,

one has to accept inaccurate forecasts due to unpredictable business environment.

After it, the opportunities will be identified to ensure the company (or its unit) not

stuck in the middle or any “end” point of multi-strategy scope. Such scientific

implications can guide the research leading to more solutions.

Finally, the radical innovation should be used as a must in high-tech industry

to measure and lead business performance in “Red Ocean” of the competition,

which is not emphasised enough in the most of manufacturing theories. It can not

be outside of the research even its focus as the optimisation for manufacturing

operation or demand-supply network.

4.4 Reliability and validity

In order to evaluate the results, it is needed to check the validation of the research

quality. The definition of validation can be found from many academic resources

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for different fields. Robert K. Yin (1994) also presents four complementary ways

to judge the quality of empirical case study research: (1) reliability, (2) construct

validity, (3) internal validity, and (4) external validity. It should be applied here to

guide the discussion.

In general, reliability is the ability of a system to perform and maintain its

functions in routine circumstances, as well as hostile or unexpected circumstances.

Reliability is necessary for validity and it is easier to achieve although it does not

guarantee validity. Stated another way, reliability can be associated with random

error and validity with systematic error.

In general, validation is the process of checking if something satisfies a

certain criterion. Validation implies one is able to testify that a solution or process

is correct or compliant with set standards or rules. With the confirmation by

examination and provision of objective evidence, it should conform to user needs

and intended uses. The particular requirements implemented through the process

can be consistently fulfilled.

Validity can be extended to internal validity as internal design of the study

and external validity as external generalisation made from results. Internal

validity is a form of experimental validity if it properly demonstrates a causal

relation between two variables. External validity is also a form of experimental

validity if the experiment’s results hold across different experimental settings,

procedures and participants.

The meaning of the above figure can be understood easily without the need

for further explanations.

There is a format to review the dimensions of research quality to check the

reliability and validity as following Table 6:

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Table 6. Dimensions of research quality in the evaluation (format from Collin, 2003).

Quality

dimension

Case study tactic (Robert K.

Yin, 1994)

Appearance in this study

Reliability -Develop case study protocol

-Develop case study

database

-For all product change cases, the implementation targets of

version changeover time and scraping cost are applied in the

same way.

-A database of CN (Change Notice) and IN (Implementation

Notice) is well constructed.

Construct

Validity

-Use multiple sources of

evidence

-Establish chain of evidence

-Have key informants review

draft case study report

-Many sources of knowledge or information were checked in

theoretical and industrial trend review.

-The research is a continuous development based on the

existing body of knowledge.

Internal

Validity

-Do pattern matching

-Do explanation building

-Do time series analysis

-The flexible optimisation or synchronisation is a pattern-like

way suitable to many solutions.

-The systematic principles were built as the abstraction from

action research cycles and product change cases (even

some were not selected as the cases for research cycles).

-Time series analysis was in line with action research cycles.

External

Validity

-Use replication logic in

multiple case studies

-Use case study protocol

-Replication logic was used in multiple cases of product

changes.

-Generalisation in action research approach is very limited

even with many other product cases ongoing at the same

time.

But, the business situation has been becoming more and more dynamic in global

manufacturing, which should be as a factor in the consideration (such as Toyota

with profit loss also in 2008 after 70 years of positive results). Here are the key

points to discuss validation and reliability in further details for this research with

the above concerns:

– The research scope was defined at the beginning for a narrow range within

suitable industries. It was related to those large corporations who engage in

high-tech manufacturing on a global scale. They should have a demand-

supply network already as part of their strategy-driven operation with

minimum product variation. It is now still valid with all the limitations

verified earlier because some companies can be far away to such a maturity if

they do not yet meet these pre-requisites. As a concern of validation, they are

as essential conditions for repeatable results of product change management

or operational improvement towards total synchronisation.

110

– Due to the importance of IT support increasing dramatically, it can be another

factor in the consideration of validation and reliability. For a global operation,

such a competence should be good enough in order to avoid the trouble of

synchronising in a manual way. A comparable level of IT competence should

be needed along with business re-engineering in the company. Besides, it can

also be interesting for business application suppliers or consultant companies

as a great opportunity for business concept innovation and technology

development direction.

– For a company in the competition, a different strategy should be used as a

situational choice on a case-by-case basis. A business benchmark sample

cannot be copied exactly to other companies, even though it was successful

under certain conditions. Even for those benchmark companies, they can not

keep a same result to themselves.

– This research proposes a new approach to dealing with the traditional

problem of inaccurate forecasts in today’s more dynamic nature environment.

Attention should be paid to more than the improvement of forecast accuracy

alone if it is not working so well in business practices.

Guessing the possible direction of the plane and its speed difference to the rocket

or the missile is a challenge. It can hit the target only if they “meet” in the

shooting. It can be quite sure that at least there would be no big chance of hitting

the plane if one just targets its current position, a situation that is similar to just

making a copy in a dynamic business.

Therefore, the degree of validation and reliability should be dependent on the

abstracting level of the solutions. As a result-oriented way, the copy cannot bring

great success in business because there can be no exactly same situation always

kept to any companies or even the enterprise benchmark itself. To deal with

business uncertainties, there is a need for abstracting the solutions, such as

happens with time-based optimisation by multiple strategies. When applying it to

the manufacturing operation, it should be as a pattern, with those principles being

the baseline only. The nature of autonomic features should be considered in

business for the success.

4.5 Research contribution & discussion

New contributions of the research can be summarised as a base of further work in

the future. It includes not only the insight to some arguments of management

111

theory, but also own discovery from this research. Each point is detailed with the

explanations to provide an overall view of research results as Table 7:

Table 7. Summarising new contributions from the research.

New Contributions of Own Insight & Discovery The Explanations from Research Results

Accept inaccurate forecast for the focus moving

to seek alternative solutions

An empirical research truly aiming for alternative

solutions how to survive by synchronising demand-

supply pace & flow even with “extra” product changes

A practical reference to support the arguments in this

field

Introduce and prove three ingredients in

manufacturing strategy (especially with the

innovation emphasised)

A study sticking on the complexity of real business and

its key challenge.

An independent “driver” separated from the leagility for

the innovation in manufacturing operation as a strategy

– deserving its research much more than what

happened in the past.

Research the reasons and the solutions for

product innovation challenges caused by supply

lead-time gaps and material liability

A deep understanding to tangible or intangible status

of demand-supply details as the 1st report trying to

reduce liability effect in product changes

Also as the 1st report about empirical research details

of using product changes to study manufacturing

improvements

Identify a good opportunity to develop new theory

of total synchronisation and IT solution (business

intelligence automation) as its utilisation in global

scale for leading companies

A simple idea leading to new thoughts of a theory:

How the principles to achieve no-scraping cost status

in product change (equal to ideal synchronisation) are

repeatable and applicable to normal time of global

manufacturing?

For those leading companies in global business, the innovation should be

emphasised as a must at strategy level. The research brings it into whole thinking

of manufacturing operation, which can be seen just a corner for the company or

its demand-supply network. The innovation can affect much wider range of

corporate performance. It explains why lean or agile strategy always has its

drawbacks.

Besides, product change is only one of the forms for the innovation when it

will have radical effects of the differentiation (such as advanced technology, cost

saving in big scale …). As a companywide view, any of similar efforts to bring

radical differentiation for the company to achieve new competitive advantage is

the definition so called “innovation”. It is the key of surviving in global

112

competition especially essential to be leading companies. Otherwise, lack of this

ingredient in company’s strategies is a clear sign to the failure or already as a path

to the end of industrial life cycle.

The innovation is the big thing to determine the winner in global competition

sooner or later, which is proved by many facts as the life cycle of industry. It is

the time to deal with the complexity of three-dimensional world in real business

and explore a new academic theory for it (such as the effort leading to “total

synchronisation” oriented by the innovation in this research).

4.6 Future research

This study presents new understanding on time-based optimisation of minimising

costs, quick delivery, and rapid product change. Further research is however

required to fully utilise the presented ideas, especially for what with the

innovation as a driving force. For further abstraction, a so called “total

synchronisation” concept is under development as next big outcome of the

research. In order to better manage in global business, new IT solutions are

needed to support this new thinking, requiring future study. The lack of studies

about business intelligence automation can be a new opportunity of research field.

In addition, the potential of web 2.0 for harnessing the creativity of people to

support the type of optimisation discussed in this thesis would be a good topic for

future study. The simulation about mobile phone industry by Reiner et al. (2009)

can be an interesting sample if applicable in mobile infrastructure manufacturing

also as research tool even though big differences do exist.

This thesis has been conducted in a single company and one business

environment, having more cases and expanding to new business areas would be

an interesting topic for future study.

Besides, the tendency of overusing the strategy of minimising costs during

economic hard times, often results in losses to those leading companies. Global

business is constantly under a turbulent change that has become normality, but is,

however, too often ignored. “Wonderful” periods between two economic

downtimes have become shorter and shorter. Too often companies use excuses

that now we have to tighten our belts, accept slower operational speed, less

product innovations and lower employee motivation as the times are harder.

Instead the companies should accept the reality. People expect that they can apply

the other strategies again when a good time is coming. As a result, their leading

position in the industry gets literally lost. The leader status is not simply

113

maintained by making structural changes in the business sector e.g. with big

acquisitions. The target of total synchronisation concept is to break such thinking

and study right manner - always with multi-strategies in mind. It will help leading

companies or new-coming challengers in the industry to win in global

competition. This is why these aspects should be studied further.

114

115

5 Summary

The main motive for this research arises from the fact that ICT has developed into

a turbulent, high clock-speed sector. Industrial globalisation has greatly changed

high-tech companies while they have created significant operations in multiple

countries. Because poor visibility and massive uncertainty are part of the

operational nature, new challenges arise continuously for companies who want to

internationalise their demand-supply network.

ICT companies face challenges in an unpredictable business environment,

where demand-supply forecasting is not accurate enough. How to optimally

manage product change process and demand-supply chain in this type of

environment? Companies face pressures to simultaneously be efficient,

responsive and innovative, i.e. to minimise costs, and shorten order delivery and

product change periods.

The effects of changes in essential parameters of inventory level, order

delivery period, and product change time were studied in this dissertation for a

real demand-supply chain of a significant international actor. Secondly, based on

these analyses, this study attempted to find new means of dealing with complex

issues in the unpredictable business environment.

This thesis included three action research cycles. Each action research cycle

sought answers by going into one extreme of minimising costs, diminishing order

delivery period, or shortening product change periods. In practice, these research

cycles included the case company changing their business accordingly for each of

these cases. Conducting required changes in the case company were economically

significant trials.

The results of this doctoral dissertation provide tips for global high tech

companies. Large international companies typically have manufacturing sites in

different parts of the world. According to the results, mental shift from local

optimisation to a global one is required for efficient manufacturing operations.

Companies have traditionally considered their strategy as a choice between

minimising costs, quick delivery, and rapid product change. Also, companies have

believed that one single strategy is adequate and applicable to all of their products.

However, according to this thesis, different products may have a different strategy.

This would allow companies to flexibly react to the needs of different customer

groups, business environments, and different competitors. In addition, strategy

can be changed relatively often, monthly, weekly, or even daily.

116

Companies typically have multiple partners and consequently a suitable

balance is required for the entire demand-supply chain. Forecasts are an important,

powerful tool for influencing the supply operations, as forecasts give information

for suppliers. A company should try to make relevant information, including

product change management, visible for both the company itself, and the entire

supply chain. This would make it easier for the subcontractors to optimise the

entire chain if it has adequate access to critical information. Two-way

communication is required to fully optimise the entire demand-supply chain.

Based on the results of this doctoral thesis, companies must harmonise their

product portfolio globally, including all their sites. Once the same product version

is at all sites, they can help each other from components supply viewpoint.

Consequently, product changes can be taken through quicker. Global product

portfolio harmonisation can be seen as a new normal situation for the high tech

business. This would enable further optimisation, covering all global operations.

117

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