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Operations Management and

Information Technology

- opportunities between the diciplines

Prof. Petri Helo

Industrial Management, University of Vaasa, Finland

Industrial Management Research Group

• Software engineering services for industrial companies

• Established in 1999 • Owned by employees

Wapice Ltd.

• 2013 Sales 18 MEUR• 260 employees• Deloitte Fast Growth 500 EMEA 08/09

4

Operations Management

Domain and methodology expertise

Information and Communication

Technologies

Data collectionInformation processingCommunication

OM – IT examples

• Manufacturing

• Service

• Supply Chains

• Networked Business Models

• Conclusions

4.2.2014 5

MANUFACTURING AND

OPERATIONS

4.2.2014 6

Manufacturing Execution Systems

MES performs four primary functions:

1. Collects data (e.g. bar-code scanning) in real time.

2. Organizes and stores data in a centralized database.

3. Makes data accessible throughout the network, and integrates critical data from other information systems.

4. Delivers and manages orders from ERP to production, with detailed scheduling reacting to real-time events, increasing productivity and quality

Layers according to ISA-95 standards

CRM•PDM/

•PLMBusiness planningCorporate

ExecutionPlant

ControlPlant

Individual devicePlant

Work instructions available all the timeReal-time view on production

1. Guiding workers

noMuda – VisualFactory

Andon

System for announcing quality

issues in production

1. Immediate action

Classification

• Material

• Procedure

• Tool

• Safety

• Component quality

Work will continue once problem

solved

2. Corrective action

Statistics based quality control

2. Quality control

Climbing up the ramp-up curve

• New product introduction– Ramp-up to quality

– Ramp-up to quantity

4.2.2014 11

timevolume

Zero series

Prototypes

Volume

production

Ramp-

down

Hot dip galvanizing MES

4.2.2014 12

3. View to production queue

MES at the galvanizing factory

Functionality• Touch panel PC for data

input– Customer identification– Batch numbers– Additional works (sand

blast)– Work queue– Quality control

• Tracking jigs by usingRFID – weightrecording before and after the bath

4.2.2014 13

Real time work queueProfit/loss calculation for everycustomer order

SERVICE OPERATIONS

4.2.2014 14

What if we could see all the

installed products all the time?

Installed base view

4.2.2014 16

Products at customer

= installed base

Communication Remote management and service

Bill of Materials

Operators

Location

Batch processing

Real-time view

”War room”

Maintenance

Remote operation

Web-portal for customers

Web based view on service apps

Communication to sites

Site service operations

4.2.2014 17

Remote management and CBM

• Functions from ISO 13374: Machine Condition Assessment

Data Processing & Information Flow Blocks

Record temp, amps and volts 1/s

Calculate daily min, max and averages

Rule triggered. Detect exception and send info to portal

Suggest replacing the component in two weeks from now

The component will last 300 hours

Diagnose: Component X has problem.

Product packages

PackagesPay per service Monthly fee Monthly fee

based on reliability

20 © Wärtsilä

4.2.2014

University of Vaasa | Logistics Systems Research Group | Petri Helo

21

4.2.2014

University of Vaasa | Logistics Systems Research Group | Petri Helo

22

SUPPLY CHAINS

4.2.2014 23

Marine traffic – AIS

4.2.2014 24

Transport companies

4.2.2014 25

http://www.trackpackages.com

FedEx: 9.8 million: Requests on Dec. 23, 2008, the most ever in one day (http://www.commercialappeal.com/news/2009/jan/30/fedex-launches-real-time-tracking/)

Tracking truck drivers

Trackers in container boxes

4.2.2014 27

Concept of real-time supply chain

4.2.2014 28

Vendor

Distribution center CustomerTransport company Transport company

Inbound Outbound

DC1

DC2

Customer

S1

S2

S2

S2

S2

C1C1

C3

C..C..

C..

C..

C..C..

C..C..

C..

Transport booking

Status: readyto go

Status: loaded

Status: received

Status: new handling unitnumber

Status: loaded

Status: received

Real-time vehicle routing

4.2.2014 29

• Collection area definitions, policy updates

• Daily route optimisation

• Fuel and emission reductions due to reduced kms

Single truck

Multi-truck

UPS Trucks turning just right

• Change of routing optimization

• “Saved the company $17 million a year, reduced emissions and made drivers safer because they aren't typically turning left into traffic”

NETWORKED BUSINESS

MODELS

4.2.2014 31

4.2.2014 32

Companies making location decisions

China 2008 ~1.36India 2007 ~1.17

VIRTUAL FACTORY“…a temporary network of independent companies, suppliers, customers –even rivals, linked by information technology to share costs, skills and access one another’s markets. It will have neither central office nor organisationalchart… no hierarchy, no vertical integration…” (Byrne)

4.2.2014 33

Business Community Management

FORM OPERATE DISSOLVE

ETO VO Management

CTO VO Management

FORM OPERATE DISSOLVE

Performance management

But how do they manage the dispersed

operations?

Shared work queue in multi-factory

environment

4.2.2014 34

Shared work queueDecentralized system – likeemail or instant messengerTransparency to customerNotifications on events

How to control non-

hierachial network?

• Project based business – number of companies changing from

project to project

• Rules and data exchange - not only manufacturing orders, but

also product data

37

Ponoko.com – Manufacturing 2.0?

A commercial plaform for

virtual manufacturers

(mainly small prototypes

or hobbyists)

Inquiry/offer

Manufacturing

Delivery

Several suppliers

Raw material types

Rapid prototyping

Electronics

Sheet metal

Plastics

Diatom Studio - SketchChair

• Cloud MES as manufacturing platform?

4.2.2014 38

CONCLUSIONS

4.2.2014 39

4.2.2014 40

Data size

Time range

Long range Short range

Small

Big

Strategic

Customer and installed

base data

Supply chainService

supply chain

PPC

Maintenance

OM development

• Do our models take the advantage of real-time information?

• Can we make better assumptions based on hard data?

• When should we use Big Data or Small data on our problems?

• Making our DSS systems accessible not only managers but everyone within the supply chain

4.2.2014 41

4.2.2014 42