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The Impact of Lean Practices on Mass Customization and

Competitive Performance of Mass-Customizing plants

Ayman Bahjat Abdallah* and Yoshiki Matsui**

Department of Business Management Systems, International Graduate School of

Social Sciences, Yokohama National University. 79-4 Tokiwadai, Hodogaya-Ku,

Yokohama 240-8501 Japan, Tel & Fax: +81-45-339-3734

Email addresses: *aymanabdallah@yahoo.com, **ymatsui@ynu.ac.jp

Proceedings of the 20th Annual Production and Operations Management Society (POMS) Conference,

Orlando, FL, USA; 05/2009

Winning paper of “Emerging Economies Young Researcher Award (EEYRA)”, the 20th Annual

Production and Operations Management Society (POMS) Conference, Orlando, FL, USA; 05/2009

Abstract

In this study we use multi-item scales to measure mass customization and seven lean

practices- just-in-time production, total quality management, total productive

maintenance, human resource management, manufacturing strategy, supplier

relationship management, and customer relationship management. We examine the

impact of lean practices on mass customization for machinery, electrical & electronics

and automobile companies for six countries- Japan, Korea, USA, Germany, Austria,

and Finland. We also examine the impact mass customization and lean practices on

competitive performance of the plant.

The results show that four lean practices, just-in-time production, manufacturing

strategy, supplier relationship management, and customer relationship management

positively affect mass customization implementation level. The results also show that

mass customization and lean practices positively affect competitive performance of

the plant. Plants that have high levels of both mass customization and lean practices

show higher competitive performance compared to plants that have high mass

customization and low lean practices levels.

Key words: Mass customization; lean production; Empirical research

1. Introduction

Since the publication of the book The Machine that Changed the World written by

Womack et al. (1990), the concept lean production has received a considerable

attention from western researchers, and many western manufacturers have

successfully replaced mass production with lean production. Although just-in-time

production and total quality management are the most prominent dimensions of lean

production; however, other lean practices include human resource management, total

productive maintenance, supplier and customer relationship management, and

manufacturing strategy (Matsui, 2007; Shah and ward, 2003; Sohel et al., 2003;

Sakakibara et al., 1997; Ramarapu et al., 1995; Mehra and Inman, 1992). There is an

agreement among researchers that lean production is expected to eliminate waste and

non-value added activities, reduce cost while improving quality, and improve

flexibility and customer responsiveness.

Mass customization has appeared as a new way of competitiveness in the era of

globalization, open markets, and short products life cycles. It has even been

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postulated that mass customization is the single way to competitiveness in today’s

dynamic business environment (Blecker and Friedrich, 2006). Mass customization is

the ability of the firm to quickly design, produce, and deliver products that meet

specific customer requirements at close to mass production prices (Tu et al., 2001).

The real challenge for manufacturers has been how to maintain low operational cost

and short lead times in a high demand uncertainty environment resulting from

offering mass customized products (Skipworth and Harrison, 2006).

While reviewing the literature, we found some contradiction concerning the linkages

between lean production and mass customization. On one hand, Pine et al. (1993)

asserted that lean production, which they referred to as continuous improvement, and

mass customization require different organizational structures, values, management

roles and systems, learning methods, and ways of relating to customers. They further

indicated that a company that has mastered continuous improvement must change radically

the way it is run to become a successful mass customizer. Christiansen et al. (2004) using

data from 75 Danish manufacturing companies found that there are no direct

relationships between lean manufacturing and mass customization practices and there

are only weak relationships between the two concepts. On the other hand, other

authors have indicated that lean production is expected to facilitate and support mass

customization (e.g. Anderson, 2004; Chandra and Grabis, 2004a; Tu et al., 2001). We

could find a very limited number of papers that have attempted to empirically

investigate the linkages between lean production, or some of its dimensions, and mass

customization; therefore, in this paper, we use an empirical data gathered from six

countries and three industries in an attempt to fill this gap and shed more light on their

linkages. We also use the empirical data to examine whether or not lean production

practices affect competitive performance of mass customizing firms.

2. Literature review

2.1. Lean Production

The term lean production became known and very popular after the publication of the

book The Machine That Changed the World written by James Womack, Daniel Jones,

and Daniel Roos, and published in 1990. The book described Toyota Production

System (TPS) and was widely read and referred to by many researchers. TPS and

Japanese production system received a considerable attention by researchers and

manufacturers after the publication of the book. Prior to that, Japanese production

techniques in general and TPS in particular received little attention from Western

researchers and manufacturers, even though some books and articles were published

in an attempt to introduce TPS to the West (e.g. Schonberger, 1982; Monden, 1983;

Ohno; 1988; Imai, 1986). Prior to 1990, the terms TPS and just-in-time (JIT)

described what became later known as lean production.

There is a general agreement among researchers that “Lean production” was initiated

by Toyota to meet their specific requirements. After visiting Ford motor company in

the 1950s, Eiji Toyoda, later the president of Toyota, and Taiichi Ohno, the

production engineer, soon concluded that mass production could never work in Japan,

and this led them to start thinking and innovating Toyota production system, and

ultimately, lean production (Womack et al., 1990).

Womack et al. (1990) described the idea of why lean production is “lean” as “because

it uses less of everything compared with mass production-half the human effort in the

factory, half the manufacturing space, half the investment in tools, half the

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engineering hours to develop a new product in half the time. Also, it requires keeping

far less than half the needed inventory on site, results in many fewer defects, and

produces a greater and ever growing variety of products”.

Shah and Ward (2007) defined lean production as: “an integrated socio-technical

system whose main objective is to eliminate waste by concurrently reducing or

minimizing supplier, customer, and internal variability”.

Ohno (1988) indicated that two pillars are needed to support TPS, JIT production and

autonomation. He explained that autonomation, or automation with human touch,

means transferring human intelligence to a machine. It prevents the production of

defective products, eliminates overproduction, and automatically stops abnormalities

on the production line allowing the situation to be investigated.

One main difference between mass production and lean production lies on their

ultimate goals. The goal of mass producers is to be “good enough” which could be

translated as acceptable number of defects, inventories, and narrow standardization.

The goal of lean producers is perfection which means declining costs, almost zero

defects and inventories, and more product variety (Womack et al., 1990). Liker (2003)

indicated that though mass producers gain some advantages such as economies of

scale and flexibility in scheduling by having similar machines and similarly skilled

workers together, it is difficult for them to move materials as well as information

between departments, and they would need to create material handling department.

On the other hand, one piece flow which is a fundamental rule of lean production

brings different benefits such as improved quality, flexibility, productivity, safety,

morale, and reduced cost.

2.2 Elements of lean production

From our extensive literature review, it was clear that there is no consensus among

researchers concerning the elements of lean production. This is due to the fact that

researchers view lean production in different ways. Many researchers avoid the usage

of the term lean production, and instead use JIT production to describe the same idea.

Those researchers usually include elements from other operational practices to their

definition of JIT production. Other operational practices include, but not limited to,

human resource management, total quality management, total productive/preventive

maintenance, and supplier relationship management (e.g. Mehra and Inman, 1992;

Brown and Inman, 1993; Koufteros and Vonderembse, 1998; Zhu and Meredith,

1995; Sohal et al., 1993; McLachlin, 1997; White and Prybutok, 2001; Fullerton et al.,

2003). The usage of the term JIT by those researchers implicitly implies that lean

production is meant which includes JIT production as well as some other operational

practices that support it and contribute to its success. Shah and ward (2007) indicated

that in the US JIT is often assumed to be TPS. The main limitation of those papers is

that they often select one element from operational practices such as HRM or TQM

while neglecting other elements from those operational practices. Selecting some

individual elements from some operational practices could lead to misunderstanding

by practitioners implying that JIT production (or lean manufacturing) consists of JIT

production core practices in addition to few single elements from other operational

practices.

Other research papers approached lean manufacturing from the perspective that it

consists of different operational practices, among which JIT production is the main.

For instance, Sakakibara et al. (1997) used the term JIT manufacturing and its

infrastructure to describe the so-called lean manufacturing. Their definition of

infrastructure practices included quality management, work force management,

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manufacturing strategy, organizational characteristics, and product design. Sohel et al.

(2003) also used the term infrastructure practices to describe operational practices that

contribute to JIT production. They defined infrastructure practices as “activities and

mechanisms that provide support for JIT practices to be effective in a plant”. The

infrastructure practices in their research included quality management, manufacturing

strategy, product technology, work integration system, and HRM policies. Shah and

ward (2003) viewed lean manufacturing as consists of four bundles, JIT bundle, TQM

bundle, TPM bundle, and HRM bundle. Sanchez and Perez (2001) viewed lean

indicators as zero-value activities elimination, continuous improvement, multi-

functional teams, JIT production and delivery, suppliers’ integration, and flexible

information systems. Spear and Bowen (1999) indicated that the main elements of

TPS included standardization of work, direct links between suppliers and customers,

uninterrupted work flows, and continuous improvement based on the scientific

method.

Another stream of research papers investigated the relationship between two or more

lean dimensions, mainly JIT production and some other practices. For instance, Flynn

et al. (1995) investigated the relationship between JIT production and total quality

management. Cua et al. (2001) investigated the relationship between JIT, TQM, and

TPM. Kannan and Tan (2005) investigated the linkages among JIT, TQM, and supply

chain management.

Our literature review revealed that in the early research papers concerning JIT

production, TPS, or lean manufacturing, it was common to include elements from

other operational practices as dimensions of JIT production. More recent papers tend

to distinguish between JIT production and other operational practices and regard the

latter as independent but related practices. Shah and Ward (2007) attributed this

tendency to the fact that those operational practices were established as an

independent constructs and are used to predict operational performance of the plant.

Our approach in this paper is to view lean production from its comprehensive

perspective. Therefore, we selected the following practices from the published

literature and defined them as lean production elements:

a) Just-in-time production (JIT)

b) Human resource management (HRM)

c) Total quality management (TQM)

d) Total productive maintenance (TPM)

e) Manufacturing strategy (MS)

f) Supplier relationship management (SRM)

g) Customer relationship management (CRM)

3. Framework and research hypotheses

This research has been based on the proposed framework (Fig. 1). The framework

considers the impact of different dimensions of lean production on mass

customization level, and the impact of mass customization and lean practices on

competitive performance of the plant. We discuss our hypothesized relationships in

this section.

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3.1. Just-in-time production

One of the main challenges facing companies implementing MC is how to deal with

the increased levels of inventory associated with offering customized products on

demand. Aigbedo (2007) found that the average amount of inventory tends to increase

in MC environment to prevent stock-out of occurring. The increased levels of

inventory tend also to increase the cost which is of great importance to maintain the

competitive advantage of the company. Chandra and Crabis (2004b) pointed to

inventory as one of the major sources for cost increase due to adoption of mass

customization. Therefore, JIT production is an ideal solution to deal with such

tendency as inventory in JIT environment is regarded as the main source of waste and

parts usually arrive at the production plant on demand. Pine, (1993) pointed to JIT and

other advances in management as an effective way in achieving both low cost and

customization with increased flexibility and responsiveness. Anderson (2006) further

described such a combination as unbeatable enabling companies to build products on

demand without forecasts, batches, inventory, or working capital. Lu et al. (2006)

argued that in an ideal Mass customization organization; there would be no inventory

of finished goods.

Berman (2002) described the successful attainment of JIT production by Dell

Company, a prominent mass customizer. It maintains inventory for only few days or

in some cases few hours with regular communication and replenishments from its

suppliers.

JIT production is based on producing in small lot sizes which is enabled by set-up

time reduction. This situation is ideal for companies implementing mass

customization to manage and deal with demand uncertainty associated with offering

customized products. Anderson (2006) pointed to flow, some times called one-piece

flow which is a key element of JIT production as of especial importance for MC

where every piece may be different. Anderson (2004) indicated that while MC must

be run separately from the operations of mass production, Build-to-order and mass

customization operations are equally efficient and very complementary.

There are two types of postponement have been pointed to as enablers to achieve cost

efficient MC: time postponement, delaying the delivery of the materials until after the

customer orders arrive, and form postponement, delaying the differentiation of the

products until the last moment (Su et al., 2005; Zinn and Bowersox 1988). Both types

Competitive performance

Lean production dimensions

JIT

TQM

HRM

TPM

MS

SRM

CRM

Fig.1. Research framework

Mass customization

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are expected to be highly facilitated by JIT techniques such as JIT delivery of

materials, cellular layout, reduced set up time, and one piece flow. In addition to that,

JIT plants are usually equipped with flexible technologies which are one of the major

requirements for MC processes (Lei et al. 1996; Lau, 1995).

We measure JIT production along the following dimensions:

3.1.1 Equipment Layout: use of manufacturing cells, elimination of forklifts and long

conveyers, and use of smaller equipment designed for flexible floor layout, all

associated with JIT.

3.1.2 JIT delivery by suppliers: assesses whether vendors have been integrated into

production in terms of using Kanban containers, making frequent (or just-in-time)

delivery and quality certification.

3.1.3 Kanban/Pull system: assesses whether or not the plant has implemented the

physical elements of a Kanban system.

3.1.4 Setup Time Reduction: assesses whether the plant is taking measures to reduce

setup times and lower lot sizes in order to facilitate JIT.

H1a. JIT production is positively associated with MC level.

H1b. JIT production contributes to competitive performance of MCers.

3.2 Total quality management

One of the major required capabilities for a mass customizing firm is producing high

quality products at a batch size of one without losing efficiency. This implies that

products must be produced correctly in the first attempt and no rework is permitted

and thus, mass customizer must constantly develop process capability (Kakati, 2002).

Defects and reworks are the major factor to inhibit MC diffusion and development,

and flexibility of the firm and its ability to lower cost and maintain fast delivery of

customized products depends heavily on its quality system. Kakati (2002) further

indicated that in the mass customized environment, people, processes and technology

need to be constantly reconfigured to give customers exactly what they want.

Therefore, a mass customizer needs to develop flexible system and flexible resources

without losing efficiency and quality is one major capability to achieve this purpose.

Pine et al. (1993) argued that companies must first achieve high levels of quality and

skills and low cost through continuous improvement to become successful mass

customizers. Quality management with continuous improvement has been pointed to

as single most important success factor in Japan’s manufacturing success (Imai, 1986;

Ono, 1988). Dean and Bowen (1994) asserted that the entire quality management

effort must be focused on achieving customer satisfaction, which is the ultimate

objective of MC. In addition to that, a firm has to align a TQM program with its

strategic planning and provide associated plans and means that are necessary for its

promotion (Chine et al., 2002).

Quality management is a necessary tool to respond to unpredictability and uncertainty

associated with MC. A predictable process, which quality management strives to

control, enables a smooth flow of goods through the process with minimum buffer

inventory (Takeuchi and Quelch, 1983). In addition to that, continuous improvements

and learning is regarded as a pillar to achieve “learning organizations” that can

respond quickly to new customer demands and marketplace changes (Hirschhorn et

al., 2001).

Total quality management has been measured along the following scales:

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3.2.1 Continuous Improvement and Learning: assesses employee’s commitment to

continuous quality improvement.

3.2.2 Feedback: assesses whether the plant provides shop floor personnel with

information regarding their performance in a timely and useful manner. The scale

measures feedback about performance in both chart and verbal form which are used in

facilitating and supporting quality and productivity improvements.

3.2.3 Process control: Measures use of statistical process in production and in office

support functions, in designing ways to “fool proof” processes, and self inspection.

3.2.4 Cleanliness and Organization: Assesses whether plant management has taken

steps to organize the work place and maintain it in order to help employees

accomplish their jobs faster and install a sense of pride in their work place.

H2a. TQM is positively associated with MC level

H2b. TQM contributes to competitive performance of MCers.

3.3 Total productive maintenance

In a mass customization environment, flexible processes, smooth operations, and

short set up times are a key for successful implementation. Equipment unplanned

downtime represents a threat to a mass customizing firm that produces products to

individual customer specifications and has to adhere to high quality and delivery

requirements. TPM is an essential management tool to control unanticipated problems

associated with machine performance variability and breakdown.

TPM aims to continually maintain and maximize the condition and effectiveness of

the equipment through complete involvement of every employee (Cua, 2000),

therefore, minimizing unexpected deviations from planned procedures. In addition to

that, TPM enables companies to achieve unique capabilities of equipment which will

provide the company according to Hayes and Wheelwright (1984) with an equipment

advantage which is a major success factor for mass customizing firm. Several benefits

are expected to be gained by a mass customizing firm implementing TPM program.

These benefits include equipment breakdown reduction, improved quality, increased

effectiveness, assured safety, reduced costs, and continuous improvement of

workforce skills and knowledge ( cua, 2000; Steinbacher and Steinbacher, 1993;

Suzuki, 1994). These benefits are important attributes of mass customization and

expected to enhance the competitive performance of a mass customizing firm and to

increase customer satisfaction.

We measure TPM along the following dimensions:

3.3.1 Autonomous Maintenance: The involvement of workers in cleaning and

inspecting their equipment, and their ability to detect and treat abnormal conditions of

their equipment.

3.3.2 Preventive Maintenance: The use of diagnostic techniques to predict equipment

lifespan, using technical analysis of major breakdowns, upgrading inferior equipment,

and redesign equipment if necessary.

3.3.3 Maintenance Support: The availability of planned maintenance, maintenance

standards plant-wide, and reliable maintenance information systems.

3.3.4 Team Based Maintenance: The availability of cross-functional teams and small

group problem solving to deal with equipment problems.

H3a. TPM is positively associated with MC level

H3b. TPM contributes to competitive performance of MCers.

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3.4 Human resource management

MC depends heavily on having the necessary human skills and abilities that can

respond effectively to design and produce products to specific customer requirements;

therefore, Human resource modifications should be undertaken prior to MC

implementation.

Pine (1993) indicated that successful mass customization requires an integrated

organization in which every function and person is focused on the individual customer,

all have eliminated waste, and each contributes to develop, produce, market, and

deliver low-cost customized products. Pine et al. (1993) pointed to the crucial role of

work force which is expected to handle an increasingly complex of tasks, such as

assembling a variety of products by expanding its range of skills. Flynn et al. (1994)

further argued that team work and group problem solving allow decision making to be

decentralized and therefore variance and uncertainty are easier to manage.

Kakati (2002) described three types of human flexibility relevant for mass

customization among which numerical and functional flexibility –Numerical

flexibility concerns the readiness to adjust the number of employees to fluctuation in

demand; functional flexibility concerns the readiness to change the tasks performed

by workers in response to varying business demands.

Pine et al. (1993) described workers in a mass customization environment as

independent, capable individuals, with efficient linkage system. This implies the need

for employee involvement, job re-design, and cross training, which allow according to

Monden (1983) greater control of processes and better communication.

Employee involvement will be enhanced by encouraging employee suggestions,

cooperation and coordination both vertically and horizontally (Forza, 1996; Aggrawal

and Aggrawal, 1985).

We measure human resource management along the following dimensions:

3.4.1 Employee suggestion- Implementation and feedback: assesses employee

perceptions regarding management’s implementation and feedback on employee

suggestions.

3.4.2 Multi-functional employees: This scale is used to determine if employees are

trained in multiple tasks/areas; that is, receive cross training so that they can perform

multiple tasks or jobs.

3.4.3 Small group problem solving: This scale is designed to assess the effective use

of teams on the shop floor for continuous improvement.

3.4.4 Training for Employees: This scale is used to determine if employees’ skill and

knowledge are being upgraded in order to maintain a work-force with cutting edge

skills and abilities.

H4a. HRM is positively related to MC level

H4b. HRM contributes to competitive performance of MCers.

3.5 Manufacturing strategy

Kakati (2002) emphasized that Mass customization requires constant innovation in

products and process capability to cope up with wide range of novel products and

with considerable design turbulence. He pointed to two approaches to stimulate

innovation for multiplying options– Techno-centric and Anthropocentric (Human

centered). Pine (1993) asserted that MC requires new ways of managing and new uses

of technology. It requires new visions and strategies, new methods of developing,

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producing, marketing, and delivering products, and new forms of organization better

suites to turbulent times.

MS should be seen as one major enabler to acquire the required innovations, resources

and capabilities that facilitate the shift towards MC environment. MS is the blueprint

for the manufacturing function that frames the acquisition, development and

elimination of manufacturing capabilities far into the future (Bates et al., 1995). Da

Siveria et al. (2001) pointed to technologies and methodologies that support the

development of the system as enablers of MC. Chandra and Grabis (2004a) further

indicated that MC methodologies address organizational and cultural perspectives of

implementation, while process technologies address manufacturing perspectives.

Hart (1995) identified four key factors for success of a MC system among which

organizational readiness. Sohel et al. (2003) suggested that plants with a well defined

manufacturing strategy are expected to be more focused than plants without a

manufacturing strategy. Therefore, MS will provide support to achieve organizational

change and readiness. This includes the adaptation of attitudes, culture and resources

of the firm to suit MC environment as well as the enhancement of leadership

capability which must be open to new ideas and aggressive in the pursuit of

competitive advantage (Chandra and Grabis, 2004a; Hart 1995).

Manufacturing firms adopt MC strategies in order to improve, or at least maintain,

their competitive position in today’s dynamic and competitive business environment.

To achieve this objective, manufacturing managers must be able to combine constant

improvement of existing manufacturing processes with judicious investment in new

processes, utilizing both human and capital resources (Schroeder and Flynn, 2001).

In addition to that, manufacturing strategy is used to coordinate manufacturing

decision making, including selection of technologies, suppliers, production planning

and control systems, work force, and qualitative practices (Bates et al., 1995).

We measure manufacturing strategy along the following dimensions:

3.5.1 Achievement of Functional Integration: Functional integration measures

whether or not the different functional areas of the company are integrated in terms of

goals, decisions made, and knowledge of one another’s areas.

3.5.2 Manufacturing-Business Strategy Linkage: Measures the consistency between

the manufacturing strategy and the business strategy and whether or not

manufacturing strategy supports the business strategy.

3.5.3 Formal Strategic Planning: Plant management involvement in strategic

planning and frequently updated strategic plans indicate a world class orientation.

3.5.4Anticipation of New Technologies: Measures whether the plant is prepared in

advance of technological breakthroughs to engage in the implementation of new

technologies when such technologies become available.

3.5.5Proprietary Equipment: Measures whether or not the plant is pursuing

development of in-house equipment as a source of competitive advantage.

H5a. MS is positively associated with MC level.

H5b. MS contributes to competitive performance of MCers.

3.6 Supplier relationship management

Chandra and Grabis (2004a) pointed to efficiency in supply chain as one of the major

determinants in achieving the main objectives of mass customization, providing low

cost and shorter delivery times. MC characterized by demand uncertainty as products

are produced to specific customer needs. The wide variety of potential customized

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products makes it difficult for a mass customizing firm seeking efficiency and low

cost to hold large quantity of inventories, thus, demand uncertainty results in supply

uncertainty. Therefore supply chain management has been described as a glue binding

together activities performed to ensure mass customization success (Gooley,

1998).Timely contact with suppliers after the customer orders are received is

potentially an ideal solution for such situation. This situation implies that

manufacturers utilizing MC policies will highly depend upon their suppliers regarding

delivery promptness, quantities, quality, etc. therefore, comprehensive supplier

selection models are necessary (Chandra and Grabis, 2004b), and managing

relationship with suppliers should be given first priority in order to avoid potential

pitfalls associated with supply uncertainty.

Yang et al. (2005) pointed to Postponement as an appropriate strategy to intentionally

delay activities, rather than starting them with incomplete information about the actual

demands. Although postponement enables company to keep its options open prior to

availability of adequate information, incorporating the flexibility to cope with risk and

uncertainty, he found that supplier delivery performance was the most significant

barrier to postponement.

As a common response to potential challenges in supply chain, Anderson (2004) and

Pine (1993) pointed to supplier lead time reduction as an effective way to reduce risks

associated with product shipments and to shorten the respond time to customer

demand. Salvador et al. (2002) and Alford et al. (2000) emphasized that mass

customizers have to coordinate activities with their suppliers at the product design

face and to involve suppliers in the design and development. Lui (2007) asserted that

informal, trust-based relationship with suppliers is expected to ensure reliable and

flexible supply of raw materials for the mass customizing firm at low cost. Salvador et

al. (2001) pointed to supplier quality improvement as an effective way to improve

internal operations control capability.

We measure supplier relation management along the following dimensions:

3.6.1 Supplier quality improvement: assesses the amount and type of interaction

which occurs with suppliers regarding quality concerns.

3.6.2 Trust based relationship with suppliers: assesses the cooperation level with

suppliers, problem-sharing, and openness of communications with them.

3.6.3 Supplier lead time reduction: assesses whether supplier lead time is given more

priority than cost, and the efforts done by the firm to encourage and assist suppliers to

reduce their lead time.

3.6.4 Supplier partnership: assesses the cooperative relations with suppliers and to

what extent suppliers are involved in NPD.

H6a. SRM is positively related with MC level

H6b. SRM contributes to competitive performance of MCers.

3.7 Customer relationship management

Firms adopt mass customization strategies as a response to market turbulence and

customer demand for variety and uniqueness. Pure customization which is the highest

level of customization is achieved when customer involvement is enabled throughout

the entire production cycle. The delivered products here are completely unique to

customer specifications (Lampel and Mintzberg, 1996). This implies that customer

relationship management is a focal point for mass customization success. Therefore,

the first step for firms considering mass customization should be the creation of a

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system that allows the firm to work closely with its customers. Such a system is

expected to assure that customers could be involved at any stage of the production

process starting from the design and ending with the post production customization.

The required level of customization by customers is the basis to select the stage of

production customers can alter. Piller et al. (2000) argued that to be successful mass

customizers, companies have to “build an integrated information flow that not only

covers one transaction but improves the knowledge base of the whole company by

information gathered during the fulfillment of a customer-specific order”.

Broekhuizen and Alsem (2002) indicated that customer involvement is a success

factor that positively affects the probability of success in mass customization. In

addition, the degree of customer involvement is one key element in defining the

configuration of processes and technologies that must be used to produce the mass

customized product (Lampel and Mintzberg, 1996; Chandra and Grabis, 2004a).

Zipkin (2001) pointed to elicitation (a mechanism for interacting with the customer

and obtaining specific information) as one of the key capabilities of Mass-

customization systems; therefore mass customization requires an elaborate system for

eliciting customers' wants and needs in addition to a strong direct-to-customer

logistics system.

The primary objective of mass customization is to maximize customer satisfaction by

providing tailor-made solutions with near mass production efficiency (Blecker and

Friedrich, 2006), therefore, from both the customer’s and the producer’s perspectives,

customer satisfaction is the most important criteria for evaluating mass customization

success. Moreover, Da Silveira et al. (2001) argued that for a successful

implementation of a mass customization system, it is essential to define value based

on the customer.

The more customers are involved in the different production stages, the better they

perceive the uniqueness of the products designed and produced upon their

requirements.

Kratochvil and Carson (2005) argued that the improved order processes that allow

better customer involvement associated with mass customization result in lower costs,

by automating or eliminating steps in a business process and by minimizing losses by

eradicating misunderstanding and misinterpretations in the order process. As a result,

better quality products are expected to be delivered and subsequently increased

loyalty and life-cycle revenue due to an improved dialog with customers. In addition

to that, more customer involvement will justify, from the customer’s perspective, any

extra cost they may occur due to customizing their products.

We measure customer relationship management along the following dimensions:

3.7.1 Customer involvement: This scale assesses the level of customer contact/

orientation/ responsiveness.

3.7.2 TQM link with customers: This scale measures whether the plant has been

integrated into customer production in terms of quality.

3.7.3 Customer focus: assesses the willingness of the plant to involve customers the

design, and the anticipation of customers’ needs, requirements, and expectations.

H7a. CRM is positively related to MC level

H7b. CRM contributes to competitive performance of MCers.

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4. Methodology

4.1 Description of data

The data used for this empirical research were collected as part of an ongoing High

Performance Manufacturing (HPM) project (previously called world class

manufacturing project (WCM)), round 3 being conducted by a team of researchers in

ten countries: Japan, Korea, USA, Germany, Italy, Austria, Sweden, Finland, Spain,

and UK. The HPM database was assembled in 2003 and 2004 and consists of

randomly selected world-class and traditional manufacturing companies from three

different industries; machinery, electrical & electronics, and transportation. Our

sample for this study comprised of 187 manufacturing plants located in Japan, Korea,

USA, Germany, Finland, and Austria representing Asia Pacific, North America, and

Europe. Table 1 shows the distribution of the plants used in this research classified by

country and by industry.

Table 1

Number of sample plants classified by country and industry Country Industry Total

Machinery Electronics Transportation

Japan 10 12 13 35

USA 9 11 9 29

Germany 13 9 19 41

Korea 10 10 11 31

Finland 6 14 10 30

Austria 7 10 4 21

Total 55 66 66 187

The measurement instrument of this project was developed after conducting an

extensive review of relevant literature by project members. The developed scales were

reviewed by a panel of 5 experts to assure content validity, and the scales were

revised as needed. The questionnaires were designed for various managers,

supervisors, and direct workers and were pre-tested at several manufacturing plants

and were pilot-tested by academics and were revised as needed. The original

questionnaire was translated into each county’s language by experts from those

countries and then translated back to English to ensure equivalency.

The selected manufacturing companies were contacted personally by members of

HPM in each country. The project members asked the executive in charge of

manufacturing operations for voluntary participation in the project. Approximately

60% of the contacted companies agreed to participate and assigned one plant manager

to be responsible for data collection. Participating plants were promised to receive a

comprehensive feedback concerning their managerial and operational practices

compared with other plants. The right respondents in terms of experience, specialty,

and knowledge were agreed upon between the team members and the assigned plant

manager.

The questionnaires were then completed by five direct workers, four supervisors, and

ten managers, each of whom received a different questionnaire, allowing respondents

to address their particular area of expertise. In addition, multiple respondents were

asked to complete each question in order to obtain greater reliability of the data and to

eliminate potential respondent bias.

13

4.2. Measurement analysis and research variables

We have used multi-item scales to measure mass customization and lean production

practices. The respondents were asked to indicate their agreement or disagreement

with the statements provided using seven-point Likert scales, where 7 indicates strong

agreement and 1 indicates strong disagreement. The measurement scales can be found

in appendices A.

4.3. Competitive performance

There are different ways to measure competitive performance. While reviewing the

literature, we found that the most widely used measures are cost, quality, flexibility,

and delivery (e.g. Hayes and Wheelwright, 1984; Hill, 1989; Ward et al., 1995;

Sakakibara et al., 1997; Cua et al., 2001; McKone et al., 2001). Respondents were

asked to evaluate their performance relative to their competitors in the same industry

on a global basis, using five-point Likert scales, where 5 indicates superior to

competitors and 1 indicates poor, low end of industry. The non-scale items to measure

competitive performance can be found in appendices B. In our study, we use these

four measures of competitive performance as follows:

1. Cost: Unit cost of manufacturing

2. Quality: Conformance to product specifications

3. Flexibility: Flexibility to change volume

4. Delivery: On time delivery performance

To ensure that the scales are reliable indicators of their constructs, factor analysis was

carried out, with principal components analysis (PCA) as the extraction method. We

selected PCA as it is preferred for purposes of data reduction, whereas the other type

of factor analysis, principal factor analysis (PFA), is preferred when the research

purpose is the detection of data structure or casual modeling. The objective of PCA is

to extract maximum variance from the data set with each component (Tabachnick and

Fidell, 2001). Our purpose was to perform within-scale factor analysis to verify that

all items loaded onto one factor. Only the items that had a factor loading of at least

0.40 and eigenvalue of at least 1 were retained.

Cronbach’s α-coefficient was used to evaluate the reliability of the scales. The

majority of the measurement scales has met the recommended standard of α ≥ 0.70

and has been considered to be internally consistent (Nunnally, 1967). The reliability

of the other scales was higher than 0.60. Nunnally recommends a minimum standard

of 0.60 for newly developed scales; therefore, we decided to retain these scales.

Cronbach’s α-coefficient for each measurement scale can be found in appendix A.

We also carried out factor analysis for the super scales of JIT, HRM, MS, TQM, TPM,

SRM, CRM, and competitive performance. All factor loadings were higher than 0.40

with eigenvalues higher than 1. Cronbach’s α-coefficient for all the super scales was

higher than 0.70 as shown in table 2.

14

Table 2

Validity and reliability of the super scales Scale JIT HRM TPM

Alpha

coefficient

0.752 0.852 0.830

Factor loading Factor 1 Factor

2

Factor

1

Factor 1 Factor

2

Factor

1

Factor 1 Factor

2

Factor

1

Scale 1 .813 .828 .690 Scale 2 .836 .814 .885 Scale 3 .735 .807 .847

Scale 4 .688 .881 .834 Eigenvalue 2.373 2.776 2.674

Proportion 59.329% 69.409% 66.839%

No. of factors 1 1 1

Scale TQM MS SRM

Alpha

coefficient

0.801 0.788 0.776

Factor loading Factor 1 Factor

2

Factor

1

Factor 1 Factor

2

Factor

1

Factor 1 Factor

2

Factor

1

Scale 1 .751 .814 .884 Scale 2 .857 .873 .874 Scale 3 .828 .817 .719

Scale 4 .751 .790 .629 Scale 5 .415 Eigenvalue 2.549 2.888 2.459

Proportion 63.734% 57.751% 61.486%

No. of factors 1 1 1

Scale CRM Competitive performance

Alpha coefficient 0.787 0.608

Factor loading Factor 1 Factor 2 Factor 1 Factor 1 Factor 2 Factor 1

Question item 1 .847 .676 Question item 2 .844 .705 Question item 3 .833 .777 Question item 4 .543 Eigenvalue 2.124 1.852

Proportion 70.787 46.302

No. of factors 1 1

Table 3 shows correlation matrix and summary of statistics of the research measures.

Table 3

Means, standard deviations, and correlations among variablesª Mean S.D. 1 2 3 4 5 6 7 8

1. MC 5.09 .620 1

2. JIT 4.58 .549 .224*** 1

3. HRM 5.20 .526 .206*** .546*** 1

4. MS 5.06 .547 .384*** .406*** .539*** 1

5. TQM 5.19 .557 .186** .470*** .719*** .526*** 1

6. TPM 4.93 .547 .196*** .595*** .707*** .679*** .651*** 1

7. SRM 5.10 .405 .190*** .668*** .648*** .450*** .660*** .616*** 1

8. CRM 5.43 .370 .297*** .272*** .559*** .443*** .721*** .474*** .618*** 1

9. Competitive

performance 3.73 .547 .235*** .371*** .370*** .526*** .342*** .328*** .245*** .291***

ªN=187 ***P ≤ 0.01

**P≤ 0.05

15

5. Results and discussion

We start our analysis by testing hypotheses H1a, H2a, H3a, H4a, H5a, H6a, and H7a,

which stated that lean practices significantly contribute to mass customization

implementation level. These hypotheses were tested by hierarchical regression

analysis using MC as a dependent variable (Table 4). In the first equation, we entered

plant size, country and industry control variables; Finland, USA, Germany, Korea,

Austria, Electronics, and Machinery. In the second equation we entered the control

variables and lean production practices. The first equation shows that the control

variables alone significantly contribute to the explanation of the variance in the level

of MC implementation (R²adj = 0.065, P < 0.05). This explanation was attributed to

the country effect as Germany (P< 0.01), USA (P< 0.1), and Austria (P< 0.1) have

significantly higher levels of MC implementation than Japan.

The second equation shows that the addition of lean practices explained a significant

portion (13.6%) of the variance in MC implementation level and development. Only

one lean practice, manufacturing strategy proved to be significant (P< 0.01) and

positively related to MC implementation level. The other practices were not

significantly related. In multiple regression analysis, multicollinearity is a potential

problem that occurs when independent variables are highly correlated (Tabachnick

and Fidell, 2001) and usually leads to unreliable estimates of the individual regression

coefficients. The correlation matrix in table 3 shows that lean practices have positive

and significant correlations with each other ranging between 0.272— 0.721. In order

to deal with this problem in our regression models, we used the variance inflation

factor (VIF) which measures the impact of collinearity among the variables in a

regression model. VIF for the seven lean practices in the second model ranged

between 2.66 and 4.73 indicating that about 75% of the variance of each practice can

be explained by other practices and the control variables (Lui et al. 2006), implying

the existing of multicollinearity in the second model of our regression.

Table 4

Hierarchical regression analysis of mass customization Variables Model (1)

Coefficient

Model (2)

Coefficient

(Constant) 4.624*** 2.352***

Ln. Size .047 -.070

FIN .099 .052

USA .160* .139

GER .400*** .327***

KOR .119 .084

AUT .179* .075

ELEC .146 .169*

MACH .050 .066

JIT .162

TPM -.090

HRM -.001

TQM -.244

MS .400***

SRM -.005

CRM .187

R² .114 .250

Adj. R² .065 .168

F 2.321** 3.051***

Change in R² .136

F change 3.556*** * P≤ 0.1; ** P≤ 0.05; *** P≤ 0.01.

16

Lui et al. (2006) faced similar situation and pointed to MANOVA as an appropriate

statistical techniques that is unaffected by multicollinearity and able to assess the

individual contribution of each independent variable.

We use one-way ANOVA to continue our analysis, and have classified the plants into

three groups of high, medium, and low MC plants. In order to account for the effect of

the control variables, we have used the standardized residuals from the first regression

model to classify the three groups. The results of ANOVA test (Table 5) show that the

three groups of MC plants differ significantly concerning JIT (P≤ 0.05), MS (P≤ 0.01),

SRM (P≤ 0.05), and marginally differ concerning CRM (P≤ 0.1). As for the other

three lean practices: HRM, TPM, and TQM, significant differences among the three

groups were not found. Clearly, based on these results, we cannot conclude that the

three insignificant lean practices should be ignored by mass customizers because they

did not show significant differences among the three groups of mass customization.

The possible explanation for this result is that mass customization requires different

way to manage human resource than lean production firms. It seems that the

traditional way of lean plants of having multi-functional employees and establishing

small groups to solve common problems doesn’t suit mass customization environment.

Highly skilled, but specialized workers could yield better results for a mass

customizing firm. The possible large variety of customer requirements and

specifications entails specialized workers and, as opposed to lean production where

the level of customization is very low, applying lean HRM practices to a mass

customizing firm may reduce the ability of workers to respond effectively to customer

requirements. Pine II (1993) asserted that mass customization requires different

organizational structures, management roles and systems, values, learning methods,

and approaches of relating to customers. The results also suggest that the traditional

lean methods of managing quality and TPM do not suit mass customizing firms. It

seems that MC requires specific approaches to quality that ensures the delivery of

highest possible levels of quality products according to customer specifications that

justify extra cost paid by the customer. Such levels of quality are usually not targeted

by traditional lean plants.

We also have conducted Scheffe pairwise comparison tests of mean differences to

further understand the differences among the three groups. The results show that

plants with high mass customization level have significantly higher levels than the

plants with low mass customization level concerning JIT (P≤ 0.05), MS (P≤ 0.01),

SRM (P≤ 0.1), and CRM (P≤ 0.1). Significant differences were not found between the

groups of high and medium mass customization level as well as between the groups of

medium and low mass customization level.

All in all, hypotheses H1a, H5a, and H6a were supported. Hypothesis H7a was

marginally supported, and hypotheses H2a, H3a, and H4a were rejected.

17

Table 5

Results of ANOVA test Lean

practices

MC implementation level Pairwise differences (Scheffe) F-value P-value

High

(N=63)

Medium

(N=62)

Low

(N=62)

JIT 4.72 4.54 4.47 (1,3)** 3.762 .025

TPM 5.01 4.90 4.89 .881 .416

HRM 5.31 5.16 5.13 2.111 .124

TQM 5.29 5.10 5.18 1.871 .157

MS 5.23 5.03 4.91 (1,3)*** 5.388 .005

SRM 5.20 5.08 5.03 (1,3)* 2.998 .052

CRM 5.50 5.44 5.36 (1,3)* 2.477 .087 * P≤ 0.1.

** P≤ 0.05. *** P≤ 0.01.

Next, we test hypotheses H1b, H2b, H3b, H4b, H5b, H6b, and H7b. we use

hierarchical regression analysis with the overall measure of competitive performance

as a dependent variable (Table 6). In the first equation, we entered plant size, country,

and industry control variables. In the second equation, we added mass customization

into the model. In the third to ninth equations, we added lean practices independently

into the models so that we can measure the incremental impact of each lean practice

on competitive performance given the impact of the control variables and mass

customization. The first equation shows that the control variables alone did not

contribute to the explanation of the variance of competitive performance. The second

equation shows that the addition of mass customization explained a significant portion

(4.7%) of the variance in competitive performance among responding plants. The

third equation shows that the addition of each of lean practices explained an

additional significant portion (ranging between 8.2% - 19.3%) of the variance in

competitive performance among responding plants.

It is interesting to note that mass customization proved to be positively associated

with competitive performance overall measure. The literature has shown some

contradiction in this regard. Christiansen et al. (2004) indicated that mass

customization might lower manufacturing performance as companies offer

customized products at almost the price of mass produced products, and they found

that only weak relationships exist between bundles of early mass customization and

performance. However, Pine (1993) argued that low costs are achieved through

economies of scope, the application of a single process to produce a greater variety of

products more cheaply and more quickly. Other researchers have indicated that mass

customization is associated with higher competitiveness (e.g. Moser, 2007; Da

Silveria et al., 2001; Kotha, 1995).

18

Table 6

Results of Hierarchical regression analysis of competitive performance Variables Eq. (1)

Eq. (2) Eq. (3) Eq. (4) Eq. (5) Eq. (6) Eq. (7) Eq. (8) Eq. (9)

(Constant) 3.682*** 2.638*** 1.630*** 1.523*** 1.280** 1.032* .227*** .781 .282 Ln. Size .052 .041 -.057 -.104 -.069 -.098 -.035 -.027 -.052

FIN -.201* -.226* -.257** -.273*** -.303*** -.147 -.268** -.382*** -.433***

USA -.111 -.139 -.185* -.113 -.180* -.046 -.199** -.191** -.288*** GER .011 -.081 -.054 -.057 -.126 -.048 -.139 -.080 -.253**

KOR -.030 -.046 -.083 -.051 -.053 -.049 -.075 -.079 -.112

AUT .099 .056 .043 -.042 -.052 -.074 -.087 -.003 -.135 ELEC -.097 -.132 -.092 -.157* -.140 -.137 -.105 -.092 -.060

MACH -.072 -.097 .020 -.053 -.041 -.067 .038 -.017 -.031

MC .229*** .166** .157* .179** .060 .190** .166** .177** JIT .372***

TPM .394***

HRM .357*** MS .529***

TQM .372***

SRM .343*** CRM .353***

R² .079 .125 .236 .244 .232 .317 .230 .216 .207

Adj. R² .023 .065 .178 .185 .173 .265 .171 .155 .146 F 1.40 2.07** 4.02*** 4.18*** 3.92*** 6.04*** 3.88*** 3.57*** 3.39***

Change in R² .046 .112 .119 .107 .193 .106 .091 .082 F change 6.91*** 18.99*** 20.43*** 18.12*** 36.67*** 17.82*** 15.04*** 13.51***

* P≤ 0.1.

** P≤ 0.05.

*** P≤ 0.01.

The literature has indicated that lean practices are highly associated with competitive

performance of the plant. Hence, in order to insure that the results presented in Table

6 was not spurious due to the powerful impact of lean practices on performance and to

insure that mass customizers who adopt lean practices have higher performance, we

have split our sample into four groups as shown in figure 2 below. We have split the

sample into high and low mass customization plants based on the mean value as a cut-

off point. In a similar manner, we also have split the sample into high and low

implementers of each lean production practice. Our concern is on the plants that have

achieved high levels of mass customization implementation, therefore, group three

(low levels of mass customization and high levels of lean practices) and group four

(low levels of mass customization and low levels of lean practices) have not be

considered in our additional analysis.

Next, we have conducted t-test to compare group one (high mass customization and

high lean practices) with group two (high mass customization and low lean practices)

concerning competitive performance of the plant as shown in Table 7.

The results of the t-test show that there are significant differences between the two

groups concerning competitive performance for all the lean practices except CRM.

This result might be surprising as customer involvement and relation management has

been widely considered in the literature as one main success factor and perquisite for

mass customization.

19

Group 2

High/Low

Group 1

High/high

Group 4

Low/low

Group 3

Low/high

Low High

Lean practices

Fig.2. levels of simultaneous implementation of MC and lean practices

It seems that CRM is more associated with customer satisfaction and maximization of

the perceived value to customers to a greater extent than competitive performance.

Hypotheses H1b – H6b were supported while we were reluctant to accept hypothesis

H7b.

Table 7

Results of t-test

No. Group Performance

mean S.D.

No. of

plants t- value p-value

1 High MC/High JIT 3.96 .571 46 2.593 .011

High MC/ Low JIT 3.64 .561 39

2 High MC/High TPM 4.00 .510 48 3.414 .001

High MC/ Low TPM 3.58 .601 37

3 High MC/High HRM 3.98 .550 47 2.993 .004

High MC/ Low HRM 3.61 .571 38

4 High MC/High MS 3.99 .510 56 4.185 .000

High MC/ Low MS 3.48 .582 29

5 High MC/High TQM 4.02 .571 48 3.878 .000

High MC/ Low TQM 3.56 .501 37

6 High MC/High SRM 4.02 .518 48 3.998 .000

High MC/ Low SRM 3.55 .565 37

7 High MC/High CRM 3.91 .531 50 1.645 .105

High MC/ Low CRM 3.69 .641 35

6. Conclusions

On the basis of our study, the following conclusions are drawn.

First, plant size, country and industry alone explained a significant portion of

variation in mass customization implementation level. This variance was explained by

country effect as USA, Germany, and Austria have higher levels of MC

implementation than Japan. Our analysis did not reveal significant differences among

the three industries-machinery, transportation, and electrical & electronics suggesting

that mass customization strategy could be equally implemented in those industries.

Plant size also did not show association with mass customization implementation

level implying that mass customization is not a strategy for bigger plants only, but

smaller plants can successfully adopt it to enhance their competitive advantage.

High

Low

Mass

Customization

20

Plant size, country and industry did not explain a significant portion of variation in

competitive performance level. This implies that competitive performance depends on

overall infrastructure, capabilities and strategies, which are the pillars of superior

performance, regardless of the size of the plant, industry or country to which the plant

belongs to.

Second, the results show that four lean practices, just-in-time production,

manufacturing strategy, supplier relationship management, and customer relationship

management are associated with mass customization implementation level. Many

companies are reluctant to adopt mass customization strategy due to demand and

supply uncertainties associated with its implementation. Our study suggests that the

ability of companies to manage their supply chains is the main success factor of mass

customization. Lean supply chain management effectively enables companies to

manage uncertainties associated with mass customization, and enables them to

efficiently and timely respond to customer needs and requirements. Manufacturing

strategy, which is often neglected in the lean production literature, proved to be a

powerful tool to increase mass customization level as it guides the efforts to build the

necessary infrastructure for successful mass customization implementation.

Three lean practices, total quality management, total productive maintenance, and

human resource management were not found to affect mass customization. Although

these practices are essential for traditional lean plants, our results suggest that these

practices are not sufficient for mass customization environment which requires

different ways to HRM and different approaches to quality management that exceed

those implemented in traditional lean plants to ensure the provision of greater value to

customers.

Third, the results show that mass customization has a direct positive impact on the

competitive performance of the plant. This implies that MC is an effective strategy to

improve the competitiveness of the plant and to gain a competitive advantage.

Companies are advised to seek ways to minimize and eliminate extra costs due to

adopting mass customization strategy.

Fourth, this study re-emphasized that lean production practices are associated with

higher competitive performance. In today’s competitive environment, companies are

recommended as never before to apply lean practices in order to maintain and

enhance their competitive advantage.

Fifth, this study shows that lean production practices are an effective tool for mass

customizing firms to improve their competitive performance. High mass customizers

with high lean practice show higher competitive performance than high customizers

with low lean practices. The only exception was customer relation management which

is an essential tool to facilitate mass customization implementation and to maximize

customer satisfaction.

The limitation of our study is that the measurement scales used for our research may

not capture all the aspects and practices implemented by the surveyed plants.

Furthermore, only three industries were included in our sample.

Similar research studies should be undertaken in case of other industries and less

developed countries to investigate the potential of mass customization and the

willingness and readiness of the firms to adopt it. Case studies are needed for

companies implementing mass customization and lean practices. Also, Additional

empirical research is needed to investigate the impact of other operational practices on

mass customization.

21

Appendix A

Multi-item measurement scales

Mass Customization (Cronbach’s α-coefficient = 0.739)

(Adapted from Tu, Vonderembse and Ragu-Nathan, 2001)

Question1 We are highly capable of large scale product customization.

Question2 We can easily add significant product variety without increasing cost.

Question3* Our setup costs, changing from one product to another, are very low.

Question4 We can customize products while maintaining high volume.

Question5 We can add product variety without sacrificing quality.

Question6* We tend to run standardized products whenever possible.

Question 7 Our capability for responding quickly to customization requirements is very high.

* Items are deleted

JIT production

Equipment Layout (Cronbach’s α-coefficient = 0.708)

Question1 We have laid out the shop floor so that processes and machines are in close proximity to

each other.

Question2 We have organized our plant floor into manufacturing cells.

Question3 Our machines are grouped according to the product family to which they are dedicated.

Question4 The layout of our shop floor facilitates low inventories and fast throughput.

Question5 Our processes are located close together, so that material handling and part storage are

minimized.

Question6 We have located our machines to support JIT production flow.

Just-in-Time Delivery by Suppliers (Cronbach’s α-coefficient = 0.660)

Question1 Our suppliers deliver to us on a just-in-time basis.

Question2 We receive daily shipments from most suppliers.

Question3 We can depend upon on-time delivery from our suppliers.

Question4 Our suppliers are linked with us by a pull system.

Question5 Suppliers frequently deliver materials to us.

Kanban (Cronbach’s α-coefficient = 0.794)

Question1 Suppliers fill our kanban containers, rather than filling purchase orders.

Question2 Our suppliers deliver to us in kanban containers, without the use of separate packaging.

Question3 We use a kanban pull system for production control.

Question4 We use kanban squares, containers or signals for production control.

Setup Time Reduction (Cronbach’s α-coefficient =0.741)

Question1 We are aggressively working to lower setup times in our plant.

Question2 We have converted most of our setup time to external time, while the machine is running.

Question3* We have low setup times of equipment in our plant.

Question4 Our crews practice setups, in order to reduce the time required.

Question5 Our workers are trained to reduce setup time.

Question6 (R) Our setup times seem hopelessly long.

* Items are deleted

22

TPM

Autonomous Maintenance (Cronbach’s α-coefficient = 0.613)

(Based on Nakajima)

Question1 Cleaning of equipment by operators is critical to its performance.

Question2 Operators understand the cause and effect of equipment deterioration.

Question3 Basic cleaning and lubrication of equipment is done by operators.

Question4* Production leaders, rather than operators, inspect and monitor equipment

performance*.

Question5 Operators inspect and monitor the performance of their own equipment.

Question6 Operators are able to detect and treat abnormal operating conditions of their

equipment.

* Items are deleted

Preventive Maintenance (Cronbach’s α-coefficient = 0.676)

(Based on Nakajima)

Question1 We upgrade inferior equipment, in order to prevent equipment problems.

Question2 In order to improve equipment performance, we sometimes redesign equipment.

Question3 We estimate the lifespan of our equipment, so that repair or replacement can be

planned.

Question4 We use equipment diagnostic techniques to predict equipment lifespan.

Question5 We do not conduct technical analysis of major breakdowns.

Maintenance Support (Cronbach’s α-coefficient = 0.673)

Question1 Our production scheduling systems incorporate planned maintenance.

Question2 Spare parts for maintenance are managed centrally.

Question3* Each of our plants establishes its own maintenance standards.

Question4 Equipment performance is tracked by our information systems.

Question5 Our systems capture information about equipment failure.

* Items are deleted

Team Based Maintenance (Cronbach’s α-coefficient = 0.655)

Question1 We find that equipment performance is improved by the work of cross-functional

teams.

Question2* Our maintenance teams are comprised of specialized maintenance personnel*.

Question3 In the past, many equipment problems have been solved through small group sessions.

Question4 Groups are formed to solve current equipment problems.

Question5* Maintenance personnel solve most maintenance problems by themselves*.

* Items are deleted

HRM

Employee Suggestions – Implementation and Feedback (Cronbach’s α-coefficient = 0.832)

Question 1 Management takes all product and process improvement suggestions seriously.

Question 2 We are encouraged to make suggestions for improving performance at this plant.

Question 3 Management tells us why our suggestions are implemented or not used.

Question 4 Many useful suggestions are implemented at this plant.

Question 5 My suggestions are never taken seriously around here.

Multi-Functional Employees (Cronbach’s α-coefficient = 0.796)

Question 1 Our employees receive training to perform multiple tasks.

Question 2 Employees at this plant learn how to perform a variety of tasks.

Question 3 The longer an employee has been at this plant, the more tasks they learn to perform.

Question 4 Employees are cross-trained at this plant, so that they can fill in for others, if

necessary.

QuestionR 5 At this plant, each employee only learns how to do one job.

23

Small Group Problem Solving (Cronbach’s α-coefficient = 0.824)

Question 1 During problem solving sessions, we make an effort to get all team members’ opinions

and ideas before making a decision.

Question 2 Our plant forms teams to solve problems.

Question 3 In the past three years, many problems have been solved through small group sessions.

Question 4 Problem solving teams have helped improve manufacturing processes at this plant.

Question 5 Employee teams are encouraged to try to solve their own problems, as much as

possible.

QuestionR 6 We don’t use problem solving teams much, in this plant.

Task-Related Training for Employees (Cronbach’s α-coefficient = 0.807)

Question 1 Our plant employees receive training and development in workplace skills, on a

regular basis.

Question 2 Management at this plant believes that continual training and upgrading of employee

skills is important.

Question 3* Employees at this plant have skills that are above average, in this industry.

Question 4 Our employees regularly receive training to improve their skills.

Question 5 Our employees are highly skilled, in this plant.

* Items are deleted

TQM

Continuous Improvement and Learning (Cronbach’s α-coefficient = 0.683)

Question1 We strive to continually improve all aspects of products and processes, rather than

taking a static approach.

Question2 If we aren’t constantly improving and learning, our performance will suffer in the long

term.

Question3 Continuous improvement makes our performance a moving target, which is difficult

for competitors to attack.

Question4 We believe that improvement of a process is never complete; there is always room for

more incremental improvement.

Question5 Our organization is not a static entity, but engages in dynamically changing itself to

better serve its customers.

Feedback (Cronbach’s α-coefficient = 0.779)

Question1 Charts showing defect rates are posted on the shop floor.

Question2 Charts showing schedule compliance are posted on the shop floor.

Question3 Charts plotting the frequency of machine breakdowns are posted on the shop floor.

Question4 Information on quality performance is readily available to employees.

Question5 Information on productivity is readily available to employees.

Process Control (Cronbach’s α-coefficient = 0.812)

Question1 Processes in our plant are designed to be “foolproof.”

Question2 A large percent of the processes on the shop floor are currently under statistical quality

control.

Question3 We make extensive use of statistical techniques to reduce variance in processes.

Question4 We use charts to determine whether our manufacturing processes are in control.

Question5 We monitor our processes using statistical process control.

Cleanliness and Organization (Cronbach’s α-coefficient = 0.799)

Question1 Our plant emphasizes putting all tools and fixtures in their place.

Question2 We take pride in keeping our plant neat and clean.

Question3 Our plant is kept clean at all times.

Question4 Employees often have trouble finding the tools they need.

Question5 Our plant is disorganized and dirty.

24

MS

Achievement of Functional Integration (Cronbach’s α-coefficient = 0.810)

Question1 The functions in our plant are well integrated.

Question2 Problems between functions are solved easily, in this plant.

Question3 Functional coordination works well in our plant.

Question4 Our business strategy is implemented without conflicts between functions.

Anticipation of New Technologies (Cronbach’s α-coefficient = 0.776)

Question1 We pursue long-range programs, in order to acquire manufacturing capabilities in advance

of our needs.

Question2 We make an effort to anticipate the potential of new manufacturing practices and

technologies.

Question3 Our plant stays on the leading edge of new technology in our industry.

Question4 We are constantly thinking of the next generation of manufacturing technology.

Formal Strategic Planning (Cronbach’s α-coefficient = 0.780)

Question1 Our plant has a formal strategic planning process, which results in a written mission, long-

range goals and strategies for implementation.

Question2 This plant has a strategic plan, which is put in writing.

Question3 Plant management routinely reviews and updates a long-range strategic plan.

Question4 (R) The plant has an informal strategy, which is not very well defined.

Manufacturing-Business Strategy Linkage (Cronbach’s α-coefficient = 0.767)

Question1 We have a manufacturing strategy that is actively pursued.

Question2 Our business strategy is translated into manufacturing terms.

Question3 Potential manufacturing investments are screened for consistency with our business

strategy.

Question4 At our plant, manufacturing is kept in step with our business strategy.

Question5 (R) Manufacturing management is not aware of our business strategy.

Question6 (R) Corporate decisions are often made without consideration of the manufacturing strategy.

Proprietary Equipment (Cronbach’s α-coefficient = 0.676)

Question1 We actively develop proprietary equipment.

Question2

(R)*

Our equipment is about the same as the rest of the industry.

Question3 We have equipment that is protected by our firm’s patents.

Question4 Proprietary equipment helps us gain a competitive advantage.

Question5 (R) We rely on vendors for most of our manufacturing equipment.

Question6 We frequently modify equipment to meet our specific needs.

* Items are deleted

SRM

Supplier Partnership (Cronbach’s α-coefficient = 0.772)

Question1 We maintain cooperative relationships with our suppliers.

Question2 We provide a fair return to our suppliers

Question3 We help our suppliers to improve their quality.

Question4 We maintain close communications with suppliers about quality considerations and

design changes.

Question5 Our key suppliers provide input into our product development projects.

Trust-Based Relationship with Suppliers (Cronbach’s α-coefficient = 0.705)

Question1 We are comfortable sharing problems with our suppliers.

Question2 In dealing with our suppliers, we are willing to change assumptions, in order to find

more effective solutions.

Question3 We believe that cooperating with our suppliers is beneficial.

Question4 We emphasize openness of communications in collaborating with our suppliers.

25

Supplier Lead Time (Cronbach’s α-coefficient = 0.600)

Question1 We seek short lead times in the design of our supply chains.

Question2 We purchase in small lot sizes, to reduce supplier lead time.

Question3* When outsourcing, we consider supplier lead time as a greater priority than cost.

Question4 Our company strives to shorten supplier lead time, in order to avoid inventory and

stockouts.

* Items are deleted

Supplier Quality Involvement (Cronbach’s α-coefficient = 0.768)

Question1 We strive to establish long-term relationships with suppliers.

Question2 Our suppliers are actively involved in our new product development process.

Question3 Quality is our number one criterion in selecting suppliers.

Question4 We use mostly suppliers that we have certified.

Question5 We maintain close communication with suppliers about quality considerations and

design changes.

Question6* We actively engage suppliers in our quality improvement efforts

Question7 We would select a quality supplier over one with a lower price

* Items are deleted

CRM

TQM Link with Customers (Cronbach’s α-coefficient = 0.722)

Question1 Quality is the number one criterion used by our customers in selecting us as a supplier.

Question2 Our processes are certified, or qualified, by our customers.

Question3 Our customers involve us in their quality improvement efforts.

Question4 Our customers can rely on us for quality products and processes.

Question5 Quality is our number one priority in dealing with our customers.

Customer Focus (Cronbach’s α-coefficient = 0.611)

Question1* Engineers are the best source of product specifications and design changes.

Question2 We believe that customers are a better judge of their needs than product designers.

Question3 We believe that organizations should be proactive in anticipating their customers’

needs.

Question4 We believe that customers are the best judge of their needs and wants.

Question5 Customer satisfaction is important to the long-term performance of our organization.

Question6 Our organization satisfies or exceeds the requirements and expectations of our

customers.

* Items are deleted

Customer Involvement (Cronbach’s α-coefficient = 0.688)

Question1 We frequently are in close contact with our customers.

Question2 Our customers seldom visit our plant.

Question3 Our customers give us feedback on our quality and delivery performance.

Question4 Our customers are actively involved in our product design process.

Question5 We strive to be highly responsive to our customers’ needs.

Question6 We regularly survey our customers’ needs.

26

Appendix B Competitive Performance Scales

Please circle the number that indicates your opinion about how your plant compares to its competition

in your industry, on a global basis.

1: Poor, low end of industry; 2: Equivalent to competitors; 3: Average; 4: Better than average; 5:

Superior

Unit cost of manufacturing 1 2 3 4 5

Conformance to product specifications 1 2 3 4 5

On tome delivery performance 1 2 3 4 5

Flexibility to change volume 1 2 3 4 5

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