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, multifunctional 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)
b)
c)
d)
e)
f)
g)
Just-in-time production (JIT)
Human resource management (HRM)
Total quality management (TQM)
Total productive maintenance (TPM)
Manufacturing strategy (MS)
Supplier relationship management (SRM)
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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Lean production dimensions
JIT
TQM
HRM
TPM
MS
SRM
CRM
Mass customization
Competitive performance
Fig.1. Research framework
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
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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,
8
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 Masscustomization 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
Japan
USA
Germany
Korea
Finland
Austria
Total
Industry
Machinery
10
9
13
10
6
7
55
Total
Electronics
12
11
9
10
14
10
66
Transportation
13
9
19
11
10
4
66
35
29
41
31
30
21
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.
12
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.
13
Table 2
Validity and reliability of the super scales
Scale
Alpha
coefficient
Factor loading
Scale 1
Scale 2
Scale 3
Scale 4
Eigenvalue
Proportion
No. of factors
Scale
Alpha
coefficient
Factor loading
Scale 1
Scale 2
Scale 3
Scale 4
Scale 5
Eigenvalue
Proportion
No. of factors
Scale
Alpha coefficient
Factor loading
Question item 1
Question item 2
Question item 3
Question item 4
Eigenvalue
Proportion
No. of factors
JIT
0.752
Factor 1
Factor
2
HRM
0.852
Factor
1
.813
.836
.735
.688
2.373
59.329%
Factor 1
Factor
2
TPM
0.830
Factor
1
.828
.814
.807
.881
2.776
69.409%
1
1
Factor
2
Factor
1
Factor 1
Factor
2
SRM
0.776
Factor
1
.814
.873
.817
.790
.415
2.888
57.751%
2.549
63.734%
1
Factor 1
Factor 1
2.124
70.787
Factor
2
Factor
1
.884
.874
.719
.629
2.459
61.486%
1
Factor 1
.847
.844
.833
Factor
1
1
MS
0.788
.751
.857
.828
.751
CRM
0.787
Factor 2
Factor
2
.690
.885
.847
.834
2.674
66.839%
TQM
0.801
Factor 1
Factor 1
1
Competitive performance
0.608
Factor 1 Factor 2 Factor 1
.676
.705
.777
.543
1.852
46.302
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
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***
2
ªN=187
***P ≤ 0.01
**P≤ 0.05
14
3
4
5
6
7
8
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
(Constant)
Ln. Size
FIN
USA
GER
KOR
AUT
ELEC
MACH
JIT
TPM
HRM
TQM
MS
SRM
CRM
R²
Adj. R²
F
Change in R²
F change
Model (1)
Coefficient
4.624***
.047
.099
.160*
.400***
.119
.179*
.146
.050
.114
.065
2.321**
* P≤ 0.1; ** P≤ 0.05; *** P≤ 0.01.
15
Model (2)
Coefficient
2.352***
-.070
.052
.139
.327***
.084
.075
.169*
.066
.162
-.090
-.001
-.244
.400***
-.005
.187
.250
.168
3.051***
.136
3.556***
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.
16
Table 5
Results of ANOVA test
Lean
practices
JIT
TPM
HRM
TQM
MS
SRM
CRM
MC implementation level
High
Medium
Low
(N=63)
(N=62)
(N=62)
4.72
4.54
4.47
5.01
4.90
4.89
5.31
5.16
5.13
5.29
5.10
5.18
5.23
5.03
4.91
5.20
5.08
5.03
5.50
5.44
5.36
Pairwise differences (Scheffe)
(1,3)**
(1,3)***
(1,3)*
(1,3)*
F-value
P-value
3.762
.881
2.111
1.871
5.388
2.998
2.477
* 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).
17
.025
.416
.124
.157
.005
.052
.087
Table 6
Results of Hierarchical regression analysis of competitive performance
Variables
Eq. (1)
Eq. (2)
Eq. (3)
Eq. (4)
Eq. (5)
(Constant)
Ln. Size
FIN
USA
GER
KOR
AUT
ELEC
MACH
MC
JIT
TPM
HRM
MS
TQM
SRM
CRM
R²
Adj. R²
F
Change in R²
F change
* P≤ 0.1.
** P≤ 0.05.
*** P≤ 0.01.
3.682***
.052
-.201*
-.111
.011
-.030
.099
-.097
-.072
2.638***
.041
-.226*
-.139
-.081
-.046
.056
-.132
-.097
.229***
1.630***
-.057
-.257**
-.185*
-.054
-.083
.043
-.092
.020
.166**
.372***
1.523***
-.104
-.273***
-.113
-.057
-.051
-.042
-.157*
-.053
.157*
1.280**
-.069
-.303***
-.180*
-.126
-.053
-.052
-.140
-.041
.179**
Eq. (6)
1.032*
-.098
-.147
-.046
-.048
-.049
-.074
-.137
-.067
.060
Eq. (7)
.227***
-.035
-.268**
-.199**
-.139
-.075
-.087
-.105
.038
.190**
Eq. (8)
.781
-.027
-.382***
-.191**
-.080
-.079
-.003
-.092
-.017
.166**
Eq. (9)
.282
-.052
-.433***
-.288***
-.253**
-.112
-.135
-.060
-.031
.177**
.394***
.357***
.529***
.372***
.343***
.079
.023
1.40
.125
.065
2.07**
.046
6.91***
.236
.178
4.02***
.112
18.99***
.244
.185
4.18***
.119
20.43***
.232
.173
3.92***
.107
18.12***
.317
.265
6.04***
.193
36.67***
.230
.171
3.88***
.106
17.82***
.216
.155
3.57***
.091
15.04***
.353***
.207
.146
3.39***
.082
13.51***
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 cutoff 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.
18
High
Group 2
High/Low
Group 1
High/high
Low
Group 4
Low/low
Group 3
Low/high
Mass
Customization
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
1
High MC/High JIT
High MC/ Low JIT
High MC/High TPM
High MC/ Low TPM
High MC/High HRM
High MC/ Low HRM
High MC/High MS
High MC/ Low MS
High MC/High TQM
High MC/ Low TQM
High MC/High SRM
High MC/ Low SRM
High MC/High CRM
High MC/ Low CRM
2
3
4
5
6
7
Performance
mean
3.96
3.64
4.00
3.58
3.98
3.61
3.99
3.48
4.02
3.56
4.02
3.55
3.91
3.69
S.D.
.571
.561
.510
.601
.550
.571
.510
.582
.571
.501
.518
.565
.531
.641
No. of
plants
46
39
48
37
47
38
56
29
48
37
48
37
50
35
t- value
p-value
2.593
.011
3.414
.001
2.993
.004
4.185
.000
3.878
.000
3.998
.000
1.645
.105
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.
19
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.
20
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
21
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.
22
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.
23
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, longrange 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
Our equipment is about the same as the rest of the industry.
(R)*
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.
24
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.
We actively engage suppliers in our quality improvement efforts
Question6*
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.
25
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
Conformance to product specifications
On tome delivery performance
Flexibility to change volume
1
1
1
1
2
2
2
2
3
3
3
3
4
4
4
4
5
5
5
5
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