Relationships between two approaches for planning manufacturing strategy: A strategic approach and a paradigmatic approach

ARTICLE IN PRESS Int. J. Production Economics 115 (2008) 349– 361

Contents lists available at ScienceDirect

Int. J. Production Economics journal homepage: www.elsevier.com/locate/ijpe

Relationships between two approaches for planning manufacturing strategy: A strategic approach and a paradigmatic approach Jian Wang a,, De-bi Cao b a b

Department of Industrial Engineering and Management, Tokyo Institute of Technology, 2-12-1 Ookayama, Meguro-ku, Tokyo 152-8550, Japan Faculty of Science and Technology, Keio University, 3-14-1 Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan

a r t i c l e in fo

abstract

Article history: Received 16 November 2006 Accepted 16 April 2008 Available online 21 June 2008

Two approaches for planning manufacturing strategy, a strategic approach and a paradigmatic approach, are introduced. The key decisions of these two approaches are, respectively, located in the choices of competitive priorities and manufacturing paradigms. Three hypothesis models on the relationships between these two approaches in a turbulent environment are founded with the help of structural equation modeling and tested with 107 samples from the Chinese manufacturing industry. The results suggest that when established the relationships between manufacturing strategy and business strategy, the mediate function of competitive priorities is not suitable for manufacturing paradigms, and it is more appropriate to make the key decisions in each approach based on business strategy directly. & 2008 Elsevier B.V. All rights reserved.

Keywords: Manufacturing strategy Business strategy Competitive priority Manufacturing paradigm Turbulent environment

1. Introduction Manufacturing strategy has different paradigms, such as competing through manufacturing, strategic choices in manufacturing and best practices (Voss, 1995). Accordingly, there are different approaches for planning manufacturing strategy. Two approaches are introduced in this paper: a strategic approach and a paradigmatic approach. In the strategic approach, the key decisions on manufacturing strategy are located in the choices of competitive priorities, which are known as the manufacturer’s choice of emphases from among key capabilities such as quality, cost, delivery and flexibility. In the paradigmatic approach, the key decisions on manufacturing strategy are located in the choices of manufacturing paradigms which include best practices and innovative manufacturing systems, such as lean production and agile manufacturing. A compatible and complementary relationship can be found

 Corresponding author. Tel.: +81 42 2020260.

E-mail addresses: [email protected] (J. Wang), [email protected] (D.-B. Cao). 0925-5273/$ - see front matter & 2008 Elsevier B.V. All rights reserved. doi:10.1016/j.ijpe.2008.04.014

between these two approaches, and there are close relationships between the choices on competitive priorities and manufacturing paradigms. However, there exist different views on planning manufacturing strategy according to these two approaches. One issue in this regard is whether the decisions on manufacturing paradigms should be made based on competitive priorities, business strategy or both. As a decision in the manufacturing section, manufacturing paradigms should be consistent with manufacturing strategy, such as the decisions on competitive priorities. On the other hand, as a paradigm of manufacturing strategy, manufacturing paradigms should be directed by business strategy. Different choices will have different planning process models for manufacturing strategy and different performance in practice, too. Since manufacturing strategy must support business strategy irrespective of the approach used, we suggest that the relationships between the two approaches be examined through an analysis of the relationships among the key decisions of the two approaches and business strategy. Research on the relationships between manufacturing paradigms, competitive priorities and business strategy

ARTICLE IN PRESS 350

J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

can be also helpful for introducing and improving innovative manufacturing systems or best practices. In this paper, 31 such innovative manufacturing systems are collected. In addition, an increasing number of such systems and tools are entering the market, covering the whole supply chain, such as SCM packages provided by SAP, Manugistics and i2. However, it is reported that many firms have failed in the innovative activities. It has been widely accepted that in the absence of good integration with strategy, these innovative activities would not lead to good performance. Usually, manufacturing strategy is a suitable choice for most firms to build the integration. One reason for this is that these innovative activities are purposed to the production sections or the SCM. The other reason is that manufacturing strategy can mediate between business strategy and business performance (Ward and Duray, 2000). Another choice is to set up a direct linkage with business strategy but not with manufacturing strategy. The difference is that the second selection does not agree with the mediatory role of manufacturing strategy, such as in terms of competitive priorities, between manufacturing paradigms and business strategy. In general, the consistency between manufacturing strategy and business strategy has been taken for granted, and the difference has not laid sufficient emphasis on both theory and practice. In academic research, when setting up the relationships between systems and strategy, different researchers employ different selections, business strategies or manufacturing strategies (Miltenburg, 1995; Duda and Cochran, 2000; Kim and Lee, 1993; Carrie et al., 1994). Relationships between manufacturing strategy and business strategy have been studied from the 1960s; however, the links need to be further researched because environmental and other changes have caused manufacturing strategy to drift away from the mainstream strategy (Brown and Blackmon, 2005; Barnes et al., 2004; Skinner, 1969). In practice, according to a survey on the Chinese manufacturing industry in 2000, the implementation of JIT may be beneficial for quality, inventory turnover and flexibility; however, it may also have a significant negative relationship with the market share improvement in China (Robb and Xie, 2001). Based on the response to the implementation of TQM in China in 2003, there is a lack of complete understanding of strategic quality management in the surveyed firms, and they only have superficial knowledge of the connotations of some quality dimensions (Lau et al., 2004). The purpose of this study is to compare different linkages between manufacturing paradigms, competitive priorities and business strategy through an empirical study, and provide suggestions on planning manufacturing strategy based on these two approaches. Questions on the relationships among manufacturing paradigms, competitive priorities and business strategy also reflect the coordination problems between two views on strategic management: the market-led view and the resource-based view. In the market-led view, changes within markets determine the market position and functional-level strategy; in the resource-based view, the firm should assemble and deploy appropriate resources that provide opportunities for sustainable competitive advantage in its chosen markets to maximize returns

(Brown and Blackmon, 2005). In the planning process, competitive priorities have often been used to reflect the market requirements and firms’ choices, and the contingency between the choices of competitive priorities and the decisions in manufacturing strategy have been studied often (Ho, 1996; Ketokivi, 2006). On the other hand, in the resource-based view researchers hold that it is more appropriate to forget the trade-offs between competitive priorities in a hyper-competitive environment, and suggest a new planning process model for manufacturing strategy, which includes developing, protecting, and leveraging resources in a dynamic manner (Gagnon, 1999). Manufacturing paradigms are important choices for building manufacturing capabilities. How to match manufacturing capabilities with market requirements has become an important question under the changing environment. Acur and Bititci (2004) demonstrate how the business process-based approach (PROPHESY) facilitates the integration of resource- and marketbased approaches to strategy management. Brown and Blackmon (2005) introduced the concept of ‘strategic resonance’ to dynamically link business-level strategy and manufacturing capabilities, market requirements and a firm’s supply network. However, few empirical studies compared the performance between the strategic and paradigmatic links when planning manufacturing strategy. One reason for this is that it is difficult to hold a common view on the manufacturing capability since the competitive environment is under constant change. Our study focuses on a turbulent environment. A turbulent environment is characterized as the changing and uncertain requirements on competition. In this environment, change is not an exception, but a rule. In order to rapidly respond to frequent and sudden changes, some innovative manufacturing systems, best practices and SCM tools have been proposed. In this study, 31 innovative manufacturing systems and four manufacturing paradigms are examined. These manufacturing paradigms are characterized as the capability of dealing with changes and uncertainty and cover the process of whole supply chains. By examining the requirements in a turbulent environment and the response in manufacturing, this study may provide suggestions to discover the coevolution between innovative activities in manufacturing and the institutional factors in a different environment, which is also included in studies on the Science of Institutional Management of Technology (SIMOT). The remainder of this paper is organized as follows. In the second section, literatures on the relationships between the two approaches are reviewed and three models on the relationships are established. The third section describes the research methodology. In the fourth section, the results of measuring manufacturing strategy and business strategy are reported. The hypotheses are tested in the fifth section. In the last section, we present the conclusion and discuss the results. 2. Literature review Today’s competitive environment is a turbulent environment full of changes such as ubiquitous availability and

ARTICLE IN PRESS J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

351

accelerated pace of technology innovation, globalization of market and increasing customer expectation (Hughes, 1997). Changes are difficult to predict in terms of success or failure and the frequency with which they occur. Correa (1994) stated that current changes have an attribute of uncertainty. These changes cause considerable complexity for manufacturing systems and the whole supply chain management (Perona and Miragliotta, 2004). Dealing with these changes and uncertainty is regarded as the most important capability for firms and also for manufacturing systems to compete in the turbulent environment (Wiendahl and Scholtissek, 1994; Prastacos et al., 2002). This is considerably different from the core aim of production in a stable competitive environment, which is to move towards the most efficient, low-cost state by investing in automation and standardization in response to predictably maturing markets (John et al., 2001). In the turbulent environment, there are different approaches for planning manufacturing strategy. Traditionally, a common theme in manufacturing strategy research has been describing a manufacturer’s choice of emphasis among key capabilities, which can be described by the term ‘competitive priorities’(Ward et al., 1995). Ward and Duray (2000) presented a context model for manufacturing strategy, among which competitive environment, business strategy and manufacturing strategy are sequentially linked and affect one another. Kim and Arnold (1996) empirically studied the constructs and linkage among the detailed components of manufacturing strategy and proposed a process model for operation, as shown in Fig. 1. This process model regarded that the components of manufacturing strategy included competitive priorities (relative importance of competitive capabilities), manufacturing objectives (relative emphasis on performance targets) and action plans (choice of improvement programs). In this paper, the approach for planning manufacturing strategy based on the choices of competitive priorities is referred to as ‘a strategic approach’. This approach focuses on the external consistency among manufacturing strategy, business strategy and other functional strategies, such as marketing strategy, and on

the internal consistency among the components of manufacturing strategy, such as competitive priorities and action plans. Although theoretically appealing, it is easy to make firms miss the step changes from the novel and creative ideas under this approach (Voss, 1995). In addition, under the turbulent environment, the strategic approach faces some other problems. For example, firms must satisfy the simultaneous demands of low cost, flexibility, speed and variety, but not any specific demand from among these (Brown, 2001; Shi and Daniels, 2003). A paradigmatic approach for planning manufacturing strategy can avoid these shortcomings. A paradigmatic approach regards that innovative manufacturing paradigms and practices can embody new rules and sets of coherences between various choices about manufacturing, and provides the best practices for benchmarking. To elaborate in detail, this approach has three components, as shown in Fig. 2. (1) Action plans: Detailed plans on practices, techniques or programs, such as just-in-time deliveries, quality cycles, Kanban, statistical process control and quality function deployment (QFD). Bolden et al. (1997) have listed over 100 manufacturing practices. (2) Manufacturing systems: Innovative systems including the systemic and coherent combinations of practices. For example, the just-in-time manufacturing system includes 10 types of techniques or practices (White, 1990). Spina (1998) refers to these systems as manufacturing models. Clark (1996) coined the terms advanced manufacturing systems (AMS) for these systems or best practices in production, design and engineering and logistics. (3) Manufacturing paradigms: limited sets of new principles that underpin the techniques and pool together various manufacturing systems or models (Spina, 1998). Though appealing to practice, this approach, too, faces some criticisms. For example, it is difficult to converge different manufacturing systems into the unifying paradigms, and it may drive companies to become similar to each other (Spina, 1998). Because of the functions and shortages of these two approaches, a compatible and complementary relationship between them has been found (Voss, 1995; Hayes and

Business Strategy

Business Strategy

Competitive Priority

Manufacturing Paradigm

Manufacturing Objective

Manufacturing System

Action Plan

Action Plan

Performance

Performance

Fig. 1. A model of the strategic approach.

Fig. 2. A model of the paradigmatic approach.

ARTICLE IN PRESS 352

J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

Pisano, 1996; Clark, 1996; Morita and Flynn, 1997; Spina, 1998). When Ward et al. (1990) proposed a top-down hierarchical process model for manufacturing strategy, they also observed that ‘the process model should be revised for the current development of conventional strategic planning activities along with capability building activities such as ‘‘total quality control’’’. Voss (1995) concluded that different paradigms of manufacturing strategy have their strengths and weaknesses and each partially overlaps the other. Clark (1996) argued that the true competitive power of manufacturing lies in integrating the capability of an AMS with the strategic management of manufacturing. Morita and Flynn (1997) empirically examined the linkage between different paradigms of manufacturing strategy. Spina (1998) provided evidence that paradigmatic views and strategic choices in manufacturing are compatible and complementary. Adopting two approaches at the same time has been widely accepted in research and practice. However, the question of how to plan a manufacturing strategy according to these two approaches has different views. To elaborate, different linkages can be found in the relationships among business strategy, competitive priority and manufacturing paradigm. First, linking manufacturing paradigms to business strategy via competitive priorities is a possible choice. This mediate linkage is based on the close relationships between manufacturing paradigms and competitive priorities and those between competitive priorities and business strategy. Manufacturing paradigms come from manufacturing systems which must reflect a company’s manufacturing strategy and the chosen competitive priorities (Safsten and Winroth, 2002). In many studies and practices, decisions on manufacturing systems have been made based on competitive priorities. For example, different types of manufacturing systems have been linked to business performance in term of cost, quality, delivery and flexibility (Miltenburg, 1995; Duda and Cochran, 2000). This proposal has also been reflected in the model proposed by Kim and Arnold (1996), which links action plans (including some best practices) to business strategy through competitive priorities. Then, direct relationships can also be found between manufacturing systems/best practice and business strategy. For example, Kim and Lee (1993) linked different types of manufacturing systems to business strategy in terms of cost efficiency and differentiation. Carrie et al. (1994) linked different manufacturing systems to business strategy in terms of defenders, prospectors, analysers and rectors. Vichkery and Droge (1993) linked different practices to business strategy. Hiltrop (1996) and Pilkington (1998) also found that without a good integration with business strategy, the best practices would not lead to good performance. Finally, based on the mediate linkage and direct linkage, we may consider that manufacturing paradigms can be linked to business strategy in two ways at the same time: direct linkage to business strategy and mediate linkage by competitive priority. Based on these three possible linkages, we set up three models, as shown in Fig. 3. Based on the mediate linkage, Model A has a serial linkage among business strategy (BS),

B.S.

C.P.

B.S.

B.S.

C.P.

M.P.

M.P.

C.P.

M.P.

Fig. 3. Three models on the relationships between two approaches.

competitive priorities (CP) and manufacturing paradigms (MP). Based on the direct linkage, Model B has parallel linkages between manufacturing paradigms and business strategy and between competitive priorities and business strategy. Based on these two proposals, Model C has a completed linkage among manufacturing paradigms, competitive priorities and business strategy. Studies on the linkages in the current turbulent environment is important for building a competitive production system with low cost and less time, and it may also distinguish the firms as good performers and poor performers (Skinner, 1969; Ward and Duray, 2000). It can also give suggestions for introducing and improving innovative manufacturing systems or best practices under the turbulent environment. In addition, studies on the question can be helpful for the coordination between the market-led view and the resource-based view on strategic management. Many studies can be found on the compatible and complementary relationship between these two approaches (Voss, 1995; Hayes and Pisano, 1996; Clark, 1996; Morita and Flynn, 1997; Spina, 1998). Studies on the direct and mediate linkages among competitive environment, business strategy, competitive priorities and performance can also been found (Ward and Duray, 2000). However, few empirical studies considered the mediate and direct linkages among business strategy, competitive priorities and manufacturing paradigms simultaneously. The purpose of this paper is to compare the performance of these three linkage models based on a survey and provide suggestions on the planning process of manufacturing strategy.

3. Research methodologies We now introduce the research methodologies in this section. In the first subsection, four important manufacturing paradigms for dealing with change are concluded from 31 innovative manufacturing systems. In the second subsection, measurement scales for the manufacturing paradigms, competitive priorities and business strategy used in the survey are developed. In the third subsection, we describe the methodologies for testing the measurement scales of the manufacturing paradigms and the methodologies for testing the hypotheses. In the last

ARTICLE IN PRESS J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

subsection, we describe the survey instrument and samples used for the survey. 3.1. Manufacturing paradigms According to the conception of manufacturing paradigms, we can conclude them based on the common characteristics of manufacturing systems. In this study, we focus on those manufacturing paradigms on dealing with change and uncertainty in a turbulent environment. In the process of dealing with the above-mentioned issues, different manufacturing systems and practices share some common characteristics, such as developing new designs for production structures and dynamic processes (Wiendahl and Scholtissek, 1994). Monostori et al. (1998) concluded that manufacturing systems deal with changes in three ways: (1) enhancing the reactivity and proactivity of a traditionally structured system by sophisticated new control systems, (2) constructing decentralized, distributed systems and (3) developing adaptive systems that can learn from past experiences. In this study, we examined the works in the area of manufacturing systems, most of which were published in refereed journals and international conferences. Over 100 papers (from 32 refereed journals, 13 international conferences, and some other publications) have been reviewed. As mentioned earlier, 31 different types of manufacturing systems were collected for analysing their capability to deal with change. The manufacturing paradigms examined in this study were derived from a qualitative analysis of the characteristics shared by these manufacturing systems. Finally, four important manufacturing paradigms were concluded and had been adopted in some studies (Cao et al., 2005; Wang et al., 2004). (1) Autonomy manufacturing paradigm: An autonomy manufacturing paradigm implies dealing with change through autonomous actions and autonomous control in manufacturing. It facilitates the entities in manufacturing systems, such as workers and work teams, to develop the capability to create and control the execution of their own plans and/or strategies. This paradigm employs local autonomy, or the decentralization of authority, which is necessary when the complexity of a manufacturing system increases, together with uncertainty in local decision making (Villa, 2002). Accordingly, it suggests a new structure for dealing with change: a conglomerate of autonomous and distributed units, which operate as a set of cooperating entities (Monostori et al., 1998; Tharumarajah et al., 1996). This paradigm can be found in 14 out of the 31 manufacturing systems examined in this study: intelligent manufacturing, holonic manufacturing, fractal factory, bionic manufacturing, random manufacturing system, genetic manufacturing, agent-based manufacturing system, interactive manufacturing, responsibility-based manufacturing, agile manufacturing, distributed networked manufacturing, modular production, flexible integrated manufacturing and lean production.

353

(2) Distribution manufacturing paradigm: A distribution manufacturing paradigm implies dealing with change through distributed structures and control systems. Distributed structures in manufacturing organizations make use of dispersed resources with flexible organizations and distributed control systems. A typical distributed structure is a networking organization, which involves a wide perspective covering geographic dispersion and interdependent coordination rather than the traditional focus on separated manufacturing sites (Shi and Gregory, 1998). Seven manufacturing systems use a distributed structure for dealing with change: agile manufacturing, contract manufacturing, distributed networked manufacturing (networked manufacturing or remote manufacturing), information-based manufacturing, Internet-based manufacturing, shared manufacturing and international manufacturing networks. Distributed control is the other aspect of this paradigm and is often associated with autonomous actions in manufacturing systems; moreover, it can be found in eight other manufacturing systems: intelligent manufacturing, holonic manufacturing, fractal factory, bionic manufacturing, random manufacturing system, genetic manufacturing, agent-based manufacturing system and interactive manufacturing. (3) Modularity manufacturing paradigm: A modularity manufacturing paradigm implies dealing with change by breaking down manufacturing systems or products into discrete pieces or units, which can communicate with one another only through standardized interfaces within a standardized architecture (Langlois, 2002). The components of manufacturing systems, such as pieces of equipment and subsystems, can be regarded as discrete pieces or units. Nine manufacturing systems utilize this approach: random manufacturing, agent-based manufacturing, agile manufacturing, reconfigurable manufacturing, modularity production, holistic model-driven manufacturing, flexible integrated manufacturing, virtual cell manufacturing and lean production. In addition, modularity characteristics can also be found in product design, which allows the decoupling of the process for developing new products, enabling these processes to become concurrent, autonomous and distributed, thus enabling in turn the adoption of modular organization designs for product development (Sanchez, 1996). (4) Integration manufacturing paradigm: An integration manufacturing paradigm implies dealing with change by integrating different manufacturing processes and integrating humans and technology in manufacturing systems. Integrative devices are often used to increase the chances for success when organizations face a changing and uncertain environment (Rondeau, 2000). The objectives of integration are several, such as human–machine integration, integration of components of systems and integration of the components of a business process (Kosanke and Klevers, 1990). Among these, two types of integration have been widely used in manufacturing systems: integration of

ARTICLE IN PRESS 354

J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

process (IOP) and integration of human and technology (IHT). Manufacturing activities, such as design, fabrication and assembly, can be integrated by using information technology. Computer integrated manufacturing (CIM) and virtual manufacturing are the manufacturing systems typically used for the integration approach. With the sources of change and uncertainty expanding, IOP needs to integrate more activities in the life cycle of a product, for example, marketing and distribution. This idea can be found in two manufacturing systems: life-cycle manufacturing and post-mass manufacturing. IHT places organizational and human competence in the forefront and requires adapted technologies. It is economically very successful in industrial sectors affected by steadily changing demands, volatile markets and the need for high adaptability to customer requirement (Eichener, 1996). This manufacturing paradigm is adopted in five manufacturing systems: learning manufacturing, human-centred manufacturing, anthropocentric production, millennium manufacturing and ultimate manufacturing. In addition, many other manufacturing systems also adopted the integration approach, including intelligent manufacturing, interactive manufacturing, responsibility-based manufacturing, agile manufacturing, information-based manufacturing, internet-based manufacturing, reconfigurable manufacturing, holistic model-driven manufacturing, flexible integrated manufacturing and lean production.

Table 1 Potential items for measuring business strategy and competitive priorities Variables

Measurements scale items

Business strategy Price Operating efficiency Competitive pricing Procurement of raw materials Reducing product costs Minimize outside financing Decreasing the number of product features Differentiation New product development Brand identification Innovation in marketing techniques and methods Control of distribution channels Advertising Competitive priorities Flexibility Lead-time reduction Setup time reduction Ability to change priorities of jobs on the shop floor Ability to change machine assignments of jobs on the shop floor Quality Statistical process control Real-time process control systems Updating process equipment Developing new processes for new products Developing new processes for old products Delivery Provide fast deliveries Meet delivery promises Cost Reduce inventory Increase capacity utilization Increase equipment utilization Reduce production costs

3.2. Item generation Business strategy and competitive priorities have been widely studied. Therefore, the potential items that can be used for measuring them can be adopted from traditional literatures. Business strategy has been widely studied as a variant of generic strategies involving a choice between differentiation and delivered cost or price; in addition, there exists an instrument for making the generic strategic types operational, which has also been frequently used in strategic researches (Ward and Duray, 2000). Four competitive priorities have often been citied in different studies: quality, cost, delivery and flexibility. (1) The quality scale includes items related to the important quality aspect of process control and process management, which can be used strategically to gain competitive advantage. (2) The cost scale constitutes emphasis on reducing production costs, inventory and increasing capability and equipment utilization. (3) The delivery scale includes emphasis on customer service as indicated by either delivery reliability or delivery speed. (4) The flexibility scale is intended to capture the importance of reducing costs associated with changing products or product mix. This type of flexibility has two aspects: range and time, while the cost of providing flexibility is sometimes used as a third aspect. The range aspect signifies the extent of product variety and the temporal aspect reflects setup time (Gerwin, 1993). Ward and Duray (2000) provided an instrument to make the four competitive priorities and the business strategy

operational. Their instrument has been adapted in this study with some modifications, for example, by using a five-point Likert scale instead of a seven-point Likert scale. Respondents are asked to indicate the degree of emphasis on some activities, as listed in Table 1, with a scale ranging from 1 to 5, where a response of 1 indicates no emphasis and 5 indicates considerable emphasis. Measurement scales for the manufacturing paradigms are listed in Table 2 and explained as follows. (1) The measurement scales of the autonomy paradigm are developed based on the definitions of autonomous actions and the characteristics of related manufacturing systems. The existence of autonomous actions in operation management implies that managers below the top management level can make decisions with strategic implications without prior approval from the top management (Andersen, 2000). (2) The measurement scales of the distribution paradigm are developed based on the definitions and characteristics of related manufacturing systems. The distribution paradigm can be used both in controlling systems and the organization structures of manufacturing systems, and we focus on the latter. (3) The measurement scales of the modularity paradigm in this paper focus on product design and manufacturing. The modularity paradigm in the design of manufacturing systems is not investigated since firms undertake the reengineering of entire manufacturing systems on an infrequent basis. Duray et al. (2000) proposed two

ARTICLE IN PRESS J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

Table 2 Potential items for measuring the manufacturing paradigms Variables

Variables Measurements scale items name

Modularity Modularity through MTF1 fabrication MTF2 MTF3 MTF4 Modularity through MTS1 standardization MTS2 MTS3 MTS4 MTS5

Components are designed to end-user specifications Components are sized for each application Components are altered to end-user specifications Component dimensions are changed for each end-user Products have interchangeable features and options Options can be added to a standard product Components are shared across products New product features are designed around a standard base unit Products are designed around common core technology

355

modularity through standardization (MTS), which is characterized as component swapping, sectional, mix and bus modularity. In this study, we adapt the instrument provided by Duray et al. (2000) to measure MTF as well as MTS. (4) Both types of integration paradigms were measured. Their measurement scales also were developed based on literatures and the characteristics of related manufacturing systems, such as instruments for measuring integration in the supply chain (Rosenzweig et al., 2003). For the manufacturing paradigms, the survey respondents were asked to indicate the degree to which the measurement scale items are current concerns in their firms. All items are represented by a five-point Likert scale of 1 ¼ strongly disagree, 2 ¼ disagree, 3 ¼ neutral, 4 ¼ agree, 5 ¼ strongly agree and NA ¼ not applicable or do not know.

3.3. Methodology for measuring paradigms and testing hypotheses

Distribution DIS1 DIS2 DIS3

Geographic dispersion Linked by networks Sharing resources (human resources, equipments, etc.) with other subsidiaries

AUT1

Managers below the top management level can make decisions with strategic implications without prior approval from the top management Machines have replaced many human workers Workers in the job shop can manage themselves and have the power to make some decisions Different departments can cooperate with and support each other

Autonomy

AUT2 AUT3

AUT4

Integration Integration of process

IOP1 IOP2 IOP3 IOP4 IOP5 IOP6

Integration of human and technology

IHT1

IHT2 IHT3 IHT4 IHT5 IHT6

Integrated closely within your own organization Integrated closely with raw material suppliers Integrated closely with distributors/ retailers Integrated closely with customers Keep good cooperation relationships with other firms Emphasize cooperation between different departments Machines have been closely integrated with humans and emphasize human factors engineering Human resources has been regarded as the most important asset Knowledge management has been emphasized Different methods or technologies can be adopted according to needs Education and training is an important function Maintaining good relationships with consulting companies

methods to realize the modularity paradigm in manufacturing: Modularity through fabrication (MTF), which is characterized as cut-to-fit and component sharing, and

The measurement scales of business strategy and competitive priorities were adapted from traditional studies and have been validated. Therefore, we only test their vertical validity in this paper with Cronbach’s Alpha. The measurement scales of the four manufacturing paradigms are developed in this paper. Therefore, it is necessary to test their construct validity and reliability. In this study, the basis for theoretical validity is established through the review of a larger body of literatures. Vertical validity is addressed via Cronbach’s alpha. In addition, factor analysis is used to further verify whether each of the scales is unidimensional, thus providing evidence for a single latent construct. As suggested by some empirical studies (Rondeau, 2000), there are five steps to set up the measurements of the manufacturing paradigms. The first step is to purify the items based on corrected item total correlation (CITC). As a guideline, items with a CITC value below 0.50 were eliminated. The item inter-correlation matrices provided by SPSS were also utilized to drop items if they did not strongly contribute to Cronbach’s alpha. Second, items related to a specific approach were subject to exploratory factor analysis to assess the dimension’s internal consistency. The principal components approach was selected for the extraction procedure, with the varimax method used for factor rotation. The MEANSUB command was used within SPSS to replace the very small number of missing values with the mean for that item. Items that did not load at 0.60 or above were eliminated unless they were considered to be important for the research. The adequacy of the sample size for factor analysis was calculated using the Kaiser–Meyer–Olkin (KMO) measure of sampling adequacy. In this study, items that had weak factor loadings were designated for modification if their content was considered as being important to the research. This is consistent with the recommendation that the researcher considers an item’s importance for the research as well as its loading during factor interpretation. Third, structural equation modeling (SEM) was used to enable further refinement for the measurement of the

ARTICLE IN PRESS 356

J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

approaches. This is because factor analysis assumes that measurement errors are not correlated, and it does not test the degree of correlation of these error terms. SEM does test the degree of correlation among the error terms. The model evaluation criteria of SEM are explained in the following paragraph. Items with path coefficients less than 0.60 were dropped until the measurement model derived a good performance and all path coefficients were above 0.60. Fourth, the external consistency of manufacturing paradigms was evaluated by simultaneously submitting the items for all paradigms to exploratory factor analysis. No constraint was placed on the number of factors that could emerge. The method of extraction was principle components with varimax rotation. Items that did not load at 0.60 or more were usually eliminated. Finally, the reliability of the remaining items was assessed using Cronbach’s alpha. Analysis of MOment Structure (AMOS), package software of the SEM, was used to set up the measurement models for business strategy, competitive priorities and manufacturing paradigms, and the structure models for the relationships between them. For model evaluation, several standard criteria were used (McKone et al., 2001): (1) the degree of freedom (DF) represents the difference between the number of independent statistics and the model parameters fitted. (2) The likelihood ratio test (LRT) statistic is minimized and is usually interpreted as a w2 variate. (3) The probability level in w2 testing should be higher than the 5% level. (4) Goodness of fit index (GFI), (5) adjusted goodness of fit index (AGFI) and (6) comparative fit index (CFI) rescale the fit of the observations and the expectations. Values of GFI, AGFI and CFI between 0.80 and 0.89 represent a good fit while values above 0.90 represent a very good fit. (7) Root mean square error of approximation (RMSEA) is a measure of the population discrepancy that is adjusted for the DF for testing the model. A value of 0.08 or less for RMSEA would indicate a reasonable error of approximation. If the model fits the data well, the magnitude of the AMOS path coefficients can be examined for statistical significance. The test for significance compares the estimated parameter to its standard error and has a t-distribution. 3.4. Survey methodology and sample characteristics The relationships between business strategy, competitive priorities and the manufacturing paradigms were surveyed with a questionnaire. The survey data was collected through structured interviews with managers of manufacturing firms in the People’s Republic of China. As the world’s fourth largest industrial producer behind the US, Japan and Germany, China manufactures more than 50% of the world’s cameras, 30% of the world’s airconditioners and televisions, 25% of the world’s washing machines and nearly 20% of all refrigerators (Leggett and Wonacott, 2002). Four universities in four different regions of China were involved in carrying out the survey. The study regions were Beijing City in the north of China, Shanghai City in the east, Wuhan City in the centre and Xi’an City in the west of China. About five graduate

Table 3 Description of firms in samples Characteristics

Number of employees p100 100–1000 41000 Gross sale (million dollars) p15 15–120 4120 Location Beijing City Wuhan City Xi’an City Others Representative products

Sample number

Percent of total (N ¼ 107)

26 40 41

24.3 37.3 38.3

47 35 25

43.9 32.7 23.3

30 28.0 31 29.0 9 8.4 37 34.6 DVD player, cloth, car, electronic equipment, beer, medicine, MOTOR, hydraulic production, elevator, display screen

students from each university participated in the data collection process and were trained to use a consistent interviewing method. The surveys began in November 2003 and concluded in January 2004. To assure the accuracy and completeness of responses, the survey data was collected through structured interviews with a purposeful sample of 118 firms. These firms were selected through the recommendations of the local university as being leading manufacturers and represented a variety of manufacturing industries. From each firm, we interviewed the persons responsible for manufacturing management, mostly general managers or department managers. We thus obtained 107 completed questionnaires used in the analysis. The sample demographics are shown in Table 3. The size of the firms ranges from around 20 employees to over 1000 employees. The gross sales in these firms vary from less than 2.5 million to over 500 million dollars. The most commonly manufactured products are machinery, computers, air-conditioners and electronic equipment. 4. Measurement results Two types of modularity paradigms (MTS and MTF) and integration paradigms (IOP and IHT) were measured. Initially, six manufacturing paradigms, i.e. AUT, DIS, MTF, MTS, IOP and IHT, were measured with 28 scales, as shown in Table 2. 4.1. Purification One original scale of the autonomy paradigm (AUT2) and one original scale of MTF2 were eliminated because of low CITC scores. Some original scales with a CITC score less than 0.50 are retained for the following analyses because they are important for the research and their scores are close to 0.50.

ARTICLE IN PRESS J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

4.2. Dimension-level factor analysis In the factor analysis of each paradigm, a single factor emerged for everyone with all item loadings greater than 0.60, except for IHT1 in the IHT paradigm. Therefore, IHT1 was dropped. The KMO measure for each construct indicates that factor analysis was possible. Single factor measurement models are specified for each paradigm with AMOS, the results show that AMOS measurement models for all paradigms except for MTS satisfied the requirements. Measurement models of the distribution paradigm and the autonomy paradigm have one path coefficient less than 0.60. We retain them for the following analysis because they are important for this research and are very close to 0.60. Some estimate criteria for the measurement model of MTS are much below than the recommended minimum level, indicating that further modification may be required. Modification indices (error correlation between items) and path coefficients are used to suggest iterative modifications to improve key model fit statistics. Finally, MTS 4 and MTS 5 were dropped from the measurement of MTS. 4.3. Construct-level factor analysis The remaining items were subjected to factor analysis. Six factors emerged, as shown in Table 4. Factor loadings above 0.50, eigenvalue (EV), percentage of variance (PV) and cumulative percentage of variance (CPV) were listed. Considering the loadings above 0.60, the first five factors can be identified as follows: F1—integration of process (IOP), F2—IHT, F3—MTF, F4—distribution (DIS) and F5—MTS. AUT1 has loadings above 0.50 on F2 and F6, and both loadings are less than 0.60. With regard to the loading of AUT1 on F6, which is higher than the loading of AUT1 on F2, and the importance of AUT1 to F6, we identified F6 as AUT (Autonomy) and did not drop AUT1. Finally, no items were dropped in this step. These factors explain 66.66% of the variance. The KMO measure indicates that factor analysis was possible. 4.4. Reliability Inter-item analysis is used to check the scales for internal consistency for reliability, which is satisfied in this study. In particular, Cronbach’s reliability coefficient alpha is calculated for each paradigm, i.e. 0.86 (F1—IOP), 0.86 (F2—IHT), 0.76 (F3—MTF), 0.65 (F4—DIS), 0.70 (F5—MTS) and 0.71 (F6—AUT). An inter-item analysis is also conducted on business strategy and competitive priorities, which is also satisfied in this paper. The Cronbach’s reliability coefficient alpha is shown in as follows: 0.68 (cost leadership), 0.83 (differentiation), 0.68 (flexibility), 0.74 (quality), 0.74 (delivery) and 0.76 (cost). 5. Results for structural modeling Higher-orders measurement models were set up for the manufacturing paradigms, since there are two types of modularity paradigms and integration paradigms. In order

357

Table 4 Factor loadings for the construct analysis Item

Factors loadings above 0.50 (KMO ¼ 0.85) F1—IOP

IOP1 IOP2 IOP3 IOP4 IOP5 IOP6 IHT2 IHT3 IHT4 IHT5 IHT6 MTF1 MTF3 MTF4 DIS1 DIS2 DIS3 MTS1 MTS2 MTS3 AUT1 AUT3 AUT4 EV PV CP

F2—IHT

F3—MTF

F4—DIS

F5—MTS

F6—AUT

0.669 0.768 0.645 0.608 0.737 0.726 0.602 0.678 0.608 0.716 0.738 0.763 0.819 0.838 0.771 0.729 0.621 0.761 0.798 0.760 0.508

3.91 17.02 17.02

3.11 13.50 30.52

2.22 9.64 40.16

2.08 9.05 49.20

2.07 9.00 58.21

0.536 0.832 0.881 1.95 8.46 66.66

to test the hypotheses, different structure models were set up according to the models proposed in AMOS 4.0. In model A, as shown in Fig. 4, business strategy has direct links with competitive priorities, which has a direct link with the manufacturing paradigms, and there is no direct link between business strategy and the manufacturing paradigms. Model B is shown in Fig. 5. It regards that while the competitive priorities and manufacturing paradigms have direct relationships with business strategy, there are no direct relationships between the competitive priorities and manufacturing paradigms. In model C, as shown in Fig. 6, both the competitive priorities and manufacturing paradigms have direct relationships with business strategy, and there is a direct link between them. The performances of the three models are shown in Table 5. We can see that model B has the highest value in GFI, AGFI and probability level. All three models have identical RMSEA values. According to Byrne (2001), whether the difference between different models is statistically significant can be tested based on w2 statistics. A necessary condition for this is that two models should be nested, which means that two models are hierarchically related to one another in the sense that their parameter sets are subsets of one another. Under this condition, ‘the differential between the models represents a measurement of the over-identifying constraints and is itself w2 distributed, with DF equal to the difference in degrees of freedom; it can thus be tested statistically, with a significant Dw2 indicating substantial improvement in model fit’. There are nested relationships between models A and C, and between models B and C. Based on the

ARTICLE IN PRESS 358

J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

Fig. 4. A serial model with one way (model A).

Fig. 5. A parallel model with two ways (model B).

difference between the values of w2, we can arrive at the following results: (1) There is a significant difference between the performances of models A and C. The Dw2 (MA–MC) is 5.897, which is significant in w2 (DF ¼ 1, Po0.05). (2) There is no significant difference between the performance of models B and C. The Dw2 (MB–MC) is 0.004, which is not significant in w2 (DF ¼ 1, Po0.05). Therefore, we can conclude that models B and C have a significant difference with model A in the performance. Then, we can pay attention to the path coefficients in models B and C. Model C has good performance; however, the path coefficient between manufacturing paradigms

and competitive priorities in model 3 is 0.04, which is not significantly different from zero at the significance level 5% (two-tailed). On the other hand, both the path coefficients in model B are significantly different from zero, when tested by t statistics. Therefore, the results of models C support the structure in model B. Finally, we can explain our results based on model B. When the modification indices are examined for model A based on model B, the indices suggest that there exists a relationship between business strategy and the manufacturing paradigms. When the modification indices are examined for model C based on model B, the indices suggest that there is no relationship between the competitive priorities and manufacturing paradigms if both are connected to business strategy. The results of model B support that manufacturing paradigms have

ARTICLE IN PRESS J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

359

Fig. 6. A combined model with two ways (model C).

Table 5 Performances of three models Criteria

Model A

Model B

Model C

Standard

GFI AGFI CFI RMSEA LRT/DF

0.934 0.888 1.000 0.000 0.990

0.942 0.902 1.000 0.000 0.862

0.942 0.900 1.000 0.000 0.881

[0,1] [0,1] [0,1] p0.08 p5

direct relationships with business strategy. The path coefficient is 0.86, where the statistical significance at level Po0.001 (t ¼ 3.32). The path coefficient between business strategy and competitive priorities is 0.84, which is also significant at the level Po0.001 (t ¼ 4.17).

6. Discussion and conclusion The results show that in the relationship between business strategy and manufacturing paradigms, there is a significant difference in the performance of models with the mediate relationships through competitive priorities and the direct relationships. The latter has a better performance in the fitness to the real situation of leading firms. The results imply that under the turbulent environment, business strategy has a direct influence on the decisions on manufacturing paradigms, and competitive priorities cannot underlay the media roles between business strategy and manufacturing paradigms in the leading manufacturing firms in China. When planning MS, the strategic and paradigmatic approaches could be adopted at the same time, and the decisions on competitive priorities and manufacturing paradigms should be made, respectively, based on business strategy. The results can provide some suggestions for the planning process of manufacturing strategy under the turbulent environment and for the implementation of innovative systems and best practices.

With regard to the planning process of manufacturing strategy under the turbulent environment, this result implies that it is difficult to coordinate the strategic and paradigmatic approaches through setting up the relationship between competitive priorities and manufacturing paradigms. One possible solution is to establish the coordination through the action plan or business-level strategy. On the practice level, detailed action plans may reflect the requirements on competitive priorities and on the manufacturing paradigms at the same time. Action plans based on competitive priorities may respond to the market requirement immediately; while, action plans based on manufacturing paradigms may respond to longterm market requirements. On the business-level strategy, business strategy may simultaneously include the direction function for competitive priorities and manufacturing paradigms. It requires the strategic planning process that needs to be re-examined based on the market-led and resource-based views, such as the studies on strategic resonance (Brown and Blackmon, 2005). To cite the example of a case study, the components of business strategy and manufacturing strategy of a leading global PC manufacturer include four views: the financial view, the customer view, the process view and the learning and growing view. The purposes in the process view support the customer view, which supports the financial view. The learning and growing view focuses on building competitive capabilities and includes the plans of introducing innovative manufacturing systems. In the detailed action plan between 2003 and 2007, four common topics can be found in its manufacturing strategy: improve quality, cost down, reduce lead time and differentiate manufacturing technologies. The content of quality, cost and delivery (QCD) changed with the improvement in the focus of manufacturing systems and supply chain. The detailed contents in the latter aspect changed annually and included some best practices or innovative manufacturing systems, such as RFID-based manufacturing systems, virtual manufacturing lines and the global SCM system for building to order/ configure to order.

ARTICLE IN PRESS 360

J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

On the adoption and improvement of innovative manufacturing systems or best practices, this result implies that competitive priorities cannot avoid the mediate roles between business strategy and manufacturing paradigms in the turbulent environment. This may be caused by the changes and uncertainty in a competitive environment, which make the linkage between strategy and systems come closer and require frequent adjustment. This necessitates a re-examination of the mediatory role of business strategy between a competitive environment and manufacturing strategy and of the role of manufacturing strategy in business strategy and production systems. Another reason for this may be the existence of a many-to-many relationship between improvement areas and innovative activities, which implies that every improvement area is generally pursued through various innovative approaches and these innovative activities in turn have different purposes (Spina, 1998). Based on the results, it is better to pay more attention to the business strategy at the requirement analysis stage when adopting or improving innovative systems or best practices. This may be difficult for the current introduction process, which focuses on the relationships between systems and functional strategy. Manufacturing paradigms are very useful; however, it may be impossible for firms to abandon the competitive priorities which are simple to understand and have been widely accepted. In practice, changing the role of competitive priorities may be a choice, such as from the direction role to the coordination role. Consider the example of this case study: a leading global PC manufacturer decided to move its factory to a place with sufficient cheap labor, easy availability of materials and a future market. However, the production technology and management departments will not be moved with the factory. One important reason for this is that a firm cannot fire employees easily. Therefore, the firm decided to build a virtual manufacturing system. The direct purpose is to reduce the paused time of the real lines by building the test environment in the virtual systems. When discussing this decision, the firm calculated the cost down from this investment and other advantages. To elaborate, three types of cost down are calculated: (1) cost down from the reduction of trip times to the real lines; (2) with the virtual systems, the time for preparing the test data can be reduced and (3) the time reduction in surveying the pause reasons in the real lines because of database technology problems and programming. Based on the investment and the analysis of competitive priorities, the general managers agree to adopt the virtual systems. In this decision process, the strategic decision on virtual manufacturing systems was made based on the business strategy of the firm, and also supported from the analysis of competitive priorities. However, the firm would not adopt the virtual manufacturing systems solely based on the competitive priorities analysis. This is not only because of the huge investment but mostly because this decision is a strategic one and needs coordination with other strategic decisions, such as the location of firms, the employment policy and market targets. In addition, the analysis of competitive priorities cannot help in discovering the whole possible profit from

this strategic decision. For example, with the virtual manufacturing systems, the firm can avoid considerable risk in technology innovation. The results in this study suggest that innovative activities in the turbulent environment should be managed with an approach different from that in the traditional environment. The relationships between strategy and innovative systems or best practices are characterized as dynamic co-evolution. This co-evolutional relationship can be influenced not only by the contextual factors in the environment but also by the contents of innovative activities. In this study, it is shown that changes and uncertainty in the competitive environment force the relationships between strategy and system closer. On the other hand, the innovative systems call for an innovative theory on strategy and significantly change the relationships between strategy and systems. Therefore, we need to consider not only innovation in technology but also innovation in the theory of the management of technology. However, this study has some limitations. This study examined four important manufacturing paradigms in the turbulent environment and cannot cover all the important ones. Further research is needed to systematically analyze the innovation in manufacturing paradigms in a turbulent environment. We tested the relationships based on the samples only from China. An important line of future research is to test the stability of the relationships globally and over time. References Acur, N., Bititci, U., 2004. A balanced approach to strategy process. International Journal of Operations & Production Management 24 (4), 388–408. Andersen, T.J., 2000. Strategic planning, autonomous actions and corporate performance. Long Range Planning 33, 184–200. Barnes, D., Hinton, M., Mieczkowska, S., 2004. The strategic management of operations in e-business. Production Planning & Control 15 (5), 484–494. Bolden, R., Waterson, P., Warr, P., Clegg, C., Wall, T., 1997. A new taxonomy of modern manufacturing practices. International Journal of Operations and Production Management 17 (11), 1112–1130. Brown, S., 2001. Managing process technology—further empirical evidence from manufacturing plants. Technovation 21, 467–478. Brown, S., Blackmon, K., 2005. Aligning manufacturing strategy and business-level competitive strategy in new competitive environments: the case for strategic resonance. Journal of Management Studies 42 (4), 793–815. Byrne, B.M., 2001. Structural Equation Modeling with AMOS: Basic Concepts, Applications, and Programming. Lawrence Erlbaum Associates, Inc., Publishers. Cao, D.B., Wang, J., Sun, L.Y., 2005. Managing complexity in manufacturing: relationships among four common approaches. Journal of Japan Industrial Management Association 56 (4), 229–236. Carrie, A.S., Macintosh, R., Scott, A., Peoples, G.A., 1994. Linking strategy to production management structures and systems. International Journal of Production Economics 34, 293–304. Clark, K.B., 1996. Competing through manufacturing and the new manufacturing paradigm. Production and Operations Management 5 (1), 42–57. Correa, H.L., 1994. Linking Flexibility, Uncertainty and Variability in Manufacturing Systems: Managing un-Planned Change in the Automotive Industry. Ashgate, Avebury. Duda, J.W., Cochran, D.S., 2000. A decomposition approach to linking strategic objectives with preliminary manufacturing system design decisions. In: Proceedings of the 11th Annual Conference of the Production and Operations Management Society, San Antonio, TX. Duray, R., Ward, P.T., Milligan, G.W., Berry, W.L., 2000. Approaches to mass customization: configurations and empirical validation. Journal of Operations Management 18, 605–625.

ARTICLE IN PRESS J. Wang, D.-B. Cao / Int. J. Production Economics 115 (2008) 349–361

Eichener, V., 1996. The impact of technical standards on the diffusion of anthropocentric production systems. International Journal of Human Factors in Manufacturing 6, 131–145. Gagnon, S., 1999. Resource-based competition and the new operations strategy. International Journal of Operations & Production Management 19 (2), 125–138. Gerwin, D., 1993. Manufacturing flexibility: a strategic perspective. Management Science 39, 395–410. Hayes, R.H., Pisano, G.P., 1996. Manufacturing strategy: at the intersection of two paradigm shifts. Production and Operations Management 5 (1), 25–41. Hiltrop, J.M., 1996. Impact of human resource management on organizational performance. European Management Journal 14 (6), 628–637. Ho, C.F., 1996. A contingency theoretical model of manufacturing strategy. International Journal of Operations & Production Management 16 (5), 74–98. Hughes, J.J., 1997. Views of the future. In: Wolkoff, R.L. (Ed.), NextGeneration Manufacturing: A Framework for Action. The Agility Forum, Bethlehem, PA. John, C.H.St., Canon, A.R., Pouder, R.W., 2001. Change drivers in the new millennium: implications for manufacturing strategy research. Journal of Operations Management 19, 143–160. Ketokivi, M., 2006. Elaborating the contingency theory of organizations: the case of manufacturing flexibility strategies. Production and Operations Management 15 (2), 215–228. Kim, J.S., Arnold, P., 1996. Operationalizing manufacturing strategy: an exploratory study of constructs and linkage. International Journal of Operation and Production Management 16 (12), 45–73. Kim, Y., Lee, J., 1993. Manufacturing strategy and production system: an integrated framework. Journal of Operations Management 11, 3–15. Kosanke, K., Klevers, T., 1990. CIMOSA: architecture for enterprise integration. Computer Integrated Manufacturing Systems 3, 317–324. Langlois, R.N., 2002. Modularity in technology and organization. Journal of Economic Behavior and Organization 49, 19–37. Lau, R.S.M., Zhao, X., Xiao, M., 2004. Assessing quality management in China with MBNQA criteria. International Journal of Quality & Reliability Management 21 (7), 699–713. Leggett, K., Wonacott, P., 2002. Burying the competition. Far Eastern Economic Review, October 17. McKone, K.E., Schroeder, R.G., Cua, K.O., 2001. The impact of total productive maintenance practices on manufacturing performance. Journal of Operations Management 19, 39–58. Miltenburg, H., 1995. How to Formulated and Implement a Winning Plan. Productivity Press, Portland, OR. Monostori, L., Ka´da´r, B., Hornya´k, J., 1998. Approaches to managing changes and uncertainties in manufacturing. CIRP Annual 47, 365–369. Morita, M., Flynn, E.J., 1997. The linkage among management systems, practices and behaviour in successful manufacturing strategy. International Journal of Operations and Production Management 17 (10), 967–993. Perona, M., Miragliotta, G., 2004. Complexity management and supply chain performance assessment: a field study and a conceptual framework. International Journal of Production Economics 90, 103–115. Pilkington, A., 1998. Manufacturing strategy regained: evidence for the demise of best practice. California Management Review 41 (1), 31–42.

361

¨ derquist, K., Spanos, Y., Wassenhove, L.V., 2002. An Prastacos, G., SO integrated framework for managing change in the new competitive landscape. European Management Journal 20 (1), 55–71. Robb, D.J., Xie, B., 2001. A survey of manufacturing strategies in Chinabased enterprises. International Journal of Production Economics 72, 181–199. Rondeau, P.J., 2000. Exploring work system practices for time-based manufacturers: their impact on competitive capabilities. Journal of Operations Management 18, 509–529. Rosenzweig, E.D., Roth, A.V., Dean Jr., J.W., 2003. The influence of an integration strategy on competitive capabilities and business performance: an exploratory study of consumer products manufacturers. Journal of Operations Management 21, 437–456. Safsten, K., Winroth, M., 2002. Analysis of the congruence between manufacturing strategy and production system in SMME. Computers in Industry 49, 91–106. Sanchez, R., 1996. Strategic product creation: managing new interactions of technology, markets, and organizations. European Management Journal 14, 121–138. Shi, D., Daniels, R.L., 2003. A survey of manufacturing flexibility: implications for e-business flexibility. IBM Systems Journal 42, 414–427. Shi, Y., Gregory, M., 1998. International manufacturing networks: to develop global competitive capabilities. Journal of Operations Management 16, 195–214. Skinner, W., 1969. Manufacturing-missing link in corporate strategy. Harvard Business Review May–June, 136–145. Spina, G., 1998. Manufacturing paradigms versus strategic approaches: a misleading contrast. International Journal of Operations and Production Management 18 (8), 684–709. Tharumarajah, A., Wells, A.J., Nemes, L., 1996. Comparison of the bionic, fractal, and holonic manufacturing system concepts. International Journal of Computer Integrated Manufacturing 9, 217–226. Vichkery, S.K., Droge, C., 1993. Production competence and business strategy: do they affect business performance. Decision Science 24 (2), 435–455. Villa, A., 2002. Autonomy versus efficiency in multi-agent management of extended enterprise. Journal of Intelligent Manufacturing 13, 429–438. Voss, C.A., 1995. Alternative paradigms for manufacturing strategy. International Journal of Operations and Production Management 15 (4), 5–16. Wang, J., Cao, D.B., Ma, S.H., 2004. Linking manufacturing systems to manufacturing strategy in a changing environment. International Journal of Innovation and Technology Management 1 (4), 415–434. Ward, P.T., Duray, R., 2000. Manufacturing strategy in context: environment, business strategy and manufacturing strategy. Journal of Operations Management 18, 123–138. Ward, P.T., Leong, G.K., Snyder, D.L., 1990. Manufacture strategy: an overview of current process and content models. In: Ettlie, D.J., Burstein, M., Fiegenbaum, A. (Eds.), Manufacturing Strategy: The Research Agenda for the Next Decade. Kluwer, Boston. Ward, P.T., Duray, R., Keong Leong, G., Sum, C., 1995. Business environment, operations strategy, and performance: an empirical study of Singapore manufacturers. Journal of Operations Management 13, 99–115. White, R.E., 1990. Post Implementation Evaluation of Just in Time Manufacturing. University Microfilms International. Wiendahl, H.P., Scholtissek, P., 1994. Management and control of complexity in manufacturing. CIRP Annual 43, 533–540.