Manufacturing flexibility operations management

Manufacturing flexibility operations management

Journal of Operations Management 21 (2003) 173–191 Manufacturing flexibility: defining and analyzing relationships among competence, capability, and customer satisfaction Qingyu Zhang a,1 , Mark A Vonderembse b,∗ , Jeen-Su Lim c,2 a Department of Economics and Decision Sciences, Arkansas State University, State University, Arkansas, AR 72467, USA b Department of Management, The University of Toledo, Toledo, OH 43606, USA c The University of Toledo, Toledo, OH 43606, USA Received 25 April 2001; accepted April 2002 Abstract Fast and dramatic changes in customer expectations, competition, and technology are creating an increasingly uncertain environment To respond, manufacturers are seeking to enhance flexibility across the value chain Manufacturing flexibility, a critical dimension of value chain flexibility, is the ability to produce a variety of products in the quantities that customers demand while maintaining high performance It is strategically important for enhancing competitive position and winning customer orders This research organizes literature on manufacturing flexibility and classifies it according to competence and capability theory It describes a framework to explore the relationships among flexible competence (machine, labor, material handling, and routing flexibilities), flexible capability (volume flexibility and mix flexibility), and customer satisfaction It develops valid and reliable instruments to measure the sub-dimensions of manufacturing flexibility, and it applies structural equation modeling to a large-scale sample (n = 273) The results indicate strong, positive, and direct relationships between flexible manufacturing competence and volume flexibility and between flexible manufacturing competence and mix flexibility Volume flexibility and mix flexibility have strong, positive, and direct relationships with customer satisfaction © 2002 Elsevier Science B.V All rights reserved Keywords: Empirical research; Flexibility; Management of technology; Manufacturing; Operations strategy Introduction Manufacturers face an increasingly uncertain external environment as the rate of change in customer ∗ Corresponding author Tel.: +1-419-530-4139; fax: +1-419-530-7744 E-mail addresses: qzhang@astate.edu (Q Zhang), mark.vonderembse@utoledo.edu (M.A Vonderembse), jlim@utnet.utoledo.edu (J.-S Lim) Tel.: +1-870-972-3416 Tel.: +1-419-530-2922 expectations, global competition, and technology accelerates (Huber, 1984; Skinner, 1985; Jaikumar, 1986; Doll and Vonderembse, 1991; Germain et al., 2001) Researchers and manufacturing managers contend that flexibility is a strategic imperative that enables firms to cope with uncertainty (Gerwin, 1987; Sethi and Sethi, 1990) Flexibility is the organization’s ability to meet an increasing variety of customer expectations without excessive costs, time, organizational disruptions, or performance losses Upton (1994, 1995) defines flexibility as increasing the range 0272-6963/02/$ – see front matter © 2002 Elsevier Science B.V All rights reserved PII: S - ( ) 0 - 174 Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 of products available, improving a firm’s ability to respond quickly, and achieving good performance over this wide range of products To attain the type of flexibility that customers want (i.e quick delivery of a variety of high-quality, low-cost products), organizations seek value chain flexibility (Zhang, 2001) Value chain flexibility is broadly defined to include product development, manufacturing, logistics, and spanning flexibilities (Zhang, 2001; Day, 1994) It focuses primarily on filling customer orders rather than on merely improving the efficiency and effectiveness of equipment and processes Such a focus requires manufacturing firms to develop cross-functional and cross-company efforts that eliminate bottlenecks, increase responsiveness, and create a level of performance that enables firms to build competitive advantage (Blackburn, 1991; Hamel and Prahalad, 1989) Manufacturing flexibility, the focus of this study, is the ability of the firm to manage production resources and uncertainty to meet customer requests (Behrbohm, 1985; Gerwin, 1993; Kathuria and Partovi, 1999; Hill, 1994; D’Souza and Williams, 2000; Koste and Malhotra, 1999) Sethi and Sethi (1990) contend that manufacturing flexibility is a hard-to-capture concept, and Upton (1995) believes that confusion and ambiguity about this concept inhibit its effective management Slack (1983, 1987) distinguishes resource flexibility (e.g., machine flexibility) from systems flexibility (e.g., mix flexibility) Correa and Slack (1996) define the attributes of systems flexibility (range and response) and types of systems flexibility (e.g product mix and production volume) Different descriptors for manufacturing flexibility overlap; as an example, process flexibility intersects with operational flexibility Some descriptors are aggregates of others; process flexibility includes routing flexibility, machine flexibility, and material handling flexibility The concept of manufacturing flexibility is confounded because the attributes of flexibility (i.e range, mobility, and uniformity) and the components of flexibility (e.g machine flexibility and volume flexibility) are often mingled (Barad, 1992; Gupta, 1993; Benjaafar, 1994) This imprecise language makes it difficult to develop valid and reliable measures of manufacturing flexibility and to improve theory development Clear definitions and accurate measures are needed to construct and test theory related to manufacturing flexibility The literature on this important subject is accumulating including case studies (Maffei and Meredith, 1995), industry specific studies (Suarez et al., 1996), and mathematical models (Kumar, 1987; Benjaafar and Ramakrishnan, 1996; Gupta, 1993; Jordan and Graves, 1995; Byrne and Chutima, 1997) Upton (1995, 1997) provides a measure of process range based on a small sample survey (54 plants) Suarez et al (1995, 1996) offer a measure of flexibility on the printed circuit board industry Gupta and Somers (1992) develop measures of manufacturing flexibility based on a large-scale survey, but they not clearly describe the dimensions underlying each type of manufacturing flexibility Some researchers emphasize manufacturing flexibility as an internal resource, a competence (Carter, 1986; Das and Nagendra, 1993) They highlight task sequencing or dispatching disciplines, and they develop flexible machining systems with totally automated functions to cope with uncertainty But flexible systems that focus on creating internal competencies (e.g routing flexibility and machine flexibility) may not enhance customer satisfaction Satisfaction increases as the firm builds capabilities (e.g mix flexibility) that provide value to customers To understand manufacturing flexibility, the internal competencies and external capabilities of flexibility should be clarified, and relationships between them should be examined This paper contributes to the manufacturing literature by: (1) delineating manufacturing flexibility into dimensions of flexible manufacturing competence (machine, labor, material handling, and routing flexibilities) and flexible manufacturing capability (volume flexibility and mix flexibility), (2) proposing a research framework, including hypotheses, that relates competence to capability and capability to customer satisfaction, (3) developing valid and reliable measures for the dimensions of competence and capability, and (4) testing the hypotheses described in the framework using structural equations modeling The results and implications of our findings are also discussed Theory development Kickert (1985) believes that “flexibility can be considered as a form of meta-control aimed at increasing Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 control capacity by means of an increase in variety, speed, and amount of responses as a reaction to uncertain future environmental development” (p 24) From this perspective, the breadth and intensity of flexibility needed to cope with changing customer requirements cannot be provided by one department or function It requires a company-wide effort to increase responsiveness and eliminate bottlenecks across the value chain (Blackburn, 1991; Hamel and Prahalad, 1989; Yusuf et al., 1999) Value chain flexibility includes product development, manufacturing, logistics, and spanning flexibilities (Day, 1994; Zhang et al., 2002) It enables firms to introduce new products quickly, support rapid product customization, shorten manufacturing lead times and costs for customized products, improve supplier performance, reduce inventory levels, and deliver products in a timely manner (Zhang et al., 2002) Integration, coordination, and communication across the value chain are essential for success regardless of how many different firms are involved and which firms own the assets (Day, 1994) Product development flexibility enables firms to respond with product modifications and new product commercialization (Sobek et al., 1999; Srinivasan et al., 1997) Such flexible design and development capabilities can increase manufacturability by simplifying product structure and standardizing component parts (Clark and Fujimoto, 1991; Gerwin, 1987; Griffin, 1993; Sethi and Sethi, 1990) This can make manufacturing faster and easier Manufacturing flexibility enables firms to produce the needed quantity of high-quality products quickly and efficiently through set-up time reduction, cellular manufacturing layouts, preventive maintenance, quality improvement efforts, and dependable suppliers These are predicated on machining, labor, material handling, and routing flexibilities (Boyer and Leong, 1996; Chen et al., 1992; Hyun and Ahn, 1992; Ramasesh and Jayakumar, 1991; Sethi and Sethi, 1990) Logistics flexibility enables the smooth flow of materials, which facilitates the production and deliveries of high-quality, value-added products (Porter and Millar, 1985) Flexibility in physical supply, purchasing, physical distribution, and demand management are key components of logistics flexibility (Lambert and Stock, 1993; Porter, 1985) Spanning flexibility insures that different departments or groups (inside and outside of the organization) 175 can coordinate product design, production, and delivery in ways that add value to customers (Day, 1994; Cooper and Zmud, 1990; Hayes and Pisano, 1994) It is within this context of value chain flexibility that manufacturing flexibility is discussed and a research framework is developed and tested 2.1 Framework for manufacturing flexibility Manufacturing flexibility is a complex, multidimensional concept that has evolved over the years (Sethi and Sethi, 1990) Early in its development, Leaver and Brown (1946) propose a series of small, functionally oriented machines that can be plugged together in different sequences to make different products Diebold (1952) recognizes manufacturing flexibility as essential for producing discrete parts effectively and efficiently Abernathy (1978) and Hayes and Wheelwright (1984) view manufacturing flexibility as a tradeoff between efficiency in production and dependability in the marketplace Achieving flexibility in large-volume production without sacrificing efficiency begins with the development of manufacturing cells and flexible manufacturing systems Efficiency and flexibility are achieved by reducing set-up time and cost, shifting to product-oriented layouts, increasing equipment reliability, and enhancing quality (Monden, 1983; Schonberger, 1986) Manufacturing flexibility is the ability of the organization to manage production resources and uncertainty to meet various customer requests Hayes and Wheelwright (1984) consider manufacturing flexibility to be a strategic element of business, along with price (cost), quality, and dependability Priorities assigned to each of these factors determine how an organization positions itself relative to it competitors Sethi and Sethi (1990) consider manufacturing flexibility as a set of elements that are integrally designed and carefully linked to facilitate the adaptation of processes and equipment to a variety of production tasks Upton (1995) identifies attributes of flexibility including potential flexibility versus demonstrated flexibility and robustness (maintaining a status quo despite a change) versus agility (instigating change rather than reacting to it) Upton (1995) also describes internal flexibility as what the firm can (competencies) and external flexibility as what the customer sees (capabilities) This distinction is central 176 Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 Fig Impact of flexible manufacturing competence on capability and customer satisfaction to the notion of internal competencies and external or customer-facing capabilities Day (1994) claims that organizations achieve customer satisfaction by building capabilities on a set of competencies Fig provides an overview of the relationships among flexible manufacturing competence, mix flexibility, volume flexibility, and customer satisfaction Flexible manufacturing competencies, which include machine, labor, material handling, and routing flexibilities, have a direct and positive impact on volume flexibility and mix flexibility Volume flexibility and mix flexibility are external elements of competition (capabilities) that should lead to increased customer satisfaction This framework is useful for studying manufacturing flexibility at the resource and organizational level and for developing and testing structural relationships among the constructs 2.1.1 Competence and capability Prahalad and Hamel (1990) contend that an organization should focus on developing core competencies that help it to create enduring customer satisfaction Teece et al (1997) extend this discussion of core competencies to include capabilities They argue that firms should not be viewed as a portfolio of assets (internal competencies) but as a set of mechanisms by which customer-pleasing capabilities are selected and built Stalk et al (1992) claim that competence emphasizes technological and production expertise at specific points along the value chain while capabilities are broadly based and encompass the entire value chain In this respect, capabilities are visible to the consumer while the internal competencies that support those capabilities rarely are Competence and capability correspond to secondary flexibility and primary flexibility as described by Watts’ et al (1993) This perspective can assist managers in identifying which capabilities are critical to their customers and which competencies support those capabilities Externally focused flexible capability can be viewed as a linkage among corporate, marketing, and manufacturing strategy (Watts et al., 1993; Kathuria and Partovi, 1999) Internally focused flexible competence provides the processes and infrastructure that enable the firm to achieve the desired levels of flexible capability Hyun and Ahn’s (1992) cone model suggests that flexible manufacturing competence including machine, labor, material handling, and routing flexibilities support volume flexibility and mix flexibility A firm’s ability to change machining operations, labor activities, material handling modes, and routes should be useable for different production volumes and product mixes (Kathuria and Partovi, 1999; D’Souza and Williams, 2000) The definition and literature support for these sub-dimensions of manufacturing flexibility are given in Table and are discussed in the following sections Each sub-dimension has three distinct attributes: range/variety, mobility/responsiveness, and uniformity (Upton, 1995; Koste and Malhotra, 1999) Range is the firm’s ability to make a large or small number Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 177 Table The definitions of sub-constructs of manufacturing flexibility Construct Definition Literature Manufacturing flexibility The ability of the organization to manage production resource and uncertainty to meet various customer requests The ability of a piece of equipment to perform different operations economically and effectively The ability of the workforce to perform a broad range of manufacturing tasks economically and effectively The ability to transport different work pieces between various processing centers over multiple paths economically and effectively The ability to process a given set of part types using multiple routes economically and effectively The ability of the organization to operate at various batch sizes and/or at different production output levels economically and effectively The ability of the organization to produce different combinations of products economically and effectively given certain capacity Chen et al (1992), Leong et al (1990) Machine flexibility Labor flexibility Material handling flexibility Routing flexibility Volume flexibility Mix flexibility of different products and to make very similar or very different products The greatest range is when a large number of very different products can be produced Mobility is the ability to change from one product to another, quickly High mobility minimizes the need for long production runs Uniformity is the ability to maintain performance standards as a firm switches among products High uniformity indicates the ability to maintain quality as the product is changed (Leeuw and Volberda, 1996; Sethi and Sethi, 1990; Upton, 1995) 2.2 Defining flexible manufacturing competencies 2.2.1 Machine flexibility It is the ability of a piece of equipment to perform different operations economically and effectively It is a key variable in shop floor scheduling and the dual resource constrained job shop The range element of machine flexibility can be assessed by the number of different operations a machine can perform and the speeds at which it operates (Gupta, 1993; Hyun and Ahn, 1992; Ramasesh and Jayakumar, 1991) Gupta (1993), Hyun and Ahn (1992), Chen et al (1992), Sethi and Sethi (1990) Upton (1994), Hyun and Ahn (1992), Ramasesh and Jayakumar (1991) Hutchinson (1991), Sethi and Sethi (1990), Coyle et al (1992) Upton (1995), Gerwin (1993), Sethi and Sethi (1990) Carlsson (1989), Gerwin (1993), Sethi and Sethi (1990) Boyer and Leong (1996), Sethi and Sethi (1990), Gupta and Somers (1992) Mobility is high when operations can be switched with short changeover time and near zero set-up cost If quality and efficiency are consistent across different operations and different operating speeds, then the machine has uniformity As machine flexibility increases, higher levels of volume flexibility and mix flexibility can be achieved 2.2.2 Labor flexibility It is the ability of the workforce to perform a broad range of manufacturing tasks economically and effectively It is an important element in the dual resource constrained literature, but the conceptual and empirical literature tends to emphasize equipment flexibility and to neglect the potential impact of labor The workforce, however, plays a vital role in most production processes Flexible workers can handle uncertainty in the production process, such as absent workers, or they can respond to changes in demand by shifting the workforce as needed The number of tasks that workers can perform, their speed of execution, and their ability to learn quickly are measures of the range element of labor flexibility The ability 178 Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 of the workforce to recognize the need for a change in work deployment and to execute the change can measure the mobility attribute The ability of the worker to maintain quality and efficiency across a variety of jobs can measure the uniformity attribute of labor flexibility As a result, workforce flexibility is a major factor in determining the extent of volume and mix flexibility (Hyun and Ahn, 1992; Ramasesh and Jayakumar, 1991; Upton, 1994) 2.2.3 Material handling flexibility It is the ability to transport different work pieces between various processing centers over multiple paths economically and effectively Hutchinson (1991) notes that insufficient consideration of the material handling subsystem can constrain the benefits of a flexible manufacturing system in terms of product mix and production volume Paths can act as bottlenecks that starve downstream stations if processing (movement) times are too long Changing over material handling equipment to accommodate different products can cause delays and increase costs From this perspective, material handling activities are like machines in the manufacturing systems with range, mobility, and uniformity attributes The number of paths between work centers and the types of materials transported capture the range attribute of material handling flexibility Mobility can be examined using the time or cost associated with adding a path Material transfer time, cost, and quality issues, such as in-transit damage, can measure uniformity (Sethi and Sethi, 1990) 2.2.4 Routing flexibility It is the ability to process a given set of part types using multiple routes economically and effectively Routing flexibility is widely studied in the flexible manufacturing system literature because it allows firms to find alternate processing centers in case of machine breakdowns or system overloads These alternate routes increase the options available to management, thereby enhancing mix flexibility These alternate routes also provide the opportunity to apply underutilized equipment to expand volume flexibility Range can be evaluated by the number of alternative routes and the extent to which a route can be varied Mobility can be evaluated by time and cost expended to make a change, and uniformity can be measured by differences in processing time and quality when alternative routes are used (Sethi and Sethi, 1990; Upton, 1995) 2.3 Volume flexibility Volume flexibility is the ability of the organization to operate at various batch sizes and/or at different production output levels economically and effectively It demonstrates the competitive potential of the firm to increase production volume to meet rising demand and to keep inventory low as demand falls (Gerwin, 1993; Sethi and Sethi, 1990) It is widely discussed in economics literature and assessed by the cost curve (Carlsson, 1989) If a cost curve is U-shaped with a long flat bottom, it is viewed as flexible because there is a wide range of production volumes with little difference in costs The level of aggregate output over which the firm sustains profitability under normal conditions indicates the range element of volume flexibility In this case, range is the number of output levels where the average cost curve is flat The time required to change output level captures the mobility element while production costs and quality levels provide a measure of uniformity 2.4 Mix flexibility Mix flexibility is the ability of the organization to produce different combinations of products economically and effectively given certain capacity It enables a firm to enhance customer satisfaction by providing the kinds of products that customers request in a timely manner Mix flexibility must be evaluated within the current production system configuration without considering major facility modifications This implies that the production system can respond to changes in demand without impacting volume and capacity, which is part of volume flexibility Without this condition, an organization could simply acquire additional resources to manufacture different sets of products The number of different products manufactured by the firm as well as the degree of differentiation of those products captures the range attribute of mix flexibility The time and cost incurred for changing product mix measure mobility/responsiveness The organization’s ability to maintain product quality and system productivity while manufacturing a variety of products measures Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 uniformity (Boyer and Leong, 1996; Dixon, 1992; Gupta and Somers, 1992; Sethi and Sethi, 1990) 2.5 Customer satisfaction Customer satisfaction is the degree to which customers perceive that they received products and services that are worth more than the price they paid (Tracey, 1996) White’s (1996) meta-analysis of manufacturing performance defines a set of variables that influence customer satisfaction including quality, delivery speed, delivery dependability, cost, flexibility, and innovation Schroeder et al (1986) report similar measures of performance Koufteros (1995) provides measure of competitive capabilities that include cost, competitive pricing, premium pricing, value-to-customer quality, product mix flexibility, product innovation, and customer service Tracey et al (1999) provide a similar set of measures: price offered, quality of products, product line breadth, order fill rate, and frequency of delivery As advocated by Slack (1987) and Swamidass and Newell (1987), this study proposes measures that are based on the perception of experienced managers to assess customer satisfaction These measures include retention, ratio of price to value, quality, product reputation, and customer loyalty Hypotheses and research methods Hypotheses are developed that relate flexible competence to flexible capability and flexible capability to customer satisfaction Research methods include the item generation, pre-test, and pilot study methods used for instrument development as well as large-scale survey methods 3.1 Relationships between flexible competence and flexible capability Flexible competence, including machine, labor, material handling, and routing flexibilities, must be planned and managed to achieve customer-desired capabilities like volume flexibility and mix flexibility (Koufteros et al., 1997) Volume flexibility is increased as set-up time reductions for machinery and material handling equipment allow more production time, 179 flexible workers learn to perform tasks faster and better, material handling systems operate at a faster rate, and alternate routes are created to engage underutilized equipment Mix flexibility is enhanced as set-up cost reductions for machinery and material handling equipment permit the production of a greater number of highly differentiated products, flexible workers increase their level of skill and learn to produce more products, and new routes are established and used easily Thus, the following hypotheses are proposed Hypothesis 1a Flexible manufacturing competence has a significant positive impact on volume flexibility Hypothesis 1b Flexible manufacturing competence has a significant positive impact on mix flexibility 3.2 Relationships between flexible capability and customer satisfaction Volume flexibility and mix flexibility are important organizational capabilities that must be planned and managed effectively to achieve customer satisfaction (Behrbohm, 1985) Firms achieve high levels of satisfaction by providing high value to their customers High value results in loyal customers who are more likely to repurchase and, thus, promotes long-term prosperity through the creation of a base of steady clients (Innis and LaLonde, 1994; McKee et al., 1989; Narver and Slater, 1995; Venkatraman and Ramanujan, 1986) Managers seek to build enduring customer satisfaction, which involves the development, accumulation, combination, and protection of unique skills and capabilities (Teece et al., 1997) Wernerfelt (1984) argues that customer satisfaction analysis should expand its focus beyond product market positioning to include a set of resources and organizational skills, like volume flexibility and mix flexibility, that shape the firm’s long-term success Volume flexibility enables firms to satisfy customer requests by producing the exact amount of product ordered It enables firms to increase production volume quickly in response to unanticipated needs and to reduce volume quickly to avoid building inventory Volume flexibility reduces or eliminates waiting time for customers when demand levels fluctuate, and it reduces costs/price by lowering inventory in the supply chain Mix flexibility enhances customer satisfaction 180 Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 by producing the product with the features and performance that customers want It enables firms to produce a wide variety of products without excessive time delays, premium prices, or declines in quality Mix flexibility reduces the waiting time for special order products that customers value highly (Kathuria, 2000; Schroeder et al., 1986; White, 1996) Thus, the following hypotheses are proposed Hypothesis 2a Volume flexibility has a significant positive impact on customer satisfaction Hypothesis 2b Mix flexibility has a significant positive impact on customer satisfaction 3.3 Instrument development methods: item generation, pre-test, and pilot test An extensive literature review was the basis for developing an initial list of items to measure the component of the manufacturing flexibility In particular, the following works were examined (Sethi and Sethi, 1990; Upton, 1995; Gupta, 1993; Hyun and Ahn, 1992; Gerwin, 1993; Gupta and Somers, 1992; Kathuria and Partovi, 1999; Koste and Malhotra, 1999; D’Souza and Williams, 2000) During structured interviews, the definitions of manufacturing flexibility and its sub-dimensions were presented to four manufacturing executives The interview results were analyzed, and the research construct and measurement items were revised These managers also participated in a Q-sort to further enhance the content, convergent, and discriminant validities Here, the practitioners acted as judges and sorted the items into separate pools The final scales for the manufacturing flexibility items were five-point, Likert-type scales with = strongly disagree, = disagree, = neutral, = agree, and = strongly agree (The same five-point Likert-type scales were used for customer satisfaction.) For the pre-test, copies of the revised definitions and measurement items were examined by 10 faculty members from the same university They had expertise in operations management, information systems, and marketing They had the opportunity to suggest changes in the definition as well as to “Keep”, “Drop”, or “Modify” each item They were instructed to suggest new items if they felt that existing ones did not cover the domain of the construct A pilot study targeted manufacturing management executives Corrected item–total correlation (CITC) was applied to 33 responses to purify the scales Factor analysis was performed on each scale to assess unidimensionality Cronbach’s (1951) alpha was used to assess scale reliability During the pilot study, items with low CITC, factor loading, or reliability were deleted or reworded In some cases, items were added to cover the domain of the construct The items that entered the large-scale survey for manufacturing flexibility are listed in Appendix A Detailed results of the pilot study are available from the authors 3.4 Large-scale survey methods The large-scale survey was conducted using a mailing list provided by The Society of Manufacturing Engineers (SME) Five SIC codes were covered in the survey: 34 “fabricated metal products”; 35 “industrial and commercial machinery”; 36 “electronic and electrical equipment and components”; 37 “transportation equipment”; 38 “instruments and measurements equipment” A second-wave mailing was conducted weeks after the first mailing Out of 314 responses received (21 undeliverables, 11 blank returns, and incomplete), 273 were usable, resulting in a response rate of 9.2% (i.e 273/(3000 − 41)) The responses across SIC codes 34, 35, 36, 37, and 38 were 83, 65, 58, 38, and 29, respectively Firm size as measured by the number of employees was 100–249 = 135; 250–499 = 63; 500–999 = 35; and 1000 or more = 40 Job titles of respondents were CEO/President = 70; Vice-president = 43; Manager = 131; and Director = 29 Non-response bias was examined using a Chi-square test; the non-significant χ test results indicate the representativeness of the respondents for the sampling frame The results in Table show that there is no significant difference between the sample and respondents for SIC code (χ = 7.6, d.f = 4, P > 0.10); number of employees (χ = 8.6, d.f = 3, P > 0.01); and job title (χ = 5.3, d.f = 3, P > 0.10) To assist in determining whether data collected by SIC codes could be summed, the means for the SIC codes were compared for flexible manufacturing competence, volume flexibility, mix flexibility, and customer satisfaction MANOVA was completed to compare the means of these variables by SIC codes Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 181 Table Comparisons of sample and respondents Variables Sample Respondents (expected fe ) Respondents (observed fo ) SIC 34 760 35 680 36 599 37 490 38 450 Chi-square test (χ = 7.6, d.f = 4, P (26%) (23%) (20%) (16%) (15%) > 0.10) 70 62 55 45 41 83 65 58 38 29 Number of employees 100–249 1280 250–499 650 500–999 419 >1000 630 Chi-square test (χ = 8.6, d.f = 3, P (43%) (22%) (15%) (20%) > 0.02) 117 60 38 58 135 63 35 40 Job title CEO/President 680 Vice-president 459 Manager 1610 Director 230 Chi-square test (χ = 5.3, d.f = 3, P (23%) (15%) (54%) (8%) > 0.10) 62 42 148 21 70 43 131 29 273 273 Total 2979a (100) Note: (1) figures in parentheses are percentage; the calculation formula χ = (fe − fo )2 /fe (2) The sample (SME) list was cleaned up by eliminating some names from the same company a 2979 = 3000 − 21, where 3000 is the sample size and 21 is the number of undeliverables The results indicate that there is no significant difference for each construct mean across the five SIC codes This provides support for aggregating the data Scale development and validation results This research develops a set of valid and reliable instruments to measure the six sub-dimensions of manufacturing flexibility In this section, the purification and factor analysis results are first reported To determine if flexible manufacturing competence is a second-order construct, a LISREL measurement model is run 4.1 Purification and factor analysis results The 34 items in Appendix A were purified by examining the CITC of each item as recommended by Churchill (1979) The results, provided in Table 3, show that the CITC scores for all items in the labor flexibility, routing flexibility, volume flexibility, and mix flexibility are >0.50 The CITC for one machine flexibility (MA3) and one material handling flexibility (MH5) are substantially 0.80 are very good for basic research (Nunnally, 1978) As shown in Table 3, Cronbach’s alpha scores are 0.83, 0.91, 0.92, 0.92, 0.90, 0.92 for machine, labor, material handling, routing, volume, and mix flexibilities, respectively The remaining items, 32 in total, were submitted to exploratory factor analysis to check for factor structures among the various sub-dimensions As a general rule, the ratio of respondents to items should at least be greater than to (Tinsley and Tinsley, 1987) The ratio of respondents to items in this research is to 1, meeting the general guideline Factor loadings >0.50 are considered significant and items with factor cross-loadings of 0.40 or above should be removed (Hair et al., 1995) To streamline the final results, factor cross-loadings 0.50 with the smallest being 182 Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 Table Purification and factor loadings for manufacturing flexibility (large scale) Sub-construct Coding CITC CITC α Factor loadings F1 Machine flexibility F2 F3 F4 F5 F6 0.733 0.590 – 0.725 0.757 0.806 MA1 MA2 MA3 MA4 MA5 MA6 0.555 0.547 0.285 0.438 0.579 0.622 0.640 0.535 – 0.515 0.693 0.785 0.83 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – Labor flexibility WO1 WO2 WO3 WO4 WO5 0.785 0.851 0.806 0.824 0.642 – – – – – 0.91 – – – – – – – – – – – – – – 0.844 0.896 0.877 0.890 0.726 – – – – – – – – – – – – – – – Material handling flexibility MH1 MH2 MH3 MH4 MH5 0.626 0.613 0.684 0.618 0.273 0.777 0.857 0.828 0.781 – 0.92 – – – – – – – – – – – – – – – – – – – 0.843 0.861 0.855 0.790 – – – – – – – – – – – Route flexibility RO1 RO2 RO3 RO4 RO5 RO6 0.759 0.791 0.762 0.793 0.812 0.777 – – – – – – 0.92 – – – – – 0.815 0.832 0.816 0.852 0.861 0.815 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – – Volume flexibility VO1 VO2 VO3 VO4 VO5 VO6 0.750 0.793 0.684 0.770 0.714 0.719 – – – – – – 0.90 – – – – – – – – – – – – – – – – – – – – – – – – – – – – – 0.755 0.790 0.679 0.683 0.663 0.674 – – – – – – Mix flexibility MI1 MI2 MI3 MI4 MI5 MI6 0.669 0.754 0.830 0.797 0.783 0.774 – – – – – – 0.92 – – – – – – – – – – – 0.755 0.802 0.874 0.860 0.845 0.808 – – – – – – – – – – – – – – – – – – – – – – – – Eigenvalue Percentage of variance Cumulative % of variance – – – – – – – – – – – – 0.59 Six factors emerge from the factor analysis with most factor loadings >0.70 No items have factor cross-loading of 0.40 or above, and all items load on their respective factors The cumulative variance explained by the six factors is 69.4% The correlation matrix (Table 4) for the remaining 32 items was examined for evidence of convergent 4.52 41.1 41.1 4.32 13.5 27.7 3.86 12.1 39.7 3.22 10.1 49.8 3.20 10.0 59.8 3.09 9.6 69.4 and discriminant validity The smallest within-factor items correlation are: machine flexibility = 0.34, labor flexibility = 0.54, material handling flexibility = 0.66, routing flexibility = 0.60, volume flexibility = 0.22, and mix flexibility = 0.51 These correlations are significantly different from zero (P < 0.01) This supports the claim that there is good convergent Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 183 184 Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 validity within the factors Discriminant validity was tested by comparing the lowest correlation for a particular item and any other item within the factor (within factor correlation) to correlations of that item and all items outside of the factor (between factor correlation) If the former correlation is less than the latter, then a violation occurs An examination of the correlation matrix in Table reveals a total of 11 violations In no case does the count for the violations for an item exceed half the potential comparisons This supports the claim that there is good discriminant validity between the factors (Campbell and Fiske, 1959; Koufteros et al., 1998) Overall, the measures have very good reliability, factorial validity, and convergent and discriminant validity The final research instruments are contained in Appendix A with the two deleted items noted (MA3 and MH5) In addition, the instrument for customer satisfaction was adapted from Tracey (1996) Two items were revised: “customer retention rate” was changed to “customers keep doing business with us,” and “generating new business through customer referrals” was changed to “our firm has a good reputation for our products.” The revised scale was purified and assessed for reliability as well as convergent and discriminant validity The CITC, reliability, and factor loadings are reported in Table 4.2 Flexible manufacturing competence as a second-order construct To test the structural model as hypothesized in Fig 1, it is necessary to show that flexible manufacturing competence is a second-order construct composed of four sub-dimensions To achieve this, structural equation modeling (LISREL) was used to assess model fit Two widely used incremental fit indices are the comparative-fit index (CFI) and normed-fit index (NFI) NFI is a relative comparison of the proposed model to the null model CFI avoids the underestimation of fit often noted in small samples for NFI (Bentler, 1990; Bagozzi et al., 1999) Many researchers interpret index scores from 0.80 to 0.89 as representing reasonable fit and scores of 0.90 or above as evidence of good fit (Joreskog and Sorbom, 1986; Byrne, 1989) The Chi-square statistic is a good global test of a model’s ability to reproduce the sample variance/covariance matrix The ratio of χ to degrees of freedom provides information on the relative efficiency of competing models For this statistic, a value 0.7 are considered good; in other words, one latent trait or construct exists underlying a set of indicators and has substantive meaning Only three items have loadings that are somewhat below 0.7; MH (0.68), CS1 (0.65), and CS4 (0.68) In terms of overall fit, the χ statistic is significant (χ = 600.75; d.f = 205; P = 0.000), and the ratio of χ to degrees of freedom is 2.93, which indicates a reasonable fit While non-significant Chi-square values are desirable, they have a greater tendency to indicate significant differences for equivalent models as the sample size exceeds 200 respondents (Hair et al., 1995) In this study, the sample size is 273 The model fit indices NFI = 0.83, CFI = 0.88 are reasonable In addition, the absolute fit measures are also good: RMSEA = 0.076 and RMR = 0.046 The results of the model in Fig support Hypotheses 1a and 1b The LISREL path coefficients are, respectively, 0.31 (t = 4.22), 0.40 (t = 5.36), which are statistically significant at the level of 0.01 Table Completely standardized loadings and errors in the LISREL model∗ Items Constructs Flexible manufacturing competence MA WO MH RO VO1 VO2 VO3 VO4 VO5 VO6 MI1 MI2 MI3 MI4 MI5 MI6 CS1 CS2 CS3 CS4 CS5 CS6 ∗ Volume flexibility Mix flexibility Customer satisfaction Errors 0.74 0.73 0.68 0.73 – – – – – – – – – – – – – – – – – – – – – – 0.81 0.84 0.73 0.81 0.75 0.76 – – – – – – – – – – – – – – – – – – – – – – 0.70 0.78 0.87 0.85 0.83 0.81 – – – – – – – – – – – – – – – – – – – – – – 0.65 0.74 0.75 0.68 0.72 0.71 0.46 0.51 0.56 0.46 0.34 0.29 0.47 0.35 0.44 0.42 0.51 0.39 0.24 0.28 0.31 0.34 0.58 0.45 0.44 0.54 0.48 0.50 All loadings are significant at the 0.01 level 186 Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 Fig Hypotheses test using structural equation model (path and measurement) This supports the claim that flexible manufacturing competence has significant, positive, and direct impacts on an organization’s ability to achieve volume flexibility and mix flexibility Hypotheses 2a and 2b are also confirmed The LISREL path coefficients are, respectively, 0.18 (t = 2.71) and 0.25 (t = 3.54), which are statistically significant at the level of 0.01 This indicates that volume flexibility and mix flexibility have significant, positive, and direct impacts on the customer satisfaction It is also important to note that flexible manufacturing competence has a significant, positive indirect impact on customer satisfaction (β = 0.16, t = 3.88) The indirect effects along the two paths of volume flexibility and mix flexibility are, respectively, 0.06 and 0.10 Based on the data in Fig 2, mix flexibility seems to have a greater impact on customer satisfaction than volume flexibility The coefficient for the path from mix flexibility to customer satisfaction (0.25) is larger than the one from volume flexibility to customer satisfaction (0.18) The indirect effects of flexible manufacturing competence on customer satisfaction are also stronger through mix flexibility (0.10) than through volume flexibility (0.06) Mix flexibility reduces uncertainty with respect to meeting customer preference, and it enhances customer satisfaction by meeting customer wants for product performance and features Volume flexibility reduces uncertainty in quantity delivered, and it increases customer satisfaction by providing the exact amount of product The data tends to support the claim that customers want a product that meets their needs without having to wait It also appears that customers may be willing to wait a little longer to get something that meets their needs precisely than to accept a lesser product that can be delivered faster Implications for managers The dichotomy of flexible manufacturing competencies and capabilities can assist managers in identifying sub-dimensions of manufacturing flexibility that are critical to their customers Customers value the Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 visible capabilities, volume flexibility and mix flexibility, rather than the internally oriented competencies because customers see how these capabilities can be used to increase their satisfaction However, volume flexibility and mix flexibility cannot be achieved directly; they are attained through the implementation of flexible manufacturing competencies, which include machine, labor, material handling, and routing flexibilities Standing alone, flexible competencies are not adequate to build a substantial competitive edge While competencies are important, customers not value them directly They are unwilling to pay more because machines and workers are flexible Customers value the manifestation of these competencies, which is the capability of the organization to provide the right product, at the right time, and in the correct quantity This dichotomy of flexible manufacturing competence and capability enables managers to develop a comprehensive view of flexibility (i.e both external advantage creating capabilities and internal supporting competencies) With a partial view of flexibility, managers may focus on flexible competencies alone, thus limiting their ability to enhance customer satisfaction Flexible competencies can be altered directly through the actions of management These competencies become the starting points or foundations for creating manufacturing flexibility Volume flexibility and mix flexibility can be achieved as management plans, organizes, and coordinates production activities to align these internal competencies with customer expectations Machine flexibility impacts volume flexibility through its ability to alter the operating rate of equipment and to reduce down time caused by set-up delays, maintenance, or failures Machine flexibility impacts product mix flexibility through its ability to adjust quickly and easily to make other products When labor is a constraint, labor flexibility impacts production volume through the speed and knowledge base of the workers If workers can adjust quickly and easily to new tasks, delays in shifting between products and the time it takes to get up to speed can be reduced sharply Workers that have multiple skill sets should be able to produce a variety of products Material handling flexibility implies variation in the speed of delivery as well as the ability to move a variety of products without impacting costs or quality Routing flexibility impacts volume flexibility through 187 the development of new routes to take advantage of idle capacity in the system It also can be used to increase the variety of products produced From a strategic perspective, the dichotomy of flexible manufacturing capabilities and competencies may help firms to achieve mass customization Increasingly sophisticated customers require firms to supply a rich variety of products with good quality and low cost Mass customization is the ability of a firm to produce customized products on a large-scale with a short lead-time and at a cost that is close to non-customized products Mix flexibility helps organizations to make products that best serve the needs of customers at a reasonable cost, and volume flexibility provides the companies with near mass production capacity and efficiency Volume flexibility and mix flexibilities are indispensable preconditions for mass customization To achieve mass customization advantage and foster customer satisfaction, managers must plan and manage manufacturing flexibilities in terms of advantage-adding capabilities and supporting competencies Conclusions, limitations, and future research This paper describes manufacturing flexibility as an integral part of value chain flexibility and discusses its key sub-dimensions It provides theoretical justification for a research model that relates flexible manufacturing competencies, volume flexibility, mix flexibility, and customer satisfaction Based on the extensive literature review, the concept and sub-dimensions of manufacturing flexibility have been defined and clarified including three distinctive attributes: range, mobility, and uniformity This study applies competence and capability theory to this issue, which brings a systematic, resource-based view of manufacturing flexibility By building a network of manufacturing flexibility constructs and conducting analysis across a large number of organizations, this study represents an initial investigation of manufacturing flexibility rooted in a comprehensive view of value chain flexibility It points out and empirically confirms that flexible manufacturing competencies support the firm’s flexible capabilities, i.e volume flexibility and mix flexibility These flexible capabilities, in turn, enhance customer satisfaction 188 Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 This paper also develops a set of valid and reliable instruments to measure the sub-dimensions of manufacturing flexibility These instruments were developed through a carefully designed large-scale data collection process that used rigorous instrument development methods The final instruments, listed in Appendix A, are short and easy to use Each scale has six or fewer items and the total number of items across six scales is only 32 The content domains of the constructs were adequately covered because care was taken during item generation and evaluation The factor structure is simple and has good loadings The instruments exceed generally accepted validity and reliability standards for basic research These scales represent substantial progress towards the establishment of standard instruments for measuring manufacturing flexibility, and they have several applications in practice They can be used to evaluate manufacturing flexibility in an organization In addition to an overall assessment, they can be used to target specific aspects of manufacturing flexibility and to determine where problems may exist These instruments can also be used to compare manufacturing flexibility among various divisions of the same company or to compare it across organizations The results of these comparisons can lead to new approaches to manufacturing and business strategies It is important to recognize that a single study does not provide valid measures in the true spirit of instrument developments This study, through successive stages of analysis and refinement, has arrived at a final list of operational indicators that satisfied important reliability and validity criteria Such a list should be replicated and refined in other research contexts Given the perceptual nature of the data used to assess the theoretical constructs, it is important to recognize problems associated with the key informant approach Although the use of one respondent per participating company has the possibility of mono-respondent bias and common methods variance, it is necessary to arrive at a list of acceptable indicators before proceeding to examine inter-informant consistency Future studies should collect new data to confirm both the manufacturing flexibility measures and the structural model results This would provide further evidence for the validity and reliability of the instruments, and it would speed the diffusion of standard instruments among the academic community Future research may include similar studies for other aspects of value chain flexibility: product development, logistic, and spanning flexibilities It may also examine relationships among the four dimensions of value chain flexibility Appendix A Manufacturing flexibility Note: These items measure manufacturing flexibility compared with competitors using a five-point Likert-type scale to indicate the extent to which the respondents agree or disagree to each statement: = strongly disagree, = disagree, = neutral, = agree, = strongly agree These items entered the large-scale study Items MA3 and MH5 were deleted during the large-scale measurement analysis Machine flexibility MA1: Machine set-up can be done quickly MA2: A typical machine can perform many types of operations MA3∗ : A typical machine can effectively use many different tools MA4: Machines often become obsolete when new operations are required MA5: Machine tools can be changed quickly MA6: Machine set-ups are easy Labor flexibility WO1: Workers can perform many types of operations effectively WO2: A typical worker can use many different tools effectively WO3: Cross-trained workers can perform a broad range of manufacturing tasks effectively in the organization WO4: Workers can operate various types of machines WO5: Workers can be transferred easily between organizational units Material handling flexibility MH1: A typical material handling system can handle different part types MH2: A typical material handling system can link different processing centers Q Zhang et al / Journal of Operations Management 21 (2003) 173–191 MH3: Material handling system can move different part types through manufacturing facilities MH4: Material handling changeovers between parts are quick MH5∗ : Material handling tools can be changed or replaced quickly Routing flexibility RO1: A typical part operation can be routed to different machines RO2: A typical part can use many different routes RO3: The system has alternative routes in case machines break down RO4: The operating sequence through which the parts flow can be changed RO5: Machine visitation sequence can be changed or replaced quickly RO6: Route changeovers are easy Volume flexibility VO1: We can operate efficiently at different levels of output VO2: We can operate profitably at different production volumes VO3: We can economically run various batch sizes VO4: We can quickly change the 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Toward a taxonomy of manufacturing flexibility dimensions Journal of Operations Management 18, 577–593 Das, S., Nagendra, P., 1993 Investigations into the impact of flexibility on manufacturing performance