- Email: [email protected]
A methodology for deploying flexibility in supply chains
Proceedings of the 14th IFAC Symposium on Information Control Problems in Manufacturing Bucharest, Romania, May 23-25, 2012
A methodology for deploying flexibility in supply chains Miryam Barad Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel (email: [email protected])
BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB Abstract Supply Chain Management (SCM) seeks to improve the internal efficiency of the supply net of an enterprise and its competitiveness with respect to other enterprises through collaboration among firms for leveraging their strategic position and their operational efficiency. To improve their capabilities for coping with changes and uncertainties companies need flexibility. Here a methodology for deploying flexibility in Supply Chains utilizing Quality Function Deployment (QFD) is presented. A conceptual model is developed using a top-down structure with QFD sequential matrices. The methodology represents a structured framework of an eventual empiric research for measuring and analyzing causal relationships between the extent and types of flexibility of an enterprise in a supply chain and its performances under various changes and uncertainties. Keywords: Flexibility, Changes, Supply Chains, Quality Function Deployment _________________________________________________________________________ 1. INTRODUCTION )OH[LELOLW\ KDV EHHQ GHILQHG LQ D EURDG VHQVH DV µDQ attribute of a system technology for coping with the variety of its environmental needs (De Groote, 1994). From a performance perspective, flexibility is a powerful ingredient that enables stable performances under changing conditions (see e.g. Swamidass and Newell, 1987, De Meyer et al, 1989, Gerwin, 1993). As most of the supply chain characteristics exhibit changes and uncertainties, flexibility in these systems may well represent a potential source of improved efficiency.
1.1 Supply chains A supply chain is a collection of suppliers, component manufacturers, end product manufacturers, logistics providers, and after-sales service providers, intended to leverage their strategic positioning and to improve their operating efficiency (Bowersox, Closs and Cooper, 2002). The causes for emergence of supply chains are manifold: global competition, customization -where the emphasis is on tailoring a product to the exact requirements of a customer-, rapid innovations that have drastically reduced product lifecycles thus forcing manufacturers to build closer links with their suppliers and distributors, and advances in information technology enabling partners to exchange information and to plan jointly to form a seamless supply chain. Intense competition and very high levels of uncertainties characterize the current markets. The added dimension of economic downturn is putting serious stress on individual links. Given such difficult circumstances, a methodology for studying the industry needs and for finding pathways for companies to take care of these needs becomes very important.
The main objectives of the methodology are: 1) Building a structured framework of an eventual empiric research for matching flexibility improvement needs to the strategic priorities of an enterprise within a supply chain. 2) Considering changes and uncertainties in a systematic way. The paper is organized as follows: section 2 briefly reviews bottom-up and top-down hierarchical structures of flexibility in the literature. Section 3 presents the conceptual model and its structured framework Quality Function Deployment. The paper ends with some concluding remarks.
1.2 Why flexibility?
978-3-902661-98-2/12/$20.00 © 2012 IFAC
752
10.3182/20120523-3-RO-2023.00319
INCOM 2012, May 23-25, 2012 Bucharest, Romania
2. FLEXIBILITY IN THE LITERATURE
2.2 Flexibility measuring dimensions To get a quantitative outlook on flexibility as a hedge against changes and uncertainties we shall start with the flexibility dimensions. The three most agreed upon dimensions for measuring flexibility are: range, response and distension (see e.g. Fitzgerald et al., 2009). The range measures the current variety of available alternatives action space- for coping with a given change. This dimension is associated with the system effectiveness and is typically measured by counting the number of options or by a normalized index. Response is the preparation time (or cost) for coping with a change within the current action space. This dimension may be thus associated with the system efficiency. Distension is the invested effort/time/cost for enhancing the current action space if needed, thus enabling to accommodate a given change whose handling is currently outside the action space.
Flexibility is a complex, multi-dimensional and hard to capture concept (Zhang et al., 2002). Considering the vast and diverging literature on this subject we shall limit our survey to an overall view and then dwell on some topics closely related to our framework. Most of the extant reviews of flexibility in the literature deal with manufacturing flexibility (see e.g. de Toni and Tonchia, 1998; D'Souza and Williams, 2000). 2.1 Two perspectives on flexibility We classify here the variety of flexibility types and frameworks in the manufacturing flexibility literature by considering two different perspectives: bottom-up and topdown. The 'classical' early flexibility frameworks were built bottom-up, matching a manufacturing hierarchy. The more recent frameworks have a top-down hierarchical structure viewing flexibility through a manufacturing strategy or a marketing perspective. This classification as such shows that the perceived advantage of manufacturing flexibility, first solely associated with intelligent manufacturing (Flexible Manufacturing Systems), has nowadays achieved strategic importance. A bottom up flexibility structure comprises three hierarchical flexibility levels: basic (related to the manufacturing elements such as 'machines'), system (related to composite activities, such as 'rerouting', i.e. the system capability of processing a part through using different routes/machines) and aggregate level such as 'production' flexibility (see e.g. Sethi and Sethi, 1990; Benjaafar and Ramakrishman, 1996, for detailed descriptions and analyses). The early researchers had a pretty good view of the basic and eventually the system flexibility types but their view of flexibility at an aggregate level was somewhat vague. The later top-down flexibility frames were built from strategies to resources, focusing on the strategic importance of flexibility (Olhager and West, 2002). This is because by the end of µ V WKH LPSRUWDQFH RI IOH[LELOLW\ started to get its main recognition from a strategic perspective. Select flexibility types became directly linked to competitive priorities of enterprises. Corporate objectives (growth, survival, profit) are linked to first order flexibilities (featured by high market/customer visibility) such as due date, volume and new products. At their turn, the first order flexibilities are also linked to resources (e.g. process, labor and suppliers), considered 'providers' or 'enablers' of flexibility. Top-down flexibility researchers do not bother to discuss flexibility of elements.
3. THE CONCEPTUAL MODEL AND ITS METHODOLOGICAL FRAMEWORK 3.1 Conceptual model Otto and Kotzab, 2003, explored the strategic metrics in Supply Chains Management (SCM). These authors found that typical competitive advantages found in the manufacturing strategy literature are consistent with the competitive advantages in SCM. However, 'quality' was not explicitly included among the SCM goal oriented metrics in the above paper. Hence, we consider a set of strategic components of an enterprise from which we exclude quality and cost. These are: delivery (fast, dependable), product diversity and new products. We prioritize each of these strategic metrics by combining the strategic importance attributed by an enterprise to each component with its competitive incapability (Narasimhan and Jayaraman, 1998). As in Barad and Gien, 2001, our model here multiplies (and normalizes) the scores of these two attributes so that the higher the importance of a strategic component and the higher is its competitive incapability, the higher is its priority for improvement. We model flexibility deployment in Supply Chains using a top-down QFD matrix structure (see Fig. 1). First, we translate the prioritized strategic metrics into priorities of first order/ customer oriented flexibility metrics (due date, volume and time to market) by considering the type and impact of changes or uncertainties that affect a given strategic metric.
753
INCOM 2012, May 23-25, 2012 Bucharest, Romania
Prioritized Strategic Metrics
Changes 1
Changes 2 I
Strategic Importance
II III
Strategic Flexibility
Internal Flexibility
Providers
Competitive Capability Deployment
Deployment
Priorities of First Order Flexibility Metrics:
Priorities of Internal Flexibility Metrics by Facets:
Priorities of Resources/ Infrastructures and Collaboration:
Due Date, Volume, Mix,Time to Market
Design, Manufacturing Human, Information/ and Collaboration Communication, Logistics infrastructures
Fig.1 The QFD Conceptual Model
The translation considers the impact of each HOW element on each WHAT element. The HOWs of a given matrix become the WHATs of the next one. The first QFD matrix is called the House of Quality. QFD has been developed at Mitsubishi in 1972 by Akao. It reached the U.S. more than ten years later (see e.g. Hauser and Clausing, 1988; Akao, 1990). It is typically carried out by teams of multidisciplinary representatives from all stages of product development and manufacturing (Lai, Ho, and Chang, 1998). In recent years the application domains of the QFD methodology have been expanded and its popularity increased tremendously. Essentially, there is no boundary for QFD potential fields of applications (Chan and Wu, 2002). Barad and Gien utilized QFD for deploying manufacturing strategies into manufacturing improvement actions. They developed a two-level model linking the improvement actions of a company with its operating and strategic improvement needs and used data provided by face-to-face interviews of managers and engineers from a sample of Small Manufacturing Enterprises. To identify generic improvement models clustering analysis was carried out. Olhager and West (2002) presented a two-level QFD approach for deploying manufacturing flexibility and its applications. Ideas from these two papers are incorporated in our proposed QFD approach.
We deem that changes drive flexibility needs and thus we incorporate potential effects of changes and uncertainties in Supply Chains that are relevant at the strategic level or at the internal level. For instance, delivery reliability is translated into due date flexibility (whose role is to make delivery reliability robust to modifications to agreements regarding due dates and into volume flexibility (for making delivery reliability robust to changes in the required volume. New products are expressed by time to market flexibility, whose role is to make the time to market duration robust to additional information on the new product features necessitating changes. The second matrix translates the first order flexibility metrics into priorities of internal flexibility capabilities by facets (design, manufacturing and collaboration), see section 3.3. The third matrix translates these flexibility capabilities into flexibility providers. The flexibility providers we consider are: human, informational and logistic infrastructures as well as collaborators (suppliers, customers and eventually competing firms). 3.2 Quality Function Deployment The design framework adopted here is Quality Function Deployment (QFD), originally a product planning methodology expressing the voice of the customer. It is initiated by a set of customer needs where each need has assigned to it a priority indicating its importance to the customer. The QFD essence is to extract the prioritized customer needs or desires, expressed in his/her own words (WHATs), to translate them into prioritized technical product quality characteristics (HOWs) and subsequently LQWR FRPSRQHQWV¶ FKDUDFWHULVWLFV RSHUDWLQJ GHFLVLRQV DQG other decisions. EacKWUDQVODWLRQRIFXVWRPHUµYRLFHV¶DQG subsequent processes uses a matrix relating the HOWs with the WHATs, associated with any specific QFD stage.
3.3 Internal flexibility capabilities by facets In contrast to most top-down studies on manufacturing flexibility that (because of the already established knowledge provided by the bottom-up studies) deliberately ignored what may be called lower order or internal flexibility types here we attempt to study them. Our rationale is that flexibility in supply chains is more complex than manufacturing flexibility, and as such still
754
INCOM 2012, May 23-25, 2012 Bucharest, Romania
(see Ernst and Kamrad, 2000). Flexibility in R&D has a human orientation and is related to flexible designers (versatile individuals with multiple capabilities) and flexible synergetic teams and can reduce design time of a product given its complexity (see Barad, 1998). From a design perspective flexible hardware is related to layout and configuration flexibility enabling a quick adaptation (reconfiguration) of the existing layout to new products (see Mehrabi et al. 2002). In the manufacturing facet, we seek to entwine flexibility capabilities reflecting 'intelligent hardware' such as versatile machines with short set up (from the earlier bottom-up studies of flexibility) with more recent 'human' aspects in manufacturing (see Hopp and Van Oyen, 2003). These are related to flexible employment, versatile operators/ teams. The flexibility types in the collaboration facet are mainly intended to enable reduction of stock levels without increasing risk.
lacks basic understanding of these internal flexibility types and their linkages. The upper linkages of the internal flexibility capabilities reflect their specific contribution to improving customer oriented flexibility capabilities, while their lower linkages reflect the extent of their dependability on the flexibility providers. We classify these internal flexibility capabilities into three facets: design, manufacturing and collaboration. Table 1 is a concise representation of the second QFD matrix in the conceptual model with eventual linkages (symbolically represented here by ) between some specific customeroriented flexibility types -the WHATs- and specific internal flexibility capabilities ± the HOWs. Interviewees will have to attribute weights to each specific linkage (see section 3.4) We envisage flexibility in design in terms of 'products', 'humans' and 'hardware', all contributing to achieve timeto-market flexibility. Interchange flexibility stands for flexible /interchangeable products. It is related to principles of modularity and can reduce design complexity
Table 1. A selective view of matrix II Internal flexibility capabilities by facets
Volume
Mix Product
Time to Market
Outsourcing
Trans-routing flexibility
(late) Product customization
Short Set-up
Collaboration
Versatile machines
Due Date
Versatile Operator
Flexible employment
Manufacturing Configuration flexibility
R&D flexibility
Interchange flexibility
Design Customer oriented Flexibility Types
In the perceptual phase data will be collected through questionnaire-based face-to-face interviews or mailed questionnaires, while in the implementation phase it will rely on in depth collaboration with select enterprises investigated in phase I. The perceptual phase of the study will identify vital flexibility improvement needs of supply chains in different industries by evaluating and analyzing causal perceived relationships between the extent and types of flexibility of a company in the surveyed industries. It will be based on input from interviewees (managers and engineers) on the
Among others we suggest flexible (delayed) product customization with respect to price marking, mixing or packaging (see Narasimhan and Jayaraman, 1998, Ernst and Kamrad, 2000) and and in transfer of stock between users at the same echelon (see Barad and Even Sapir, 2003). 3.4 Data Collection and Analysis The proposed structured framework here can be used to design an empirical study in two phases: a perceptual phase eventually followed by an adaptive/ implementation phase.
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INCOM 2012, May 23-25, 2012 Bucharest, Romania
evaluation. Int. J. of Production Economics, 85, 155170. Benjaafar, S. and Ramakrishnan, R. (1996). Modeling, measurement and evaluation of sequencing flexibility in manufacturing systems. Int. J. of Production Research, 34(5), 1195-1220. Bowersox D.J., Closs D.J., and Cooper, M.B. (2002). Supply Chain Logistics Management, McGraw Hill, New York. Chan, L.K. and Wu, M.L. (2002). Quality Function Deployment: A literature review. European Journal of Operations Research, 143, 463-497. De Groote, X. (1994). The flexibility of production processes: a general framework. Management Science, 40(7), 933-945. De Meyer, A., Nakane, J., Miller, J.G. and Ferdows, K. (1989). Flexibility the next competitive battle. Strategic Management Journal, 10(2), 135-144. De Toni, A. and Tonchia, S. (1998). Manufacturing flexibility: a literature review. Int. J. of Production Research 36(6), 1587-1617. D'Souza, D. E. and Williams, F. P. (2000). Towards a taxonomy of manufacturing flexibility dimensions. J. of Operations Management, 18(5) 577-593. Ernst, R. and Kamrad, B., 2000, "Valuation of supply chain structures through modularization and postponement", European Journal of Operations Research, 124, 495-510. Gerwin, D. (1993). Manufacturing Flexibility: A Strateg ic Perspective. Management Science 39(4) 395-410. Fitzgerald, G., Barad, M., Papazafeiropoulou, A. and Alaa, G. (2009). A framework for analyzing flexibility of generic objects. Int. J. of Production Economics, 122(1), 329-339. Hauser, J.R. and Clausing, D. (1988). The House of Quality, Harvard Business Review, 66, 1-27. Hill, C. H. (1995). Manufacturing Strategy. 2nd ed., Macmillan,London. Hopp, J. W. and Van Oyen, M.P. (2003). Agile Workforce Evaluation: A framework for cross training and coordination Lai, Y. J, E. Ho, A. and Chang, S. I. (1998). Identifying Customer Preferences in QFD using Group DecisionMaking Techniques. In: Integrated Product and Process Development, Wiley, New York. Mehrabi, M.G., Ullsoy, A.G., Kore, Y. and Heytler, P. (2002). Trends and perspectives in flexible and reconfigurable manufacturing systems. Journal of Intell igent Manufacturing, 13(2), 135-146. Narasimhan, R. and Jayaranan, J. (1998). Causal linkages in Supply Chain Management. Decision Science, 29(3), 579-605. Olhager, J. and West, B. M. (2002). The house of flexibility: using the QFD approach to deploy manufacturing flexibility. Int. J. of Operations and Production Management 22(1), 50-79.
importance of the strategic priorities and on their respective competitive incapability in their enterprises as well as on their perceived impact of given changes on these capabilities. Other input variables from the interviewees will concern their perception of the strength of the relationships between the HOWs and the WHATs in the three QFD matrices. The adaptive phase of the research will address the implementation perspective to be realized through a bottom-up path of prioritized flexibility improvement actions of a given enterprise. The prioritized flexibility improvement actions detected in phase I will be analyzed using additional decision making actions and for following and measuring the expected causal research. These concern improved levels of measured flexibility and improved levels of flexibility oriented performance measures of the enterprise. The latter will be expressed in terms of universal measures such as time, costs and eventually quality and human oriented performances.
4. CONCLUDING REMARKS This proposed empirical research framework deals with two fields of academic arena that have generated considerable amount of research interest ± supply chains and flexibility. It presents an integrated perspective on flexibility in supply chains and can be used in one or two research phases: perceptual and adaptive. While flexibility has been an active topic for the past twenty years, most of the works have concentrated on manufacturing, which is only one of the supply chain aspects. Studies on supply chain flexibility are still in a nascent stage. The proposed methodology utilizes Quality Function Deployment (QFD) a multi agent technique for supporting flexibility deployment in three stages. Changes are incorporated in the QFD model. The model emphasizes internal flexibility capabilities by facets: design, manufacturing and collaboration.
REFERENCES Akao, Y. (1990). Quality Function deployment: Integrating Customer Requirements into Product Design, Productivity Press, Cambridge, MA. Barad, M. (1998). Flexibility performance measurement systems-a framework for design. In: Neely A.D., Waggoner, D. B. (Eds.), Proceedings of the First International Conference on Performance Measurement, University of Cambridge, UK, 78-85. Barad, M and Gien, D. (2001). Linking improvement models to manufacturing strategies. Int. J. of Production Research, 39(12), 2675-2695. Barad, M. and Even-Sapir, D. (2003). Flexibility in logistic systems ± modeling and performance
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Otto, A. and Kotzab, H. (2003). Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. European J. of Operational Research, 144, 306-32. Sethi, A.K. and Sethi, S.P. (1990). Flexibility Flexible manufacturing Systems, 2, 289- in Manufacturing: A survey. Int. J. of 328. Skinner, W. (1992). A strategy for competitive manufacturing. Management Review, 76(8), 54-56. Swamidass, P.M. and Newell, W.T. (1987). Manufacturing strategy, environmental uncertainty and performance: a path analytic model. Management Science, 33(4), 509-524. Zhang, Q., Vonderembse, M.A. and Lim,J.S. (2002).Value chain flexibility: a dichotomy of competence and capability. Int. J. of Production Research 40(3), 561-583.
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A methodology for deploying flexibility in supply chains Miryam Barad Department of Industrial Engineering, Tel Aviv University, Tel Aviv, Israel (email: [email protected])
BBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBBB Abstract Supply Chain Management (SCM) seeks to improve the internal efficiency of the supply net of an enterprise and its competitiveness with respect to other enterprises through collaboration among firms for leveraging their strategic position and their operational efficiency. To improve their capabilities for coping with changes and uncertainties companies need flexibility. Here a methodology for deploying flexibility in Supply Chains utilizing Quality Function Deployment (QFD) is presented. A conceptual model is developed using a top-down structure with QFD sequential matrices. The methodology represents a structured framework of an eventual empiric research for measuring and analyzing causal relationships between the extent and types of flexibility of an enterprise in a supply chain and its performances under various changes and uncertainties. Keywords: Flexibility, Changes, Supply Chains, Quality Function Deployment _________________________________________________________________________ 1. INTRODUCTION )OH[LELOLW\ KDV EHHQ GHILQHG LQ D EURDG VHQVH DV µDQ attribute of a system technology for coping with the variety of its environmental needs (De Groote, 1994). From a performance perspective, flexibility is a powerful ingredient that enables stable performances under changing conditions (see e.g. Swamidass and Newell, 1987, De Meyer et al, 1989, Gerwin, 1993). As most of the supply chain characteristics exhibit changes and uncertainties, flexibility in these systems may well represent a potential source of improved efficiency.
1.1 Supply chains A supply chain is a collection of suppliers, component manufacturers, end product manufacturers, logistics providers, and after-sales service providers, intended to leverage their strategic positioning and to improve their operating efficiency (Bowersox, Closs and Cooper, 2002). The causes for emergence of supply chains are manifold: global competition, customization -where the emphasis is on tailoring a product to the exact requirements of a customer-, rapid innovations that have drastically reduced product lifecycles thus forcing manufacturers to build closer links with their suppliers and distributors, and advances in information technology enabling partners to exchange information and to plan jointly to form a seamless supply chain. Intense competition and very high levels of uncertainties characterize the current markets. The added dimension of economic downturn is putting serious stress on individual links. Given such difficult circumstances, a methodology for studying the industry needs and for finding pathways for companies to take care of these needs becomes very important.
The main objectives of the methodology are: 1) Building a structured framework of an eventual empiric research for matching flexibility improvement needs to the strategic priorities of an enterprise within a supply chain. 2) Considering changes and uncertainties in a systematic way. The paper is organized as follows: section 2 briefly reviews bottom-up and top-down hierarchical structures of flexibility in the literature. Section 3 presents the conceptual model and its structured framework Quality Function Deployment. The paper ends with some concluding remarks.
1.2 Why flexibility?
978-3-902661-98-2/12/$20.00 © 2012 IFAC
752
10.3182/20120523-3-RO-2023.00319
INCOM 2012, May 23-25, 2012 Bucharest, Romania
2. FLEXIBILITY IN THE LITERATURE
2.2 Flexibility measuring dimensions To get a quantitative outlook on flexibility as a hedge against changes and uncertainties we shall start with the flexibility dimensions. The three most agreed upon dimensions for measuring flexibility are: range, response and distension (see e.g. Fitzgerald et al., 2009). The range measures the current variety of available alternatives action space- for coping with a given change. This dimension is associated with the system effectiveness and is typically measured by counting the number of options or by a normalized index. Response is the preparation time (or cost) for coping with a change within the current action space. This dimension may be thus associated with the system efficiency. Distension is the invested effort/time/cost for enhancing the current action space if needed, thus enabling to accommodate a given change whose handling is currently outside the action space.
Flexibility is a complex, multi-dimensional and hard to capture concept (Zhang et al., 2002). Considering the vast and diverging literature on this subject we shall limit our survey to an overall view and then dwell on some topics closely related to our framework. Most of the extant reviews of flexibility in the literature deal with manufacturing flexibility (see e.g. de Toni and Tonchia, 1998; D'Souza and Williams, 2000). 2.1 Two perspectives on flexibility We classify here the variety of flexibility types and frameworks in the manufacturing flexibility literature by considering two different perspectives: bottom-up and topdown. The 'classical' early flexibility frameworks were built bottom-up, matching a manufacturing hierarchy. The more recent frameworks have a top-down hierarchical structure viewing flexibility through a manufacturing strategy or a marketing perspective. This classification as such shows that the perceived advantage of manufacturing flexibility, first solely associated with intelligent manufacturing (Flexible Manufacturing Systems), has nowadays achieved strategic importance. A bottom up flexibility structure comprises three hierarchical flexibility levels: basic (related to the manufacturing elements such as 'machines'), system (related to composite activities, such as 'rerouting', i.e. the system capability of processing a part through using different routes/machines) and aggregate level such as 'production' flexibility (see e.g. Sethi and Sethi, 1990; Benjaafar and Ramakrishman, 1996, for detailed descriptions and analyses). The early researchers had a pretty good view of the basic and eventually the system flexibility types but their view of flexibility at an aggregate level was somewhat vague. The later top-down flexibility frames were built from strategies to resources, focusing on the strategic importance of flexibility (Olhager and West, 2002). This is because by the end of µ V WKH LPSRUWDQFH RI IOH[LELOLW\ started to get its main recognition from a strategic perspective. Select flexibility types became directly linked to competitive priorities of enterprises. Corporate objectives (growth, survival, profit) are linked to first order flexibilities (featured by high market/customer visibility) such as due date, volume and new products. At their turn, the first order flexibilities are also linked to resources (e.g. process, labor and suppliers), considered 'providers' or 'enablers' of flexibility. Top-down flexibility researchers do not bother to discuss flexibility of elements.
3. THE CONCEPTUAL MODEL AND ITS METHODOLOGICAL FRAMEWORK 3.1 Conceptual model Otto and Kotzab, 2003, explored the strategic metrics in Supply Chains Management (SCM). These authors found that typical competitive advantages found in the manufacturing strategy literature are consistent with the competitive advantages in SCM. However, 'quality' was not explicitly included among the SCM goal oriented metrics in the above paper. Hence, we consider a set of strategic components of an enterprise from which we exclude quality and cost. These are: delivery (fast, dependable), product diversity and new products. We prioritize each of these strategic metrics by combining the strategic importance attributed by an enterprise to each component with its competitive incapability (Narasimhan and Jayaraman, 1998). As in Barad and Gien, 2001, our model here multiplies (and normalizes) the scores of these two attributes so that the higher the importance of a strategic component and the higher is its competitive incapability, the higher is its priority for improvement. We model flexibility deployment in Supply Chains using a top-down QFD matrix structure (see Fig. 1). First, we translate the prioritized strategic metrics into priorities of first order/ customer oriented flexibility metrics (due date, volume and time to market) by considering the type and impact of changes or uncertainties that affect a given strategic metric.
753
INCOM 2012, May 23-25, 2012 Bucharest, Romania
Prioritized Strategic Metrics
Changes 1
Changes 2 I
Strategic Importance
II III
Strategic Flexibility
Internal Flexibility
Providers
Competitive Capability Deployment
Deployment
Priorities of First Order Flexibility Metrics:
Priorities of Internal Flexibility Metrics by Facets:
Priorities of Resources/ Infrastructures and Collaboration:
Due Date, Volume, Mix,Time to Market
Design, Manufacturing Human, Information/ and Collaboration Communication, Logistics infrastructures
Fig.1 The QFD Conceptual Model
The translation considers the impact of each HOW element on each WHAT element. The HOWs of a given matrix become the WHATs of the next one. The first QFD matrix is called the House of Quality. QFD has been developed at Mitsubishi in 1972 by Akao. It reached the U.S. more than ten years later (see e.g. Hauser and Clausing, 1988; Akao, 1990). It is typically carried out by teams of multidisciplinary representatives from all stages of product development and manufacturing (Lai, Ho, and Chang, 1998). In recent years the application domains of the QFD methodology have been expanded and its popularity increased tremendously. Essentially, there is no boundary for QFD potential fields of applications (Chan and Wu, 2002). Barad and Gien utilized QFD for deploying manufacturing strategies into manufacturing improvement actions. They developed a two-level model linking the improvement actions of a company with its operating and strategic improvement needs and used data provided by face-to-face interviews of managers and engineers from a sample of Small Manufacturing Enterprises. To identify generic improvement models clustering analysis was carried out. Olhager and West (2002) presented a two-level QFD approach for deploying manufacturing flexibility and its applications. Ideas from these two papers are incorporated in our proposed QFD approach.
We deem that changes drive flexibility needs and thus we incorporate potential effects of changes and uncertainties in Supply Chains that are relevant at the strategic level or at the internal level. For instance, delivery reliability is translated into due date flexibility (whose role is to make delivery reliability robust to modifications to agreements regarding due dates and into volume flexibility (for making delivery reliability robust to changes in the required volume. New products are expressed by time to market flexibility, whose role is to make the time to market duration robust to additional information on the new product features necessitating changes. The second matrix translates the first order flexibility metrics into priorities of internal flexibility capabilities by facets (design, manufacturing and collaboration), see section 3.3. The third matrix translates these flexibility capabilities into flexibility providers. The flexibility providers we consider are: human, informational and logistic infrastructures as well as collaborators (suppliers, customers and eventually competing firms). 3.2 Quality Function Deployment The design framework adopted here is Quality Function Deployment (QFD), originally a product planning methodology expressing the voice of the customer. It is initiated by a set of customer needs where each need has assigned to it a priority indicating its importance to the customer. The QFD essence is to extract the prioritized customer needs or desires, expressed in his/her own words (WHATs), to translate them into prioritized technical product quality characteristics (HOWs) and subsequently LQWR FRPSRQHQWV¶ FKDUDFWHULVWLFV RSHUDWLQJ GHFLVLRQV DQG other decisions. EacKWUDQVODWLRQRIFXVWRPHUµYRLFHV¶DQG subsequent processes uses a matrix relating the HOWs with the WHATs, associated with any specific QFD stage.
3.3 Internal flexibility capabilities by facets In contrast to most top-down studies on manufacturing flexibility that (because of the already established knowledge provided by the bottom-up studies) deliberately ignored what may be called lower order or internal flexibility types here we attempt to study them. Our rationale is that flexibility in supply chains is more complex than manufacturing flexibility, and as such still
754
INCOM 2012, May 23-25, 2012 Bucharest, Romania
(see Ernst and Kamrad, 2000). Flexibility in R&D has a human orientation and is related to flexible designers (versatile individuals with multiple capabilities) and flexible synergetic teams and can reduce design time of a product given its complexity (see Barad, 1998). From a design perspective flexible hardware is related to layout and configuration flexibility enabling a quick adaptation (reconfiguration) of the existing layout to new products (see Mehrabi et al. 2002). In the manufacturing facet, we seek to entwine flexibility capabilities reflecting 'intelligent hardware' such as versatile machines with short set up (from the earlier bottom-up studies of flexibility) with more recent 'human' aspects in manufacturing (see Hopp and Van Oyen, 2003). These are related to flexible employment, versatile operators/ teams. The flexibility types in the collaboration facet are mainly intended to enable reduction of stock levels without increasing risk.
lacks basic understanding of these internal flexibility types and their linkages. The upper linkages of the internal flexibility capabilities reflect their specific contribution to improving customer oriented flexibility capabilities, while their lower linkages reflect the extent of their dependability on the flexibility providers. We classify these internal flexibility capabilities into three facets: design, manufacturing and collaboration. Table 1 is a concise representation of the second QFD matrix in the conceptual model with eventual linkages (symbolically represented here by ) between some specific customeroriented flexibility types -the WHATs- and specific internal flexibility capabilities ± the HOWs. Interviewees will have to attribute weights to each specific linkage (see section 3.4) We envisage flexibility in design in terms of 'products', 'humans' and 'hardware', all contributing to achieve timeto-market flexibility. Interchange flexibility stands for flexible /interchangeable products. It is related to principles of modularity and can reduce design complexity
Table 1. A selective view of matrix II Internal flexibility capabilities by facets
Volume
Mix Product
Time to Market
Outsourcing
Trans-routing flexibility
(late) Product customization
Short Set-up
Collaboration
Versatile machines
Due Date
Versatile Operator
Flexible employment
Manufacturing Configuration flexibility
R&D flexibility
Interchange flexibility
Design Customer oriented Flexibility Types
In the perceptual phase data will be collected through questionnaire-based face-to-face interviews or mailed questionnaires, while in the implementation phase it will rely on in depth collaboration with select enterprises investigated in phase I. The perceptual phase of the study will identify vital flexibility improvement needs of supply chains in different industries by evaluating and analyzing causal perceived relationships between the extent and types of flexibility of a company in the surveyed industries. It will be based on input from interviewees (managers and engineers) on the
Among others we suggest flexible (delayed) product customization with respect to price marking, mixing or packaging (see Narasimhan and Jayaraman, 1998, Ernst and Kamrad, 2000) and and in transfer of stock between users at the same echelon (see Barad and Even Sapir, 2003). 3.4 Data Collection and Analysis The proposed structured framework here can be used to design an empirical study in two phases: a perceptual phase eventually followed by an adaptive/ implementation phase.
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evaluation. Int. J. of Production Economics, 85, 155170. Benjaafar, S. and Ramakrishnan, R. (1996). Modeling, measurement and evaluation of sequencing flexibility in manufacturing systems. Int. J. of Production Research, 34(5), 1195-1220. Bowersox D.J., Closs D.J., and Cooper, M.B. (2002). Supply Chain Logistics Management, McGraw Hill, New York. Chan, L.K. and Wu, M.L. (2002). Quality Function Deployment: A literature review. European Journal of Operations Research, 143, 463-497. De Groote, X. (1994). The flexibility of production processes: a general framework. Management Science, 40(7), 933-945. De Meyer, A., Nakane, J., Miller, J.G. and Ferdows, K. (1989). Flexibility the next competitive battle. Strategic Management Journal, 10(2), 135-144. De Toni, A. and Tonchia, S. (1998). Manufacturing flexibility: a literature review. Int. J. of Production Research 36(6), 1587-1617. D'Souza, D. E. and Williams, F. P. (2000). Towards a taxonomy of manufacturing flexibility dimensions. J. of Operations Management, 18(5) 577-593. Ernst, R. and Kamrad, B., 2000, "Valuation of supply chain structures through modularization and postponement", European Journal of Operations Research, 124, 495-510. Gerwin, D. (1993). Manufacturing Flexibility: A Strateg ic Perspective. Management Science 39(4) 395-410. Fitzgerald, G., Barad, M., Papazafeiropoulou, A. and Alaa, G. (2009). A framework for analyzing flexibility of generic objects. Int. J. of Production Economics, 122(1), 329-339. Hauser, J.R. and Clausing, D. (1988). The House of Quality, Harvard Business Review, 66, 1-27. Hill, C. H. (1995). Manufacturing Strategy. 2nd ed., Macmillan,London. Hopp, J. W. and Van Oyen, M.P. (2003). Agile Workforce Evaluation: A framework for cross training and coordination Lai, Y. J, E. Ho, A. and Chang, S. I. (1998). Identifying Customer Preferences in QFD using Group DecisionMaking Techniques. In: Integrated Product and Process Development, Wiley, New York. Mehrabi, M.G., Ullsoy, A.G., Kore, Y. and Heytler, P. (2002). Trends and perspectives in flexible and reconfigurable manufacturing systems. Journal of Intell igent Manufacturing, 13(2), 135-146. Narasimhan, R. and Jayaranan, J. (1998). Causal linkages in Supply Chain Management. Decision Science, 29(3), 579-605. Olhager, J. and West, B. M. (2002). The house of flexibility: using the QFD approach to deploy manufacturing flexibility. Int. J. of Operations and Production Management 22(1), 50-79.
importance of the strategic priorities and on their respective competitive incapability in their enterprises as well as on their perceived impact of given changes on these capabilities. Other input variables from the interviewees will concern their perception of the strength of the relationships between the HOWs and the WHATs in the three QFD matrices. The adaptive phase of the research will address the implementation perspective to be realized through a bottom-up path of prioritized flexibility improvement actions of a given enterprise. The prioritized flexibility improvement actions detected in phase I will be analyzed using additional decision making actions and for following and measuring the expected causal research. These concern improved levels of measured flexibility and improved levels of flexibility oriented performance measures of the enterprise. The latter will be expressed in terms of universal measures such as time, costs and eventually quality and human oriented performances.
4. CONCLUDING REMARKS This proposed empirical research framework deals with two fields of academic arena that have generated considerable amount of research interest ± supply chains and flexibility. It presents an integrated perspective on flexibility in supply chains and can be used in one or two research phases: perceptual and adaptive. While flexibility has been an active topic for the past twenty years, most of the works have concentrated on manufacturing, which is only one of the supply chain aspects. Studies on supply chain flexibility are still in a nascent stage. The proposed methodology utilizes Quality Function Deployment (QFD) a multi agent technique for supporting flexibility deployment in three stages. Changes are incorporated in the QFD model. The model emphasizes internal flexibility capabilities by facets: design, manufacturing and collaboration.
REFERENCES Akao, Y. (1990). Quality Function deployment: Integrating Customer Requirements into Product Design, Productivity Press, Cambridge, MA. Barad, M. (1998). Flexibility performance measurement systems-a framework for design. In: Neely A.D., Waggoner, D. B. (Eds.), Proceedings of the First International Conference on Performance Measurement, University of Cambridge, UK, 78-85. Barad, M and Gien, D. (2001). Linking improvement models to manufacturing strategies. Int. J. of Production Research, 39(12), 2675-2695. Barad, M. and Even-Sapir, D. (2003). Flexibility in logistic systems ± modeling and performance
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Otto, A. and Kotzab, H. (2003). Does supply chain management really pay? Six perspectives to measure the performance of managing a supply chain. European J. of Operational Research, 144, 306-32. Sethi, A.K. and Sethi, S.P. (1990). Flexibility Flexible manufacturing Systems, 2, 289- in Manufacturing: A survey. Int. J. of 328. Skinner, W. (1992). A strategy for competitive manufacturing. Management Review, 76(8), 54-56. Swamidass, P.M. and Newell, W.T. (1987). Manufacturing strategy, environmental uncertainty and performance: a path analytic model. Management Science, 33(4), 509-524. Zhang, Q., Vonderembse, M.A. and Lim,J.S. (2002).Value chain flexibility: a dichotomy of competence and capability. Int. J. of Production Research 40(3), 561-583.
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