IMPROVING SUPPLY CHAIN PERFORMANCE: AN INSIGHT ON THE RELATIONSHIP BETWEEN COLLABORATION, MODELING AND STRUCTURE

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1 IMPROVING SUPPLY CHAIN PERFORMANCE: AN INSIGHT ON THE RELATIONSHIP BETWEEN COLLABORATION, MODELING AND STRUCTURE by Marini Nurbanum Mohamad and Wong Wai Peng School of Management, University Science Malaysia Pulau Pinang, Malaysia ABSTRACT The supply chain has recently been identified as an avenue for companies to create competitive advantage. Supply chain management literature covers various aspects of the supply chain, including the debates on the definition, models for designing a supply chain, implementation strategies and improvement approaches. Companies now proactively seek ways to improve their supply chain as they can no longer depend on the conventional strategy of competition based on cost. Two main approaches in improving supply chain management are discussed in this paper. The first approach is collaboration in the supply chain and the second approach is enhancing supply chain solutions with the application of the Chaos Theory. Companies can use these approaches to develop core competencies in their supply chain through continuous improvement in order to sustain their competitive position in their respective industry. This paper provides some insights on the issues pertaining to the two approaches in improving supply chain. One common stumbling block in their implementation is the structure of the supply chain. An integrative framework is proposed to examine the relations between these approaches with the structural heredity and their combination effects on supply chain This framework can be used as a complementary tool for practitioners to align the strategies with modeling applications and structures in order to improve chain KEYWORDS Supply Chain Management, Chaos Theory, Collaboration, Structure INTRODUCTION The rapid growth of technology, globalization and instability of market conditions are several factors that is currently changing the business environment. To stay ahead, businesses must find their way to stay competitive and adapt to the new business environment. Supply chain includes all activities and processes to supply a product or service to the end customer. All portions of the material production, from raw materials to the end customer, are considered to be a linked chain. In supply chain management, one must understand the network of suppliers and customers along the chain, planning material and information flows efficiently along the chain to maximize cost efficiency, effectiveness, delivery, and flexibility (Arnold, Chapman and Clive, 2008). Supply chain management is the integration of key business processes among the network of interdependent suppliers, manufacturers, distribution centers and retailers in order to improve the flow of goods, service and information from original suppliers to final consumers with the objective of reducing system wide costs while maintaining required service levels (Stapleton, Hanna and Ross, 2006). Supply chain management plays an important role in today s business practice. With the existing supply chain system, we should analyze the possible ways to improve and enhance the system. The existing system may no longer be used in adapting to new business practices. Advanced technology for example, should be used to improve the supply chain management. Since any changes in the supply chain affects other participants in the chain, it is crucial to take a clear look upon the decision that will be made. When the negative effect occurs in one of the chain participants, it would then affect the whole chain members. By enhancing the existing supply chain, it could provide organizations added value in terms of enhancing customer service and reducing cost. This paper gives a brief review on what are some of the ways to improve the supply chain and possible future research that can be carried out in this field. The crux of this paper is we proposed a new framework to model supply chain improvement. This framework will be tested in our future research using adaptive case studies. It is hoped that this

2 framework will be able to help managers to appropriately maneuver their strategies to achieve improvements in the supply chain. The rest of this paper will proceed as follows. Next section will be a brief literature review on how the supply chain can be improved; the benefits and issues of each method is discussed. Section 3 will introduce a framework that model supply chain improvement. Lastly, the paper provides future research directions in this area. LITERATURE REVIEW In this section, we will discuss the ways that companies can improve the supply chain and elaborate the issues concerning each of the approaches. As the world evolves, the business environment is also changing. The traditional applications of supply chain management may no longer be effective to ensure that companies nowadays stay competitive. Past research showed that companies with more mature supply chain practices are reducing costs faster than their less mature peers. By this advantage, industry leaders are able to increase market share and drive out their competition. To compete in the new world environment, companies like Dell, Wal-Mart, and Zara worked on supply chain innovation to stay ahead in their own industries and thus transforming the industries in which they compete (Hoole, 2005). Collaboration Supply chain sounds simple; however it is actually a complex process which needs a lot of understanding and cooperation from all the participants in the supply chain. Collaboration is one way in improving the supply chain (Cassivi, 2006; Boddy et al., 2000). Collaboration between participants in the supply chain undoubtedly shapes and influences the type and volume of information being shared. The greater the level of collaboration among the supply chain members, the greater will be the volume of the information passed through the chain; hence, this could lead the path to more accurate information which will then help to reduce the bullwhip effect in the chain and enhance overall chain performance (Boddy et al., 2000). Several frameworks and roadmaps which raised the important points about collaboration in a supply chain had been highlighted by Lummus et al. (1998), Corbett et al. (1999) and Boddy et al. (2000). Particularly, collaborative planning, forecasting and replenishment (CPFR), which introduces a sequential approach that defines key actions to be undertaken during collaboration initiation is increasingly popular in use. CPFR has its origins in a series of programs implemented in the 1980s and 1990s to optimize inventory and replenishment activities (VICS, 1998). It includes programs such as Vendor Managed Inventory (VMI) and Capacity Requirement Planning (CRP), which are designed to bring supply chain partners closer together through operational mechanisms that facilitate information exchange in the multi-tiered supply chain. In brief, the CPFR method is segmented into three stages i.e., stage one planning which includes front-end agreement and joint business plan; stage two forecasting which includes collaboration in sales and order forecast; stage three replenishment or order generation. Details of the CPFR mechanism as well as enhancement of the method can be referred from Aviv (2001) and Esper and Williams (2003). The advantages of collaboration e.g., through CPFR are indeed significant. It has been proven that collaborating partners experienced sales increases, inventory reductions and improved customer service (Holmstrom et al., 2002). Collaboration also strengthened relationships among partners by improving and fostering trust in their exchanges (Rubiano Ovalle and Crespo Marques, 2003). Nevertheless, the implementation of collaboration in the supply chain is not easy. A number of issues need to be understood in order to maximize the success of such collaboration (Baratt, 2004). For instance, many organizations fail to take into account internal plans and activities that will undoubtedly impact the outcome of the collaboration. Companies tend to focus on the initial stage more heavily than the subsequent and last stage that involves the execution of collaboration actions. Hence, the expected positive results of collaboration effort is hampered and not fully realized. Cassivi (2006) further advocated that the execution the collaboration activities (e.g., delivery and tracking, replenishment and procurement) need to be given equal focus as the planning activities; as a critical element in maintaining an efficient supply chain is to obtain visibility through planning and execution actions with upstream and downstream partners effectively. On the other hand, innovations in collaborations through e-commerce though have helped to manage supply chain activities more efficiently by improving visibility of information in the supply chain, the lack of a structured

3 approach in managing the supply chain had impeded the entire effort of collaboration (Simchi-Levi, 2005). For instance, if the supply chain members viewed the communication channels as too complex, they may hesitate to share latest information e.g., on sales and order data, to avoid confusion and miscommunication. Mentzer et al. (2001) highlighted that the structure of the supply chain influences the nature of supply chain activities, the efficiency and effectiveness of the supply chain, and relationships with other members within the entire supply chain. This identifies that the overall state of the collaboration is associated with the supply chain structure. Randall et al. (2003) further iterated that many collaboration efforts among the supply chain members failed due to the misfit of supply chain structures, hence strategy and supply chain structures need to be aligned appropriately to achieve overall Enhancing solutions through application of Chaos theory One important component of effective supply chain is accuracy in planning and forecasting. An accurate assessment of customer demand impacts inventory levels, supplier behavior and transportation (Bowersox et al., 2000). Accuracy in forecasting customer demands is challenging and so complex as it describes customers behaviors that approach pure randomness (Hannon, 2003). In order to manage the stochasticity of customer demand, Chaos theory could help to best manage a supply chain. Chaos theory is developed from mathematics and the physical and natural sciences. Recently, it has been widely used by business practitioners as a new way of approaching business problems (Bonabeau and Meyer, 2001). Chaos theory attempts to explain apparent disorder in a very ordered way. The root of the theory states that things are not really random, just complex; and these apparent random events can be represented by simple computation that when iterated many times, produces complex results (Wheatley, 1999). In application to a system, Chaos theory can explain the behavior of the system by a set of nonlinear equations where the output of one calculation is taken as the input of the next (Burns, 2002). The complexity of the supply chain systems can be explained by the laws of chaos (Doherty and Delener, 2001). For example, when forecasting customer demand, each customer interprets key variables of importance to them and makes a buying decision. The complexity designs with the difficulty associated with understanding and predicting the decision making process of each customer. Expounding from the bullwhip effects, the forecasting and demand planning becomes exponentially more complex when the buying behaviors of many customers, each with a unique decision set, are aggregated together. This aggregation is then formed into a formula which is constantly repeated (as of analyzing the data), and the results is a model that can enhance the prediction of the behavior. The grounded idea behind Chaos theory is sensitivity to initial conditions, where the output of the previous stage is the inputs to the next. As such, the values that result from each stage of the formula or equations of the model are repeatedly fed back into the next stage until a complete and robust model is formulated. Small deviations in the results of each formula could only mildly diverge the results from models (Doherty and Delener, 2001); the final results obtained are therefore reliable and accurate. Hence, effective application of Chaos theory, e.g., to enhance the understanding of variables like forecasting that impact supply chain operations, can lead to enhanced supply chain performance levels. The core difficulty in the application of Chaos theory in supply chain stems from the structure of the supply chain itself. It has been a challenge for any organization to best structure the supply chain for long term while at the same time focusing on day-to-day activities (Mentzer et al., 2001). Internal basic structure of supply chain balancing act, as well as the external environment e.g., market demands, customer service, transportation and logistics considerations and pricing constraints all must be understood in order to structure and adjust the supply chain effectively. The constantly changing behavior of each variable and their interactions further complicates the application of the Chaos theory for supply chain solutions (Cheung and Turnbull, 1998). PROPOSED FRAMEWORK In this section, we will propose a framework to examine supply chain improvement in an interesting way. From the past works of scholars and researchers in this field, we found that an integrated framework that encompassed supply chain strategy, structure and modeling application has not yet existed. Distinguished scholars examined the subject matter mostly from a unitary domain perspective; modeling application has been largely segregated and examined using another disciplinary platform. The proposed framework is interesting as it will provide a transdisciplinary approach in gauging the subject matter. This simple framework is aimed to complement existing work, not substituting them, and hope to provide some contributive insights to the supply chain research.

4 In this framework, we focus on collaboration planning strategy, which encompassed mainly four types of activities: front-end agreement and joint business plan for both upstream and downstream perspectives. These items were based on the study of Cassivi (2001). We then model, how the level or depth of this strategy would be affected by supply chain structure and the application of modeling techniques (i.e. Chaos theory) leads to improved We classify supply chain structure into three basic types following Fisher (1997), Huang et al. (2002) and Naylor et al. (1999), that are lean, agile and hybrid. Figure 1 depicts our framework: FIGURE 1 PROPOSED ANALYTICAL FRAMEWORK Degree of Collaboration a) Front-end agreement with supplier. b) Joint business plan with supplier. c) Front-end agreement with customer d) Joint business plan with customer. Structure - Lean - Agile - Hybrid Supply Chain performance Application of Chaos theory A lean supply chain structure (SCS) is organized to maximize operational efficiency and minimize overall cost. Lean arrangements are typically used for higher volume product lines that have stable demand and standardized technologies. An agile SCS is organized to achieve flexibility and speed in responding to dynamic market conditions and customer needs. Agile arrangements are used for the lower volume product lines subject to more uncertain demand and innovative technologies. A hybrid SCS combines features of the two. The measurement variables and corresponding scales for SCS in this study will be developed based on the previous studies of SCS and design (Cherrington et al., 2001; Fisher, 1997; Mintzberg, 1979; Randall et al., 2003). Based on past observations in Section 2, collaboration effectiveness is affected by supply chain structure, which in turn affects the firm Therefore hypotheses 1 and 2 are proposed: H 1 : Degree of collaboration significantly affects supply chain H 2 : The relationship between collaboration and supply chain performance is mediated by supply chain structure. We advocate that the application of Chaos theory will enhance the collaboration effort in improving supply chain, thus: H 3 : The relationship between collaboration and supply chain performance is mediated by application of Chaos theory. H 4 : The relationship between application of Chaos theory and supply chain performance is mediated by the supply chain structure. The hypotheses above can be further break down into itemized level to test each construct in more detail. In summary, the hypotheses can be detailed as follows: H 1a : Higher extent in front-end agreement with supplier improves supply chain H 1b : Joint business plan with supplier improves supply chain H 1c : Higher extent in front-end agreement with customer improves supply chain H 1d : Joint business plan with customer improves supply chain H 2a : Supply chain structure mediates the extent of front-end agreement with supplier in improving supply chain H 2b : Supply chain structure mediates the extent of joint business plan with supplier in improving supply chain

5 H 2c : Supply chain structure mediates the extent of front-end agreement with customer in improving supply chain H 2d : Supply chain structure mediates the extent of joint business plan with customer in improving supply chain H 3a : Application of Chaos theory mediates the extent of front-end agreement with supplier towards supply chain H 3b : Application of Chaos theory mediates the extent of joint business plan with supplier towards supply chain H 3c : Application of Chaos theory mediates the extent of front-end agreement with customer towards supply chain H 3d : Application of Chaos theory mediates the extent of joint business plan with customer towards supply chain In the next step of our research, we will test our framework in the electronic industry. As supply chain collaboration has been widely adopted in the electronic environment, it will not be difficult to gather data required for the analysis from the industry. A qualitative-quantitative sequential approach (Creswell, 1994; Tashakkori and Teddlie, 1998), namely a multiple-case study and an electronic survey, will be carried out to gather empirical evidence from a supply chain in the electronic industry. This model aims to produce some insights to managing collaboration in the supply chain. This model will be able to tell us how collaboration activities should be maneuvered and aligned with structure and modeling application, in order to achieve an improved supply chain. FUTURE RESEARCH AND CONCLUSION Collaboration and application of Chaos theory have provided an avenue or springboard to further improve the supply chain. Complexity of the structure of supply chain has impeded the implementation of the two above approaches. Future research could look into how to possibly align the strategy, structure and the incorporation of quantitative modeling approach to improve the supply chain. This paper developed an integrative framework to examine the relations between these approaches with the structural heredity and their combination effects on supply chain This framework will be tested in our future research. We hope that this framework can be used as a complementary tool for practitioners to align the strategies with modeling applications and structures in order to improve chain REFERENCES Aviv, Y. (2001), The effect of collaborative forecasting on supply chain performance, Management Science, Vol.47, No.10, pp Arnold, J.R.T., Chapman, S.N. and Clive, L.M. (2008), Introduction to materials management, Introduction to Materials Management, 6 th Edition, pp Barratt, M. (2004), Understanding the meaning of collaboration in the supply chain, Supply Chain Management: An International Journal, Vol. 9, No. 1, pp Bonabeau, E. and Meyer, C. (2001), Swarm intelligence: a whole new way to think about business, Harvard Business Review, Vol.79, No.5, pp Boddy, D., MacBeth, D. and Anger, B. (2000), Implementing collaboration between organizations: an empirical study of supply chain partnering, Journal of Management Studies, Vol.37, No.7, pp Bowersox, D.J., Closs, D.J., and Stank, T.P. (2000), Ten mega-trends that will revolutionize supply chain logistics, Journal of Business Logistics, Vol.21, No.2, pp Burns, J.S. (2002), Chaos theory and leadership studies: exploring uncharted seas, Journal of Leadership and Organizational Studies, Vol.9, No.2, pp Cassivi, L. (2006), Collaboration planning in a supply chain, Supply Chain Management: An International Journal, Vol.11, No.3, pp

6 Cobertt, C.J., Blackburn, J.D. and Van Wassenhove, L.N. (1999), Partnerships to improve supply chains, Sloan Management Review, Vol.40, No.4, pp Cheung, M.Y.S. and Turnbull, P. (1998), A review of the nature and development of inter-organisational relationships: a network perspective, in Naude, P. and Turnbull, P. (Eds), Network Dynamics in International Marketing, Elsevier Science, Amsterdam, pp Cherrington, D.J., Bischoff, S.J., Dyer, W.G., Stephan, E.G. and Stewart, G.L. (2001), Organizational Effectiveness, Brigham Young University Press, Provo, UT. Creswell, J.W. (1994), Qualitative and Quantitative Approaches, Sage Publications, Thousand Oaks, CA. Doherty, N. and Delener, N. (2001), Chaos theory: marketing and management implications, Journal of Marketing Theory and Practice, Vol.9, No.4, pp Esper, T.L. and Williams, L.R. (2003), The value of collaborative transportation management: its relationship to CPFR and information technology, Transportation Journal, Vol.42, No.4, pp Fisher, M.L. (1997), What is the right supply chain for your product?, Harvard Business Review, Vol.75 No.2, pp Hannon, D. (2003), Demand forecasting rises on logisticians radar screens, Purchasing, Vol.32, No. 12, pp.55. Holmstrom, J., Framling, K., Kaipia, R. and Saranen, J. (2002), Collaborative planning, forecasting and replenishment: new solutions needed for mass collaboration, An International Journal of Supply Chain Management, Vol.7, No.3, pp Hoole, R. (2005), Five ways to simplify your supply chain, Supply Chain Management: An International Journal, Vol. 10, No.1, pp. 3-6 Huang, S.H., Uppal, M. and Shi, J. (2002), A product driven approach to manufacturing supply chain selection, Supply Chain Management, Vol.7, No.4, pp, Lummus, R.R., Vokurka, R.J., and Alber, K.L. (1998), Strategic supply chain planning, Production and Inventory Management Journal, Vol.39 No.3, pp Mentzer, J.T., DeWitt, W., Keebler, J., Min, S., Nix, N., Smith, C. and Zacharia, Z. (2001), Defining supply chain management, Journal of Business Logistics, Vol.22, No.2, pp Mintzberg, H. (1979), The Structuring of Organizations, Prentice-Hall, Englewood Cliffs, NJ. Naylor, J.B., Naim, M.M. and Berry, D. (1999), Leagility: integrating the lean and agile manufacturing paradigms in the total supply chain, International Journal of Production Economics, Vol.62, No.1, pp Randall, T., Morgan, R.M. and Morton, A.R. (2003), Efficient versus responsive supply chain choice: an empirical examination of influential factors, Journal of Product Innovation Management, Vol.20, pp Rubiano Ovalle, R. and Crespo Marquez, A. (2003), The effectiveness of using e-collaboration tools in the supply chain: an assessment study with system dynamics, Journal of Purchasing and Supply Management, Vol.9, No.4, pp Simchi-Levi, D., Kaminsky, P. and Simchi-Levi, E. (2005), Designing and Managing the Supply Chain, 3 rd ed., Irwin McGraw-Hill, New York, NY. Stapleton, D., Hanna, J.B. and Ross, J.R. (2006), Enhancing supply chain solutions with the application of Chaos theory, Supply Chain Management: An International Journal, Vol. 11, No. 2, pp Tashakkori, A. and Teddlie, C. (1998), Mixed Methodology: Combining Qualitative and Quantitative Approaches, Sage, Thousand Oaks, CA.

7 Voluntary Interindustry Commerce Standards Association (VICS) (1998), Collaborative Planning, Forecasting and Replenishment Voluntary Guidelines, Uniform Code Council, Lawrenceville, NJ. Wheatley, M.J. (1999), Leadership and the New Science, 2 nd ed., Berrett-Koehler, San Francisco, CA.

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