Engineering patterns of supply chain optimization to manage oscillation effect

  • Received November 29, 2016;
    Accepted January 12, 2017;
    Published June 8, 2017
  • Author(s)
    Thokozani Patmond Mbhele
  • DOI
    http://dx.doi.org/10.21511/ppm.15(2).2017.12
  • Article Info
    Volume 15 2017, Issue #2, pp. 124-139
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  • 664 Downloads

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The cascading order variability from downstream trumping up the upstream site of the supply chain network indicates the deleterious effect to the performance of the fast moving consumer goods industry. The fundamental likelihood to optimization in this industry requires dexterous flows of quasi-real-time information, as well as reliable product availability. In this context, this study analyzes the challenges of bullwhip effect on the perspective of ingenious optimization strategies, and further contemplates to establish the engineering patterns of interrelationships on the magnitude of pooling the resources to advance supply chain capabilities. The suppression of bullwhip effect on underlying optimization strategies is sought to elevate accelerated responsiveness, improve network demand visibility and reduce volatility in frequencies to inventory replenishment. A rigorous and disciplined quantitative approach afforded the tentatively development of pattern of interrelated supply chain dimensions. The factor analysis method was used on 448 responses and insightful findings were produced from the compelling purposive sampling technique. The findings indicate that the magnitude of better ameliorating bullwhip effect, the value of competitive economic information and strength of selected optimization strategies depend on the model of unified engineering patterns. This paper provides insights to FMCG industry on using innovative strategies and modern technology to enhance supply chain visibility through integrated systems networks.

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    • Figure 1. Global optimization strategies on bullwhip effect – part one
    • Figure 2. Global Optimization strategies on bullwhip effect – part two
    • Figure 3. Engineering circular patterns of supply chain optimization strategies and leagility system
    • Table 1. Descriptive and factor analysis on KMO and Bartlett’s test rotated components
    • Bartlett, J. E., II, Kotrlik, J. W., and Higgins, C. (2001). Organisational research: Determining appropriate sample size for survey research. Information Technology, Learning and Performance Journal, 19(1), 43-50.
    • Blumberg, B., Cooper, D. R., and Schindler, P. S. (2008). Business Research Methods. New York: McGraw-Hill International.
    • Borgatii, S. P., and Li, H. (2009). On social network analysis in a supply chain. Journal of Supply Chain Management, 45(2), 5-22.
    • Bowersox, D. J., Closs, D. J., Cooper, M. B., and Bowersox, J. C. (2013). Supply Chain Logistics Management. (4th Ed.). Boston: McGraw-Hill.
    • Browne, K. (2010). Trolley Psychology: Choice unlocks the psychological secrets of the supermarket and shows you how to avoid spending more than you mean to. Australian Consumer’s Association Choice Magazine, 4(60).
    • Burt, R. S. (1992). Structural Holes. Cambridge, M. A.: Harvard University Press.
    • Burt, R. S. (1997). The contingent value of social capital. Administration Science Quartley, 42(2), 339-365.
    • Cachon, G., and Terwiesch, C. (2009). Matching supply with Demand: An introduction to Operations Management. Boston: McGraw-Hill Irwin.
    • Cai, X., and Du, D. (2009). On the Effects of Risks Pooling in Supply Chain Management: Review and Extensions. Acta Methematic Applicatae, 25(4), 709-722.
    • Cao, M., and Zhang, Q. (2011). Supply chain collaboration: Impact on collaborative advantage and firm performance. Journal of Operations Management, 29, 163-180.
    • Cardi, M., Moretto, A., Perego, A., and Tumino, A. (2014). The benefits of supply chain visibility: A value assessment model. 151, 1-19.
    • Chang, T., Fu, H., Lee, W., Lin, Y., and Hsueh, H. (2007). A study of an augmented CPFR model for the 3C retail industry. Supply Chain Management: An International Journal, 12(3), 200-209.
    • Chen, I. J., Paulraj, A., and Lado, A. (2004). Strategic purchasing, supply management, and performance. Journal of Operations Management, 22(5), 505-523.
    • Chen, L. (2013). Dynamic supply chain coordination under consignment and vendor-managed inventory in retailer-centric B2B electronic markets. Industrial Marketing Management, 42, 518-531.
    • Chen, T. H., and Chang, H. M. (2010). Optimal ordering and pricing policies for deteriorating items in one-vendor multi-vendor supply chain. International Journal of Advanced Manufacturing Technology, 49(1-4), 341-355.
    • Cho, D. W., and Lee, Y. H. (2011). The value of information sharing in a supply chain with a seasonal demand process. Computers and Industrial Engineering. http://dx.doi.org/10.1016/j. cie.2011.12.004
    • Choi, T. M., and Sethi, S. (2010). Innovative quick response programs: A review. International Journal of Production Economics, 127(1), 1-12.
    • Chopra, S., and Meindl, P. (2007). Supply chain management: Strategy, planning and operations. (3rd Ed.). New Jersey: Pearson International edition.
    • Christopher, M., and Towill, D. R. (2005). An integrated model for the Design of Agile Supply Chains. International Journal of supply chain management, 6(5).
    • Christopher, M. (2011). Logistics and Supply chain management. (4th Ed.). Boston: Pearson Publicashing.
    • Claro, D. P. (2004). Managing Business Networks and the Buyer- Supplier Relationships: How Information Obtained from the Business Network Affects Trust, Transaction Specific Investments, Collaboration and Performance in the Dutch Potted Plant Industry.
    • Cooper, B. R., and Schindler, P. S. (2008). Business Research Methods. (10th Ed.). New York: McGraw- Hill International.
    • Costello, A. B., and Osborne, J. W. (2005). Best practices in exploratory factor analysis: Four recommendations for getting the most from your analysis. Practical Assessment, Research and Evaluation, 10(7), 1-9.
    • Dominguez, R., Cannella, S., and Framinan, J. M. (2014). On bullwhip-limiting strategies in divergent supply chain networks. Computers and Industrial Engineering, 73, 85-95.
    • Fitzsimmons, J. A., and Fitzsimmons, M. J. (2006). Service Management: Operations, Strategy, Information Technology. (5th Ed.), Boston: McGraw-Hill.
    • Flynn, B. B., Huo, B., and Zhao, X. (2010). The impact of Supply chain integration on performance: A contingency and configuration approach. Journal of Operations Management, 28(1), 58-71.
    • Fu, D., Ionescu, C. M., Aghezzaf, E., and Keyser, R. D. (2014). Decentralised and centralised model predictive control to reduce the bullwhip effect in supply chain management. Computers and Industrial Engineering, 73, 21-31.
    • Gajanayake, R., Gajanayake, S., and Surangi, H. (2011). The impact of selected visual merchandising techniques on patronage intentions in supermarkets, International Conference on Business and Economic Research, 1130-1165.
    • Galaskiewicz, J. (2011). Studying supply chains from a social network perspective. Journal of Supply Chain Management, 47(1), 4-8.
    • Ganesan, S., George, M., Jap, S., Palmatier, R. W., and Weitz, B. (2009). Supply Chain Management and Retailer Performance: Emerging Trends, Issues, and Implications for Research and Practice. Journal of Retailing, 85(1), 84-94.
    • Garson, G. D. (2012). Factor Analysis. North Carolina: Statistical Associates Publishing.
    • Gronovetter, M. (2005). The impact of social structure on economic outcomes. Journal of Economic Perspective, 19(1), 33-50.
    • Gulati, R. (1998). Alliances and networks. Strategic Management Journal, 19(4), 293-317.
    • Gunasekaran, A. and Ngai, E. W. T. (2009). Modeling and analysis of build-to-order supply chains. European Journal of Operational Research, 195(2), 319-334.
    • Hair, J. J., Anderson, R. E., Tatham, R. L. and Black, W. C. (1998). Multivariate data analysis. (5th Ed.) New Jersey: Prentice-Hall International.
    • Hair, Jr, J. F., Babin, B., Money, A. H., and Samouel, P. (2003). Essentials of Business Research Methods. New York: John Wiley & Sons, Inc.
    • Heizer, J., and Render, B. (2014). Principles of Operations Management: Sustainability and Supply Chain Management. (9th Ed.), New Jersey: Pearson Education.
    • Hopp, W. J., and Spearman, M. L. (2008). Factory Physics. (3rd Ed.). Boston: McGraw-Hill International Edition.
    • Kaiser, H. F. (1970). A second generation Little Jiffy. Psychometrika, 35, 401-415.
    • Kang, J. H., and Kim, Y. D. (2012). Inventory control in a two-level supply chain with risk pooling effect. International Journal of Production Economics, 135(1), 116-124.
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