Proactive inventory policy intervention to mitigate risk within cooperative supply chains

Abstract: This exploratory paper will investigate the concept of supply chain risk management involving supplier monitoring within a cooperative supply chain. Inventory levels and stockouts are the key metrics. Key to this concept is the assumptions that (1) out-of-control supplier situations are causal triggers for downstream supply chain disruptions, (2) these triggers can potentially be predicted using statistical process monitoring tools, and (3) carrying excess inventory only when needed is preferable as opposed to carrying excess inventory on a continual basis. Simulation experimentation will be used to explore several supplier monitoring strategies based on statistical runs tests, specifically "runs up and down" and/or "runs above and below" tests. The sensitivity of these tests in detecting non-random supplier behavior will be explored and their performance will be investigated relative to stock-outs and inventory levels. Finally, the effects of production capacity and yield rate will be examined. Results indicate out-of-control supplier signals can be detected beforehand and stock-outs can be significantly reduced by dynamically adjusting inventory levels. The largest benefit occurs when both runs tests are used together and when the supplier has sufficient production capacity to respond to downstream demand (i.e., safety stock) increases. When supplier capacity is limited, the highest benefit is achieved when yield rates are high and, thus, yield loss does not increase supplier production requirements beyond its available capacity.

How to cite this paper

 Kurano, T., McKay, K & Black, G. (2014). Proactive inventory policy intervention to mitigate risk within cooperative supply chains.International Journal of Industrial Engineering Computations , 5(2), 249-264.

Refrences

Aelker, J., Bauernhansl, T., & Ehm, H. (2013). Managing complexity in supply chains: a discussion of current approaches on the example of the semiconductor industry. Procedia CIRP, 7, 79-84.

Albani, A., & Dietz, J. L. (2009). Current trends in modeling inter-organizational cooperation. Journal of Enterprise Information Management, 22 (3), 275-297.

Angerhofer, B. J., & Angelides, M. C. (2000). System dynamics modelling in supply chain management: Research review. Proceedings of the 2000 Winter Simulation Conference, 342–351.

Beamon, B. M. (1998). Supply Chain Design and Analysis: Models and Methods. International Journal of Production Economics, 55 (3), 281-294.

Blackhurst, J., Craighead, C. W., Elkins, D., & Handfield, R. B. (2005). An empirically derived agenda of critical research issues for managing supply-chain disruptions. International Journal of Production Research, 43(19), 4067–4081.

Blackhurst, J. V., Scheibe, K. P., & Johnson, D.J. (2008). Supplier risk assessment and monitoring for the automotive industry. International Journal of Physical Distribution & Logistics Management, 38 (2), 143-165.

Caballini, C., & Revetria, R. (2008). A system dynamics Model for The Simulation Of a Non Multi Echelon Supply Chain: Analysis of Optimization Utilizing The Berkeley Madonna Software. International Journal of Mathemarical Models and Methods in Applied Sciences, 4 (2), 503-512.

Cigolini, R., Pero, M., Rossi, T., & Sianesi, A. (2014). Linking supply chain configuration to supply chain performance: a discrete event simulation model. Simulation Modelling Practice and Theory, 40, 1-11.

Croom, S., Romano, P., & Giannakis, M. (2000). Supply chain management: an analytical framework for critical literature review. European Journal of Purchasing & Supply Management, 6, 67-83.

Disney, S. M., & Towill, D. R. (2003). On the bullwhip and inventory variance produced by an ordering policy. Omega, 31 (3). 157-167.

Dom?nguez, R., & Framinan, J.M. (2013). A decision management tool: modelling the order fulfilment process by multi-agent systems. International Journal of Management and Decision Making, 12(3), 240-258.

Fransoo, J. C., & Wouters, M. J. F. (2000). Measuring the bullwhip effect in the supply chain. Supply Chain Management, 5 (2), 78-89.

Giunipero, L. C., Hooker, R. E., Joseph-Matthews, S., Brudvig, S., & Yoon, T. (2008), A decade of SCM literature: past, present, and future implications. Journal of Supply Chain Management, 44 (4), 66-86.

Goswami, S., Engel, T., & Krcmar, H. (2013). A comparative analysis of information visibility in two supply chain management information systems. Journal of Enterprise Information Management, 26 (3), 276-294.

Hallikas, J., Karvonen, I., Pulkkinen, U., Virolainen, V. M., & Tuominen, M. (2004). Risk management processes in supplier networks. International Journal of Production Economics, 90 (1), 47-58.

Hendricks, K., & Singhal, V. (2003). The effect of supply chain glitches on shareholder wealth. Journal of Operations Management, 21, 501–522.

Hennies, T., Reggelin, T., Tolujew, J., & Piccut, P.A. (2013). Mesoscopic supply chain simulation. Journal of Computational Science, forthcoming.

Hill, J. F. (1996). Monitoring information and materials to enhance logistics performance. Logistics Information Management, 9 (2), 10-15.

Juttner, U., Peck, H., & Christopher, M. (2003). Supply Chain Risk Management: Outlining an Agenda for Future Research. International Journal of Logistics: Research and Applications, 6 (4), 197-210.

Knight, R., & Pretty, D. (1996). The impact of catastrophes on shareholder value. The Oxford Executive Research Briefings, Oxford: University of Oxford

Lee, H. T., & Wu, J. C. (2006). A study on inventory replenishment policies in a two-echelon supply chain system. Computer & Industrial Engineering, 51, 257-263.

Lian, Q., & Jia, S.L. (2013). Research and simulation of supply chain disruption based on contract. Applied Mechanics and Materials, 380-384, 4815-4822.

March, J., & Shapira, Z. (1987). Managerial perspectives on risk and risk taking. Management Science, 33 (11), 1404-1418.

McKay, K. N. (1992). Production Planning and Scheduling: A Model for Manufacturing Decisions Requiring Judgment. PhD Thesis, University of Waterloo, Waterloo, Canada.

Minegishi, S., & Theil, D. (2000). System dynamics modelling and simulation of a particular food supply chain. Simulation Practice and Theory, 8 (5), 321-339.

Mobini, M., Sowlati, T., & Sokhansanj, S. (2013). A simulation model for the design and analysis of wood pellet supply chains. Applied Energy, 111, 1239-1249.

Moraga, R., Rabelo, L., & Sarmiento, A. (2008). Towards a methodology for monitoring and analyzing the supply chain behavior, in: Laha, D., Mandal, P. (Eds.), Handbook of Computational Intelligence in Manufacturing and Production Management. IGI Global, Hershey, pp. 186-208.

Perea, E., Grossmann, I. E., Ydstie, B. E., & Tahmassebi, T. (2000). Dynamic modeling and classical control theory for supply chain management. Computers and Chemical Engineering, 24, 1143– 1149.

Pezeshki, Y., Baboli, A., Cheikhrouhou, N., Modarres, M., & Akbari Jokar, M.R. (2013). A rewarding-punishing coordination mechanism based on trust in a divergent supply chain.
European Journal of Operational Research, 230 (3), 527-538.

Pfohl, H.C., Cullmann, O., & Stolzle, W. (1999). Inventory management with statistical process control: Simulation and evaluation. Journal of Business Logistics, 20 (1). 101-121.

Ramanathan, U. (2014). Performance of supply chain collaboration - a simulation study. Expert Systems with Applications, 41(1), 210-220.

Reggelin, T., & Tolujew, J. (2011). A Mesoscopic Approach to Modeling and Simulation of Logistics Processes, in: Proceedings of the 2011Winter Simulation Conference, Phoenix, Arizona, 1513-1523.

Rice, J., & Caniato, F. (2003). Building a Secure and Resilient Supply Chain. Supply Chain Management Review, 7 (5), 22–30.

Riddalls, C.E., Bennett, S., & Tipi, N.S. (2000). Modelling the dynamics of supply chains. International Journal of Systems Science, 31, 969–976.

Sambasivan, M., Nandan, T., & Mohamed, Z. (2009). Consolidation of performance measures in a supply chain environment. Journal of Enterprise Information Management, 22 (6), 660-689.

Smew, W., Young, P., & Geraghty, J. (2013). Supply chain analysis using simulation, gaussian process modelling and optimisation. International Journal of Simulation Modelling, 12 (3), 178-189.

Sterman, J. D. (2000). Business dynamics – system thinking and modelling for a complex world, New York: McGraw Hill

Tang, C. S. (2006). Perspectives in supply chain risk management. International Journal of Production Economics, 103, 451-488.

Towill, D. (1993). System Dynamics – background, methodology and applications. Computing and Control Engineering Journal, 4 (6), 261-268.

Wagner, S. M., & Bode, C. (2009). Dominant Risks and Risk Management Practices in Supply Chains, in: Zsidisin, G.A., Ritchie, B. (Eds.), Supply Chain Risk: A Handbook of Assessment, Management, and Performance. Springer, New York, pp. 271-290.

Watts, C. A., Hahn, C. K., & Sohn, B. K. H. (1994). Monitoring the performance of a reorder point system: A control chart approach. International Journal of Operations and Production Management, 14 (2), 51-62.