Wednesday , December 19 2018

Cost-benefit Analysis of Decentralized Ordering on
Multi-tier Supply Chain by Risk Simulator

Masakatsu MORI1, Ryoji KOBAYASHI2, Masaki SAMEJIMA2, Norihisa KOMODA2
1 Yokohama Research Laboratory, Hitachi, Ltd.
292, Yoshida-cho, Totsuka-ku, Yokohama, Kanagawa, 244-0817, Japan masakatsu.mori.vr@hitachi.com
2 Graduate School of Information Science and Technology, Osaka University
1-5, Yamadaoka, Suita, Osaka, 565-0871, Japan
kobayashi.ryoji@ist.osaka-u.ac.jp, samejima@ist.osaka-u.ac.jp,
komoda@ist.osaka-u.ac.jp

Abstract: For the retailer on supply chain, decentralized ordering to multiple suppliers is an effective method to mitigate the risk that the retailer cannot sell the product to customers when the suppliers are down by catastrophic disasters. But decentralized ordering costs the retailer because the retailer procures products from the suppler whose procurement cost is expensive. For the retailers’ cost-benefit analysis of the decentralized ordering, we address developing the risk simulator on multi-tier supply chain to evaluate the effect of risk mitigation and the cost by decentralized ordering. In order to develop the risk simulator for the multi-tier supply chain, we combine the risk simulator for the 2-tier supply chain as a building block. In addition, when the 2-tier supply chain is combined, the risk simulator calculates propagation of the risk and the cost from a 2-tier supply chain to the others. Applying the risk simulator to the real supply chain with different parameter values, the authors confirmed that the risk simulator enables to find the relationship between the cost-benefit characteristic and the multi-tier supply chain model.

Keywords: cost-benefit analysis, conditional value at risk, decentralized ordering, multi-tier supply chain, risk simulator.

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CITE THIS PAPER AS:
Masakatsu MORI, Ryoji KOBAYASHI, Masaki SAMEJIMA, Norihisa KOMODA, Cost-benefit Analysis of Decentralized Ordering on Multi-tier Supply Chain by Risk Simulator, Studies in Informatics and Control, ISSN 1220-1766, vol. 23 (3), pp. 229-238, 2014. https://doi.org/10.24846/v23i3y201401

  1. Introduction

Supply chain is a sequence of operations such as procurement, production, logistics and sale for supplying products from suppliers to final consumers. Retailers in the supply chain procure products from suppliers and sell products to consumers. In supply chain, there is a problem that catastrophic disasters such as earthquakes may prevent the procurement from suppliers[1][2]. The retailers suffer losses as the procurement risks if they cannot sell the products along with the consumers’ demand. So, it is necessary for the retailers to consider not only the procurement cost but also the procurement risk in procurement planning. In this paper, the authors define the risk as the possibility that the retailer cannot sell the products to customers when the catastrophic events happen.

Representative ways to reduce the procurement risk are to increase the urgent stock of the products and decentralize procurement of the product from multiple suppliers [3][4]. The decision-maker of the retailer has to consider how much cost and risk are changed by the urgent stock and decentralizing orders. Conventional researches have addressed evaluating the cost and the risk by simulation on a supply chain model with catastrophic events. The simulation methods to be used are Petri Net [5][6][7] and Monte Carlo method [8][9][10][11].

Most of the conventional researches address evaluating the effect of mitigating risks by the urgent stock [8][9]. The echelon inventory in a multi-tier supply chain can be used to mitigate the risk when the some suppliers are down by catastrophic events. However, it has been pointed out that the large amount of stocks needs much management cost of the stock. And the supplier cannot store the stock for more than several days. Only the urgent stock is not enough as a risk mitigation way.

Because of the problems only in using the urgent stock, some conventional researches address evaluating the effect of mitigating risks by decentralizing orders [10][11][12]. The simulation of the cost and the risk uses Monte Carlo method to decide the amount of the stock stochastically. But these researches are for 2-tier supply chain model, i.e. the supply chain model consists of a retailer and its suppliers. Actually, there are many multi-tier supply chains, which must be addressed as the support of decision-making. In addition, the conventional researches have not considered that the retailer and the suppliers change the procurement plan when the catastrophic disaster happens.

For example, when some suppliers are down, the retailer places orders to the surviving suppliers and suppliers sell the products to the retailer for the suppliers’ own profit.

The purpose of this research is to support the cost-benefit analysis of decentralized ordering on the multi-tier supply chain with developing the risk simulator based on Monte Carlo method. In order to develop the simulator, the authors utilize the conventional researches on the 2-tier supply chain model, which is described in Chapter 2. The simulation process with propagating the cost and the risk on the multi-tier supply chain model is described in Chapter 3. The authors’ discussion on cost-benefit analysis of decentralized ordering on the real supply chain model is described in Chapter 4. Chapter 5 describes the conclusion of this paper.

REFERENCES

  1. WAGNER, S.M., C. BODE, An Empirical Investigation into Supply Chain Vulnerability, Journal of Purchasing and Supply Management, Elsevier, vol. 12, no. 6, 2006, pp. 301-312.
  2. KNEMEYER, A. M., W. ZINN, C. EROGLU, Proactive Planning for Catastrophic Events in Supply Chains, Journal of Operations Management, Elsevier, vol. 27, no. 2, 2009, pp. 141-153.
  3. CHOPRA, S., M. S. SODHI, Managing Risk to Avoid Supply-chain Breakdown, MIT Sloan Management Review, MIT, vol. 46, no.1, 2004, pp. 53-61.
  4. KLEINDORFER, P. R., G. H. SAAD, Managing Disruption Risks in Supply Chains, Production and Operations Management, Wiley-Blackwell, vol. 14, no. 1, 2005, pp. 53-68.
  5. ZEGORDI, S. H., H. DAVARZANI, Developing a Supply Chain Disruption Analysis Model: Application of Colored Petri-nets, Expert Systems with Applications, Elsevier, vol. 39, no. 2, 2012. pp. 2102-2111.
  6. LIU, R., A. KUMAR, W. V. D. AALST, A Formal Modeling Approach for Supply Chain Event Management, Decision Support Systems, Elsevier, vol. 43, 2007, pp. 761–778.
  7. BLACKHURST, J., T. WU, C. W. CRAIGHEAD, A Systematic Approach for Supply Chain Conflict Detection with a Hierarchical Petri-net Extension, Omega, Elsevier, vol. 36, no. 5, 2008, pp. 680-696.
  8. KLIBI, W., A. MARTEL, Scenario-based Supply Chain Network Risk Modeling, European Journal of Operational Research, Elsevier, vol. 223, no. 3, 2012, pp. 644-658.
  9. SCHMITT, A. J., M. SINGH, A Quantitative Analysis of Disruption Risk in a Multi-echelon Supply Chain, International Journal of Production Economics, Elsevier, vol. 139, no. 1, 2012, pp. 22-32.
  10. SAWIK, T., Selection of Supply Portfolio under Disruption Risks, Omega, Elsevier, vol. 39, no. 2, 2011, pp. 194-208.
  11. YU, H., A. Z. ZENG, L. ZHAO, Single or Dual Sourcing: Decision-making in the Presence of Supply Chain Disruption Risks, Omega, Elsevier, vol. 37, no. 4, 2009, pp. 788-800.
  12. MORI, M., R. KOBAYASHI, M. SAMEJIMA, N. KOMODA, An Evaluation Method of Reduced Procurement Risks by Decentralized Ordering in Supply Chain, in Proceedings of 11th IEEE International Conference on Industrial Informatics (INDIN 2013), IEEE, 2013, pp. 80-85.
  13. ISO 22301: Societal Security – Business Continuity Management Systems – Requirements, the International Organization for Standardization, 2012.
  14. ROCKAFELLAR, R. T., S. URYASEV, Conditional Value-at-risk for General Loss Distributions, Journal of Banking and Finance, Elsevier, vol. 26, no.7, 2002, pp. 1443-1471.
  15. AHMED, S., U. ÇAKMAK, A. SHAPIRO, Coherent Risk Measures in Inventory Problems, European Journal of Operational Research, Elsevier, vol. 182, no. 1, 2007, pp. 226-238.
  16. MITSUKUNI, K., F. KOMIYA, K. SUGIYAMA, Y. Tomita, H. MAKI, N. KOMODA, Coupling Point Production Control System for Quick Response to Orders and Minimum Inventories, In Proceedings of IEEE 6th International Conference on Emerging Technologies and Factory Automation, IEEE, 1997, pp.154-159.
  17. The Headquarters for Earthquake Research Promotion http://www.jishin.go.jp/main/yosokuchizu/index.html (in Japanese)