427807 Multi-Criteria Decision Making Supplier Selection and Auction Based Procurement in Supply Chain Management

Monday, November 9, 2015: 4:31 PM
Salon G (Salt Lake Marriott Downtown at City Creek)
Nihar Sahay, Department of Chemical and Biochemical Engineering, Rutgers, The State University of New Jersey, Piscataway, NJ and Marianthi Ierapetritou, Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ

Multi-Criteria Decision Making Supplier Selection and Auction based Procurement in Supply Chain Management

Nihar Sahay and Marianthi Ierapetritou

Department of Chemical and Biochemical Engineering, Rutgers University, Piscataway, NJ

In a multi-enterprise supply chain network, the objective of each enterprise is to maximize its own objectives. Model development for such supply chain networks needs to incorporate the interactions among the different enterprises in order to assess their impact on supply chain operations. In this work, we study a multi-enterprise supply chain network where manufacturing sites and warehouses belong to a particular enterprise while raw material suppliers and retailers belong to different enterprises. In particular, the procurement of raw materials from suppliers and the distribution of products from warehouses to the retailers are studied in this work.

Over the recent years, advances in information technology have reformed the practice of supply chain operations. High-speed communication and better connectivity provide the opportunity for enterprises to participate in Internet based marketplaces. E-marketplaces offer benefits like providing a medium to the companies for developing their pricing policies, dynamically deciding the optimal mix of products, and allocating the correct capacities. Compared to the Business-to-Consumer (B2C) front, the adoption of e-marketplaces on the Business-to-Business (B2B) front has been slow and limited as B2B transactions are more complex, usually carried out based on long-term contracts, and involve multiple attributes apart from price like transportation, reliability etc.1 However, they have gained momentum in the recent past and the advances in information technology stress the incorporation of auction-based procurement in supply chain operations. Companies can participate in auctions to distribute their products to a large number of participating retailers and take into account various factors like the number of retailers, their locations, price being offered and demand from different retailers. Peleg et al.2 study three different procurement strategies: strategic partnership with a single supplier, online search for multiple suppliers and a combination of the two strategies. They assess the effect of the procurement process on the costs and showed that the performance of the procurement process depends on the cost and demand parameters. Jin and Yu3consider a two supplier, one buyer system and study the supply chain performance in a procurement setting with competition between the suppliers. They compare different auction mechanisms based on the payoffs for the buyer and the suppliers.

Supplier selection and order allocation is another problem that has been extensively studied in the supply chain literature. These are the most significant issues in the purchasing division of enterprises. It is important to have an efficient order allocation procedure that can be integrated into the ERP system of an organization.4 Supplier selection usually involves evaluating multiple criteria like price, lead time, reliability etc. Different techniques, broadly classified into multi-criteria decision making (MCDM) techniques, mathematical programming techniques and artificial intelligence techniques, have been proposed to solve the multi-criteria supplier selection problem.5 Due to the relative practicality of the approach, multi-criteria decision making methods are a popular class of solution techniques that are widely used to solve such problems in industrial settings.6-8

In this work, a multi-enterprise supply chain network is modeled where the production sites and warehouses belong to one enterprise while the raw material suppliers and retailers belong to different enterprises. The distribution of products from warehouses to retailers is based on auctions. A multi-attribute double auction is modeled as both the seller and buyer are considered to bid the in the process based on multiple attributes like price and amount of product. The supplier selection is modeled using the Analytic Hierarchy Process (AHP). The ranking procedure assigns a utility value to each supplier. The utility values are used as a basis for ranking or choice of suppliers. The AHP method requires pairwise comparisons of the different criteria and the alternatives. Such comparisons are usually imprecise. In order to model the imprecision in the pairwise comparisons required for the AHP method, fuzzy numbers are used to represent the pairwise comparisons. The relative ranking of the suppliers obtained from the solution of the fuzzy AHP is used to allocate orders to the raw material suppliers. An agent-based simulation model is developed that captures the supply chain operations including the auction based distribution of products and the allocation of orders among raw materials based on fuzzy AHP. The approach is used for small scale case studies in order to demonstrate the effects of procurement and supplier selection policies.



1.            Simchi-Levi D, Wu SD, Shen Z-J. Handbook of Quantitative Supply Chain Analysis: Modeling in the E-Business Era (International Series in Operations Research \\& Management Science): Springer-Verlag New York, Inc.; 2004.

2.            Peleg B, Lee HL, Hausman WH. Short-term e-procurement strategies versus long-term contracts. Production and Operations Management. Win 2002;11(4):458-479.

3.            Jin MZ, Yu AJ. Procurement auctions and supply chain performance. International Journal of Production Economics. Apr 2015;162:192-200.

4.            Lin CT, Chen CB, Ting YC. An ERP model for supplier selection in electronics industry. Expert Systems with Applications. Mar 2011;38(3):1760-1765.

5.            Chai JY, Liu JNK, Ngai EWT. Application of decision-making techniques in supplier selection: A systematic review of literature. Expert Systems with Applications. Aug 2013;40(10):3872-3885.

6.            Levary RR. Using the analytic hierarchy process to rank foreign suppliers based on supply risks. Computers & Industrial Engineering. 2008;55(2):535-542.

7.            Sevkli M. An application of the fuzzy ELECTRE method for supplier selection. International Journal of Production Research. 2010/06/15 2009;48(12):3393-3405.

8.            Farzipoor Saen R. Developing a new data envelopment analysis methodology for supplier selection in the presence of both undesirable outputs and imprecise data. The International Journal of Advanced Manufacturing Technology. 2010/12/01 2010;51(9-12):1243-1250.


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