Two Stage Supply Chain Optimization for Perishable Products Under Fuzzy Environment

Two Stage Supply Chain Optimization for Perishable Products Under Fuzzy Environment

Sandhya Makkar (Lal Bahadur Shastri Institute of Management, Delhi, India)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/IJRCM.2019070103

Abstract

In the changing market scenario, supply chain management is getting phenomenal importance amongst researchers. Studies on supply chain management have emphasized the vitality of a long-term strategic relationship between the supplier, distributor and retailer. In this article, the authors have studied a two-stage supply chain coordination problem under uncertain costs and demand information when integrated procurement and distribution decisions of supply chain has to be employed. The model incorporates a single supplier transporting its products to multiple destinations of a retailer. This process becomes tedious, as when items have an inventory carrying cost incurred due to perishable nature of products. Different discount policies are offered to procure and transport goods from the one stage to other stage. Fuzzy set theory is applied to estimate the uncertainty associated with the input parameters and triangular fuzzy numbers are used to analyze the model. A case is presented to validate the procedure.
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1. Introduction

Today’s global supply chains are complex systems of organizations, people, processes, technology, and information that move products or services from concept to finished goods and from suppliers to customers. To gain the competitive edge supply chain should be flexible and agile which in turn help organizations streamline operations, increase delivery reliability, and maximize profits. Procurement and distribution in supply chain are relatively more important decisions when the demand is imprecise, and products are perishable in nature. This necessitates the high inventory level that makes the situation worst. Therefore, it is required to find out the optimum levels of ordered quantity, so that total procurement and distribution cost can be minimized.

Modeling of two stage models taking into considerations the procurement and distribution decisions for perishable products under fuzzy environment has least been studied by researchers, though subjects have been studied separately and extensively. Thus, this work is motivated to bridge the gap in the literature by proposing a two stage supply chain coordination model through quantity and freight discount policy for perishable products under uncertain environment. The review of literature is discussed as follows. Firstly, two stage supply chain models are presented. Secondly, integration of quantity discounts and freight cost i.e. procurement and distribution models are discussed. Next is the review of perishable products in supply chain and lastly the historical review of fuzzy models in supply chain are talked about. Regarding the two stage supply chains, Kaminsky and David (2003) developed a two stage model of a manufacturing supply chain. This two stage production-transportation model featured capacitated production in two stages, and a fixed cost for transporting the product between the stages. They show that their model reduces to a related model, with one capacitated production stage with linear production cost, and transportation between two inventory locations with non-linear transportation cost. However, Hyun-Soo and Kaminsky (2005) considered a model of a two-stage push-pull production-distribution supply chain. In their model, orders arrive at the final stage according to a Poisson process. Two separate operations, which take place at different places with exponential service times, were located to convert the raw materials into finished goods. When the first operation is completed the intermediate inventory is held at the first stage and then transported to the second stage where the items are produced to order. Kalaiarasi and Ritha (2011) studied two stage supply chain with fuzzy parameters. The optimal policy developed by them is determined by using Lagrangian Conditions after defuzzification of the cost function with the graded mean integration method. Another model proposed by Sinha and Sarmah (2008) studied a two-stage supply chain coordination problem under uncertain cost and demand information. The aim of the paper is to design a coordination mechanism through quantity discount policy under asymmetric information environment that allows the system to perform as closely as that of under complete information. Fuzzy set theory is applied to estimate the uncertainty associated with the input parameters and triangular membership function has been used to analyze the model.

A wide range of literature is available on supply chain coordination based on quantity discounts and transportation policies. A detailed review on quantity discounts is presented by Goyal and Gupta (1989), Benton and Park (1996) and Sarmah et al. (2006). An integrated supply chain model is studied by Hwang et al. (1990) for determining optimal order quantity when all units’ quantity discounts are available on purchasing price and freight cost. Tersine and Barman (1991) assumed a constant demand rate and developed a model with freight and price discounts, where freight discount structure is based on weight. Ertogral (2008) took a single stage multi incapacitated dynamic lot sizing problem (MILSP) with transportation cost and assumed finite planning horizon with dynamic demand. He considered all unit inventory management models to formulate the problem with piece wise linear transportation cost function. Mendoza and Ventura (2008) developed an unconstrained integrated inventory-transportation model to decide optimal order quantity for inventory system over a finite horizon.

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