Selecting suppliers in the global supply chain is the very difficult and complicated decision making problem particularly due to the various types of supply risk in addition to the uncertain performance of the potential suppliers. This paper proposes a multi-phase decision making model for supplier selection under supply risks in global supply chains. In the first phase, the model suggests supplier selection solutions suitable to a given condition of decision making using a rule-based expert system. The expert system consists of a knowledge base of supplier selection solutions and an “if-then” rule-based inference engine. The knowledge base contains information about options and their consistency for seven characteristics of 20 supplier selection solutions chosen from articles published in SCIE journals since 2010. In the second phase, the model computes the potential suppliers’ general performance indices using a technique for order preference by similarity to ideal solution (TOPSIS) based on their scores obtained by applying the suggested solutions. In the third phase, the model computes their risk indices using a TOPSIS based on their historical and predicted scores obtained by applying a risk evaluation algorithm. The evaluation algorithm deals with seven types of supply risk that significantly affect supplier’s performance and eventually influence buyer’s production plan. In the fourth phase, the model selects Pareto optimal suppliers based on their general performance and risk indices. An example demonstrates the implementation of the proposed model. The proposed model provides supply chain managers with a practical tool to effectively select best suppliers while considering supply risks as well as the general performance.

With the increased globalization, selecting suppliers has become more challenging problem, especially because of increased risks among suppliers. This paper proposes a four-step supplier selection procedure in global supply chains under risk. In the first step, appropriate supplier selection solutions are determined by using a rule-based expert system. In the second one, these solutions are used to evaluate suppliers’ general performance. In the third one, suppliers’ risks are evaluated by using a risk evaluation algorithm. In the fourth one, Pareto optimal suppliers are selected based on their general performance and risk evaluation outputs. An example demonstrates the implementation of the proposed procedure.

Due to their accessibility and 24-hr availability, convenience stores are an integral part of daily lives. Because they sell a limited number of products and have small shelf space, shelf space allocation and inventory replenishment are important considerations for inventory management that critically affect profit. In this paper, we propose five solutions for the vendor-managed inventory problem of convenience stores that maximize profit while considering stock-out-based product substitutions. The performance of the proposed solutions is evaluated via simulation to reflect the demand uncertainty and marketing activity.

본 연구에서는 편의점에서 고객이 처음 원하는 제품의 재고가 없을 때 다른 제품으로의 대체구매가 존재하는 상황에서 수익을 극대화하는 제품들의 진열공간 크기와 보충시점 결정을 위한 5가지 재고관리 해법을 제안한다. 그리고 고객수요의 불확실성과 마케팅 요소를 반영한 시뮬레이션 모형을 구축하여 제안된 해법들의 성능을 평가한다.

The operation of vending machine system presents a decision-making problem which consists of determining the product allocation to vending-machine storage compartments, replenishment intervals of vending machines, and vehicle routes, all of which have cri

S plastic injection molding factory located at Namdong Industrial Complex in Incheon produces plastic parts for semiconductor, vacuum cleaners, office furniture, etc. It produces the parts to customers’ order and delivers them directly to customers at due

The bullwhip effect is known as the significant factor which causes unnecessary inventory, lost sales or cost increase in supply chains. Therefore, the causes of the bullwhip effect must be examined and removed. In this paper, we develop two analytical to