Group decision making process for supplier selection with VIKOR under fuzzy environment

During recent years, how to determine suitable suppliers in the supply chain has become a key strategic consideration. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The VIKOR method was developed to solve multiple criteria decision making (MCDM) problems with conflicting and non-commensurable (different units) criteria, assuming that compromising is acceptable for conflict resolution, the decision maker wants a solution that is the closest to the ideal, and the alternatives are evaluated according to all established criteria. In this paper, linguistic values are used to assess the ratings and weights for these factors. These linguistic ratings can be expressed in trapezoidal or triangular fuzzy numbers. Then, a hierarchy MCDM model based on fuzzy sets theory and VIKOR method is proposed to deal with the supplier selection problems in the supply chain system. A numerical example is proposed to illustrate an application of the proposed model.     

A fuzzy model for supplier selection in quantity discount environments

Traditionally, supplier selection should simultaneously take into account numerous heterogeneous criteria, and then is a tedious task for the purchasing decision makers. It becomes especially complicated when quantity discounts are considered at the same time. Under such manner, most studies often formulate such a problem as a Multi-Objective Linear Programming (MOLP) problem, and then scale it down to a Mixed Integer Programming (MIP) problem to handle the inherited multi-objectives simultaneously. However, this approach often neglects to consider scaling and subjective weighting issues. In order to ease the problem mentioned above and to obtain a more reasonable compromise solution for allocating order quantities among suppliers with their quantity discount rate offered, the Analytical Hierarchy Process (AHP) and fuzzy compromise programming are introduced in this study. An illustrated example is presented to demonstrate the proposed model and to illuminate two kinds of attitudes for decision makers. The information from the experiments can be utilized further to explain the suppliers’ possible improvement and to help create win–win policies.     

A novel fuzzy multi-criteria decision framework for sustainable supplier selection with incomplete information

Both academic and corporate interest in sustainable supply chains has increased in recent years. Supplier selection process is one of the key operational tasks for sustainable supply chain management. This paper examines the problem of identifying an effective model based on sustainability principles for supplier selection operations in supply chains. Due to its multi-criteria nature, the sustainable supplier evaluation process requires an appropriate multi-criteria analysis and solution approach. The approach should also consider that decision makers might face situations such as time pressure, lack of expertise in related issue, etc., during the evaluation process. The paper develops a novel approach based on fuzzy analytic network process within multi-person decision-making schema under incomplete preference relations. The method not only makes sufficient evaluations using the provided preference information, but also maintains the consistency level of the evaluations. Finally, the paper analyzes the sustainability of a number of suppliers in a real-life problem to demonstrate the validity of the proposed evaluation model.     

基于灰色关联度的供应商评价体系研究*

将层次分析法与灰色关联度相结合,提出了一种新的供应商评价方法。针对供应商评价指标间关联性比较强的特点,采用层次分析法确定评价指标权值,以灰色关联度作为评价标准,对复杂多目标的供应商对象进行综合评价,其结果具有良好的性质且方法简单、利于实现。     

Single sourcing versus dual sourcing under conditions of learning

This paper considers a buyer who has to decide whether to select a single or two sources of supply for a homogeneous product. The production processes of the suppliers are subject to learning effects, which reduce the production costs and increase the production capacities of the suppliers. This, in turn, enables the suppliers to reduce the sales price, which results in lower acquisition costs at the buyer. As the supplier selection decision influences the individual production quantity of a supplier, the learning effect has to be considered when deciding how many and which suppliers to select. Since the effect of learning on the supplier selection problem has not been investigated in the literature, this paper addresses this limitation and derives models for continuous learning and when learning plateaus. Numerical results indicate that the supplier selection decision can comprehensively influence the learning process for the suppliers and therewith the total costs of the system under study. The results also show that it is not necessarily optimal solely to select the supplier with the highest learning rate.     

An integrated model for closed-loop supply chain configuration and supplier selection: Multi-objective approach

Reverse logistics consists of all operations related to the reuse of products. External suppliers are one of the important members of reverse logistics and closed loop supply chain (CLSC) networks. However in CLSC network configuration models, suppliers are assessed based on purchasing cost and other factors such as on-time delivery are ignored. In this research, a general closed loop supply chain network is examined that includes manufacturer, disassembly, refurbishing, and disposal sites. Meanwhile, it is managed by the manufacturer. We propose an integrated model which has two phases. In the first phase, a framework for supplier selection criteria in RL is proposed. Besides, a fuzzy method is designed to evaluate suppliers based on qualitative criteria. The output of this stage is the weight of each supplier according to each part. In the second phase, we propose a multi objective mixed-integer linear programming model to determine which suppliers and refurbishing sites should be selected (strategic decisions), and find out the optimal number of parts and products in CLSC network (tactical decisions). The objective functions maximize profit and weights of suppliers, and one of them minimizes defect rates. To our knowledge, this model is the first effort to consider supplier selection, order allocation, and CLSC network configuration, simultaneously. The mathematical programming model is validated through numerical analysis.     

A novel hybrid MCDM approach based on fuzzy DEMATEL, fuzzy ANP and fuzzy TOPSIS to evaluate green suppliers

It is well known that “green” principles and strategies have become vital for companies as the public awareness increased against their environmental impacts. A company’s environmental performance is not only related to the company’s inner environmental efforts, but also it is affected by the suppliers’ environmental performance and image. For industries, environmentally responsible manufacturing, return flows, and related processes require green supply chain (GSC) and accompanying suppliers with environmental/green competencies. During recent years, how to determine suitable and green suppliers in the supply chain has become a key strategic consideration. Therefore this paper examines GSC management (GSCM) and GSCM capability dimensions to propose an evaluation framework for green suppliers. However, the nature of supplier selection is a complex multi-criteria problem including both quantitative and qualitative factors which may be in conflict and may also be uncertain. The identified components are integrated into a novel hybrid fuzzy multiple criteria decision making (MCDM) model combines the fuzzy Decision Making Trial and Evaluation Laboratory Model (DEMATEL), the Analytical Network Process (ANP), and Technique for Order Performance by Similarity to Ideal Solution (TOPSIS) in a fuzzy context. A case study is proposed for green supplier evaluation in a specific company, namely Ford Otosan.     

Promotion Routes for Women Engineering Managers

ABSTRACT

Total quality management (TQM) has been increasingly accepted as a management philosophy. Criteria such as those contained in the Malcolm Baldrige National Quality Award (MBNQA) often guide TQM implementation. However, reports indicate that fully assessing TQM efforts is difficult and hiring experts to help with this task is expensive. Using survey instruments is an attractive alternative to full-scale assessment. Despite the availability of many papers covering organizational performance assessment, few instruments specifically address TQM evaluation. Furthermore, none of the TQM assessment instruments has been subjected to rigorous validation; thus, only modest confidence can be placed in results obtained from their use. This article proposes a quick and cost-effective instrument that is a simple questionnaire designed to assist organizations in conducting self-evaluations of their TQM programs. The proposed instrument is based on the seven categories of the MBNQA criteria and was developed in cooperation with the Excellence in Missouri Foundation, which administers the Missouri Quality Award (MQA). This article highlights how questionnaire results can be used to guide further TQM implementation efforts.     

Optimization of cost and service level in the presence of supply chain disruption risks: Single vs. multiple sourcing

The coordinated supplier selection and customer order scheduling in the presence of supply chain disruption risks is studied for single and multiple sourcing strategies. Given a set of customer orders for products, the decision maker needs to select a single supplier or a subset of suppliers for purchasing parts required to complete the customer orders, and schedule the orders over the planning horizon, to mitigate the impact of disruption risks. The suppliers are located in different geographic regions and the supplies are subject to different types of disruptions: to random local disruptions of each supplier individually, to random regional disruptions of all suppliers in the same region simultaneously and to random global disruptions of all suppliers simultaneously. For any combination of suppliers hit by different types of disruptions, a formula for calculating the corresponding disruption probability is developed. The obtained combinatorial stochastic optimization problem is formulated as a mixed integer program with conditional value-at-risk as a risk measure. The problem objective is either to minimize expected worst-case cost or to maximize expected worst-case customer service level, i.e., the expected worst-case fraction of customer orders filled on or before their due dates. The risk-averse solutions that optimize worst-case performance of a supply chain under disruptions risks are compared for the two sourcing strategies and the two objective functions. Numerical examples and computational results are presented and some managerial insights on the choice between the two sourcing strategies are reported.     

基于SPA／FCE／GCF的维修器材供应商选择模型

针对供应商选择模型的现状,按照“决策定量化、多方法融合、多人决策”的原则,构建了基于SPA／FCE／GCF的维修器材供应商选择模型,并通过实例验证了模型的可行性,对维修器材供应商选择具有一定的指导意义。