Supplier Selection Decisions Research Articles

Green supplier selection problem using TOPSIS extended by D numbers in tractor manufacturing industry

This research aims to evaluate the suppliers by a combined decision-making method with a new concept under uncertainty with D-number structure. Due to the importance of suppliers as a level of supply chain, determining factors for supplier selection decision is of key importance. Criteria in this study all are taken from the most recent articles so far. To consider the issue of environmental indicators related to this case can be considered sharp. In the existing evaluation methods, experts can propose their opinion under uncertainty only in one level. But in new proposed decision-making method, experts can proposed their idea about each alternative versus criteria in multi-level simultaneously. Therefore, in this method a group D-number structure with TOPSIS as a compromised decision making method to evaluate supplier selection has been applied. Finally to clearing the proposed method, the tractor company of Tabriz is studied as a case study.

Green supplier selection problem using TOPSIS extended by D numbers in tractor manufacturing industry

This research aims to evaluate the suppliers by a combined decision-making method with a new concept under uncertainty with D-number structure. Due to the importance of suppliers as a level of supply chain, determining factors for supplier selection decision is of key importance. Criteria in this study all are taken from the most recent articles so far. To consider the issue of environmental indicators related to this case can be considered sharp. In the existing evaluation methods, experts can propose their opinion under uncertainty only in one level. But in new proposed decision-making method, experts can proposed their idea about each alternative versus criteria in multi-level simultaneously. Therefore, in this method a group D-number structure with TOPSIS as a compromised decision making method to evaluate supplier selection has been applied. Finally to clearing the proposed method, the tractor company of Tabriz is studied as a case study.

INTEGRATING SUSTAINABILITY INTO SUPPLIER SELECTION: A GREY-BASED TOPSIS ANALYSIS

Sustainable supplier selection plays an important role in sustainable supply chain management operations and implementation. In this paper a novel formal modeling approach is conceptually developed and presented to address sustainable supplier selection. Grey theory and TOPSIS, a distance based multiple criteria method, are used for the integration and evaluation of sustainable supplier performance for sustainable supplier selection. From a research perspective, TOPSIS is improved to more effectively deal with grey numbers by integrating a degree of likelihood rather than converting grey numbers into crisp numbers functions, and it provides more flexible supplier rankings. This methodology strengthens the sustainable supplier selection process, and can be applied to other multiple criteria decision making problems. Illustrative calculations are made using data on sustainable supplier selection and evaluation published by Bai and Sarkis (2010). The technique is relatively accurate, matching well with results from a published grey rough set approach. The methodology easily implementable with minimal complex calculations required. It can also provide support for sustainable supplier selection, benchmarking, and improvement decisions. This is one of the first papers to integrate a broad set of sustainability factors for grey-based TOPSIS and supplier selection.

Supplier Selection with Fuzzy TOPSIS and VIKOR Method in a Trailer Manufacturing Company

The supplier decision has become a strategic decision for companies to achieve competitive advantage due to growth and increased competition, technology and reduced profit margins. The right choice of the supplier is one of the most important elements that will ensure the production and distribution of the products with the desired quality, flexibility, lower cost and high speed. There may be complexity and uncertainty in supplier selection decision as it includes many factors and multiple decision makers. For this purpose, the right supplier to be cooperated has been decided by using fuzzy TOPSIS and VIKOR methods in Microsoft Visual Basic by evaluating existing suppliers in a trailer factory operating in Turkey.

An inquiry into the supplier selection decision from the business-to-consumer (B2C) perspective

PurposeThe purpose of this paper is to provide insights into what selection criteria consumers find critical when making the supplier selection decision. Much research exists on the business-to-business (B2B) perspective, so this research seeks to investigate the business-to-consumer (B2C) viewpoint.Design/methodology/approachA survey is used to identify the areas that consumers find to be important when seeking suppliers. Factor analysis is used to identify selection criteria that are important to consumers.FindingsThe results suggest that consumers value several factors quite highly, including delivery, price and quality. Service, flexibility and relationship are also rated relatively high, while consumer concern with technology and the environment seemed to be lower.Research limitations/implicationsThis study is limited to a group of consumers represented by university students. Additional research methods may provide additional detail to supplement these results.Practical implicationsThe consistency the authors found between firm and consumer preferences should make it a relatively simple process for firms to consider the desires of customers when dealing with their own B2B relationships.Originality/valueThe B2C perspective on the supplier selection decision has seen limited research coverage. The B2C collaboration perspective considers the contributions consumers can make in supply chain decisions, which may lead to new supply chain management strategies.

Multi-Criteria Supplier Selection Decisions in Supply Chain Networks : A Multi-Objective Optimization Approach

Multi-Criteria Supplier Selection Decisions in Supply Chain Networks : A Multi-Objective Optimization Approach

A resilient-sustainable based supplier selection model using a hybrid intelligent method

Abstract Supplier selection literature with contributing sustainability issues observes exponential growth in the number of publications. However, a few studies have focused on the more extensive effects of sustainability paradigm in the supplier selection that arise with disruptions. Hence, a tool is required to help purchasing managers to consider resilience and sustainability approaches in the supplier selection decision under disruption situation. This paper fulfills this necessity with proposing a resilient-sustainable framework based on the supplier selection indicators. Moreover, a fuzzy set theory is applied to cope with the uncertainty in the supplier selection decision. Furthermore, a modular Fuzzy Inference System is designed to calculate the affinity indices of suppliers to resiliency and sustainability issues. The modular FIS system supports to have a comprehensive supplier selection model with any number of indicators and suppliers. Then, the results of the proposed modular FIS are passed to an Assurance Region DEA method (AR-DEA) to determine the weights of indicators to rank the suppliers. The applicability of the proposed integrated intelligent model investigated by a real case. To show the effectiveness of the proposed model, sensitivity analysis has been adopted.

The role of political risk in service offshoring entry mode decisions

This research investigates the effect of political risk on the offshore service industry. The study empirically examines how an extended political risk definition, operationalised into a model consisting of 12 political risk variables, helps predict location decisions across offshoring entry modes and activity types. The research focuses on captive offshoring and offshore outsourcing entry modes, and Information Technology Outsourcing (ITO), Business Process Outsourcing (BPO) and Knowledge Process Outsourcing (KPO) activity types. The research indicated that political risk factors accounted for 38% of the variability in offshore outsourcing flows, implying that concerns about service disruptions and/or cost implications of external uncertainties feature as a key factor in supplier selection and location decisions. The findings further confirm a positive relationship between institutional and regulatory factors in host locations, and the flow of offshoring activities with a high knowledge content. The research contributes to enhancing the explanatory ability of Transaction Cost Economics by re-operationalising the concept of political risk in the context of both offshore outsourcing and captive offshoring. For practitioners, these findings provide a clear indication of the political risks that can affect service offshoring decisions; for policymakers, they highlight the importance of strengthening institutional and regulatory factors to attract investment.

An empirical approach to the evaluation of defence contractor performance

ABSTRACTSupplier assessment and selection is critical in the world of international logistics and supply chain management. Despite this criticality, related processes and methodologies are largely subjective and anecdotal. A more quantitative empirical approach is needed. Improving the way government agencies and commercial businesses select suppliers can clearly result in cost savings, better schedule adherence, and higher-quality products. A key aspect of supplier selection for large government projects is effective evaluation and use of suppliers’ past performance data. However, organisations continue to search for better ways to adequately assess and utilise that data. This study uncovers new ways to more effectively use the United States government’s primary supplier performance evaluation system, called the Contractor Performance Assessment Reporting System. Based on the results of the study, a strong correlation has been shown to exist between contractors’ past and subsequent scores in terms of cost, schedule, and technical performance. The findings further indicate that the global commercial logistics community can use similar quantitative measures of contractors’ past performance to improve the effectiveness of their own supplier selection decisions.

A novel multi-objective optimization model for integrated problem of green closed loop supply chain network design and quantity discount

Abstract With the increasing importance of the efficient purchasing function, supplier selection decisions have become more strategic in supply chain management. In the past few years, the closed-loop green supply chain network has faced increasing attention with regard to the environmental regulations, social awareness and customer pressure. In order to cope with these two issues, this paper proposes an integrated mathematical programming model for multi-period, multi-product and capacitated closed loop green supply chain in which suppliers offer quantity discounts in order to motivate buyers to purchase more. The model objective functions are the minimization of economic cost and environmental emissions and maximization of customer satisfaction with determining best suppliers, purchasing amount, location-allocation facilities, transportation mode, technology type, carbon dioxide emissions, inventory levels and flows between facilities. Sensitivity analysis is conducted to validate the model for some test problems. Computational results show the effectiveness and applicability of the proposed model. Also, results have revealed that supply chain total costs are significantly reduced by considering quantity discount.

Integrated pricing and supplier selection in a two-stage supply chain

This article presents an inventory replenishment model with supplier selection and pricing decisions in a two-stage supply chain consisting of a manufacturer stage followed by a retailer stage. A decision maker controls both stages. The manufacturer can procure the required raw material from a list of potential suppliers, each of whom has restrictions regarding capacity and quality. In this model, the manufacturer periodically replenishes the retailer's inventory, whose demand is sensitive to price changes. We propose a mixed integer nonlinear programming model designed to determine the number of orders placed to the selected suppliers per order cycle, the amount of inventory replenished at each stage, and the selling price that maximizes the profit per time unit. Additionally, we provide upper and lower bounds for the retailer's selling price and for the manufacturer's lot size multiplicative factor, which results in a tight feasible search space.

Supplier diversification and ordering policy under yield randomness in inventory models

In this paper we consider supplier diversification in inventory models with multiple suppliers and random yields. We attempt to determine the optimal order quantities and expected long-run average cost from multiple suppliers with different yields and prices. We provide the mathematical formulation for cost minimization problem and show that the total annual cost function is convex. Numerical example is performed to provide some insights for supplier selection and lot sizing decisions. A sensitivity analysis of the optimal solution to these parameters is carried out. Managerial implications are also included.

Low carbon supply chain: a state-of-the-art literature review

PurposeThe purpose of this paper is to review the relevant literature on low carbon supply chain management (LCSCM) and classify it on contextual base. It also aims at identifying key decision-making issues in LCSCM. This paper also highlights some of the future challenges and scope of research in this domain.Design/methodology/approachA content analysis is carried out by systematically collecting the literature from major academic sources over a period of 18 years (2000-2017), identifying structural dimensions and classifying it on contextual base.FindingsThere is an increasing trend of research on LCSCM, but this research is still in a nascent stage. All supply chain functions such as supplier selection, inventory planning, network design and logistic decisions have been redefined by integrating emissions-related issues.Research limitations/implicationsLimitation of this study is inherent in its unit of analysis. Only peer-reviewed journal articles published in English language have been considered in this study.Practical implicationsFindings of prior studies on low carbon inventory control, transportation planning, facility allocation, location selection and supply chain coordination have been highlighted in this study. This will help supply chain practitioners in decision making.Originality/valueThough there are an increasing number of studies about carbon emission-related issues in supply chain management, the present literature lacks to provide a review of the overarching publications. This paper addresses this gap by providing a comprehensive review of literature on emissions-related issues in supply chain management.

Self-induced learning vs. project-based supplier development for production ramp-up with two supply options

Due to the important role suppliers play in the generation of customer value today, supplier development has attracted the attention of many companies in recent years. One challenge companies wishing to engage in supplier development face is to select both the right suppliers for supplier development as well as adequate supplier development measures and to align supplier development initiatives with the company's remaining sourcing activities. This problem becomes even more pressing during production ramp-up, where an increasing customer demand forces companies to expand the capacities of their supplier base as well. Albeit research on supplier development has enjoyed an increasing popularity in recent years, only a few mathematical models exist to date that support companies in making optimal supplier development decisions. The paper at hand contributes to closing this research gap by developing a decision support model that assists companies in selecting suppliers, in scheduling supplier development projects and in assigning order quantities to selected suppliers. In developing the proposed model, the paper accounts for the fact that suppliers often improve their performance also in the absence of supplier development, and thus considers both self-induced performance improvements at the suppliers as well as performance improvements induced by supplier development projects. The results of a numerical experiment imply that the supplier selection, order allocation and supplier development decisions are subject to complex interactions. The model proposed in this paper supports companies in fitting their supplier development activities to the requirements of the sourcing scenario they face with the intention to maximize profit.

The influence of business and political ties on supplier selection decisions: the case of the Nigerian public sector

Extant relevant literature suggests that nonmarket elements such as social ties are important strategic options that may help organisations enhance their competitiveness relative to other competito...

Models for supplier selection and risk mitigation: a holistic approach

According to a study conducted by PwC and the Business Continuity Institute in 2013, 75% of companies experience at least one major supply chain disruption a year and majority of the disruptions were caused by supply-related problems. With an increasing emphasis on upstream risk, risk management in supplier selection has become a critical issue faced by companies. Although previous studies proposed different methods and tools for effective and efficient supplier selection, only few approaches have attempted to incorporate risk mitigation strategies in supplier selection decisions. Our study aims to fill this gap by considering a wide range of quantitative and qualitative risk factors in supplier selection and evaluates the efficacy of alternative risk mitigation strategies in this context. Moreover, we suggest that both upstream and downstream strategies should be utilised simultaneously rather than relying on a single type of strategy. We further suggest that it is critical to align upstream and downstream risk mitigation strategies to reduce risk. We employ multi-objective optimization-based simulation in developing a decision model and consider data from an automotive parts manufacturer to demonstrate the application of our approach.

Supplier selection for a sustainable supply chain

PurposeThe purpose of this paper is to integrate the “triple bottom line (3BL)” approach in the supplier selection decision. It also aims to consider the feedback effect of the decision on strategic factors that determine the future viability of the firm in the market.Design/methodology/approachA multi-criteria decision model is developed that considers simultaneously the impact of three dimensions of 3BL approach and their sub-dimensions on the supplier selection decision. The proposed model is evaluated for a large white goods manufacturer using the analytic network process (ANP) approach.FindingsThe ANP considers the impact of variables, sub-variables, and their interdependencies simultaneously. The outcome of the model is the relative priorities for the firms considered as potential suppliers.Practical implicationsThis research was conducted in one of the largest developing economies. The impact of integrating sustainability would be widespread due to the huge market in which the company operates. The results of this research can provide support to the decision makers in arriving at an optimal decision considering all sustainability dimensions.Originality/valueThe novelty of the approach lies in the application of multi-criteria model integrating sustainability dimensions with a feedback effect for supplier selection. The case company would benefit by showing its commitment toward environment and social responsibility leading to improved brand image and sustainable business.

A Simulation for Managing Complexity in Sales and Operations Planning Decisions

ABSTRACTWithin the classroom it is often difficult to convey the complexities and intricacies that go into making sales and operations planning decisions. This article describes an in‐class simulation that allows students to gain hands‐on experience with the complexities in making forecasting, inventory, and supplier selection decisions as part of the sales and operations planning process. The activity may be run during one class period and is flexible enough to accommodate almost any class size. During the simulation, students may apply forecasting techniques, inventory management concepts, and supplier selection processes, while experiencing the effects of supply chain disruptions. This simulation is recommended to be used after forecasting, inventory management, and supplier selection topics have been discussed. An overview of the exercise and evidence of its effectiveness is provided.

AN INTEGRATED FUZZY MCGDM APPROACH FOR SUPPLIER SELECTION PROBLEM

Materials constitute a large proportion of the total project cost and the absence of right materials in the right quantities and quality on site when needed is one of the most commonly experienced causes of delays in construction projects. Although supplier selection is a strategic issue, contractors generally select suppliers based on their past experi­ences, which may result in selecting wrong suppliers. Supplier selection decision is generally made by multiple decision makers and is affected by several criteria. Therefore, selecting the right supplier among many alternatives considering several compromising and conflicting criteria is a multi-criteria group decision-making (MCGDM) problem. This paper proposes an integrated fuzzy MCGDM approach, which employs fuzzy analytic hierarchy process (AHP) and fuzzy Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) together, for the supplier selection problem. In the proposed approach, fuzzy AHP is used to analyse the structure of the supplier selection problem and to determine the weights of the criteria, and the fuzzy TOPSIS method is employed to rank the alternative suppliers. The proposed approach is applied to a problem of selecting the most appropriate rail supplier and company management found the proposed decision approach satisfactory and implementable in future supplier selection problems.

Application of multi-objective optimization based on genetic algorithm for sustainable strategic supplier selection under fuzzy environment

Purpose: The incorporation of environmental objective into the conventional supplier selection practices is crucial for corporations seeking to promote green supply chain management (GSCM). Challenges and risks associated with green supplier selection have been broadly recognized by procurement and supplier management professionals. This paper aims to solve a Tetra “S” (SSSS) problem based on a fuzzy multi-objective optimization with genetic algorithm in a holistic supply chain environment. In this empirical study, a mathematical model with fuzzy coefficients is considered for sustainable strategic supplier selection (SSSS) problem and a corresponding model is developed to tackle this problem.Design/methodology/approach: Sustainable strategic supplier selection (SSSS) decisions are typically multi-objectives in nature and it is an important part of green production and supply chain management for many firms. The proposed uncertain model is transferred into deterministic model by applying the expected value mesurement (EVM) and genetic algorithm with weighted sum approach for solving the multi-objective problem. This research focus on a multi-objective optimization model for minimizing lean cost, maximizing sustainable service and greener product quality level. Finally, a mathematical case of textile sector is presented to exemplify the effectiveness of the proposed model with a sensitivity analysis.Findings: This study makes a certain contribution by introducing the Tetra ‘S’ concept in both the theoretical and practical research related to multi-objective optimization as well as in the study of sustainable strategic supplier selection (SSSS) under uncertain environment. Our results suggest that decision makers tend to select strategic supplier first then enhance the sustainability.Research limitations/implications: Although the fuzzy expected value model (EVM) with fuzzy coefficients constructed in present research should be helpful for solving real world problems. A detailed comparative analysis by using other algorithms is necessary for solving similar problems of agriculture, pharmaceutical, chemicals and services sectors in future.Practical implications: It can help the decision makers for ordering to different supplier for managing supply chain performance in efficient and effective manner. From the procurement and engineering perspectives, minimizing cost, sustaining the quality level and meeting production time line is the main consideration for selecting the supplier. Empirically, this can facilitate engineers to reduce production costs and at the same time improve the product quality.Originality/value: In this paper, we developed a novel multi-objective programming model based on genetic algorithm to select sustainable strategic supplier (SSSS) under fuzzy environment. The algorithm was tested and applied to solve a real case of textile sector in Pakistan. The experimental results and comparative sensitivity analysis illustrate the effectiveness of our proposed model.