A new DEA-GAHP method for supplier selection problem

Supplier selection is one of the most important decisions made in supply chain management. Supplier evaluation problem has been in the center of supply chain researcher’s attention in these years. Managers regard some of these studies and methods inappropriate due to simple, weight scoring methods that generally are based on subjective opinions and judgments of decision maker units involved in the supplier evaluation process yielding imprecise and even unreliable results. This paper seeks to propose a methodology to integrate data envelopment analysis (DEA) and group analytical hierarchy process (GAHP) for evaluating and selecting the most efficient supplier. We develop a methodology, which consists of 6 steps, one by one has been introduced in lecture and finally applicability of proposed method is indicated by assessing 12 suppliers in a numerical example

SUPPLIER QUALITY ASSURANCE – STEP TO COMPETITIVE ADVANTAGE

This paper aims to provide the main directions for the standardization of Suppliers Quality Assurance (SQA) processes. The standardization of such processes is to provide a “same face” to SQA areas, allowing their interaction, exchange of information, adoption of best practices and achievement of better and comparable results, besides the recognition, by global Suppliers point of view, of only one company

A novel model to measure supplier performance in the supplier selection process

Supplier evaluation has become a significant topic over the past decades, as companies have started to become more outsourced oriented. However, previous research on this topic has not paid adequate attention to the limitations associated with availability of accurate and reliable data relating to the performance of potential suppliers. In an attempt to address this issue, this paper proposes a novel supplier evaluation model that can handle imprecise quantitative and qualitative data. Additionally, Decision Maker’s opinions regarding both qualitative and quantitative criteria are incorporated into this model so that a more comprehensive and realistic assessment of supplier performance can be achieved. The model combines five separate methods that have specific capabilities to handle multiple limitations in the existing methods: Fuzzy Analytical Hierarchy Process and Fuzzy TOPSIS method are used to analyse qualitative criteria/data; Analytical Hierarchy Process and Axiomatic Design are used to analyse quantitative criteria/data, with a particular focus on handling variability in performance data; and Data Envelopment Analysis is used to integrate the results of the two approaches above so as to comparative assessment of supplier performance. This model is verified using a numerical example

A Multi objective model for supplier evaluation and selection in the presence of both cardinal and imprecise data

Imprecise data envelopment analysis (IDEA) has been applied for supplier selection in the presence of both cardinal and imprecise data. In addition to its popularity, IDEA has some drawbacks such as unrealistic inputs-outputs weights and poor discrimination power among all DMUs. To alleviate these deficiencies, this paper develops a multi objective imprecise data envelopment analysis (MOIDEA) based on the common weights. The proposed MOIDEA model is utilized for supplier evaluation and selection in the case where there exist both cardinal and imprecise data. To show both robustness and discriminating power of the proposed approach, it is applied on a numerical example taken from the literature. The results reveal several merits of the common weight MOIDEA model for supplier selection

Supplier Selection by the Pair of Nondiscretionary Factors-Imprecise Data Envelopment Analysis Models

Discretionary models for evaluating the efficiency of suppliers assume that all criteria are discretionary, that is, controlled by the management of each supplier and varied at its discretion. These models do not assume supplier selection in the conditions that some factors are nondiscretionary. The objective of this paper is to propose a new pair of nondiscretionary factors-imprecise data envelopment analysis (NF-IDEA) models for selecting the best suppliers in the presence of nondiscretionary factors and imprecise data. A numerical example demonstrates the application of the proposed method.Full Tex

Strategic sourcing:a combined QFD and AHP approach in manufacturing

Purpose – This paper aims to develop an integrated analytical approach, combining quality function deployment (QFD) and analytic hierarchy process (AHP) approach, to enhance the effectiveness of sourcing decisions. Design/methodology/approach – In the approach, QFD is used to translate the company stakeholder requirements into multiple evaluating factors for supplier selection, which are used to benchmark the suppliers. AHP is used to determine the importance of evaluating factors and preference of each supplier with respect to each selection criterion. Findings – The effectiveness of the proposed approach is demonstrated by applying it to a UK-based automobile manufacturing company. With QFD, the evaluating factors are related to the strategic intent of the company through the involvement of concerned stakeholders. This ensures successful strategic sourcing. The application of AHP ensures consistent supplier performance measurement using benchmarking approach. Research limitations/implications – The proposed integrated approach can be principally adopted in other decision-making scenarios for effective management of the supply chain. Practical implications – The proposed integrated approach can be used as a group-based decision support system for supplier selection, in which all relevant stakeholders are involved to identify various quantitative and qualitative evaluating criteria, and their importance. Originality/value – Various approaches that can deal with multiple and conflicting criteria have been adopted for the supplier selection. However, they fail to consider the impact of business objectives and the requirements of company stakeholders in the identification of evaluating criteria for strategic supplier selection. The proposed integrated approach outranks the conventional approaches to supplier selection and supplier performance measurement because the sourcing strategy and supplier selection are derived from the corporate/business strategy

Exploring potential R&D collaboration partners through patent analysis based on bibliographic coupling and latent semantic analysis

The aim of the present research is to provide a new systematic methodology to explore potential R&D collaboration partners using patent information. The potential R&D collaboration partners are visualized as a patent assignee level-map based on technological similarity between patents by using network analysis. The proposed framework utilises two analytic methods to measure technological similarity. The first method, bibliographic coupling analysis, measures technological similarity based on the citation relationship using patent bibliographic information. Second, latent semantic analysis is utilized based on semantic similarity using patent textual information. The fuel cell membrane electrode assembly (MEA) technology field is selected and applied to illustrate the proposed methodology. The proposed approach allows firms, universities, research institutes, governments to identify potential R&D collaborators as a systematic decision-making support tool.This is an Accepted Manuscript of an article published by Taylor & Francis in Technology Analysis and Strategic Management on the 22nd of October 2014, available online: http://wwww.tandfonline.com/10.1080/09537325.2014.971004. This version will be under embargo until the 22nd of April 2016

Supplier selection: comparison of DEA models with additive and reciprocal data

Supplier evaluation is one of the most important fields of application for data envelopment analysis (DEA). Criteria may include negative data in both input and output factors. Data translation can solve this problem, but interpretation is not evident from the literature. Use of an additive model is one method of managing the problem of negative data. This paper addresses this issue in relation to the supplier ranking problem. It describes the development of a ranking with cross-efficiency that incorporates negative data into the additive model. The additive model we describe is compared with previously used DEA models in which data is replaced with reciprocal values when necessary. We present a supplier-evaluation-related example of this case. After the efficiency evaluation, a supplier ranking system is constructed. To do this, we use the cross-efficiencies obtained from the additive model. Aggregate efficiencies help display the suppliers in descending order of efficiency. Finally, the paper compares self- and peer-appraisal indicators for reciprocal and additive DEA models

Supplier Selection and Evaluation Using Restricting Weights Method in Presence of Dual Factor under Fuzzy Environment.

As businesses grow more complex, so do their supply chains. Data envelopment analysis (DEA) is a useful method for supplier selection. Weight restrictions allow for the integration of managerial preferences in terms of relative importance levels of various inputs and outputs. In some situations there are some factors which play both input and output roles as well. The purpose of this research is to propose a method for selecting the best suppliers in the presence of weight restrictions and dual-role factors. This study shows the supplier selection process through a DEA model, while allowing for the incorporation of decision make

Supplier Selection and Evaluation Using Restricting Weights Method in Presence of Dual Factor under Fuzzy Environment.

As businesses grow more complex, so do their supply chains. Data envelopment analysis (DEA) is a useful method for supplier selection. Weight restrictions allow for the integration of managerial preferences in terms of relative importance levels of various inputs and outputs. In some situations there are some factors which play both input and output roles as well. The purpose of this research is to propose a method for selecting the best suppliers in the presence of weight restrictions and dual-role factors. This study shows the supplier selection process through a DEA model, while allowing for the incorporation of decision make