Analytic Hierarchy Process and Supply Chain Management: A Bibliometric Study

AbstractA comparative study was used to outline the literature in the research topic. This paper aims to present a bibliometric study ofmulti-criteria decision-making methods most applied in publications from 1990 to 2014. Our research presented relations of papers published in the Web of Science Core Collection, regarding the following keywordsAnalytic Hierarchy Process and Supply Chain. The research evidenced that the Analytic Hierarchy Process has been the method mostapplied in publications from 1993. It also showed the analysis of the predecessor and successor citation network for the selected publications under topics as supplier selection, supply development, performance measurement and value chain through the CitNetExplore software

A Fuzzy Criticality Assessment System of Process Equipment for Optimized Maintenance Management.

yesIn modern chemical plants, it is essential to establish an effective maintenance strategy which will deliver financially driven results at optimised conditions, that is, minimum cost and time, by means of a criticality review of equipment in maintenance. In this article, a fuzzy logic-based criticality assessment system (FCAS) for the management of a local company’s equipment maintenance is introduced. This fuzzy system is shown to improve the conventional crisp criticality assessment system (CCAS). Results from case studies show that not only can the fuzzy logic-based system do what the conventional crisp system does but also it can output more criticality classifications with an improved reliability and a greater number of different ratings that account for fuzziness and individual voice of the decision-makers

A fuzzy decision tool to evaluate the sustainable performance of suppliers in an agrifood value chain

Sustainable supply chain management has received much attention from both academia and industry due to various issues such as economic stability, environment conservation, and social ethics. To improve the sustainable performance of a value chain, its members need to carefully select their suppliers in relation to their own strategy. Thus, an effective tool for sustainable supplier selection and evaluation is essential, which considers the triple bottom line (TBL) of economic, environmental and social aspects by means of criteria adapted to the situation analysed. This paper develops a fuzzy decision tool to evaluate the sustainable performance of suppliers according to TBL. Sustainability criteria are identified to take into account the real hotspots in a food value chain. The proposed model integrates triangular fuzzy numbers (TFN), AHP (Analytic Hierarchy Process) and TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) in a novel way to consider quantitative and qualitative criteria as well as objective and subjective data. This is missing in most existing research when building their fuzzy models for supplier selection, but critical in dealing with the heterogeneous data available for TBL assessment. The application in a sustainable agrifood value chain illustrates the effectiveness of the proposed tool

A review of fuzzy AHP methods for decision-making with subjective judgements

Analytic Hierarchy Process (AHP) is a broadly applied multi-criteria decision-making method to determine the weights of criteria and priorities of alternatives in a structured manner based on pairwise comparison. As subjective judgments during comparison might be imprecise, fuzzy sets have been combined with AHP. This is referred to as fuzzy AHP or FAHP. An increasing amount of papers are published which describe different ways to derive the weights/priorities from a fuzzy comparison matrix, but seldomly set out the relative benefits of each approach so that the choice of the approach seems arbitrary. A review of various fuzzy AHP techniques is required to guide both academic and industrial experts to choose suitable techniques for a specific practical context. This paper reviews the literature published since 2008 where fuzzy AHP is applied to decision-making problems in industry, particularly the various selection problems. The techniques are categorised by the four aspects of developing a fuzzy AHP model: (i) representation of the relative importance for pairwise comparison, (ii) aggregation of fuzzy sets for group decisions and weights/priorities, (iii) defuzzification of a fuzzy set to a crisp value for final comparison, and (iv) consistency measurement of the judgements. These techniques are discussed in terms of their underlying principles, origins, strengths and weakness. Summary tables and specification charts are provided to guide the selection of suitable techniques. Tips for building a fuzzy AHP model are also included and six open questions are posed for future work

Supplier evaluation and selection in fuzzy environments: a review of MADM approaches

In past years, the multi-attribute decision-making (MADM) approaches have been extensively applied by researchers to the supplier evaluation and selection problem. Many of these studies were performed in an uncertain environment described by fuzzy sets. This study provides a review of applications of MADM approaches for evaluation and selection of suppliers in a fuzzy environment. To this aim, a total of 339 publications were examined, including papers in peer-reviewed journals and reputable conferences and also some book chapters over the period of 2001 to 2016. These publications were extracted from many online databases and classified in some categories and subcategories according to the MADM approaches, and then they were analysed based on the frequency of approaches, number of citations, year of publication, country of origin and publishing journals. The results of this study show that the AHP and TOPSIS methods are the most popular approaches. Moreover, China and Taiwan are the top countries in terms of number of publications and number of citations, respectively. The top three journals with highest number of publications were: Expert Systems with Applications, International Journal of Production Research and The International Journal of Advanced Manufacturing Technology

A Decision Tool for Supplier Selection That Takes into Account Power and Performance

Companies select their suppliers to provide required performance while being successful partners. An important aspect of collaboration is the power relationship between the company and its suppliers. Although the significance of power in supplier selection is acknowledged, published work rarely includes assessment of power. An empirical study on selecting suppliers for new product developments in a major European diesel engine manufacturing company, supported by three smaller studies with electronic engineering companies, frames overall questions regarding the importance of incorporating power into supplier selection and how this might be achieved. This research proposes an approach that assesses both performance and power and integrates the assessment results by modelling the relative effects of power and performance. It positions the suppliers into six scenarios (ideal, satisfying, tolerable, unfavourable, risky and tough) which depict to what extent a supplier is ‘suitable’ to work with. A reverse analysis reviews the relationship when several suppliers appear suitable. An assessment method is developed incorporating both subjective and objective data for qualitative and quantitative criteria. It combines two decision making methods, AHP and TOPSIS, with triangular fuzzy numbers. Multiple judgements from several decision makers are synthesised. This method is adapted for performance assessment of single, group and cross-group suppliers. Weights are calculated for the criteria, and combined with calculations of supplier performance against each criterion to provide an overall assessment and supplier profile. Power is quantified against a set of power determinants and power relations (supplier dominance, buyer dominance and balanced) are determined. The effects of supplier perceptions (objective, optimistic and pessimistic) are estimated in the calculation. The proposed approach involves complex calculations and a prototype software tool is developed with graphical interfaces. The tool includes performance criteria and power determinants collected from literature and allows users to define new ones. Application to an agriculture case enables the sustainable performance of suppliers (farmers) to be evaluated and compared

Evaluation of Leanness, Agility and Leagility Extent in Industrial Supply Chain

The focus of Lean Manufacturing (LM) is the cost reduction by eliminating non value added activities (waste i.e. muda) and enabling continuous improvement; whereas, Agile Manufacturing (AM) is an approach which is mainly focused on satisfying the needs of customers while maintaining high standards of quality and controlling the overall costs involved in the production of a particular product. This approach has geared towards companies working in a highly turbulent as well as competitive business environment, where small variations in performance and product delivery can make a huge difference in the long term to a company’s survival and reputation amongst the customers. Leagility is basically the aggregation of lean and agile principles within a total supply chain strategy by effectively positioning the decoupling point, consequently to best suit the need for quick responding to a volatile demand downstream yet providing a level scheduling upstream from the marketplace. A leagile system adapts the characteristics of both lean and agile systems, acting together in order to exploit market opportunities in a cost-efficient way. The present research aims to highlight how these lean, agile as well as leagile paradigms may be adapted according to particular marketplace requirements. Obviously, these strategies are distinctly different, since in the first case, the market winner is cost; whereas, in the second case, the market winner is the availability. Agile supply chains are required to be market sensitive and hence nimble. This means that the definition of waste is different from that appropriate to lean supply. The proper location of decoupling point for material flow and information flow enables a hybrid supply chain to be better engineered. This encourages lean (efficient) supply upstream and agile (effective) supply downstream, thus bringing together the best of both paradigms. While implementing leanness/agility/leagility philosophy in industrial supply chain in appropriate situations, estimation of a unique quantitative performance metric (evaluation index) is felt indeed necessary. Such an index can help the industries to examine existing performance level of leanness/agility/leagility driven supply chain; to compare ongoing performance extent to thedesired/expected one and to benchmark best practices of lean/agile/leagile manufacturing/supply chain, wherever applicable. The present research attempts to assess the extent of leanness, agility as well as leagility, respectively, for an organizational supply chain using fuzzy/grey based Multi- Criteria Decision Making (MCDM) approaches. During this research, different