Development of a Rough-MABAC-DoE-based Metamodel for Supplier Selection in an Iron and Steel Industry

Abstract

In the context of supply chain management, supplier selection can be defined as the process by which organizations score and evaluate a range of alternative suppliers to choose the best possible one who can provide superior quality of raw materials at cheaper rate and lesser lead time. It is a decision making process with multiple trade-offs between various conflicting criteria which in turn helps the organizations identify the suitable suppliers that would establish a robust supply chain assisting in maintaining a competitive edge. The main objective of supplier selection is thus focused on reducing purchase risk, maximizing overall value to the organization, and developing closeness and long-term relationships between the suppliers and the organization. In this paper, while selecting the most suitable supplier for gearboxes in an Indian iron and steel industry, assessments of three decision makers on the performance of five candidate suppliers with respect to five evaluation criteria are first aggregated using rough numbers. The definitive distances of those rough numbers are then treated as the inputs to a 25 full-factorial design plan with the corresponding multi-attributive border approximation area comparison (MABAC) scores as the output variables. Finally, a design of experiments (DoE)-based metamodel is formulated to interlink the computed MABAC scores with the considered criteria. The competing suppliers are ranked based on this rough-MABAC-DoE-based metamodel, which also easies out the computational steps when new suppliers are included in the decision making process.

Evaluating Suppliers Using AHP in a Fuzzy Environment and Allocating Order Quantities to Each Supplier in a Supply Chain

“…e author offered a new way to solving a vendor selection issue in the iron and steel sector by combining rough statistics with the MABAC approach and DOE to create a metamodel. e system begins with the aggregate of five competing vendors' relative efficiency scores using rough numbers to allow for the uncertainty considered in the decision-making process [11].…”

A New Fuzzy Extension of the Simple WISP Method

“…where λ denotes the index of optimism, and λ ∈ [0, 1]. When giving a higher value to the index of optimism λ, the value of the right endpoint (optimistic attitudes) has a greater influence on the decision and vice versa; when giving a lower value to the coefficient λ, the left endpoint (pessimistic attitudes) has a greater influence.…”

“…Multicriteria decision-making (MCDM) was first used in the 1970s and has been quickly evolving since then. Many significant MCDM approaches have been proposed as a result of rapid development and use for tackling a broad range of decision-making problems [1][2][3][4][5]. Some of the MCDM methods frequently encountered in the literature are as follows; SAW [6], CP [7], ELECTRE [8], AHP [9], TOPSIS [10], PROMETHEE [11], MACBETH [12], MULTIMOORA [13], and ARAS [14].…”

Integrated Fuzzy-MSGP Methods for Clothing and Textiles Supplier Evaluation and Selection in the COVID-19 Era

“…Fu et al [45] applied a combination of fuzzy-AHP, fuzzy-ARAS (additive ratio assessment), and MSGP methods to assess in-flight duty-free products suppliers' selection. Chattopadhyayet al [46] developed a Rough-MABAC-DoE-(interval rough analytic hierarchy processmultiattributive border approximation area comparison- e most recent research works in supplier selection under COVID-19, such as the work of Kilic and Yalcin [48], combine an improved two-stage fuzzy goal programming (GP) model with fuzzy TOPSIS technique to green supplier selection. Orji and Ojadi [1] integrated MCDM method to analyze the interrelationship between COVID-19 pandemic response strategies and TBL criteria for supplier selection.…”

“…While this study does present some limitations, such as the selection of industry suppliers, it successfully utilizes fuzzy-TOPSIS and MSGP as proposed research methods for research evaluation. erefore, this paper also invites future research on this topic which can utilize other MCDMs, such as analytic network process, structural equation modelling (SEM) or interpretive structural modelling, FUCOM-Rough SAW model (e.g., [43]), fuzzy ARAS, MSGP methods (e.g., [45]), and a Rough-MABAC-DoE-based metamodel (e.g., [46]), to estimate the criteria for supplier selection presented in the current case, after which a comparison can be made with the results acquired in this study. Furthermore, the research presented in this paper can be extended in substantial ways; the proposed method can address various marketing and management MCDM issues, such as logistics and marketing, or C&T product development and design issues when faced with inaccuracies, ambiguities, and uncertainties in the information available.…”