With increasingly competitive global world markets, companies are under intense pressure to find ways to cut production and material costs to survive and sustain their competitive position in their respective markets. Since a qualified supplier is a key element and a good resource for a buyer in reducing such costs, evaluation and selection of the potential suppliers has become an important component of supply chain management. Hence, development of an effective and rational supplier selection model is naturally desirable. Several evaluation and selection models for supplier selection have been proposed and reported in the supply chain literature. This paper reveals the findings of a wide ranging literature review of supplier selection practices and models. Altogether 147 refereed academic journal articles are reviewed and classified into five categories. A list and summary of the papers falling into each category with brief annotations is provided. The areas that have received little attention or lack of research interest are discussed and some new research settings are also suggested.

Review and critique of supplier selection process and practices

The conveyor system plays a vital role in improving the performance of flexible manufacturing cells (FMCs). The conveyor selection problem involves the evaluation of a set of potential alternatives based on qualitative and quantitative criteria. This paper presents an integrated multi-criteria decision making (MCDM) model of a fuzzy AHP (analytic hierarchy process) and fuzzy ARAS (additive ratio assessment) for conveyor evaluation and selection. In this model, linguistic terms represented as triangular fuzzy numbers are used to quantify experts’ uncertain assessments of alternatives with respect to the criteria. The fuzzy set is then integrated into the AHP to determine the weights of the criteria. Finally, a fuzzy ARAS is used to calculate the weights of the alternatives. To demonstrate the effectiveness of the proposed model, a case study is performed of a practical example, and the results obtained demonstrate practical potential for the implementation of FMCs.

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An Integrated MCDM Model for Conveyor Equipment Evaluation and Selection in an FMC Based on a Fuzzy AHP and Fuzzy ARAS in the Presence of Vagueness

Material handling equipment (MHE) selection is a rapidly increasing multi-criteria decision-making (MCDM) issue with a plethora of factors affecting the selection process. The proper selection of MHE is a crucial issue for industrial organization’s efficiency and productivity in the global market. The selection of the most suitable MHE for a specific engineering application is a costly as well as time-consuming process in which many candidate alternatives available on the market are considered as preliminary choices. Despite the facts that there are already a large number of mathematical methods available to evaluate, choose, and rate the alternatives for a given engineering application, this paper investigates the ability and applicability of two MCDM methods, namely, Additive Ratio ASsessment (ARAS) and COmplex PRoportional ASsessment (COPRAS). The present research article investigates three real-time MHE selection problems, i.e., conveyor selection, automated guided vehicles (AGV) selection, and selection of robots. All the three MCDM problems are evaluated and alternative rankings are proposed using these two methods, while the objective weighted system, entropy is used to assess the relative weights of the criteria. The current outcome rankings are also validated by using other six MCDM methods and comparing them to the findings of the previous researchers. Each example provides a listing of all the possible options from optimum to worst MHE that could potentially improve the previously proposed rankings and sensitivity analysis is computed to demonstrate that these two MCDM methods are robust enough and capable of being used effectively in solving MHE selection problems. Finally, conveyor 3, AGV 5, and ASEA-IRB 60/2 robot are found to be the best choice, while conveyor 1, AGV 3, and Unimation PUMA 500/600 robot are the worst alternative for the three MHE selection issues respectively.

Solving Material Handling Equipment Selection Problems in an Industry with the Help of Entropy Integrated COPRAS and ARAS MCDM techniques

Manufacturing technology selection is traditionally a human-driven approach where the trade-off of alternative manufacturing investments is steered by a group of experts. The problem is a semi-structured and subjective-based decision practice influenced by the experience and intuitive feeling of the decision-makers involved. This paper presents a distinct experience-based decision support system that uses factual information of historical decisions to calculate confidence factors for the successful adoption of potential technologies for a given set of requirements. A fuzzy-decision-tree algorithm is applied to provide a more objective approach given the evidence of previous manufacturing technology implementation cases. The model uses the information relationship of key technology decision variables, project requirements of an implemented technology case and the success outcome of a project to support decision problems. An empirical study was conducted at an aircraft manufacturer to support their technology decision for a typical medium complexity assembly investment project. The experimental analysis demonstrated encouraging results and practical viability of the approach.

A fuzzy-decision-tree approach for manufacturing technology selection exploiting experience-based information

The objective of this research is to develop and implement a new methodology for multi-attribute selection of automated guided vehicle (AGV) for the material handling. The present paper proposes a modified grey relational analysis (M-GRA) method combined with analytical hierarchy approach (AHP). AHP method is used to assign the relative importance between AGV selection attributes and M-GRA method is applied to determine AGV selection utility index. A proposed AHP/M-GRA technique is more suitable for the decision-making in the presence of vagueness. The methodology is illustrated by means of an example. The study was followed by the validation and the sensitivity analysis of the AHP/M-GRA technique. The results of the study build confidence in suitability of AHP/M-GRA technique for selection of AGV alternatives.

A multi-attribute selection of automated guided vehicle using the AHP/M-GRA technique

Conveyor equipment selection is a complex, and sometimes, tedious task since there are literally hundreds of equipment types and manufacturers to choose from The expert system approach to conveyor selection provides advantages of unbiased decision making, greater availability, faster response, and reduced cost as compared to human experts This paper discusses the development of a prototype expert system for industrial conveyor selection The system, which was developed on Level V Object, provides the user with a list of conveyor solutions for their material handling needs along with a list of suppliers for the suggested conveyor devices Conveyor types are selected on the basis of a suitability score, which is a measure of the fulfillment of the material handling requirements by the characteristics of the conveyor The computation of the score is performed through the Weighted Evaluation Method, and the Expected Value Criterion for decision making under risk The prototype system was successfully validated through two industrial case studies

A knowledge-based system for conveyor equipment selection

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A knowledge-based system for conveyor equipment selection