Search results

The aim of this paper is to classify suppliers into some clusters based on sustainability factors. However, there might be some unqualified suppliers and we should identify and remove those suppliers before clustering.

First, using fuzzy screening system, the authors identify and remove the unqualified suppliers. Then, the authors run their proposed clustering method. This paper proposes a data envelopment analysis (DEA) algorithm to cluster suppliers.

This paper presents a two-aspect DEA-based algorithm for clustering suppliers into clusters. The first aspect applied DEA to consider efficient frontiers and the second aspect applied DEA to consider inefficient frontiers. The authors examine their proposed clustering approach by a numerical example. The results confirmed that their method can cluster DMUs into clusters.

The main contributions of this paper are as follows: This paper develops a new clustering algorithm based on DEA models. This paper presents a new DEA model in inefficiency aspect. For the first time, the authors’ proposed algorithm uses fuzzy screening system and DEA to select suppliers. Our proposed method clusters suppliers of MPASR based on sustainability factors.

The application of fuzzy multiple attribute decision making (FMADM) approach in evaluation of organizations has grown recently, and it is combined with knowledge‐based university evaluation parameters in this study. The paper seeks to propose a FMADM approach for measuring university performance on the four knowledge‐based perspectives of a balanced scorecard.

The approach first summarizes the evaluation indexes extracted from the university performance literature. Then, the relative weights of the chosen evaluation indexes are calculated using the fuzzy analytic hierarchy process (FAHP). The fuzzy sets theory was adapted to university performance analysis.

The results reveal the critical aspects of the evaluation criteria as well as the gaps to improve university performance in order to achieve the aspired/desired level.

The paper reveals the key issues in the existing performance evaluation method, especially in the university context.

This research analyses the performance of a university based on the knowledge‐based indexes in the four BSC perspectives, using a FME‐MADM approach. It considers specific knowledge‐based metrics for each perspective.

Although implementation of the performance measures in universities are now widespread, there is no considerable literature that sufficiently addresses the various issues faced by organizations during university implementation. The paper proposes application of the balanced knowledge‐based scorecard to universities aiming at evaluating performance annually.

In order to answer which opportunities are better to pursue, the purpose of this paper is to propose and empirically test a decision-making model for evaluating and selecting entrepreneurial opportunities.

First, the authors identified common evaluation criteria through a systematic review of 45 high quality articles published in top entrepreneurship and management journals between 2000 and 2017. Second, fuzzy screening technique has been employed to offer the decision-making model. Third, the authors used data of six evaluations provided by five experts at a medium-sized biotech firm to test the model.

The study shows that common decision criteria for evaluating entrepreneurial opportunities fall into seven categories. According to these criteria and using fuzzy screening technique, a multi-expert multi-criteria decision-making (ME–MCDM) model has been suggested for evaluating and selecting opportunities.

This model can be served in situations in which decision makers should select a small number of opportunities among the larger set with regard to opportunity profile and minimal information. More opportunities and more decision makers can be included in the model. When the number of opportunities and decision makers are high, it is possible to use programming for fast, accurate and easy calculation.

This study is the first systematic review of opportunity evaluation criteria. It is also the first considering opportunity evaluation as a multi-expert decision-making process.

This paper aims to develop a framework for competitive advantage by systematic quantitative methodology based on resource-based view and dynamic capability theory. Strategic agility was used as a dynamic capability.

Data were collected from a survey aimed at manufacturing companies from five manufacturing industry in Semnan, Iran. A total of 102 questionnaires were received from 13 companies using convenience sampling. Fuzzy two-stage data envelopment analysis model (DEA) was used to analyse the data collected.

The results indicate that there is close internal relationship among firm resources, strategic agility and competitive advantage, and their inherent relationship makes constant returns to scale (CRS) scores closer to 1. In most of the companies, the second process which transforms strategic agility to competitive advantage is the main cause for unsatisfactory performance in gaining competitive advantage.

The innovation of this paper is in its model and method. There is no research has been ever done on the relationship among firm resources, strategic agility and competitive advantage. Moreover, to obtain a competitive advantage structure, DEA technique was adopted which is a new approach in this area.

The main purpose of this study is to provide a comprehensive review and critical insights of the application of fuzzy methods in modeling, assessing and understanding the various aspects of green and sustainable supply chains (SSCs).

The present study conducts a systematic literature review (SLR) and bibliometric analysis of 252 research articles. This study employs various tools such as VOSviewer version 1.6.10, Publish or Perish, Mendeley and Excel that aid in descriptive analysis, bibliometric analysis and network visualization. These tools have been used for performing citation analysis, top authors' analysis, co-occurrence of keywords, cluster and content analysis.

The authors have divided the literature into seven application areas and discussed detailed insights. This study has observed that research in the social sustainability area, including various issues like health and safety, labor rights, discrimination, etc. is scarce. Integration of the Industry 4.0 technologies like blockchain, big data analytics, Internet of Things (IoT) with the sustainable and green supply chain (GSC) is a promising field for future research.

The authors' contribution primarily lies in providing the integrated framework which shows the changing trends in the use of fuzzy methods in the sustainability area classifying and consolidating green and sustainable supply chain management (SSCM) literature in seven major areas where fuzzy methods are predominantly applied. These areas have been obtained after the analysis of clusters and content analysis of the literature presenting key insights from the past and developing the conceptual framework for future research studies.

The existing literature expresses a strong need to develop tools that support the manufacturing reshoring decision-making process. This paper aims to examine the suitability of analytical hierarchy process (AHP)-based tools for initial screening of manufacturing reshoring decisions.

Two AHP-based tools for the initial screening of manufacturing reshoring decisions are developed. The first tool is based on traditional AHP, while the second is based on fuzzy-AHP. Six high-level and holistic reshoring criteria based on competitive priorities were identified through a literature review. Next, a panel of experts from a Swedish manufacturing company was involved in the overall comparison of the criteria. Based on this comparison, priority weights of the criteria were obtained through a pairwise analysis. Subsequently, the priority weights were used in a weighted-sum manner to evaluate 20 reshoring scenarios. Afterwards, the outputs from the traditional AHP and fuzzy-AHP tools were compared to the opinions of the experts. Finally, a sensitivity analysis was performed to evaluate the stability of the developed decision support tools.

The research demonstrates that AHP-based support tools are suitable for the initial screening of manufacturing reshoring decisions. With regard to the presented set of criteria and reshoring scenarios, both traditional AHP and fuzzy-AHP are shown to be consistent with the experts' decisions. Moreover, fuzzy-AHP is shown to be marginally more reliable than traditional AHP. According to the sensitivity analysis, the order of importance of the six criteria is stable for high values of weights of cost and quality criteria.

The limitation of the developed AHP-based tools is that they currently only include a limited number of high-level decision criteria. Therefore, future research should focus on adding low-level criteria to the tools using a multi-level architecture. The current research contributes to the body of literature on the manufacturing reshoring decision-making process by addressing decision-making issues in general and by demonstrating the suitability of two decision support tools applied to the manufacturing reshoring field in particular.

This research provides practitioners with two decision support tools for the initial screening of manufacturing reshoring decisions, which will help managers optimize their time and resources on the most promising reshoring alternatives. Given the complex nature of reshoring decisions, the results from the fuzzy-AHP are shown to be slightly closer to those of the experts than traditional AHP for initial screening of manufacturing relocation decisions.

This paper describes two decision support tools that can be applied for the initial screening of manufacturing reshoring decisions while considering six high-level and holistic criteria. Both support tools are applied to evaluate 20 identical manufacturing reshoring scenarios, allowing a comparison of their output. The sensitivity analysis demonstrates the relative importance of the reshoring criteria.

This paper investigates the suitability of fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Two fuzzy-logic-based support tools are developed together with experts from a Swedish manufacturing firm. The first uses a complete rule base and the second a reduced rule base. Sixteen inference settings are used in both of the support tools.

The findings show that fuzzy-logic-based support tools are suitable for initial screening of manufacturing reshoring decisions. The developed support tools are capable of suggesting whether a reshoring decision should be further evaluated or not, based on six primary competitiveness criteria. In contrast to existing literature this research shows that it does not matter whether a complete or reduced rule base is used when it comes to accuracy. The developed support tools perform similarly with no statistically significant differences. However, since the interpretability is much higher when a reduced rule base is used and it require fewer resources to develop, the second tool is more preferable for initial screening purposes.

The developed support tools are implemented at a primary-criteria level and to make them more applicable, they should also include the sub-criteria level. The support tools should also be expanded to not only consider competitiveness criteria, but also other criteria related to availability of resources and strategic orientation of the firm. This requires further research with regard to multi-stage architecture and automatic generation of fuzzy rules in the manufacturing reshoring domain.

The support tools help managers to invest their scarce time on the most promising reshoring projects and to make timely and resilient decisions by taking a holistic perspective on competitiveness. Practitioners are advised to choose the type of support tool based on the available data.

There is a general lack of decision support tools in the manufacturing reshoring domain. This paper addresses the gap by developing fuzzy-logic-based support tools for initial screening of manufacturing reshoring decisions.

Good management strives to align and corporate processes for more attention being paid to supply chain management. Firms realize that greater co-operation and improved coordination can help to manage the entire supply chain more efficiently. The imperfect quality item is one of the most important issues that affect the expected profit of green supply chain. The imprecise cost with screening process of poor quality items posed in supply chain is the subject of this study.

The present study explores production model for imperfect items having uncertain cost parameters with three-layer supply chain encompassing supplier, manufacturer and retailer. The model is considering the impact of business tactics such as order size, production rate, production cost and appropriate times in various sectors on collaborative marketing systems. Due to imprecise cost parameters, the pentagonal fuzzy numbers are set to fuzzify the total cost and defuzzifition by using graded mean integration.

This study offers an explicit condition in uncertain environment to manage the imperfect quality item to increase the potential profit of the supply chain. The influence of changes in parameter values on the optimal inventory policy under fuzziness is provided managerial insights.

This model makes a significant contribution to fuzzy inference. The results of the study provide a trading strategy for the industry to avoid losses. The prescribed study can be suitable for the industries like sculpture, jewelry, pottery, etc.

The purpose of this paper is to ascertain and understand the relationship dynamics among the quality-enabled factors (QEFs) affecting the initiation stage of production system life cycle (PSLC). This study presents an approach for refining the decision making in the initiation stage of the production system.

In this paper, ten QEFs have been identified for the initiation stage of PSLC. An interpretive structural modelling (ISM) approach has been utilized to cultivate an organizational association among these identified QEFs. The results of ISM approach are used as an input to fuzzy Matriced’ Impacts Croisés Multiplication Appliquée á un Classement (MICMAC) analysis, to identify the driving and dependence power of QEFs.

The key consequences of this paper are to prioritize the strategic QEFs in reducing the risks linked with initiation stage of production system. The integrated model obtained by ISM-fuzzy MICMAC illustrates that there exists two clusters of QEFs, one is having high driving power and low dependency power which requires extreme consideration and of strategic importance (such as honesty and sincerity in collecting and analyzing field data) and other is having high dependence power and low driving power and are resultant effects (such as strategic decision-making ability).

The integrated ISM-fuzzy MICMAC model developed is not statistically corroborated; consequently structural equation modelling (SEM) approach which is also known as linear structural relationship approach could be utilized to examine the validity of developed hypothetical model.

This is first study to identify ten QEFs in initiation stage of production system and further, to deploy integrated ISM-fuzzy MICMAC approach to recognize and categorize the QEFs influencing the initiation stage of production system.