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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.

For organizations competing in volatile environments, strategic agility is the key for sustaining in the market. It is essential for such organizations to identify the main agility indicators that contribute to their strategic core. The purpose of this paper is to propose and test a systematic methodology that identifies key agility indicators through prioritization and establishing the intra- and inter-relationships among them.

The methodology consists of four phases. Phase I forms a pool of agility key performance indicators (KPIs). Phase II categorizes and ranks the KPIs based on their importance and the gap that exists between their current and desired level. Using interpretive structural modeling, phase III establishes the intra-relationships among the KPIs as well as agility attributes, agility enablers and improvement paths, collectively referred to as agility indicators. Finally, phase IV analyzes the inter-relationships among agility indicators using three consecutive houses of quality.

To demonstrate the capability of the proposed methodology, it was applied to a dairy food company operating in a competitive environment. The application could address the shortcomings of previous agility methodologies and helped the company to assign resources to the right agility indicators with the highest influence on strategic agility.

The methodology was applied to a single organization only. The application does not include long-term post-implementation observations and measurement of strategic agility.

Practitioners can benefit from the methodology to identify the right agility indicators of their organization and assign organizational resources for the improvement of such indicators. The methodology ensures selecting indicators that contribute to organization’s strategic agility, although ostensibly seem unrelated.

The paper contributes to the literature of strategic and organizational agility by proposing a systematic methodology that considers both intra- and inter-relationships among agility indicators. The methodology also makes a decent contribution in addressing organizational ambidexterity by analyzing mutually exclusive indicators pertaining to current and future opportunities for the organization.

Delivering premium services and quality products are critical strategies for success in manufacturing. Continuous improvement (CI), as an underlying foundation for quality management, is an ongoing effort allowing manufacturing companies to see beyond the present to create a bright future. We propose a novel integrated fuzzy framework for analyzing the barriers to the implementation of CI in manufacturing companies.

We use the fuzzy failure mode and effect analysis (FMEA) and a fuzzy Shannon's entropy to identify and weigh the most significant barriers. We then use fuzzy multi-objective optimization based on ratio analysis (MOORA), the fuzzy technique for order of preference by similarity to ideal solution (TOPSIS) and fuzzy simple additive weighting (SAW) methods for prioritizing and ranking the barriers with each method. Finally, we aggregate these results with Copeland's method and extract the main CI implementation barriers in manufacturing.

We show “low cooperation and integration of the team in CI activities” is the most important barrier in CI implementation. Other important barriers are “limited management support in CI activities,” “low employee involvement in CI activities,” “weak communication system in the organization,” and “lack of knowledge in the organization to implement CI projects.”

We initially identify the barriers to the implementation of CI through rigorous literature review and then apply a unique integrated fuzzy approach to identify the most important barriers based on the opinions of industry experts and academics.

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.

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.

In this research, extracting the innovation drivers of successful crowdfunding from the literature review, screening them for the entrepreneurial small- and medium-sized enterprises (SMEs), analysing the cause-and-effect relationship amongst them and presenting a basic causal conceptual model and eventually determining the importance/weight of each relevant driver were the primary purposes of this research. As a result, the authors have also designed a score function to measure the future innovative crowdfunding score for SMEs.

A multi-layer multi-criteria decision-making (MCDM) approach has been designed and employed to achieve research objectives. After extracting the initial list of drivers, Fuzzy Delphi was applied to screen the relevant innovation drivers of successful crowdfunding for entrepreneurial SMEs. Decision-making trial and evaluation laboratory (DEMATEL) was used to analyse the cause-and-effect relationship amongst the drivers and illustrate a basic conceptual model. Analytical network process (ANP) and Stepwise Weight Assessment Ratio Analysis (SWARA) were applied to determine the importance of the drivers and by aggregating them to measure the innovative crowdfunding score.

Initially, 28 innovation drivers of successful crowdfunding were extracted from the literature. Then by employing the first-round Delphi fuzzy method amongst 15 international entrepreneurs in SMEs, the relevant drivers, including eleven items, were screened and selected. Then by implementing the DEMATEL method, the relationship amongst these screened drivers was identified, and seven drivers were determined as causes and the rest as effects. Subsequently, a conceptual model based on the causal analysis of the drivers from the DEMATEL method was designed. Eventually, by aggregating the weight of drivers emanated from SWARA, DEMATEL and DANP, the score function for measuring the situation of an SME was designed.

According to the crowdfunding scores in this research from entrepreneurs of SMEs, influential factors in developing countries were recognised as two times more prominent in developing countries. This might be rooted in the circumstances of developing countries where many startups and SMEs are emerging in vast areas and different fields due to investment in innovation management. In these countries, the authorities and officials support these companies to empower their capabilities and innovative ideas to (1) deal with the severe competitive market and (2) benefit from them as potential economic engines. Therefore, crowdfunding platforms and public initiatives can be considered one of the most effective government supports, which may involve financial risks.

To the best knowledge of the authors, investigating the innovation drivers of successful crowdfunding via quantitative analysis by multi-layer decision-making approaches has not been considered previously. Moreover, the authors have designed a crowdfunding score function to determine the situation of an entrepreneurial SME in this area. A combination of different MCDM methods, including Fuzzy Delphi, SWARA, DEMATEL, ANP and DANP, to investigate the innovation drivers of successful crowdfunding in SMEs has not been considered previously.

The purpose of this paper is to identify and screen continuous innovation capability enablers (CICEs) in Indonesia’s manufacturing sectors, develop a relationship among these enablers and determine their driving power and dependence power in the sector.

The initial CICEs identification process is based on a literature review, while a fuzzy Delphi method (FDM) was used for the screening process of CICEs. Total interpretive structural modelling (TISM) was used to develop contextual relationships among various CICEs. The results of the TISM are used as an input for the matrix of cross-impact multiplications applied to classification (MICMAC) to classify the driving power and dependence powers of the CICEs.

This paper selected 16 CICEs classified in seven dimensions. TISM results and MICMAC analysis show that leadership, as well as climate and culture, are enablers with the highest driving power and lowest dependence powers; followed by information technology. The results of this study indicate that efforts to continuously develop innovation capabilities in the Indonesian manufacturing industries are strongly influenced by their leadership capability, climate and culture, also information technology-related capability.

The framework assessed in this study provides business managers and policymakers to obtain a bigger picture in developing policies with evidence-based strategy and priority in regard to continuous innovation capability.

The results will be useful for business managers and policymakers to understand the relationship between CICEs and identify key CICEs in Indonesia’s manufacturing sectors, which were previously non-existent.

The present study aims to explore the barriers to Lean Six Sigma (LSS) implementation in the healthcare sector and develop the ranking of finalized barriers using the Decision-Making Trial and Evaluation Laboratory (DEMATEL) approach under a fuzzy environment.

The LSS barriers are identified through the literature review and validated by the expert's opinion and statistical analysis. A total of 124 experts were identified through the purposive sampling method for conducting this study. A questionnaire survey method is used to collect the data related to identified LSS barriers in the healthcare sector. The screened barriers are ranked through the Fuzzy DEMATEL approach.

In this study, a total of 21 barriers were identified with the help of a systematic literature review and screened 13 significant barriers by the expert opinions of healthcare personnel. The result reveals that “Lack of top management commitment and support, lack of awareness about LSS”, “resistance to culture change and inadequate resources emerges as the most critical barriers”. The prioritization of barriers facilitates the managers to make effective policies and guidelines for LSS implementation in healthcare organizations.

To avoid LSS implementation failure, the practitioners and researchers need to focus on LSS barriers as per suggested ranking more conventionally and make plans and adoption policies accordingly.

This study is unique in terms of investigation and empirical analysis of LSS implementation barriers in the healthcare sector in the Indian context. The outcomes of the present study will help the managers of healthcare organizations to make the strategies and policies for LSS implementation as per the recommended LSS barriers.