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The purpose of this paper is to find excellent banks on the basis of identified variables. First of all, banks are evaluated based on operation costs, deposits, staff, investments, net profit, and loans variables. Subsequently, these variables are categorized into inputs and outputs. The performances of the banks based on these variables are analyzed by data envelopment analysis (DEA) method to find efficiency and inefficiency of decision making units (DMUs).

This research is aimed to determine the best banks based on predetermined indicators. The indicators are categorized into inputs and outputs. DEA method is used to find efficiency and inefficiency of DMU. However, the aim is to find the efficient banks and to implement the model by using AP Super Efficiency method in order to find the most efficient unit for benchmarking. However, some inputs and outputs have more priority for banks than the others, as a result it will require some changes.

The results indicate that among 13 banks, including ten public and three private, solely five public banks are efficient. Moreover, DEA is used as a benchmarking tool for inefficient banks to be efficient. Among these banks ten of them are public banks and three are private. Among efficient ones, all are public banks. Moreover, five of public banks and three of private are inefficient.

In some cases, inputs and outputs have more priority for DMs than the others, as a result it will require some changes. Also, if one of the inputs or outputs is larger in number than the others, the DMU becomes efficient, despite its low priority. Thus, for solving this problem, the indicators of this research are ranked by Rembrandt method considering the existing ones to find the best banks (best DMU) based on their performance and the relevant indicators.

The purpose of this paper is to develop a multi-item economic production quantity (EPQ) strategy under grey environment and space constraint. Since the “demand” cannot be predicted with certainty, it is assumed that data behave under grey environment and compare the proposed inventory model with other studies using crisp or fuzzy environments.

This paper is to optimise the cycle time and total cost of the multi-item EPQ inventory model. For this purpose, the Lagrangian coefficient is used to solve the constrained optimisation problem. The grey relational analysis approach and grey data are applied in developing the EPQ inventory model.

The results are compared with the analysis using crisp and fuzzy data. Sensitivity analysis is done to illustrate the effect of parameter variations on the optimal solution. The results of the study demonstrate that crisp data outperform the other two data in all scales problems in terms of cycle time and cost; grey data perform better in all scales problems than fuzzy data.

The contribution of this research is the use of grey data in developing the EPQ inventory model with space constraint.

The aim of this paper is to find and prioritise multiple critical success factors (CSFs) for the implementation of LSS in the oil and gas industry.

Based on a preselected list of possible CFSs, experts are involved in screening them with the Delphi method. As a result, 22 customised CSFs are selected. To prioritise these CSFs, the step-wise weight assessment ratio analysis (SWARA) method is applied to find weights corresponding to the decision-making preferences. Since the regular permutation-based weight assessment can be classified as NP-hard, the problem is solved by a metaheuristic method. For this purpose, a genetic algorithm (GA) is used.

The resulting prioritisation of CSFs helps companies find out which factors have a high priority in order to focus on them. The less important factors can be neglected and thus do not require limited resources.

Only a specific set of methods have been considered.

The resulting prioritisation of CSFs helps companies find out which factors have a high priority in order to focus on them.

The methodology supports respective evaluations in general.

The paper contributes to the very limited research on the implementation of LSS in the oil and gas industry, and, in addition, it suggests the usage of SWARA, a permutation method and a GA, which have not yet been researched, for the prioritisation of CSFs of LSS.

This study aims to find the best location for constructing green energy facilities in India and reducing CO₂ emissions. Incorporating green energy is a priority for many countries under the Paris Agreement. This task is challenging due to factors that affect implementation, and making the wrong decision wastes resources. India’s goals are net-zero emissions by 2070 and 50% renewable electricity by 2030. Other developing nations should emulate India’s renewable energy strategy. India ranks fourth in renewable energy and wind power, and fifth in solar power capacity. This research aims to identify the best locations in India for implementing green energy projects.

To identify the optimal green energy implementation sites in India, this research uses the hybrid multicriteria decision analysis (MCDA) in an uncertain environment. This research uses the Delphi method to identify the most suitable green energy implementation sites in India. It adapts the elements for this investigation. In addition, the utilization of the Fermatean fuzzy weighted aggregated sum product assessment technique is implemented to effectively prioritize the factors that impact the selection of these sites. This study used the MEREC method (method based on the removal effects of criteria) to identify the most suitable areas in India for implementing green energy. The highest accuracy is attained through the amalgamation of these hybrid methods.

Following the computation data by hybrid MCDA in uncertainty environment, NP Kunta in Andhra Pradesh emerges as the recommended green energy site among the 11 considered. Also among the factors political strategies and objectives hold the highest priority among them.

This study is pioneering in its efforts to provide a comprehensive perspective on the development and management of green energy operations in India. The study proves advantageous for diverse sites in the successful adoption and management of green energy. The study is additionally valuable in informing policy development aimed at promoting the use of green energy by employees through the utilization of MCDA methods in uncertain environments.

This paper aims to assess and prioritize manufacturing companies in the healthcare industry based on critical success factors (CSFs) of their reverse logistics (RL). The research involves seven medical device companies located in the Tehran Province, Iran.

To identify and prioritize companies based on CSFs of RL, the study proposes a three-phase decision-making framework that integrates the Delphi method, the best-worst method (BWM) and the Additive Ratio Assessment (ARAS) method with Z-numbers. The weights required for this method are obtained by a variant of the BWM based on Z-numbers, denoted as Z-numbers Best-Worst Method, or ZBWM. Since decision-makers face an uncertain environment, Z-numbers, which are a kind of fuzzy numbers, are applied.

First, after customizing CSFs by the Delphi method and obtaining 15 CSFs of RL, these are ranked by the hybrid BWM-ARAS method with Z-numbers. Results reveal which company appears to perform best with respect to their RL implementations. Based on this result, healthcare device companies should choose the highest priority company based on the selected RL CSFs and results from using the BWM-ARAS method with Z-numbers.

The contribution of this paper is using a hybrid ARAS-BWM method based on Z-numbers. Each of these methods has some merits compared to other similar methods. The combination of these methods contributes a new approach for prioritizing companies based on RL CSFs with high accuracy and reliability.

The oil and gas industry is a crucial economic sector for both developed and developing economies. Delays in extraction and refining of these resources would adversely affect industrial players, including that of the host countries. Supplier selection is one of the most important decisions taken by managers of this industry that affect their supply chain operations. However, determining suitable suppliers to work with has become a phenomenon faced by these managers and their organizations. Furthermore, identifying relevant, critical and important criteria needed to guide these managers and their organizations for supplier selection decisions has become even more complicated due to various criteria that need to be taken into consideration. With limited works in the current literature of supplier selection in the oil and gas industry having major methodological drawbacks, the purpose of this paper is to develop an integrated approach for supplier selection in the oil and gas industry.

To address this problem, this paper proposes a new uncertain decision framework. A grey-Delphi approach is first applied to aid in the evaluation and refinement of these various available criteria to obtain the most important and relevant criteria for the oil and gas industry. The grey systems theoretic concept is adopted to address the subjectivity and uncertainty in human judgments. The grey-Shannon entropy approach is used to determine the criteria weights, and finally, the grey-EDAS (evaluation based on distance from average solution) method is utilized for determining the ranking of the suppliers.

To exemplify the applicability and robustness of the proposed approach, this study uses the oil and gas industry of Iran as a case in point. From the literature review, 21 criteria were established and using the grey-Delphi approach, 16 were finally considered. The four top-ranked criteria, using grey-Shannon entropy, include warranty level and experience time, relationship closeness, supplier’s technical level and risks which are considered as the most critical and influential criteria for supplier evaluation in the Iranian oil and gas industry. The ranking of the suppliers is obtained, and the best and worst suppliers are also identified. Sensitivity analysis indicates that the results using the proposed methodology are robust.

The proposed approach would assist supply chain practicing managers, including purchasing managers, procurement managers and supply chain managers in the oil and gas and other industries, to effectively select suitable suppliers for cooperation. It can also be used for other multi-criteria decision-making (MCDM) applications. Future works on applying other MCDM methods and comparing them with the results of this study can be addressed. Finally, broader and more empirical works are required in the oil and gas industry.

This study is among the first few studies of supplier selection in the oil and gas industry from an emerging economy perspective and sets the stage for future research. The proposed integrated grey-based MCDM approach provides robust results in supplier evaluation and can be used for future domain applications.

This study focuses on the Critical Success Factors (CSF) for developing sustainable entrepreneurship in the Pakistani telecommunication industry. Despite the efforts made by governments and stakeholders to stimulate sustainable entrepreneurship initiatives, contributions in the telecommunications sector are lacking. Therefore, this study has the major objective of identifying a transformation path for these firms. This is done by providing a theoretical framework for sustainable entrepreneurship in the telecommunications industry, focusing on managerial and operational practices that should be modified according to a set of CSFs identified by experts in Pakistani firms.

This article proposes a novel Multiple Attribute Decision Making (MADM) approach based on Grey Systems Theory (GST) and Best-Worst Method (BWM) while unveiling endogenous relationships among current managerial/operational practices and the CSFs for sustainable entrepreneurship in the telecommunications industry.

CSFs for achieving sustainable entrepreneurship in the Pakistani telecommunications industry were found to rely on a tripod, based on effectiveness, transparency, and accountability that are embedded within the ambit of managerial and operational practices, such as focusing and reducing digital illiteracy, targeting poor communities, helping the young in structuring start-ups.

This article contributes to the MADM research stream by proposing a novel use of the BWM technique based on GST to promote sustainable entrepreneurship CSFs in Pakistani telecommunications firms.

This paper aims to propose a new benchmarking framework that uses a series of existing intuitive and analytical methods to systematically capture both objective data and subjective beliefs and preferences from a group of decision makers (DMs).

The proposed framework combines the excellence model developed by the European Foundation for Quality Management with the Rembrandt method, the entropy concept, the weighted‐sum approach, and the theory of the displaced ideal. Hard data and personal judgments are synthesized to evaluate a set of business units (BUs) with two overall performance scores plotted in a four quadrant model.

The two performance scores are used to benchmark the performance of the BUs in accordance with their Euclidean distance from the “ideal” BU. Quadrants are used to classify the BUs as efficacious, productive ineffectual, proficient unproductive, and inefficacious. The efficacious BUs, referred to as “excellent”, fall in the competency zone and have the shortest Euclidean distance from the ideal BU relative to their peers.

The benchmarking framework presented in this study has some obvious attractive features. First, the generic nature of the framework allows for the subjective and objective evaluation of a finite number of BUs by a group of DMs. Second, the information requirements of the framework are stratified hierarchically allowing DMs to focus on a small area of the large problem. Third, the framework does not dispel subjectivity; it calibrates the subjective weights with the objective weights determined through the entropy concept.

This research aims to understand the primary challenges encountered by entrepreneurs operating in emerging economies, where entrepreneurship plays a vital role. The study places a particular emphasis on entrepreneurs in Brazil.

The research methodology involved the analysis of data obtained from interviews, using both content analysis and Grey Relational Analysis techniques.

The analysis revealed several prominent difficulties that entrepreneurs face in these domains. These challenges encompassed issues such as grappling with intricate taxation systems and the associated tax burden, navigating government bureaucracy, securing access to essential financing and initial investments, contending with the absence of supportive government programs and addressing the dynamic nature of market conditions. The findings on the most critical barriers reveal potential pathways for entrepreneurs, policymakers and universities to act in developing the entrepreneurial ecosystem in emerging economies.

The insights garnered from this research have the potential to inform the formulation of robust public policies aimed at fostering entrepreneurship and innovation in emerging countries. Furthermore, these findings can serve as a valuable resource for planning initiatives designed to train engineers to become successful entrepreneurs.