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This paper aims to evaluate and prioritize the key supplier selection indicators and to establish the relationship between available alternatives and selected indicators by using step-wise weight assessment ratio analysis (SWARA) and weighted aggregated sum product assessment (WASPAS).

Authors have extracted the supplier selection criteria from literature and used a combined SWARA-WASPAS method to evaluate and rank the criteria’s. SWARA is applied for evaluating and weighting selection criteria, whereas WASPAS helped in evaluating different available alternatives based on supplier selection indicators.

Finding from SWARA suggests that supplier management is the high weighted criteria followed by information sharing and joint actions. WASPAS was used to evaluate the available alternatives and supplier A1 got the highest priority. Additionally, sensitivity analysis indicates the different scenarios for the best supplier selection.

Working executives can use the SWARA for assessment of weights of finalized indicators for their firm in the cement industry. Further, the calculated weights can be used for product and sum weightage through WASPAS to finalize the best supplier.

The originality of the manuscript lies in the sector and methodology. Author(s) applied the SWARA and WASPAS method for supplier selection in the Indian cement industry that will help working executives to evaluate their supply chain partners.

Effective educational methods are critical for successfully training future supply chain talent. The paper proposes a fuzzy multi-criteria decision-making model to evaluate and select the best educational method for tomorrow's supply chain leaders integrating skill development priorities in an uncertain environment.

The Grounded theory scheme is used to identify SC leaders' skillsets criteria and educational method alternatives. Fuzzy step-wise weight assessment ratio analysis sets the priority and determines the weight of 17 criteria. Eight decision-makers evaluate 13 alternatives using fuzzy linguistic terms. Fuzzy technique for order preference by similarity to ideal solution ranks and shows the most effective educational method. Sensitivity analysis presents the applicability of this study.

Its implementation in a university-industry collaboration case in Brazil, Mentored learning from industry experts is the best educational method. The skill development priorities are data analytics ability, end-to-end supply chain vision and problem-solving. Technical skills are the most important criteria that influence the selection of the optimal option and educational methods related to learning from others rank in the top teaching pool, including multidisciplinary cross-cultural training.

This paper is among the first to evaluate educational methods with skill development priorities integration for supply chain students using fuzzy SWARA–fuzzy TOPSIS. It provides actionable insights: a decision-making procedure for educational method selection, a broad skills profile for supply chain professional success and educational methods that professors can bring to in classroom/virtual environment.

In the recent scenario, there has been an increasing trend toward lean practices and implementation in production systems for the improvement of an organization’s performance as its basic nature is to eliminate the wastes. The increasing interest of customers in customized products and the fulfillment of customers’ demand with good productivity and efficiency within time are the challenges for the manufacturing organization; that is why adopting lean manufacturing concept is very crucial in the current scenario.

In this paper, the authors considered three different methodologies for fulfilling the objective of our research. The analytical hierarchy process, best–worst method and fuzzy step-wise weight assessment ratio analysis are the three methods employed for weighting all the enablers and finding the priority among them and their final rankings.

Further, the best results among these methodologies could be used to analyze their interrelationships for successful lean supply chain management implementation in an organization. In this paper, 35 key enablers were identified after the rigorous analysis of literature review and the opinion of a group of experts consisting of academicians, practitioners and consultants. Thereafter, the brainstorming sessions were conducted to finalize 28 lean supply chain enablers (LSCEs).

For lean manufacturing practitioners, the result of this study can be beneficial where the manufacturer is required to increase efficiency and reduce cost and wastage of resources in the lean manufacturing process.

This paper is the first of the research papers that considered deep literature review of identified LSCEs as the initial step, followed by finding the best priority weightage and developing the ranking of various lean enablers of supply chain with the help of various methodologies.

The purpose of this study is to present a new failure mode and effects analysis (FMEA) approach based on fuzzy multi-criteria decision-making (MCDM) methods and multi-objective programming model for risk assessment in the planning phase of the oil and gas construction projects (OGCP) in Iran.

This research contains multiple steps. First, 19 major potential health and safety executive (HSE) risks in OGCP were classified into six categories with the Delphi method. These factors were distinguished by the review of project documentation, checklist analysis and consulting with experts. Then, using the fuzzy SWARA method, the authors calculated the weights of major HSE risks. Subsequently, FMEA and PROMETHEE approaches were used to identify the priority of main risk factors. Eventually, a binary multi-objective linear programming approach was developed to select the risk response strategies, and an augmented e-constraint method (AECM) was used.

Regarding the project triple well-known constraints of time, cost and quality, which organizations usually confront, the HSE risks of OGCP were identified and prioritized. Also, the appropriate risk response strategies were also suggested to the managers to be adopted regarding the situations.

The present research points at the HSE risks’ assessment integrating the fuzzy FMEA, step-wise weight assessment ratio analysis and PROMETHEE techniques with the AECM. Further to the authors’ knowledge, the quantitative assessment of the HSE risks of OGCP has not been done using the combination of the fuzzy FMEA, MCDM and AECMs.

The purpose of this paper is to propose a new framework for assessing the risks of turnaround projects in upstream oil process plants.

This study represents a new hybrid framework for turnaround project risk assessment. First, according to experts’ opinions, the project risks were identified using interviews and brainstorming. The most important risks selected by experts and a hybrid multiple-attribute decision-making (MADM) method used to assess and prioritize them. The proposed MADM method uses fuzzy step-wise weight assessment ratio analysis (SWARA) and fuzzy evaluation based on distance from average solution (EDAS) methods based on trapezoidal fuzzy numbers.

In this research, 28 usual risks of turnaround projects are identified and 10 risks are then selected as the most important ones. The findings show, that among the risks of upstream oil industry turnaround projects from the perspective of experts, the risk of timely financing by the employer, with an appraisal score of 0.83, has the highest rank among the risks and the risk of machine and equipment failure during operation, with an appraisal score of 0.04, has the lowest rank.

The risk analysis based on inputs collected from the experts in the Iranian upstream oil industry, and so the generalization of the results is limited to the context of developing countries, especially oil producer ones. However, the proposed risk analysis methodology and key insights developed can be useful for researchers and practitioners in any other process industry everywhere.

A novel framework for risk assessment is introduced for turnaround projects in the oil industry using MADM methods. There is no literature on using MADM methods for turnaround project risk analysis in the oil and gas industries. Furthermore, this paper presents a hybrid fuzzy method based on SWARA and EDAS.

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.

The implementation of circular economy strategies is one of the central objectives of several governments seeking a transition toward a sustainable development. Circular economy in agriculture deals with the production of agricultural commodities making an efficient use of resources and avoiding unnecessary waste and carbon emission generation. Disruptions in the production and supply of critical agricultural products can have serious negative repercussions for firms and consumers of the food supply chain. In recent decades, disruptions generated by natural disasters such as hurricanes, thunderstorms and floods have greatly impacted social communities and industrial sectors. Supply chain risks approaches are seen to contribute key elements to address the impacts of natural disaster toward the implementation of circular economy in agriculture, helping to prevent collapses in the production and supply of food. The purpose of this paper is to study and identify flood risk drivers and their effects on the sustainability of an agriculture supply chain in connection with a circular economy strategy. By using an extended Step-wise Weight Assessment Ratio Analysis method combined with a multi-criteria decision analysis, the most essential flood drivers with a degree of importance are reported here. Then, the authors propose an Evaluation of Data based on average ASsessment method, to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. The application of this research lies within the framework of a real agricultural project founded by the European Commission Scientific Section, called RUC-APS.

The authors use management science-based tools to address circular economy in agriculture. The authors propose a multi-criteria-based methodology to assess the risks of flooding in crops areas. To validate the proposed methodology, a case example from Spain is discussed to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas.

The proposed multi-criteria methodology confirmed a successful application to rank different agricultural projects that pretend to mitigate the flood risks and its impacts on crop areas. Organizations and firms in the agricultural business can use the methodology to identify risks drivers and to detect the best projects to mitigate the highest impacts of flooding risks in crops areas.

The authors use supply chain risks approaches to address the impacts of natural disaster on the implementation of circular economy in agriculture. The authors propose a robust multi-criteria-based methodology to assess the risks of flooding in crops areas and we used to determine the best mitigating projects to face flooding risks on crop areas.

With the rapid advancement of technology, companies use new technologies to produce their products and services to maintain a competitive advantage. As companies alone cannot research and develop their technologies, they should use knowledge sources outside the organization that may exist throughout the world; hence, organizations need technology transfer. Because the success rate of technology transfer projects is low, the need to accurately assess and investigate the critical success factors of technology transfer projects is felt. In this regard, this study aims to identify and prioritize the critical success factors in technology transfer projects.

In this research, 56 critical success factor (CSF) were extracted from the context of the articles and were adjusted using experts’ opinions in different phases, as well as the fuzzy-Delphi approach. Finally, 15 factors were categorized in the form of steps of the technology transfer model: STAGE-GATE. In the next step, the set of criteria needed to prioritize CFSs was extracted from the literature and finalized with the help of the experts. Then, how each of the CSF influences the identified criteria was scored according to the organization’s export opinions. Finally, the priority of each key success factor was calculated using the additive ratio assessment (ARAS) method.

The results obtained for prioritization of the critical success factors show that experience in technology transfer in the transferee company, the existence of experienced technology transfer managers, sufficient organizational infrastructure and documenting project problems, achievements and experiences are four critical success factors of the technology transfer projects. Considering the long-term and short-term specific goals of the technology transfer process and the choice of technology in line with the company’s commercial strategy are also the critical success factors with the next priorities.

The combination of ARAS and step-wise weight assessment ratio analysis methods for identifying and prioritizing managerial decisions in the high-tech industries is a value of this research. Also, a combination of novel multi-attribute decision-making methods by the older framework of new product development is another contribution of this research.

The present study is aimed to develop a firm export performance measurement model. Increased levels of globalization are forcing firms, especially SMEs (i.e. small and medium-sized enterprises) and entrepreneurial firms, to enter new markets and increase their exports. Consequently, export performance measurement has become a vital tool for achieving competitive advantages, and this type of measurement is considered an important topic of research in the field of international marketing. However, the literature lacks a comprehensive framework for measuring export performance, even though many criteria for gauging this performance's parameters have been discovered and applied.

A literature review was conducted, and the most important criteria for export performance measurement was identified using weights obtained by stepwise weight assessment ratio analysis method. Then, the export performance of five firms was evaluated using the additive ratio assessment method with interval-valued triangular fuzzy numbers.

The 15 most significant criteria were identified and categorized into three dimensions: economic, strategic, and other. The strategic dimension was identified as the most important, while the criteria “strategic goals achievement” and “return on investment” were found to be the most relevant.

A comprehensive framework including a definitive set of the most prominent, useful evaluation criteria was established which enables both scholars and practitioners to assess export performance.

The current study attempts to fill the gaps in the literature by presenting a comprehensive framework for export performance measurement.

Country and company bankruptcies at international level have put the economies of the world and countries in a difficult position. As a result of these negative developments, the measurement of the effectiveness of internal audits system together with accounting and audits have become important. The unit that plays a key role in measuring the effectiveness of internal control, whether in the private or public sector, is internal audit. In this respect, the purpose of the present study was to outline the criteria that increase the effectiveness of internal audit in public institutions. For this purpose, the SWARA Method was used. The SWARA Method is a multi-criterion decision-making method that is employed by decision-makers to determine the weights of the criteria and to sort them out. The Questionnaire of the study was applied to a participant group that consisted of 11 experts. According to the findings, the criteria that had the highest importance were “the presence of an independent internal audit activity and impartial internal auditors,” “Performing internal audit activities in line with ethical rules, standards, and relevant regulations,” and “Determining the risks regarding the objectives and the purpose of the institution, and measuring the effects of these risks.”