This paper aims to identify resilient supplier selection criteria in the desalination supply chain and analyze the interactions between them. We identify 89 criteria from the literature. In the second step, nineteen main criteria were identified based on the literature. Two different multi-criteria decision-making techniques, i.e. interpretive structural modeling (ISM) and fuzzy decision-making testing and evaluation laboratory (fuzzy-DEMATEL), were used to identify driving criteria. Furthermore, standard criteria from each technique, their hierarchies and inter-relationships have been established. The criteria modeling using ISM and Fuzzy-DEMATEL shows that the most influential/driving criteria are management, financial status, and culture. On the other hand, five criteria viz. service, quality, relationship building, adaptive capacity, and responsiveness were commonly identified as the most dependent resilient supplier selection criteria in the desalination supply chain across two different modeling techniques. The findings allow decision-makers to select the desired and drop undesired criteria to select resilient suppliers in desalination. The most common driving criteria can be given high priority over others and can significantly improve the resilient supplier selection. MCDM-based hierarchical models and a causal diagram for resilient supplier selection criteria depicting contextual inter-relationships have been proposed. The main novelty of this research is to identify and modeling resilient supplier selection criteria. Also, multi-criteria decision-making techniques have not been used in desalination supply chain.

In recent years, the selection of a robot for particular industrial purposes is one of the most challenging problems in the manufacturing environment based on automation and smartness for real-time decision-making. At present, several types of industrial robots with various capabilities, features, facilities, and specifications are available in the market. This makes the decision-making process more and more complicated due to the increase in complexity, advanced technologies, and features that are continually being incorporated into the robots by several manufacturers. The decision-maker needs to identify and select the best-suited robot to attain the desired output with precise application ability, and minimum cost. This paper tries to solve the robot selection problem using Fuzzy Best-Worst Method and PROMETHEE as the two most appropriate multi-criteria decision-making (MCDM) methods for weighting criteria and ranking of decision alternatives, respectively.

The notion of Industry 4.0 encompasses the adoption of new information technologies that enable an enormous amount of information to be digitally collected, analyzed, and exploited in organizations to make better decisions. Therefore, finding how organizations can adopt big data (BD) components to improve their performance becomes a relevant research area. This issue is becoming more pertinent for small and medium enterprises (SMEs), especially in developing countries that encounter limited resources and infrastructures. Due to the lack of empirical studies related to big data adoption (BDA) and BD’s business value, especially in SMEs, this study investigates the impact of BDA on SMEs’ performance by obtaining the required data from experts. The quantitative investigation followed a mixed approach, including survey data from 224 managers from Iranian SMEs, and a structural equation modeling (SEM) methodology for the data analysis. Results showed that 12 factors affected the BDA in SMEs. BDA can affect both operational performance and economic performance. There has been no support for the influence of BDA and economic performance on social performance. Finally, the study implications and findings are discussed alongside future research suggestions, as well as some limitations and unanswered questions.

The purpose of this study is to ‘investigate the impact of coercive and non-coercive drivers of supply chain sustainability in chain performance, with regard to the role of intermediary monitoring and collaboration’ in one of the automotive companies. In this study, a developed conceptual model was designed and validity and reliability of designed questionnaire were tested and confirmed. To gather the data, the statistical population included all stuff of reputable automaker. A statistical sample of 188 persons was selected using Cochran formula by simple random sampling method. The results of the hypothesis with 95% confidence interval using structural equation modelling using AMOS 26 software showed that: monitoring has an impact of 0.66 on sustainability while collaboration effect is 0.22. Also, monitoring has no impact on sustainability. Meanwhile, a non-coercive driver impact on collaboration is about 0.26 VS coercive drivers have no impact. Contrarily non-coercive driver’s impact on monitoring is about 0.39 while a coercive driver is 0.74. Sustainable Supply Chain Management is a popular subject in the area of operations management for both researchers and practitioners. Supply chain sustainability is availability to reduce long-term risks associated with resources consumption, energy cost fluctuations, product commitments, and pollution management, which establishes an appropriate balance between economic development, environmental monitoring, and social assets.

Evaluating organizational readiness for adopting new technologies always was an important issue for managers. This issue for complicated subjects such as Big Data is undeniable. Managers tend to adopt Big Data, with the best readiness. But this is not possible unless they can assess their readiness. In the present paper, we propose a model to evaluate the organizational readiness for Big Data adoption. To accomplish this objective, firstly, we identified the criteria that impact organizational readiness based on a comprehensive literature review. In the next step using Principal Component Analysis (PCA) for criterion reduction and integration, twelve main criteria were identified. Then the hierarchical structure of criteria was developed. Further, Fuzzy Best- Worst Method (FBWM) has been used to identify the weight of the criteria. The finding enables decision-makers to appropriately choose the more important criteria and drop unimportant criteria in strengthening organizational readiness for Big Data adoption. Statistics-based hierarchical model and MCDM based criteria weighting have been proposed, which is a new effort in evaluating organizational readiness for Big Data adoption.

The notion of Industry 4.0 encompasses the adoption of new information technologies that enable an enormous amount of information to be digitally collected, analysed, and exploited in organizations to make better decisions. Therefore, finding how organizations can adopt big data components to improve their performance became a relevant research area. This issue is becoming more pertinent for SMEs. This study investigates the impact of big data adoption on SMEs performance by obtaining the required data from experts. The quantitative investigation followed a mixed approach, including survey data from 224 managers from Iranian SMEs, and a SEM methodology for the data analysis. Results showed that 12 factors affected the BDA in SMEs. BDA can affect both operational performance and economic performance, there has been no support for the influence of BDA and economic performance on social performance. Finally, the study implications and findings are discussed alongside future research avenues and limitations.