A FBWM-PROMETHEE approach for industrial robot selection

Industrial engineering; Multidisciplinary design optimization; Manufacturing engineering; Technology management; Operations management; Industry management; Business management; Industrialization; Industrial robots; Fuzzy best-worst method; PROMETHEE; MCDM; Robot selection; Criteria.publishersversionpublishe

The impact of big data adoption on smes’ performance

Funding Information: Acknowledgments: This work was supported by the Portuguese Foundation for Science and Technology (FCT) and the Center of Technology and Systems (CTS). Publisher Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland.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.publishersversionpublishe

Simultaneous interpretive structural modelling and weighting (SISMW)

Multi-criteria decision-making (MCDM) methods have been implemented in many fields. In the meantime, several methods have been proposed to obtain the weight of the criteria determined by various methods in different ways. In this paper, a new approach, called simultaneous interpretive structural modelling and weighting (SISMW), is proposed to solve a multi-criterion decision-making (MCDM) problem. Using SISMW, the weight of the criteria and the relationship between them could be determined simultaneously. In this approach, like the ISM method, pair comparison between criteria was made by the decision-maker to determine the relationships among the different criteria. With the help of this data, the weight of the criteria, as well as the causal (cause and effect) relationships between them, were determined in 12 steps. The main advantage of this method is that only one stage of data collection is required for obtaining weights and modelling, and so the research process may be faster. This may increase the reliability of the collected data because, in a one-step survey, the impact of time is minimized. This process can be useful for conceptualizing and developing theories to help decisionmakers understand the problem better