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Nowadays, the implementation of sustainability concepts and sustainability assessment frameworks are crucial factors for ensuring the competitive advantage of many industries. Many pioneer firms have spread sustainability frameworks through their supply chain from the upstream (strategic) level to the downstream (operational) level. So, sustainability assessment has as a valuable tool is increasingly considered in senior-level decision makings. The long term and dynamic behavior of parameters at the senior level are considered as challenges for the successful accomplishment of sustainability assessment. This gets more important as, by the advent of Industry 4.0 paradigm, firms are engaged in a more dynamic changing environment and have faced with rapid decision-making. While considering the role of sustainability assessment for protecting firms according to its three pillars, there would be a challenge among Industry 4.0 and sustainability assessment in smart manufacturing. In this paper, the resiliency concept has been focused on fulfilling this challenge. This paper considers the dynamic behavior of data analytics in Industry 4.0, affecting three domains of the firm, environment, and society, and uses an analytical Markov decision process-based approach to address the sustainability issues. This enables an adaptive and proactive sustainability assessment model that encompasses the vast and seamless communication of data in Industry 4.0 from factory, environment, and social perspectives and supports the robust decision-making structure for sustainability realization. The paper has elaborated on the capabilities of Markov Decision Processes (MDP) as a statistical tool for treating the dynamic of data analysis in the Industry 4.0 context while fulfilling sustainability assessment through resilience structure. A case study is designed to investigate the details of framework capabilities for supporting decision making in senior levels of the firm.
Valilai et al. (Wed,) studied this question.
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