An Entropy-Based Formulation for the Support of Sustainable Mass Customization 4.0

Abstract

Industry 4.0, an information and communication umbrella of terms that includes the Internet of Things (IoT) and cyber-physical systems, aims to ensure the future of the manufacturing industry competing in a proper environment of mass customization: demand for short delivery time, high quality, and small-lot products. Within this context of an Industry 4.0 mass customization environment, success depends on its sustainability, where the latter can only be achieved by the manufacturing efficiency of the smart factory-based Industry 4.0 transforming processes. Even though Industry 4.0 is associated with an optimal resource and energy productivity/efficiency, it becomes necessary to answer if the integration of Industry 4.0 elements (like CPS) has a favorable sustainability payoff. This requires performing energy consumption what-if analyses. The original contribution of this paper is the use of the entropy-based formulation as an alternative way of performing the initial steps of the energy consumption what-if analyses. The usefulness of the proposed approach is demonstrated by comparing the results of a discrete-event simulation model of mass customization 4.0 environment and the values obtained by using the entropy-based formulation. The obtained results suggest that the entropy-based formulation acts as a fairly good trend indicator of the system’s performance parameters increase/decrease. The managerial implications of these findings are presented at the end of this document.

Systemic and cyber-physical methodology for the implementation of Industry 4.0 in Mexican companies of the automotive manufacturing industry

“…For this, there are several existing methodologies to analyze the complexity of production systems (Efthymiou, Mourtzis, Pagoropoulos, Papakostas, & Chryssolouris, 2016). Among these, those that have already had the focus on quantifying the complexity in systems where the implementation of I4.0 has been standing out, like methods based on Shannon's entropy (Martínez-Olvera, 2020a, 2020bMourtzis, Fotia, Boli, & Vlachou, 2019), where those that introduce fractional measures of one or more parameters stand out recently (Lopes & Machado, 2020; Ramirez Arellano, Hernández Simón, & Bory Reyes, 2021; Ramirez Arellano, Sigarreta Almira, & Bory Reyes, 2020); the vector analysis (Dimitris, Sophia, Nikoletta, & Pietro, 2018;Mourtzis, Fotia, & Boli, 2017); the Strategic Complexity Management (SCM) framework (Freund, Al-Majeed, & Millard, 2021a, 2021b; as well as Models-Based on Systems Engineering (MBSE) (Akundi & Lopez, 2021).…”