Multi-Criteria Decision-Making for Industry 4.0: A Comprehensive AHP (Analytic Hierarchy Process) Approach to Selecting IoT Devices, AI Models, and Simulation Tools in Automotive Manufacturing

¹ IESI Laboratory, ENSET Mohammedia, Hassan 2nd University of Casablanca, Morocco
² Laboratory of Management and Systems Engineering, National School of Mines of Rabat, Morocco

^* Corresponding author: elhailouchhayat@gmail.com

Abstract

The advent of Industry 4.0 has profoundly transformed industrial ecosystems through the integration of the Internet of Things (IoT), Artificial Intelligence (AI), and advanced simulation tools. In fact, production planning and internal logistics have been substantially enhanced due to these technologies. They have also enabled the transition to sustainable smart factories. However, choosing these technological alternatives remains a multifaceted challenge for manufacturers, due to the diversity and number of criteria to be considered, such as cost, performance, interoperability, scalability, and environmental impact. This paper provides both theoretical and practical contributions, through which we propose one of the most intersting multi-criteria decision-making (MCDM) methods, specifically the Analytic Hierarchy Process (AHP) method to help optimally select IoT devices, AI approaches and simulation software in the smart automotive factories context. The methodology establishes a hierarchy of criteria, namely investment costs, forecast accuracy, compatibility with existing systems, explainability, energy usage, and carbon footprint.