Revolutionizing End-of-Life Product Recovery with Product 4.0: An Examination of Intelligent Products in Industry 4.0

Valentina Popolo (), Silvestro Vespoli, Mosè Gallo and Andrea Grassi
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Valentina Popolo: Dipartimento di Ingegneria, Università Telematica Pegaso, 80143 Napoli, Italy
Silvestro Vespoli: Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università Degli Studi di Napoli Federico II, 80125 Napoli, Italy
Mosè Gallo: Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università Degli Studi di Napoli Federico II, 80125 Napoli, Italy
Andrea Grassi: Dipartimento di Ingegneria Chimica, dei Materiali e della Produzione Industriale, Università Degli Studi di Napoli Federico II, 80125 Napoli, Italy

Sustainability, 2024, vol. 16, issue 24, 1-18

Abstract: In the context of growing environmental concerns and the increasing impact of the manufacturing sector on sustainability, this paper introduces the concept of “Product 4.0” (P4.0) as a novel approach to harnessing the potential of Artificial Intelligence (AI) within Industry 4.0 (I4.0) technologies. P4.0 focuses on optimizing the performance of the product throughout its lifecycle and improving recovery strategies at End of Use (EoU) and End of Life (EoL) stages. Through a comprehensive review of the literature, this study identifies critical gaps in the current application of AI within I4.0 for sustainable manufacturing, particularly in regard to smart product systems and their interactions with external environments. To address these gaps, the paper proposes a holistic approach for the P4.0 that leverages AI-driven data analysis and decision making to facilitate efficient product recovery and resource utilization. Additionally, a Causal Loop Diagram (CLD) model is developed to illustrate the relationships between sustainability dimensions—environmental, economic, and social—and product demand influenced by P4.0, while also discussing the challenges and limitations associated with its implementation. By bridging theoretical insights with practical recovery solutions, this research contributes to the sustainable manufacturing discourse and offers actionable directions for future investigations into AI-enhanced P4.0 applications within the manufacturing industry.

Keywords: smart product; product 4.0; end-of-life; recovery options; industry 4.0 (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2024
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