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Leveraging AI in Industry 4.0: Overcoming Challenges and Seizing Opportunities for Sustainable Operations Management

A special issue of Sustainability (ISSN 2071-1050).

Deadline for manuscript submissions: 30 September 2025 | Viewed by 1999

Special Issue Editors

Dr. Marco Mosca

E-Mail Website
Guest Editor
DIME—Department of Mechanics, Energetics, Management and Transportation Engineering, University of Genoa, 16145 Genoa, Italy
Interests: engineering 4.0; mechanical industrial systems
Prof. Dr. Flavio Tonelli

E-Mail Website
Guest Editor
Department of Mechanical Engineering, Energetics, Management and Transportation (DIME), Polytechnic School University of Genoa, 16145 Genova, Italy
Interests: industrial sustainability; digital manufacturing; supply chain management; performance management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The advent of the fourth industrial revolution has introduced a series of innovative technologies, with artificial intelligence (AI) playing a pivotal role in redefining how companies manage their operations and drive sustainability. The following Special Issue aims to explore Industry 4.0 in depth within the field of operations management, focusing on AI-driven technological innovations and emerging trends shaping the future of industries toward sustainable practices.

Authors are encouraged to contribute original articles delving into topics such as:

  1. The implementation and integration of AI with other Industry 4.0 technologies in operational processes, including the IIoT, big data analytics, cloud computing, augmented reality and CPS, with an emphasis on sustainability.
  2. The applications of AI with advanced technologies such as 3D printing, CNC, collaborative robotics and intelligent automation to enhance efficiency, flexibility and sustainability in production processes.
  3. The development of innovative, AI-enabled business models facilitated by Industry 4.0, such as servitization, distributed manufacturing, smart factories and their contributions to sustainability.
  4. The impact of AI in Industry 4.0 on the design and management of supply chains, including product traceability, visibility along the entire distribution chain and sustainable practices.
  5. Challenges and solutions in adopting AI within Industry 4.0 technologies, including workforce training, cybersecurity, organizational change management and strategies for achieving sustainability goals.

The following Special Issue aims to provide a comprehensive overview of the latest research and best practices in the field of AI within Industry 4.0 in operations management, promoting debate and knowledge exchange among academics and industry professionals internationally, with a strong focus on sustainability.

Dr. Marco Mosca
Prof. Dr. Flavio Tonelli
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • sustainability
  • artificial intelligence
  • Industry 4.0
  • operations management
  • IIOT
  • CPS
  • automation
  • robotics
  • digital transformation

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Published Papers (2 papers)

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Research

21 pages, 2949 KiB  
Article
Memetic Optimization of Wastewater Pumping Systems for Energy Efficiency: AI Optimization in a Simulation-Based Framework for Sustainable Operations Management
by Agostino G. Bruzzone, Marco Gotelli, Marina Massei, Xhulia Sina, Antonio Giovannetti, Filippo Ghisi and Luca Cirillo
Sustainability 2025, 17(14), 6296; https://doi.org/10.3390/su17146296 - 9 Jul 2025
Viewed by 307
Abstract
This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of [...] Read more.
This study investigates the integration of advanced optimization algorithms within energy-intensive infrastructures and industrial plants. In fact, the authors focus on the dynamic interplay between computational intelligence and operational efficiency in wastewater treatment plants (WWTPs). In this context, energy optimization is thought of as a hybrid process that emerges at the intersection of engineered systems, environmental dynamics, and operational constraints. Despite the known energy-intensive nature of WWTPs, where pumps and blowers consume over 60% of total power, current methods lack systematic, real-time adaptability under variable conditions. To address this gap, the study proposes a computational framework that combines hydraulic simulation, manufacturer-based performance mapping, and a Memetic Algorithm (MA) capable of real-time optimization. The methodology synthesizes dynamic flow allocation, auto-tuning mutation, and step-by-step improvement search into a cohesive simulation environment, applied to a representative parallel-pump system. The MA’s dual capacity to explore global configurations and refine local adjustments reflects both static and kinetic aspects of optimization: the former grounded in physical system constraints, the latter shaped by fluctuating operational demands. Experimental results across several stochastic scenarios demonstrate consistent power savings (12.13%) over conventional control strategies. By bridging simulation modeling with optimization under uncertainty, this study contributes to sustainable operations management, offering a replicable, data-driven tool for advancing energy efficiency in infrastructure systems. Full article
(This article belongs to the Special Issue Leveraging AI in Industry 4.0: Overcoming Challenges and Seizing Opportunities for Sustainable Operations Management)
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18 pages, 1113 KiB  
Article
Revolutionizing End-of-Life Product Recovery with Product 4.0: An Examination of Intelligent Products in Industry 4.0
by Valentina Popolo, Silvestro Vespoli, Mosè Gallo and Andrea Grassi
Sustainability 2024, 16(24), 11017; https://doi.org/10.3390/su162411017 - 16 Dec 2024
Cited by 2 | Viewed by 1036
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. [...] Read more.
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. Full article
(This article belongs to the Special Issue Leveraging AI in Industry 4.0: Overcoming Challenges and Seizing Opportunities for Sustainable Operations Management)
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