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AI Adoption in Industry 4.0 and Smart Factories as a Sustainability Enabler

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Management".

Deadline for manuscript submissions: 30 April 2027 | Viewed by 1902

Editors

Prof. Dr. Paulo Ávila

E-Mail Website
Guest Editor
1. ISEP—School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
2. INESC TEC—Instituto de Engenharia de Sistemas e Computadores, 4200-465 Porto, Portugal
Interests: resource selection; process and quality improvement; virtual and agile enterprises; optimization; sustainability development; AI applications
Dr. Alzira Mota

E-Mail Website
Guest Editor
1. ISEP—School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
2. INESC TEC—Instituto de Engenharia de Sistemas e Computadores, 4200-465 Porto, Portugal
Interests: entropy; modeling; evolutionary computing; nonlinear optimization; meta-heuristics
Dr. Luís Pinto Ferreira

E-Mail Website
Guest Editor
1. ISEP, Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 4249-015 Porto, Portugal
2. Associate Laboratory for Energy, Transports and Aerospace (LAETA-INEGI), Rua Dr. Roberto Frias 400, 4200-465 Porto, Portugal
Interests: industrial simulation; manufacturing; Industry 4.0
Special Issues, Collections and Topics in MDPI journals
Dr. Hélio Castro

E-Mail Website
Guest Editor
1. ISEP—School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
2. INESC TEC—Instituto de Engenharia de Sistemas e Computadores, 4200-465 Porto, Portugal
Interests: agile manufacturing; manufacturing engineering; ubiquitous manufacturing; virtual organizations; organizations complexity; meta-organization
Prof. Dr. João Bastos

E-Mail Website
Guest Editor
1. ISEP—School of Engineering, Polytechnic of Porto, 4200-072 Porto, Portugal
2. INESC TEC—Instituto de Engenharia de Sistemas e Computadores, 4200-465 Porto, Portugal
Interests: production planning; scheduling; meta-heurisitcs; machine learning tools

Special Issue Information

Dear Colleagues,

The adoption of Artificial Intelligence (AI) within Industry 4.0 and smart factories represents a crucial step toward achieving sustainable industrial development. This Special Issue will explore how AI serves as a key enabler of sustainability by optimizing energy consumption, reducing waste, extending equipment life, and improving resource efficiency across manufacturing processes. The scope includes the integration of AI with other Industry 4.0 technologies—such as IoT, big data analytics, and cyber-physical systems—to create intelligent, adaptive production environments that align with environmental, social, and economic sustainability goals. Real and modelled use cases are also welcome.

The existing literature highlights the transformative impact of AI on operational performance, but sustainability is still emerging as a focal point of academic and industrial research. While prior studies have examined AI’s potential efficiency gains and automation technology, fewer works have systematically addressed its role in enabling sustainable manufacturing practices. Recent research trends emphasize the importance of digital technologies in supporting the UN Sustainable Development Goals (SDGs), especially within industrial contexts. This Special Issue will build upon such insights, offering a comprehensive view of AI not just as a tool for productivity, but as a strategic resource for driving green innovation, reducing environmental footprints, and fostering long-term resilience in manufacturing systems.

Prof. Dr. Paulo Ávila
Dr. Alzira Mota
Dr. Luís Pinto Ferreira
Dr. Hélio Castro
Prof. Dr. João Bastos
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 250 words) can be sent to the Editorial Office for assessment.

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-anonymized 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

  • artificial intelligence
  • Industry 4.0
  • smart factory
  • sustainability
  • green innovation
  • use cases

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Published Papers (3 papers)

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Research

38 pages, 881 KB  
Article
Digitalisation and Sustainable Operational Performance in Sub-Saharan African Mining Companies: Evidence from Panel Data
by Shabir Ahmed and Lawrence Ogechukwu Obokoh
Sustainability 2026, 18(16), 8474; https://doi.org/10.3390/su18168474 - 18 Aug 2026
Viewed by 209
Abstract
Digital transformation is reshaping the mining industry by improving resource efficiency and environmental performance. However, empirical evidence explaining how, why, and under which organizational and institutional conditions digitalisation enhances sustainable operational performance (SOP) in Sub-Saharan African mining remains limited, despite the region’s strategic [...] Read more.
Digital transformation is reshaping the mining industry by improving resource efficiency and environmental performance. However, empirical evidence explaining how, why, and under which organizational and institutional conditions digitalisation enhances sustainable operational performance (SOP) in Sub-Saharan African mining remains limited, despite the region’s strategic role in global mineral supply. This study examines the effect of digitalisation on sustainable operational performance using longitudinal panel data from 48 mining companies operating in Sub-Saharan Africa between 2013 and 2022. Digitalisation is conceptualized as a multidimensional organizational capability and measured through a Digitalisation Index. The index was systematically derived from corporate annual environmental, social and governance reports using transparent coding procedures and Principal Component Analysis, enhancing measurement transparency and reproducibility. SOP is measured using a composite index encompassing operational efficiency, equipment utilization and maintenance effectiveness, resource utilization and environmental sustainability, and occupational health and safety. Fixed effects panel regression serves as the primary estimator, while the two-step System Generalized Method of Moments addresses endogeneity and dynamic persistence, with robustness analyses confirming result stability. The findings show that digitalisation significantly enhances sustainable operational performance by transforming digital resources into organizational capabilities that strengthen operational resilience, optimize resource allocation, and improve sustainability outcomes. By integrating the Resource-Based View, Dynamic Capabilities Theory, the TOE framework, and the Natural Resource-Based View into a unified explanatory framework, this study advances theory while providing practical guidance for digital capability development and Industry 4.0 investment and informing policies that strengthen digital infrastructure, institutional readiness, and regulatory support for sustainable mining in Sub-Saharan Africa. Full article
(This article belongs to the Special Issue AI Adoption in Industry 4.0 and Smart Factories as a Sustainability Enabler)
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21 pages, 2503 KB  
Article
Artificial Intelligence as Effectiveness Enabler of Dynamic Reconfiguration of Systems Architecture in Industry 5.0
by Luís Ferreira, Eduardo Gonçalves, Goran D. Putnik, João Pedro Silva and Paulo Ávila
Sustainability 2026, 18(15), 7913; https://doi.org/10.3390/su18157913 - 4 Aug 2026
Viewed by 302
Abstract
Industrial operations increasingly face high-stakes decisions that involve people, data streams, simulations, and control systems. Urgent sessions often require external expertise, retrieval of documents and live telemetry, running what-if simulations, and verifying safety constraints. These scenarios highlight the need for secure interoperability, explainable [...] Read more.
Industrial operations increasingly face high-stakes decisions that involve people, data streams, simulations, and control systems. Urgent sessions often require external expertise, retrieval of documents and live telemetry, running what-if simulations, and verifying safety constraints. These scenarios highlight the need for secure interoperability, explainable decision support, and human-in-the-loop control. This paper presents a proposal of a technology-agnostic reference architecture that builds on Industry 4.0 frameworks by incorporating the human-centric, resilient, and sustainable principles of Industry 5.0. Its intelligent layer enables the new approach to human involvement in the process, facilitating meaningful human–machine collaboration. The proposed research provides a practical and conceptual framework for systems engineers, industrial software architects, and operations managers seeking to transition legacy operational plants into human-aligned ecosystems. Its feasibility is evaluated through a simulation-based underground mining testbed, where heterogeneous data sources and communication protocols are integrated into a common operational environment. The proof of concept shows how telemetry, data storage, machine learning models, and operator feedback can be combined to support auditable, explainable, and human-contestable industrial decisions, demonstrating the classification accuracy, remaining useful life forecasting capabilities, and enhanced recommendation precision enabled by iterative operator feedback loops. Full article
(This article belongs to the Special Issue AI Adoption in Industry 4.0 and Smart Factories as a Sustainability Enabler)
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22 pages, 1066 KB  
Article
PFAD: Parameter-Efficient Framework for Cross-Domain Anomaly Detection for Sustainable Manufacturing
by Bokuk Joo and Hail Jung
Sustainability 2026, 18(13), 6684; https://doi.org/10.3390/su18136684 - 1 Jul 2026
Viewed by 522
Abstract
Deploying visual anomaly detection in industrial production requires retraining models for each product domain, leading to substantial costs in data collection, computational resources, and energy consumption that scale poorly across diverse manufacturing environments. This paper proposes PFAD, a parameter-efficient framework for cross-domain anomaly [...] Read more.
Deploying visual anomaly detection in industrial production requires retraining models for each product domain, leading to substantial costs in data collection, computational resources, and energy consumption that scale poorly across diverse manufacturing environments. This paper proposes PFAD, a parameter-efficient framework for cross-domain anomaly detection without retraining, enabling the direct deployment of a source model trained on a benchmark dataset to unseen industrial settings in a zero-shot manner. PFAD leverages a frozen vision transformer backbone and introduces Soft Anomaly-Aware Feature Selection (Soft AFS), which assigns continuous weights to feature channels based on anomaly discriminability, preserving information while enhancing cross-domain generalization without relying on synthetic anomalies or target-domain data. Extensive experiments on both public benchmarks and real-world industrial datasets demonstrate that PFAD achieves strong cross-domain performance, including an image-level AUROC of 0.945 for semiconductor PCB inspection using only a public dataset for training. Furthermore, PFAD supports an optional one-shot inference extension, where a single normal reference image improves detection performance in scenarios with large domain gaps (up to +10.4 pp), most effectively when zero-shot transfer leaves meaningful headroom. These results demonstrate that PFAD provides a practical and scalable solution for industrial anomaly detection by eliminating repeated retraining cycles and reducing associated computational and energy overhead, while maintaining high performance across heterogeneous domains. Full article
(This article belongs to the Special Issue AI Adoption in Industry 4.0 and Smart Factories as a Sustainability Enabler)
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