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Smart Technologies for Sustainable Production
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Smart Technologies for Sustainable Production

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

Deadline for manuscript submissions: closed (30 April 2026) | Viewed by 1062

Special Issue Editor

Prof. Dr. Manuela Ingaldi

E-Mail Website1 Website2
Guest Editor
Department of Production Engineering and Safety, Faculty of Management, Czestochowa University of Technology, 42-201 Czestochowa, Poland
Interests: sustainability; production engineering; quality engineering; service quality; e-commerce; Industry 4.0 and 5.0
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In the face of dynamic climate change, limited natural resources, and increasing pressure for environmental and social responsibility, the industrial sector is undergoing a fundamental transformation. Traditional production models must give way to modern, flexible, and sustainable technological solutions. In this context, smart technologies are playing an increasingly vital role—not only by enhancing operational efficiency but also by supporting the achievement of the Sustainable Development Goals (SDGs).

Artificial intelligence, advanced robotics, cyber–physical systems, digital twins, the Internet of Things (IoT), and emerging models of human–machine collaboration (e.g., collaborative robots or autonomous mobile robots) are redefining how industrial processes are designed, managed, and optimized. Their contribution to building green, resilient, and intelligent value chains is becoming increasingly evident, both in industrial practice and academic research.

This Special Issue aims at exploring how technologies such as artificial intelligence, robotics (including collaborative robots and autonomous mobile robots), IoT, digital twins, and cyber–physical systems can contribute to increased energy efficiency, waste reduction, improved working conditions, and greater flexibility and resilience in production systems.

We welcome original research articles, case studies, and comprehensive reviews on topics including, but not limited to, the following:

  • Smart and sustainable technological solutions enhancing environmental, economic, and social performance in industrial settings;
  • Innovative applications of robotics and automation aligned with the visions of Industry 4.0 and Industry 5.0;
  • The impact of intelligent technologies on production sustainability, including energy efficiency, waste reduction, and improved worker well-being;
  • Patent landscape analyses and reviews of emerging technological trends in sustainable manufacturing;
  • Integration of artificial intelligence, big data, IoT, and digital twins with lean and green manufacturing principles;
  • Human-centric and collaborative approaches in smart factories, including the use of collaborative robots (cobots) and autonomous mobile systems;
  • Data-driven decision-making and predictive maintenance in sustainable production environments;
  • Socio-technical challenges and opportunities in adopting smart technologies across different industrial sectors.

We encourage contributions from researchers, engineers, industry professionals, and technology developers who aim at shaping the future of smart and sustainable production through interdisciplinary innovation.

Prof. Dr. Manuela Ingaldi
Guest Editor

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-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

  • smart manufacturing
  • sustainable production
  • Industry 4.0
  • Industry 5.0
  • collaborative robots (cobots)
  • autonomous mobile robots (AMR)
  • artificial intelligence (AI)
  • digital twins
  • green and lean manufacturing
  • industrial innovation

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Further information on MDPI's Special Issue policies can be found here.

Published Papers (2 papers)

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Research

27 pages, 4029 KB  
Article
Sustainable District-Heating Transition in Poland: The Case of the City of Ustka
by Ireneusz Zagrodzki, Mateusz Bryk, Piotr Józef Ziółkowski, Tomasz Kowalczyk, Pedro Jesus Cabrera Santana and Janusz Badur
Sustainability 2026, 18(10), 4971; https://doi.org/10.3390/su18104971 - 15 May 2026
Viewed by 166
Abstract
The energy transition of district heating systems in Poland requires the simultaneous consideration of energy efficiency, operating costs, technical feasibility, and local environmental constraints. This study addresses an identified gap in the literature by combining real operational time series from a municipal district [...] Read more.
The energy transition of district heating systems in Poland requires the simultaneous consideration of energy efficiency, operating costs, technical feasibility, and local environmental constraints. This study addresses an identified gap in the literature by combining real operational time series from a municipal district heating system with time-resolved market signals and site-specific resource constraints in a single OPEX-based operational screening framework. A case study is conducted for the city of Ustka using a configuration-based comparison of hybrid supply systems that include a gas-fired combined heat and power (CHP) unit, air-source and ground-source heat pumps, thermal energy storage, and a peak-load boiler. The optimisation model was implemented in MS Excel using the GRG Nonlinear algorithm (Solver) and was driven by the district heating operational data for 2021–2022 together with electricity and natural gas prices from the Polish Power Exchange day-ahead market (TGE RDN), evaluated under both hourly and daily settlement assumptions. The results indicate an optimal capacity split of 1.2 MWel/1.3 MWth for the CHP unit and 1.5 MWel/3.0 MWth for the heat pump system, supported by a required peak boiler capacity of 8.23 MWth. Within the adopted OPEX-based assessment, the lowest value of the unit heat generation indicator was obtained for the CHP-led configuration with combined ground-source and air-source heat pumps (38.45–38.55 PLN/GJ). A distinctive element of the study is the explicit verification of whether an operationally favourable configuration remains practically feasible when local resource constraints are considered. The site assessment indicates limited practical feasibility of the borehole heat exchanger at the analysed location in Ustka, showing that the lowest OPEX result should not be interpreted as a final investment recommendation. The study provides a replicable approach for the Polish district heating operators to screen hybrid transition pathways under real market conditions and to avoid technology choices that are favourable in dispatch models but constrained in practice. From a sustainability perspective, the proposed framework supports more energy-efficient, resilient, and locally feasible district heating transition planning in municipal heat systems. Full article
(This article belongs to the Special Issue Smart Technologies for Sustainable Production)
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26 pages, 11742 KB  
Article
Towards Cost-Optimal Zero-Defect Manufacturing in Injection Molding: An Explainable and Transferable Machine Learning Framework
by Lucas Greif, Jonas Ortner, Peer Kummert, Andreas Kimmig, Simon Kreuzwieser, Jakob Bönsch and Jivka Ovtcharova
Sustainability 2026, 18(4), 2001; https://doi.org/10.3390/su18042001 - 15 Feb 2026
Viewed by 518
Abstract
In the era of Industry 4.0, Zero-Defect Manufacturing is critical for injection molding but faces three major hurdles: severe class imbalance, the “black-box” nature of AI models, and the lack of scalability across machines. This study presents a comprehensive framework addressing these challenges. [...] Read more.
In the era of Industry 4.0, Zero-Defect Manufacturing is critical for injection molding but faces three major hurdles: severe class imbalance, the “black-box” nature of AI models, and the lack of scalability across machines. This study presents a comprehensive framework addressing these challenges. Using industrial datasets, we evaluated state-of-the-art supervised algorithms. Results show that CatBoost outperforms other architectures. Crucially, we demonstrate that maximizing accuracy is insufficient; instead, we introduce a cost-sensitive threshold optimization that minimizes economic risk, identifying an optimal classification threshold significantly lower than the standard. To enhance trust, SHAP analysis reveals that motor power and specific nozzle temperatures are the primary defect drivers. Finally, we validate a transfer learning approach using LightGBM, proving that models can be adapted to new datasets with minimal retraining. The implementation of cost-sensitive thresholding reduces total failure costs by over 75% compared to standard classification, while the transfer learning approach cuts the data requirements for new machine adaptation by more than half, providing a high-impact, scalable solution for sustainable smart manufacturing. Full article
(This article belongs to the Special Issue Smart Technologies for Sustainable Production)
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