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Sustainability
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Achieving Sustainability in New Product Development and Supply Chain
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Achieving Sustainability in New Product Development and Supply Chain

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

Deadline for manuscript submissions: 12 May 2026 | Viewed by 4159

Special Issue Editor

Prof. Dr. Mihai Dragomir

E-Mail Website
Guest Editor
Department of Design Engineering and Robotics, Technical University of Cluj-Napoca, Cluj-Napoca, Romania
Interests: quality engineering and management; low-carbon production; digital transformation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The Special Issue will provide the opportunity to disseminate and discuss recent developments in the field of sustainability in product development and supply chains, key stages in establishing a mutually beneficial relationship between the industrial sector and the natural and social environment. The focus will be on the intersection of emerging technologies such as AI, Digital Twins and Internet of Things and more human- and environmental-centric approaches to product development and supply chain management.

Existing literature in the field that contains an in-depth understanding of the complexities of the production sector is still limited, and there are competing interpretations in various schools of thought. By providing space for case studies and concrete applications, while at the same time critically analyzing the benefits and drawbacks of various models, this Special Issue will enable academics and practitioners to advance the sustainability impact of their work.

Within this context, this Issue aims to analyze the relationship between the concept of sustainability and related frameworks that strive to achieve complementary goals such as circular economy, ESG, low carbon/net zero production, etc. Establishing synergies and sharing good practices is more important than ever, as complex transformations are taking place within the world economy and its various component societies.

Prof. Dr. Mihai Dragomir
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 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

  • sustainable product development
  • sustainable supply chains
  • human-centered engineering

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Published Papers (4 papers)

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Research

26 pages, 5305 KB  
Article
Development of Real-Time IoT-Based Air Quality Forecasting System Using Machine Learning Approach
by Onem Yildiz and Hilmi Saygin Sucuoglu
Sustainability 2025, 17(19), 8531; https://doi.org/10.3390/su17198531 - 23 Sep 2025
Viewed by 1294
Abstract
Air quality monitoring and forecasting have become increasingly critical in urban environments due to rising pollution levels and their impact on public health. Recent advances in Internet of Things (IoT) technology and machine learning offer promising alternatives to traditional monitoring stations, which are [...] Read more.
Air quality monitoring and forecasting have become increasingly critical in urban environments due to rising pollution levels and their impact on public health. Recent advances in Internet of Things (IoT) technology and machine learning offer promising alternatives to traditional monitoring stations, which are limited by high costs and sparse deployment. This paper presents the development of a real-time, low-cost air quality forecasting system that integrates IoT-based sensing units with predictive machine learning algorithms. The proposed system employs low-cost gas sensors and microcontroller-based hardware to monitor pollutants such as particulate matter, carbon monoxide, carbon dioxide and volatile organic compounds. A fully functional prototype device was designed and manufactured using Fused Deposition Modeling (FDM) with modular and scalable features. The data acquisition pipeline includes on-device adjustment, local smoothing, and cloud transfer for real-time storage and visualization. Advanced feature engineering and a multi-model training strategy were used to generate accurate short-term forecasts. Among the models tested, the GRU-based deep learning model yielded the highest performance, achieving R2 values above 0.93 and maintaining latency below 130 ms, suitable for real-time use. The system also achieved over 91% accuracy in health-based AQI category predictions and demonstrated stable performance without sensor saturation under high-pollution conditions. This study demonstrates that combining embedded hardware, real-time analytics, and ML-driven forecasting enables robust and scalable air quality management solutions, contributing directly to sustainable development goals through enhanced environmental monitoring and public health responsiveness. Full article
(This article belongs to the Special Issue Achieving Sustainability in New Product Development and Supply Chain)
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21 pages, 399 KB  
Article
The Interaction of Diversification Strategies, Resilience, and Digital Capabilities in Driving Supply Chain Sustainability in Saudi Arabia
by Sami Mohammed Alhaderi
Sustainability 2025, 17(18), 8299; https://doi.org/10.3390/su17188299 - 16 Sep 2025
Viewed by 1199
Abstract
This study explores the impact of supply chain diversification strategies (SCDS) on supply chain sustainability performance (SCSP), with focus on the mediating role of supply chain resilience (SCR) and the moderating effect of digital supply chain capabilities (DSC). The research objectives are (1) [...] Read more.
This study explores the impact of supply chain diversification strategies (SCDS) on supply chain sustainability performance (SCSP), with focus on the mediating role of supply chain resilience (SCR) and the moderating effect of digital supply chain capabilities (DSC). The research objectives are (1) to assess whether SCR enables diversified supply bases to deliver sustainability outcomes and (2) to examine whether DSC strengthens the effectiveness of diversification in achieving environmental, social, and economic performance. A quantitative, cross-sectional survey was administered to 329 supply chain managers from medium-to-large manufacturing and retail firms in Saudi Arabia. Data were collected using multi-item 5-point Likert scale. Validity and reliability were ensured through EFA, Cronbach’s alpha, and composite reliability. Mediation and moderation effects were tested using PROCESS Macro in SPSS version 27. Findings revealed that 52% of the variance in supply chain sustainability performance (SCSP) was explained. (SCDS) had a strong positive effect on (SCR) (B = 0.612, p < 0.001), which in turn significantly predicted SCSP (B = 0.431, p < 0.001). The total effect of SCDS on SCSP was significant (B = 0.572, p < 0.001), while the direct effect remained strong (B = 0.308, p < 0.001). The indirect effect (a × b = 0.264, 95% CI [0.194, 0.343]) confirmed that SCR partially mediates the relationship, showing that diversification enhances sustainability both directly and indirectly through resilience. Theoretically, this study extends RBV and DC theory, while practically offering managers actionable insights on integrating diversification, resilience, and digitalization to balance supply continuity with long-term sustainability goals. Full article
(This article belongs to the Special Issue Achieving Sustainability in New Product Development and Supply Chain)
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32 pages, 1813 KB  
Article
Compressing and Decompressing Activities in Multi-Project Scheduling Under Uncertainty and Resource Flexibility
by Marzieh Aghileh, Anabela Tereso, Filipe Alvelos and Maria Odete Monteiro Lopes
Sustainability 2025, 17(18), 8108; https://doi.org/10.3390/su17188108 - 9 Sep 2025
Viewed by 631
Abstract
In multi-project environments characterized by resource constraints and high uncertainty, traditional scheduling approaches often fail to respond effectively to dynamic project conditions. Fixed activity durations and rigid resource allocations limit adaptability, leading to inefficiencies and delays. To address this, the paper proposes a [...] Read more.
In multi-project environments characterized by resource constraints and high uncertainty, traditional scheduling approaches often fail to respond effectively to dynamic project conditions. Fixed activity durations and rigid resource allocations limit adaptability, leading to inefficiencies and delays. To address this, the paper proposes a novel heuristic-based scheduling method that compresses and decompresses activity durations dynamically within the context of multi-project scheduling under uncertainty and resource flexibility—while preserving resource and precedence feasibility. The technique integrates Critical Path Method (CPM) calculations with heuristic rules to identify candidate activities whose durations can be reduced or extended based on slack availability and resource effort profiles. The objective is to enhance scheduling flexibility, improve resource utilization, and better align project execution with organizational priorities and sustainability goals. Validated through a case study at an automotive company in Portugal, the method demonstrates its practical effectiveness in recalibrating schedules and balancing resource loads. This contribution offers a timely and necessary innovation for companies aiming to enhance responsiveness and competitiveness in increasingly complex project landscapes. It provides an actionable framework for dynamic schedule adjustment in multi-project environments, helping companies to respond more effectively to uncertainty and resource fluctuations. Importantly, the proposed approach also supports sustainability objectives in new product development and supply chain operations. For practitioners, the method offers a responsive and sustainable planning tool that supports real-time adjustments in project portfolios, enhancing resource visibility and execution resilience. For researchers, the study contributes a reproducible, Python-based implementation grounded in Design Science Research (DSR), addressing gaps in stochastic multi-project scheduling and sustainability-aware planning. Full article
(This article belongs to the Special Issue Achieving Sustainability in New Product Development and Supply Chain)
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25 pages, 3532 KB  
Article
Sustainable Design and Lifecycle Prediction of Crusher Blades Through a Digital Replica-Based Predictive Prototyping Framework and Data-Efficient Machine Learning
by Hilmi Saygin Sucuoglu, Serra Aksoy, Pinar Demircioglu and Ismail Bogrekci
Sustainability 2025, 17(16), 7543; https://doi.org/10.3390/su17167543 - 21 Aug 2025
Viewed by 722
Abstract
Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were [...] Read more.
Sustainable product development demands components that last longer, consume less energy, and can be refurbished within circular supply chains. This study introduces a digital replica-based predictive prototyping workflow for industrial crusher blades that meets these goals. Six commercially used blade geometries (A–F) were recreated as high-fidelity finite-element models and subjected to an identical 5 kN cutting load. Comparative simulations revealed that a triple-edged hooked profile (Blade A) reduced peak von Mises stress by 53% and total deformation by 71% compared with a conventional flat blade, indicating lower drive-motor power and slower wear. To enable fast virtual prototyping and condition-based maintenance, deformation was subsequently predicted using a data-efficient machine-learning model. Multi-view image augmentation enlarged the experimental dataset from 6 to 60 samples, and an XGBoost regressor, trained on computer-vision geometry features and engineering parameters, achieved R2 = 0.996 and MAE = 0.005 mm in five-fold cross-validation. Feature-importance analysis highlighted applied stress, safety factor, and edge design as the dominant predictors. The integrated method reduces development cycles, reduces material loss via iteration, extends the life of blades, and facilitates refurbishment decisions, providing a foundation for future integration into digital twin systems to support sustainable product development and predictive maintenance in heavy-duty manufacturing. Full article
(This article belongs to the Special Issue Achieving Sustainability in New Product Development and Supply Chain)
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