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Systems Methodology in Sustainable Supply Chain Resilience
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Systems Methodology in Sustainable Supply Chain Resilience

A special issue of Systems (ISSN 2079-8954). This special issue belongs to the section "Supply Chain Management".

Deadline for manuscript submissions: 31 July 2025 | Viewed by 3515

Special Issue Editors

Dr. Towfique Rahman

E-Mail Website
Guest Editor
Department of Business Strategy and Innovation, Griffith University, Gold Coast, QLD 4222, Australia
Interests: operations and supply chain management
Prof. Dr. Syed Mithun Ali

E-Mail Website
Guest Editor
Department of Industrial and Production Engineering, Bangladesh University of Engineering and Technology, Dhaka 1000, Bangladesh
Interests: artificial intelligence and machine learning; operations management; operations research and decision analysis; supply chain management
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

In today's rapidly evolving global environment, building resilient and sustainable supply chains has become a crucial challenge for both present and future generations.  Experts in systems methodology and supply chain management are seeking to address this challenge by leveraging advanced technologies and innovative approaches to enhance supply chain resilience while promoting sustainability [1,2]. The integration of systems thinking into supply chain management offers a comprehensive approach to understanding and managing the complex interdependencies that characterize modern supply networks [3,4]. Recent studies have underscored the importance of adopting holistic frameworks that encompass not only the physical and operational aspects of supply chains but also the environmental, social, and economic dimensions of sustainability [5,6]. This Special Issue seeks to gather pioneering research and practical insights that contribute to the advancement of systems methodology in the context of sustainable supply chain resilience. We particularly encourage contributions that explore novel methodologies, tools, and frameworks for integrating resilience and sustainability into supply chain design and management. By fostering collaboration between academia and industry, this issue aims to push the boundaries of current knowledge and practice, ultimately leading to supply chains that are not only robust and adaptive but also aligned with the principles of sustainability.

We welcome submissions that address a broad range of topics, including, but not limited to, the following:

  • New systems methodologies for enhancing sustainable supply chain resilience;
  • Empirical studies on the implementation of sustainable and resilient supply chain practices;
  • The role of digital technologies in sustainable and resilient supply chain management;
  • Integrating circular economy principles into resilient supply chain systems;
  • Challenges and opportunities in multi-stakeholder collaboration for sustainable and resilient supply chains;
  • Simulation and modeling techniques for resilient and sustainable supply chains;
  • Artificial intelligence and data-driven approaches in sustainable supply chain resilience;
  • Case studies highlighting successful implementation of systems methodology in sustainable and resilient supply chains.

We look forward to receiving your valuable contributions to this Special Issue, which aims to make a significant impact on the field of sustainable supply chain resilience.

References

  1. Nasir, S. B., Ahmed, T., Karmaker, C. L., Ali, S. M., Paul, S. K., Majumdar, A. Supply chain viability in the context of COVID-19 pandemic in small and medium-sized enterprises: implications for sustainable development goals. J. Inf. Manag, 2022, 35(1), 100–124.
  2. Shahed, K. S., Azeem, A., Ali, S. M., Moktadir, M. A. (2021). A supply chain disruption risk mitigation model to manage COVID-19 pandemic risk. Environ Sci Pollut Res, 2021.
  3. Rahman, T., Paul, S. K., Shukla, N., Agarwal, R., Taghikhah, F. Supply chain resilience initiatives and strategies: A systematic review. Comput. Ind. Eng., 2022, 170, 108317.
  4. Rahman, T., Taghikhah, F., Paul, S. K., Shukla, N., Agarwal, R. An Agent-Based Model for Supply Chain Recovery in the Wake of the COVID-19 Pandemic An Agent-Based Model for Supply Chain Recovery in the Wake of the COVID-19. Comput. Ind. Eng., 2021, 158.
  5. Shin, N.; Park, S. Evidence-Based Resilience Management for Supply Chain Sustainability: An Interpretive Structural Modelling Approach. Sustainability 2019, 11, 484.
  6. Taghikhah, F., Voinov, A., Shukla, N. Extending the supply chain to address sustainability. J. Clean. Prod., 2019, 229, 652–666.

Dr. Towfique Rahman
Prof. Dr. Syed Mithun Ali
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. Systems is an international peer-reviewed open access monthly 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 supply chain
  • resilience
  • systems methodology
  • digital technologies
  • circular economy
  • multi-stakeholder collaboration
  • artificial intelligence
  • supply chain design
  • simulation techniques
  • empirical studies

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

Published Papers (6 papers)

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Research

29 pages, 6318 KiB  
Article
Economic and Environmental Sustainability Performance Improvements in the Outdoor Wood Furniture Industry Through a Lean-Infused FMEA-Supported Fuzzy QFD Approach
by Melike Nur Ince, Emrecan Arpaci, Cagatay Tasdemir and Rado Gazo
Systems 2025, 13(3), 211; https://doi.org/10.3390/systems13030211 - 19 Mar 2025
Viewed by 245
Abstract
Fiercer competition across all industries has made identifying and eliminating lean wastes to enhance sustainability performance an effective route that many companies take. This study focuses on the production process of wood park/garden benches at a company that manufactures outdoor wood furniture. The [...] Read more.
Fiercer competition across all industries has made identifying and eliminating lean wastes to enhance sustainability performance an effective route that many companies take. This study focuses on the production process of wood park/garden benches at a company that manufactures outdoor wood furniture. The goal was to identify lean wastes within a sustainability framework across seven operations and integrate multi-criteria decision making (MCDM) methodologies for waste elimination. Eleven lean KPIs addressing economic and environmental sustainability were used to develop and prioritize 13 lean failure modes (LFMs) with Risk Priority Numbers (RPNs) above 100, leading to lean project proposals for each LFM. Eighteen lean tools were ranked using the Fuzzy Quality Function Deployment (Fuzzy QFD) method. A total of eight improvement propositions, namely, Kaizen and continuous improvement, upgrade machinery for energy efficiency, Just-In-Time (JIT), optimize production processes with lean methodologies, implement cost reduction strategies, Total Productive Maintenance (TPM), Investing in Automation, and Andon were implemented. Significant improvements were observed post-implementation: total lead time was reduced by approximately 38.46%, value-added time by 22.05%, and non-value-added time by 47.64%. The required number of workers decreased by 14.29%, and the total inventory decreased by approximately 57.31%. The results contribute to sustainability goals by reducing energy consumption and waste while increasing economic efficiency. It also provides a robust framework for decision making in fuzzy environments, guiding practitioners and academics in lean management and sustainability. Full article
(This article belongs to the Special Issue Systems Methodology in Sustainable Supply Chain Resilience)
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16 pages, 1626 KiB  
Article
Portfolio Procurement Strategies with Forward and Option Contracts Combined with Spot Market
by Nurul Anastasya Talaba and Pyung-Hoi Koo
Systems 2025, 13(3), 210; https://doi.org/10.3390/systems13030210 - 18 Mar 2025
Viewed by 223
Abstract
Increasing supply chain uncertainty due to market volatility has heightened the need for more flexible procurement strategies. While procurement through long-term forward contracts provides supply stability and cost predictability, it limits adaptability. Option contracts offer procurement flexibility, but require additional upfront premiums. Meanwhile, [...] Read more.
Increasing supply chain uncertainty due to market volatility has heightened the need for more flexible procurement strategies. While procurement through long-term forward contracts provides supply stability and cost predictability, it limits adaptability. Option contracts offer procurement flexibility, but require additional upfront premiums. Meanwhile, the spot market enables real-time purchasing without prior commitments, enhancing flexibility but exposing buyers to price volatility. Despite the growing adoption of portfolio procurement—combining forward contracts, option contracts, and spot market purchases—the existing research primarily examines these channels in isolation or in limited combinations, lacking an integrated perspective. This study addresses this gap by developing a comprehensive procurement model that simultaneously optimizes procurement decisions across all three channels under uncertain demand and fluctuating spot prices. Unlike prior studies, which often analyze one or two procurement channels separately, our model presents a novel, holistic framework that balances cost efficiency, risk mitigation, and adaptability. Our findings demonstrate that incorporating the spot market significantly enhances procurement flexibility and profitability, particularly in environments with high demand uncertainty and price volatility. Additionally, sensitivity analysis reveals how fluctuations in spot prices and demand uncertainty influence optimal procurement decisions. By introducing a new, practical approach to portfolio procurement, this study provides managerial insights that help businesses navigate complex and uncertain supply chain environments more effectively. However, this study assumes unlimited spot market capacity and reliable suppliers, highlighting a limitation that future research should address. Full article
(This article belongs to the Special Issue Systems Methodology in Sustainable Supply Chain Resilience)
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29 pages, 3122 KiB  
Article
Complexity to Resilience: Machine Learning Models for Enhancing Supply Chains and Resilience in the Middle Eastern Trade Corridor Nations
by Wajid Nawaz and Zhaolei Li
Systems 2025, 13(3), 209; https://doi.org/10.3390/systems13030209 - 18 Mar 2025
Viewed by 247
Abstract
The durable nature of supply chains in the Middle Eastern region is critical, given the region’s strategic role in global trade corridors, yet geopolitical conflicts, territorial disputes, and governance challenges persistently disrupt key routes like the Suez Canal, amplifying vulnerabilities. This study addresses [...] Read more.
The durable nature of supply chains in the Middle Eastern region is critical, given the region’s strategic role in global trade corridors, yet geopolitical conflicts, territorial disputes, and governance challenges persistently disrupt key routes like the Suez Canal, amplifying vulnerabilities. This study addresses the urgent need to predict and mitigate supply chain risks by evaluating machine learning (ML) models for forecasting economic complexity as a proxy for resilience across 18 Middle Eastern countries. Using a multidimensional secondary dataset, we compare gated recurrent unit (GRU), support vector regression (SVR), gradient boosting, and other ensemble models, assessing performance via MSE, MAE, RMSE, and R2. The results demonstrate the GRU model’s superior accuracy (R2 = 0.9813; MSE = 0.0011), with SHAP, sensitivity, and sensitivity analysis confirming its robustness in identifying resilience determinants. Analyses reveal infrastructure quality and natural resource rents as pivotal factors influencing the economic complexity index (ECI), while disruptions like trade embargoes or infrastructure failures significantly degrade resilience. Our findings underscore the importance of diversifying infrastructure investments and stabilizing governance frameworks to buffer against shocks. This research advances the application of deep learning in supply chain resilience analytics, offering actionable insights for policymakers and logistics planners to fortify regional trade corridors and mitigate global ripple effects. Full article
(This article belongs to the Special Issue Systems Methodology in Sustainable Supply Chain Resilience)
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18 pages, 3613 KiB  
Article
Application of the Non-Dominated Sorting Genetic Algorithm II (NSGA-II) in a Two-Echelon Cold Supply Chain
by Aslı Acerce and Berrin Denizhan
Systems 2025, 13(3), 206; https://doi.org/10.3390/systems13030206 - 17 Mar 2025
Viewed by 278
Abstract
A two-stage cold supply chain manages the transportation, storage, and distribution of temperature-sensitive products like frozen food, fresh/green products, and pharmaceuticals, which makes it costly. It consists of three key elements: a supplier, a warehouse, and multiple customers. Procurement planning can be conducted [...] Read more.
A two-stage cold supply chain manages the transportation, storage, and distribution of temperature-sensitive products like frozen food, fresh/green products, and pharmaceuticals, which makes it costly. It consists of three key elements: a supplier, a warehouse, and multiple customers. Procurement planning can be conducted for various products, and this study assumes the transport of a fresh/green product with gradually decreasing quality due to its perishable nature. In a two-stage cold supply chain, multiple objective functions can be defined, including cost minimization, product quality optimization, and transportation/storage condition optimization. We developed a mathematical model to optimize these objectives, incorporating two specific functions, cost minimization and product age reduction, to ensure efficient supply chain performance. Traditional solution methods often struggle with multi-objective mathematical models due to their complexity. Therefore, the Non-Dominated Sorting Genetic Algorithm II (NSGA-II), a Genetic Algorithm-based approach, was applied to solve the model efficiently. NSGA-II optimized planning for a 7-day period under specific demand conditions, ensuring better resource allocation. The results showed that NSGA-II was better than traditional methods at making decisions and routing efficiently in the two-stage cold supply chain. This led to much better outcomes, with lower costs, less waste, and better product quality throughout the process. Full article
(This article belongs to the Special Issue Systems Methodology in Sustainable Supply Chain Resilience)
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21 pages, 1526 KiB  
Article
Strategic Inventory Management with Private Brands: Navigating the Challenges of Supply Uncertainty
by Junjie Guo, Huanhuan Wang, Guang Song, Hanxing Cui and Qilan Zhao
Systems 2025, 13(3), 203; https://doi.org/10.3390/systems13030203 - 15 Mar 2025
Viewed by 354
Abstract
In the context of globalized and complex supply chains, supply uncertainty occurs frequently. To reduce dependence on suppliers, retailers often consider holding strategic inventory and introducing private brands. To explore the relationship between private brands and strategic inventory strategies, and to determine the [...] Read more.
In the context of globalized and complex supply chains, supply uncertainty occurs frequently. To reduce dependence on suppliers, retailers often consider holding strategic inventory and introducing private brands. To explore the relationship between private brands and strategic inventory strategies, and to determine the optimal strategic decisions, this paper constructs a two-stage supply chain model. Using game theory methods, we calculate the equilibrium outcomes of the supply chain under two scenarios: one with only national brands and the other with the introduction of private brands. The main findings are as follows. First, we identify the optimal decisions for both suppliers and retailers in each scenario. The influencing factors include perceived quality, inventory costs, and supply stability. Second, we find that there are constraints for retailers to activate strategic inventory, but these constraints are less restrictive when private brands are introduced. Finally, introducing private brands benefits retailers in implementing strategic inventory, although the extent of this impact depends on the conditions under which the strategic stockpile is implemented. These findings fill the gap in the existing literature on the impact of private brand introductions on strategic inventory under supply uncertainty and highlight valuable implications for business decision-makers. Full article
(This article belongs to the Special Issue Systems Methodology in Sustainable Supply Chain Resilience)
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18 pages, 1780 KiB  
Article
Enhancing Efficiency in the Healthcare Sector Through Multi-Objective Optimization of Freight Cost and Delivery Time in the HIV Drug Supply Chain Using Machine Learning
by Amirkeyvan Ghazvinian, Bo Feng and Junwen Feng
Systems 2025, 13(2), 91; https://doi.org/10.3390/systems13020091 - 31 Jan 2025
Viewed by 1011
Abstract
The purpose of this paper is to examine the optimization of the HIV drug supply chain, with a dual focus on minimizing freight costs and delivery times. With the help of a dataset containing 10,325 instances of supply chain transactions, key variables, including [...] Read more.
The purpose of this paper is to examine the optimization of the HIV drug supply chain, with a dual focus on minimizing freight costs and delivery times. With the help of a dataset containing 10,325 instances of supply chain transactions, key variables, including “Country”, “Vendor INCO Term”, and “Shipment Mode”, were examined in order to develop a predictive model using Artificial Neural Networks (ANN) employing a Multi-Layer Perceptron (MLP) architecture. A set of ANN models were trained to forecast “freight cost” and “delivery time” based on four principal design variables: “Line Item Quantity”, “Pack Price”, “Unit of Measure (Per Pack)”, and “Weight (Kilograms)”. According to performance metrics analysis, these models demonstrated predictive accuracy following training. An optimization algorithm, configured with an “active-set” algorithm, was then used to minimize the combined objective function of freight cost and delivery time. Both freight costs and delivery times were significantly reduced as a result of the optimization. This study illustrates the potent application of machine learning and optimization algorithms to the enhancement of supply chain efficiency. This study provides a blueprint for cost reduction and improved service delivery in critical medication supply chains based on the methodology and outcomes. Full article
(This article belongs to the Special Issue Systems Methodology in Sustainable Supply Chain Resilience)
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