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: 28 February 2025 | Viewed by 1174

Special Issue Editors


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Guest Editor
Department of Business Strategy and Innovation, Griffith University, Gold Coast, QLD 4222, Australia
Interests: operations and supply chain management

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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
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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

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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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Published Papers (1 paper)

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Research

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 395
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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