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Intelligent Optimization and Business Analytics for Supply Chain and Logistics...
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Intelligent Optimization and Business Analytics for Supply Chain and Logistics Management in the Age of Industry 4.0

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

Deadline for manuscript submissions: 31 January 2027 | Viewed by 5874

Editors

Dr. Young Sik Cho

E-Mail Website
Guest Editor
College of Business Administration, Gyeongsang National University, Jinju-si 52828, Republic of Korea
Interests: supply chain management; operations management; quality management; blockchain; machine learning; metacognition; empirical research; aerospace supply chains
Special Issues, Collections and Topics in MDPI journals
Dr. Euisung Jung

E-Mail Website
Guest Editor
Department of Information Operations and Technology Management, John B. and Lillian E. Neff College of Business and Innovation, University of Toledo, Toledo, OH 43606, USA
Interests: AI; machine learning; data mining; text mining; healthcare analytics

Special Issue Information

Dear Colleagues,

Intelligent optimization in supply chain management is revolutionizing the efficiency of the entire supply chain process by reducing costs, mitigating potential risks, and improving supply chain agility. In today’s digital age, intelligent optimization of supply chain and logistics systems using AI, machine learning, big data, blockchain, and IoT technologies is emerging as an essential capability for companies in order for them to gain a competitive edge in global competition.

This Special Issue welcomes original research, empirical analysis, theoretical developments, conceptual frameworks, real-world implementations, and case studies for submission, with the aim of providing an open forum for both researchers and practitioners to engage in in-depth and multifaceted discussions on intelligent optimization in supply chain and logistics systems.

This Special Issue covers various topics related to intelligent optimization and decision analytics for supply chain management, including, but not limited to, the following:

  • AI-powered intelligent supply chain management;
  • AI-driven demand forecasting;
  • AI-based supply chain risk management;
  • Intelligent optimization of inventory and warehouse operations;
  • Blockchain-based systems for logistics management;
  • Systematic review of intelligent optimization in supply chain management;
  • Real-world case studies on intelligent optimization in supply chain management.

We look forward to your submissions.

Dr. Young Sik Cho
Dr. Euisung Jung
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. 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

  • intelligent optimization
  • supply chain management
  • logistics
  • AI
  • machine learning
  • big data
  • blockchain
  • business analytics

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Published Papers (4 papers)

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Research

30 pages, 1786 KB  
Article
Intelligent Optimization of Supply Chain Disruption Prediction: A Particle Swarm Optimization-Enhanced Random Forest Framework with Explainable Analytics
by Essam Aboshaera, Oluwatayomi Rereloluwa Adegboye and Ahmad Bassam Alzubi
Systems 2026, 14(8), 1004; https://doi.org/10.3390/systems14081004 - 17 Aug 2026
Viewed by 233
Abstract
The need for intelligent optimization of supply chain operations has become imperative for organizations seeking to adapt quickly to an increasingly complex logistics environment. The complexity of predicting delivery delays stems from the large number of interdependent operational and external factors that may [...] Read more.
The need for intelligent optimization of supply chain operations has become imperative for organizations seeking to adapt quickly to an increasingly complex logistics environment. The complexity of predicting delivery delays stems from the large number of interdependent operational and external factors that may affect delivery performance. In this study, we propose a hybrid machine learning framework that employs both the Random Forest Classifier (RF) and the Particle Swarm Optimization (PSO) to provide more accurate predictions of delivery delays through automated hyperparameter tuning. We evaluated our proposed PSO–RF model using a publicly available benchmark dataset of shipment records, along with their respective operational characteristics and contextual risk indicator values. In the comparative experiments PSO–RF produced the best single configuration among the metaheuristic-tuned models, reaching an accuracy of 94.6% and a Matthews correlation coefficient of 0.739 at a swarm size of 10. This advantage is specific to the small-swarm setting: at swarm sizes of 20 and 40 the four optimizers examined the (PSO, SCA, GWO, DE) cluster within a narrow MCC band of 0.71–0.74, and SCA–RF is the most consistent, so no optimizer is uniformly superior across swarm sizes. Against the conventionally configured baseline classifiers the improvement is far larger and holds in every configuration, which indicates that the principal gain arises from optimizing the Random Forest hyperparameters rather than from the choice of search algorithm. Additionally, to provide actionable supply chain intelligence, we employed SHapley Additive exPlanations (SHAP) to conduct model interpretability analyses. The SHAP analysis identified that the most influential features within the model’s delay classification decisions include shipping costs, order weights, geopolitical risks, scheduled lead-time days, transportation mode, and base lead-time days. Full article
(This article belongs to the Special Issue Intelligent Optimization and Business Analytics for Supply Chain and Logistics Management in the Age of Industry 4.0)
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31 pages, 4550 KB  
Article
A Runtime Enforcement Framework for Vulnerable Smart Contracts of Crowdsourcing Logistics
by Tianhuan Miao and Yang Liu
Systems 2026, 14(6), 600; https://doi.org/10.3390/systems14060600 - 23 May 2026
Viewed by 289
Abstract
Blockchain-based crowdsourcing logistics is a promising decentralized paradigm for solving the “last-mile delivery” problem, in which smart contracts automatically execute the business logic. Since crowdsourcing logistics inherently involves frequent fund transfers, its smart contracts are particularly susceptible to reentrancy vulnerabilities. Existing works address [...] Read more.
Blockchain-based crowdsourcing logistics is a promising decentralized paradigm for solving the “last-mile delivery” problem, in which smart contracts automatically execute the business logic. Since crowdsourcing logistics inherently involves frequent fund transfers, its smart contracts are particularly susceptible to reentrancy vulnerabilities. Existing works address reentrancy by inserting a lock mechanism at design-time, which lacks dynamic responsiveness and incurs additional gas overhead. To overcome this limitation, we propose RE4SC, the first runtime enforcement framework for vulnerable smart contracts. RE4SC contains two components: off-Blockchain granularity segmentation and on-Blockchain granular block reordering. At the off-Blockchain level, bytecode is segmented into granular blocks through control flow analysis. This yields a finer granularity than conventional basic blocks in a control flow graph. These granular blocks are then organized into a tree structure that captures their hierarchical nesting relationships. A data flow analysis further ensures data dependency consistency after reordering. At the on-Blockchain level, a runtime enforcer retrieves the pre-computed reordering specifications from off-Blockchain analysis. It applies a depth-first reordering algorithm to reposition key state variable assignments before transfer operations, eliminating reentrancy vulnerabilities without introducing additional bytecode. We implement a prototype tool and make it open-source. Experiments on self-constructed crowdsourcing logistics contracts and three public datasets demonstrate that RE4SC repairs vulnerable contracts with zero gas overhead, outperforming existing approaches. Full article
(This article belongs to the Special Issue Intelligent Optimization and Business Analytics for Supply Chain and Logistics Management in the Age of Industry 4.0)
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14 pages, 276 KB  
Article
Layered Control Architectures for AI Safety: A Cybersecurity-Oriented Systems Framework
by Young B. Choi, Paul C. Hong and Young Soo Park
Systems 2026, 14(4), 447; https://doi.org/10.3390/systems14040447 - 20 Apr 2026
Cited by 1 | Viewed by 1617
Abstract
As artificial intelligence (AI) systems become increasingly autonomous, scalable, and embedded in critical digital infrastructure, AI safety has emerged as a significant consideration for cybersecurity, system reliability, and institutional trust. Advances in large language models and agentic systems expand the threat surface to [...] Read more.
As artificial intelligence (AI) systems become increasingly autonomous, scalable, and embedded in critical digital infrastructure, AI safety has emerged as a significant consideration for cybersecurity, system reliability, and institutional trust. Advances in large language models and agentic systems expand the threat surface to include misalignment, large-scale misuse, opaque decision-making, and cross-border risk propagation, while existing debates remain fragmented across technical, ethical, and geopolitical domains. This paper conducts a structured comparative analysis of AI safety perspectives from ten influential thinkers, examining them across five dimensions and reframing their insights through a cybersecurity lens spanning national governance, industry standards, and firm-level design. Building on this synthesis, the study proposes a layered control architecture that organizes technical safeguards, governance mechanisms, and human oversight into a defense-in-depth structure. The framework is conceptual and theory-building, intended to clarify system-level security reasoning and support future empirical refinement across diverse institutional contexts. Full article
(This article belongs to the Special Issue Intelligent Optimization and Business Analytics for Supply Chain and Logistics Management in the Age of Industry 4.0)
26 pages, 2636 KB  
Article
The Impact of Blockchain Technology on Lean Supply Chain Management: Cross-Validation Through Big Data Analytics and Empirical Studies of U.S. Companies
by Young Sik Cho, Euisung Jung and Paul C. Hong
Systems 2026, 14(1), 3; https://doi.org/10.3390/systems14010003 - 19 Dec 2025
Cited by 1 | Viewed by 2918
Abstract
Despite significant research interest, the understanding of how to systematically implement Lean practices in supply chains remains limited. Therefore, this study analyzes the impact of blockchain technology on implementing Lean principles within supply chain networks. A theoretical model was developed based on a [...] Read more.
Despite significant research interest, the understanding of how to systematically implement Lean practices in supply chains remains limited. Therefore, this study analyzes the impact of blockchain technology on implementing Lean principles within supply chain networks. A theoretical model was developed based on a comprehensive literature review, utilizing innovation diffusion theory, agency theory, and transaction cost economics. The LDA topic modeling, based on big data from the past decade, was employed to explore key areas and essential industry practices related to blockchain technology. By cross-validating big data analysis and survey results, we also developed reliable metrics that can be used to study blockchain utilization in SCM. The hypotheses were empirically tested using survey data from 219 US enterprises that have adopted blockchain technology. The empirical results revealed that blockchain adoption significantly improved Lean management practices within supply chain networks. Furthermore, research has shown that blockchain can significantly enhance operational performance, including cost reduction, quality improvement, delivery capacity, and greater flexibility. These compelling results suggest that blockchain has the potential to serve as a powerful platform for systematically integrating and orchestrating Lean management practices across the entire supply chain network, thereby achieving operational excellence. An in-depth discussion of the study’s practical implications and theoretical contributions is presented. Full article
(This article belongs to the Special Issue Intelligent Optimization and Business Analytics for Supply Chain and Logistics Management in the Age of Industry 4.0)
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