Supply Chain Applications Research Articles

Enhancing salmon freshness monitoring with sol-gel cellulose nanocrystal colorimetric paper sensors and deep learning methods

Accurate shelf-life estimation of food products has the potential to improve the safety, reliability, and sustainability of food supply chains. Here, a scalable system for accurately indicating the freshness-related information of salmon for consumers was developed and characterized. The system comprises two parts, an optically changeable colorimetric sensor by dye incorporated with sol-gel functionalized paper sensor and a deep convolutional neural network (DCNN)-based freshness estimation system based on images. The paper sensor was produced based on paper and immersed, coated, or printed with (3-aminopropyl) triethoxysilane (APTMS) and tetraethyl orthosilicate (TEOS) sol-gel particles and indicators (e.g., pH indicator, ammonia gas indicator, oxidation indicator, and other volatile organic compounds indicators). After being augmented by machine learning, the limit of detection reaches as low as 17.1 ppm for ammonia detection and the accuracy achieved up to 99.2% for salmon freshness estimation, which satisfied the application in food supply chains. The system balanced the trade-off between detection sensitivity, stability, and cost with the ability for scalable batch production.

Smart supply chain management in business education: Reflection on the pandemics

AbstractSmart supply chain management (SSCM) has recently attracted significant attention from both industry and academia, particularly in light of the COVID pandemic. This article reviews current literature on information and integration, process automation, advanced analytics, and related business curriculum in SSCM. Our survey results demonstrate a concerning lack of adoption of SSCM in business curriculum and reveal several major challenges faced by educators when teaching SSCM, including a scarcity of available teaching resources, a need for cross‐disciplinary knowledge, and a lack of industry experience among students. These findings lead to our exploration of SSCM application in various supply chain and operations functions (e.g., product design, sourcing and procurement, production, quality management, warehouse management and distribution, delivery, and supply chain risk management) during the COVID‐19 pandemic. Drawing on our analysis, we conclude by offering several recommendations for promoting SSCM in business education.

QAmplifyNet: pushing the boundaries of supply chain backorder prediction using interpretable hybrid quantum-classical neural network

Supply chain management relies on accurate backorder prediction for optimizing inventory control, reducing costs, and enhancing customer satisfaction. Traditional machine-learning models struggle with large-scale datasets and complex relationships. This research introduces a novel methodological framework for supply chain backorder prediction, addressing the challenge of collecting large real-world datasets with 90% accuracy. Our proposed model demonstrates remarkable accuracy in predicting backorders on short and imbalanced datasets. We capture intricate patterns and dependencies by leveraging quantum-inspired techniques within the quantum-classical neural network QAmplifyNet. Experimental evaluations on a benchmark dataset establish QAmplifyNet’s superiority over eight classical models, three classically stacked quantum ensembles, five quantum neural networks, and a deep reinforcement learning model. Its ability to handle short, imbalanced datasets makes it ideal for supply chain management. We evaluate seven preprocessing techniques, selecting the best one based on logistic regression’s performance on each preprocessed dataset. The model’s interpretability is enhanced using Explainable artificial intelligence techniques. Practical implications include improved inventory control, reduced backorders, and enhanced operational efficiency. QAmplifyNet also achieved the highest F1-score of 94% for predicting “Not Backorder” and 75% for predicting “backorder,” outperforming all other models. It also exhibited the highest AUC-ROC score of 79.85%, further validating its superior predictive capabilities. QAmplifyNet seamlessly integrates into real-world supply chain management systems, empowering proactive decision-making and efficient resource allocation. Future work involves exploring additional quantum-inspired techniques, expanding the dataset, and investigating other supply chain applications. This research unlocks the potential of quantum computing in supply chain optimization and paves the way for further exploration of quantum-inspired machine learning models in supply chain management. Our framework and QAmplifyNet model offer a breakthrough approach to supply chain backorder prediction, offering superior performance and opening new avenues for leveraging quantum-inspired techniques in supply chain management.

Retracted: Intelligent Application of Raw Material Supply Chain Planning System Based on Genetic Algorithm

Retracted: Intelligent Application of Raw Material Supply Chain Planning System Based on Genetic Algorithm

A Bibliometric Analysis of Blockchain Research in Supply Chain Management

Blockchain technology has garnered increasing research attention because of its potential to transform supply chain management (SCM) through enhanced transparency, security, and efficiency. This study performed a bibliometric analysis of the emerging body of literature on the intersection of blockchain technology and SCM to elucidate the intellectual structure, knowledge diffusion patterns, and research trajectories within this field. A dataset of 2,500 English-language articles published between 2020-2023 was retrieved from Dimensions and analyzed using VOSviewer and Bibliometrix. The findings revealed that the research landscape focused predominantly on technical topics grounded in computer science, with an emerging interest in supply chain applications. Bibliographic coupling revealed clusters of literature on core blockchain development, implementation challenges, and healthcare use cases. The citation analysis identified influential contributors and geographic concentrations of research productivity in China and East Asia. While currently anchored in technical disciplines, blockchain-SCM research displays a burgeoning interdisciplinarity. This study provides a multidimensional perspective on the topography and evolution of this rapidly developing domain. These insights will orient scholars and practitioners to high-impact contributions and productive directions for future blockchain integration in SCM contexts.

Fresh Food Quality Deterioration Detection and Labeling: a Review of Recent Research and Application in Supply Chain

Fresh Food Quality Deterioration Detection and Labeling: a Review of Recent Research and Application in Supply Chain

Leveraging Milk Permeate Fermentation to Produce Lactose-Free, Low-In-Glucose, Galactose-Rich Bioproducts: Optimizations and Applications

Previous studies highlighted Brettanomyces claussenii as a versatile yeast that produces ethanol or acetic acid from lactose, or selectively metabolizes glucose while leaving behind galactose, depending on different operational conditions. This flexibility enables the production of galactose-rich bioproducts from liquid dairy residues. The purpose of this study is to: (i) optimize the anaerobic fermentation of milk permeate (MP) by B. claussenii to maximize the yields of galactose and ethanol and minimize leftover glucose, and (ii) combine the optimized process with distillation and drying and characterize its multiple products. Response surface methodology was used for the optimization. Three fermentation parameters were chosen as input factors: temperature (25–35 °C), inoculation level (7.0–8.5 log cfu/mL), and time (4–40 days), with three metabolites as responses: galactose, glucose, and ethanol. The optimal combination of parameters resulted in temperature, 28 °C; inoculation level, 7.6 log cfu/mL; and time, 33 days. Under these conditions, the fermented product was predicted to have 63.6 g/L galactose, 4.0% v/v ethanol, and 0 g/L residual glucose. The optimal parameters were used to run 18 L fermentations followed by distillation and freeze-drying. As a result, four product streams were obtained and characterized for relevant physicochemical and nutritional attributes. Our results show that the partial fermentation of MP by B. claussenii can be the first step to develop lactose-free, low-in-glucose, galactose-rich bioproducts, which improve the value of this residue and broaden its applications in the food supply chain.

An Improved Integrity-Based Hybrid Multi-User Data Access Control for Cloud Heterogeneous Supply Chain Databases

Cloud-based supply chain applications play a vital role in the multi-user data security framework for heterogeneous data types. The majority of the existing security models work effectively on small to medium-sized datasets with a homogenous data structure. In contrast, Supply Chain Management (SCM) systems in the real world utilize heterogeneous databases. The heterogeneous databases include a massive quantity of raw SCM data and a scanned image of a purchase quotation. In addition, as the size of the database grows, it becomes more challenging to provide data security on multi-user SCM databases. Multi-user datatypes are heterogeneous in structure, and it is complex to apply integrity and confidentiality models due to high computational time and resources. Traditional multi-user integrity algorithms are difficult to process heterogeneous datatypes due to computational time and variation in hash bit size. Conventional attribute-based encryption models such as "Key-policy attribute-based encryption" (KP-ABE), "Ciphertext-Policy Attribute-Based Encryption" (CP-ABE) etc., are used to provide strong data confidentiality on large textual data. Providing security for heterogeneous databases in a multi-user SCM system requires a significant computational runtime for these conventional models. An enhanced integrity-based multi-user access control security model is created for heterogeneous databases in the cloud infrastructure to address the problems with heterogeneous SCM databases. A non-linear integrity model is developed to provide strong integrity verification in the multi-user communication process. A multi-user based access control model is implemented by integrating the multi-user hash values in the encoding and decoding process. Practical results proved that the multi-user non-linear integrity-based multi-access control framework has better runtime and hash bit variation compared to the conventional models on large cloud-based SCM databases.

A Study on Sustainable Hospital Supply Chain Management towards Societal Advancement in the Post-Pandemic Era

Abstract— This study delves into the intricate relationships between Hospital Infrastructure Development (HID), Collaboration Information Flow (CIF), Forecasting and Planning Management (FPM), Technology Supply Chain Application (TSCA), Sustainable Hospital Supply Chain Management (SHSCM), and Sustainable Societal Advancement (SSA) within the healthcare sector of Bangladesh. Utilizing a structured questionnaire, data was collected from a diverse group of 22 respondents, including doctors, administrators, and patients. Frequencies Analysis, conducted using IBM SPSS, provided a comprehensive demographic overview, while the Alpha Cronbach test affirmed the reliability of the research instrument with a high value. The fitness index analysis, performed using IBM AMOS software, subsequent modifications based on the Modification Index (MI) results led to a model that closely met the criteria of CMIN, degrees of freedom (df), CMIN/df, incremental fit CFI IFI, and absolute fit RMSEA, underscoring the pivotal role of the aforementioned variables in enhancing SHSCM and contributing to SSA. The findings emphasize the significance of a robust healthcare supply chain in ensuring optimal healthcare delivery and broader societal benefits. This research offers valuable insights for healthcare administrators, policymakers, and stakeholders, providing a roadmap for building resilient, efficient, and sustainable healthcare systems in the post-pandemic era.Keywords—Sustainable Hospital Supply Chain Management (SHSCM), Sustainable Societal Advancement (SSA), IBM SPSS, IBM AMOS.

Blockchain application in maritime supply chain: a systematic literature review and conceptual framework

ABSTRACT This research aims to establish the link between blockchain technology adoption in the maritime and shipping industry and its impact on maritime supply chain integration via a systematic review of both the academic and practice literature. In total 148 articles were identified and analysed. Blockchain applications identified from the literature are categorized into three domains: document management, transaction management, and cargo/vessel/terminal operations. An analysis of the benefits and challenges that influence the deployment of blockchain technology for maritime supply chain integration leads to the development of an integrated and extended Technology, Organization, and Environment (TOE) framework. This study is among the first to examine the current state of blockchain diffusion within the maritime supply chain, making a significant contribution to the field. The extended TOE framework offers guidance for future research and understanding of the relationship between blockchain adoption and maritime supply chain integration. It can be used to assist organisations in successfully adopting blockchain technology in their supply chain operations.

Blockchain in supply chain management: a feature-function framework for future research

PurposeBlockchain technology offers numerous venues for supply chain applications and research. However, the connections between specific blockchain features and future applications have been unclear to date in its evolution. The purpose of this study is to fill this void.Design/methodology/approachThe authors advance the understanding of blockchain in supply chain management by providing a new research framework built on unique blockchain features as applied across core supply chain functions.FindingsThis study’s framework is a feature-function matrix that integrates four overarching supply chain functions (i.e. supplier management, logistics, production processes and customer management) with nine blockchain features (i.e. traceability/provenance, accessibility, visibility, immutability, distributed/shared ledger, validity, peer-to-peer transacting, pseudonymity and programmability). This study’s feature-function framework is supported by a structured, systematic review of reviews using PRISMA methods. The authors use the framework to present a future blockchain research agenda in supply chain management.Originality/valueThe authors provide a new blockchain feature/supply chain function framework and provide a structured path for future research.

A pentagonal type-2 fuzzy variable defuzzification model with application in humanitarian supply chains

Disasters can be caused by humans or natural hazards, and they impact the vulnerability of communities worldwide. This study considers disasters caused by humans, including the rescue operation taken post-accidents. The objective of the research is to minimise time and cost to save people’s lives in the early stages of rescue operations from the fuzzy logic perspective by proposing a novel pentagonal type-2 fuzzy variable (PT2FV) defuzzification method. The multi-objective multi-modal services at the crucial early hours of a disaster solve a solid transportation problem mathematically using a weighted sum method and neutrosophic compromise programming in Lingo solver software. The novelty of the research has been justified by the sensitivity analysis test on various levels of credibility at different intervals of PT2FV. The various credibility level intervals have also been discussed, and the best possible interval is suggested. The outcome suggests an effective plan for humanitarian relief in difficult areas using multiple modes of transit for delivery of reliefs, availing rescue team and saving of victims along with a notion of an efficient contingency planning.

Co-authorship network analysis of AI applications in sustainable supply chains: Key players and themes

This research article presents a novel approach to examining the utilization of artificial intelligence (AI) in sustainable supply chains (SSCs) by applying social network analysis (SNA). In contrast to previous literature that primarily relied on bibliometric analysis, this study investigates the intricate relationships among AI applications in SSCs using SNA. The primary objective is to promote collaborative research and offer valuable insights into influential authors, international co-authorship networks, collaborations among scientific institutions, and research categories identified through keyword analysis.Data were collected from the Web of Science (WOS) database to conduct this research, enabling a comprehensive co-authorship network analysis. This network revealed the collaboration patterns of 1400 authors and identified the most significant authors in this field using centrality measures and the TOPSIS technique.The study also explores institutional co-authorship networks, highlighting India's National Institute of Technology (NIT) as the most active institution. By considering geographical proximity and research specialization, the institutional network was divided into distinct clusters. A keyword analysis and thematic map also shed light on Sustainability and Supply Chain Management themes, revealing their connections with AI methodologies. Based on the thematic map, E-waste, Green supply chain management (GSCM), and Green innovation themes are potential future trends. The analysis of the country network showcased varying levels of development and scientific output, with China leading the field of AI applications in SSC research. Countries were further classified into clusters based on their investment in AI for sustainable supply chains, facilitating knowledge exchange and collaboration.The outcomes of this research contribute significantly to the field by utilizing social network analysis to explore co-authorship networks, leading to a comprehensive understanding of collaborative dynamics and knowledge dissemination. The insights gained from this study can inform decision-making, investments, and strategic collaborations in the domain of AI applications for sustainable supply chains, ultimately driving progress toward sustainability objectives.

Q-learning-based hyper-heuristic evolutionary algorithm for the distributed assembly blocking flowshop scheduling problem

Distributed shop scheduling problems (DSSPs) have attracted increasing interest in recent years due to the technical trends of smart manufacturing and Industry 4.0. The distributed assembly blocking flowshop scheduling problem (DABFSP) is a critical class of DSSPs with widespread applications in modern supply chains and manufacturing systems. In this paper, a Q-learning-based hyper-heuristic evolutionary algorithm (QLHHEA) is proposed to solve DABFSP with the objective of minimizing the makespan. Firstly, a mathematical model of DABFSP is formulated, and two insertion-based speedup strategies are devised to conserve the computational cost of evaluating solutions and to accelerate the search efficiency. Secondly, a problem-specific constructive heuristic is developed to produce high-quality initial solutions. Thirdly, twelve efficient heuristics are designed to construct low-level heuristics (LLHs). The Q-learning-based evolutionary algorithm is applied as a high-level strategy to manipulate the LLHs, which are then executed in order to search the solution space. Moreover, suitable solution encoding and decoding schemes are provided to produce feasible scheduling schedules. The design of experiments is implemented to investigate the impact of the parameters. Finally, a comprehensive comparison campaign is carried out based on a total of 1710 well-known instances to evaluate the efficacy of the proposed algorithm against several state-of-the-art algorithms. Experimental results and statistical analysis show that QLHHEA significantly outperforms the existing algorithms by a significant margin, demonstrating the effectiveness and efficiency of QLHHEA in solving DABFSP.

A Critical Discourse Analysis of Blockchain Application in Supply Chains: From Power to Justice

A Critical Discourse Analysis of Blockchain Application in Supply Chains: From Power to Justice

An efficient adaptive large neighborhood search algorithm based on heuristics and reformulations for the generalized quadratic assignment problem

Operations Research (OR) analytics play a crucial role in optimizing decision-making processes for real-world problems. The assignment problem, with applications in supply chains, healthcare logistics, and production scheduling, represents a prominent optimization challenge. This paper focuses on addressing the Generalized Quadratic Assignment Problem (GQAP), a well-known NP-hard combinatorial optimization problem. To tackle the GQAP, we propose an OR analytical approach that incorporates efficient relaxations, reformulations, heuristics, and a metaheuristic algorithm. Initially, we employ the Reformulation Linearization Technique (RLT) to generate various linear relaxation models, carefully selecting the most efficient ones. Building upon this foundation, we introduce a robust reformulation based on Benders Decomposition (BD), which serves as the basis for an iterative optimization algorithm applied to the GQAP. Furthermore, we develop a constructive heuristic algorithm to identify near-optimal solutions, followed by an enhancement utilizing an Adaptive Large Neighborhood Search (ALNS) metaheuristic algorithm. This ALNS algorithm is enhanced through the integration of a tabu list derived from Tabu Search (TS) and a decision rule inspired by Simulated Annealing (SA). To validate our approach and evaluate its performance, we conduct a comparative analysis against state-of-the-art algorithms documented in the literature. This comparison confirms the significant improvements achieved in terms of solution quality and computational efficiency through our proposed methodology. These advancements contribute to the state of the art in solving the GQAP and hold the potential to enhance decision-making processes in a wide range of domains. Our methodology demonstrates remarkable improvements in solution quality and computational efficiency when compared to existing approaches, as evidenced by the comparative results with state-of-the-art algorithms. The potential implications of our research extend to optimizing decision-making processes in diverse fields, rendering it highly relevant and impactful.

A Unified Approach to Sustainable Hospital Supply Chain Management in the Post-Pandemic Era

Sustainable Hospital Supply Chain Management (SHSCM) has become crucial in the post-pandemic era, balancing economic, environmental, and social dimensions of healthcare. This paper proposes an integrated approach to SHSCM, combining four core elements, particularly Hospital Infrastructure Development (HID), Collaboration and Information Flow (CIF), Forecasting and Planning Management (FPM), and Technology Supply Chain Application (TSCA). This approach addresses the complexities and challenges in healthcare supply chains, promoting resilience, efficiency, and sustainability. The authors present potential applications from two different healthcare systems scenarios to demonstrate the application and effectiveness of the proposed approach. The findings of this study suggest that the integration of HID, CIF, FPM, and TSCA provides a comprehensive and systematic way to enhance SHSCM, with implications for research and practice in this important field.

Pricing and inventory decision in a two-layer supply chain under the Weibull distribution product deterioration: an application of NSGA-II

Academicians and practitioners have focused a lot of attention on the separate issues of pricing and inventory control in a competitive setting. However, integrating these choices in a competitive environment has received scant attention for deteriorating inventory systems from academics despite being crucial to practitioners. From this perspective, our research focuses on designing a supply chain model with inventory coordination to reflect time systems with improved accuracy and optimal control systems. In this research, we develop a two-layer supply chain model consisting of one manufacturer and one retailer incorporating the inventory classification of the retailer. Price-sensitive market demand and two-parameter time-varying Weibull distribution deterioration have been assumed to develop the mathematical model. First, a collective decision on price and inventory control of a deteriorating product has been evaluated in a duopoly environment. Secondly, to explore the decentralized scenario, we have proposed the NSGA-II algorithm to solve the bi-objective programming problem of the two-layer supply chain. The paper aims to explore product collaborative pricing policies and the inventory decision of the deteriorating item in two-layer supply chain coordination. Finally, numerical research is conducted to execute the centralized supply chain and NSGA-II application in a decentralized supply chain. The research findings can provide valuable insights for members of the two-layer supply chain to make optimal product pricing and inventory scheduling decisions.

The Role of Latest Technology in Sustainable Hospital Supply Chain Management

The future of sustainable hospital supply chain management (SHSCM) hinges on the effective adoption and application of advanced technologies. As discussed previously, Hospital Infrastructure Development (HID), Collaboration & Information Flow (CIF), and Forecasting & Planning Management (FPM) are crucial for an effective and sustainable SCM. However, leveraging the latest Technology Supply Chain Applications (TSCA) could further enhance these efforts, ensuring not only sustainability but also a higher level of service efficiency and patient care.

Inventory-Responsive Donor-Management Policy: A Tandem Queueing Network Model

Problem definition: In the blood-donor-management problem, the blood bank incentivizes donors to donate, given blood inventory levels. We propose a model to optimize such incentivization schemes under the context of random demand, blood perishability, observation period between donations, and variability in donor arrivals and dropouts. Methodology/results: We propose an optimization model that simultaneously accounts for the dynamics in the blood inventory and the donor’s donation process, as a coupled queueing network. We adopt the Pipeline Queue paradigm, which leads us to a tractable convex reformulation. The coupled setting requires new methodologies to be developed upon the existing Pipeline Queue framework. Numerical results demonstrate the advantages of the optimal policy by comparing it with the commonly adopted and studied threshold policy. Our optimal policy can effectively reduce both shortages and wastage. Managerial implications: Our model is the first to operationalize a dynamic donor-incentivization scheme, by determining the optimal number of donors of different donation responsiveness to receive each type of incentive. It can serve as a decision-support tool that incorporates practical features of blood supply-chain management not addressed thus far, to the best of our knowledge. Simulations on existing policies indicate the dangers of myopic approaches and justify the need for smoother and forward-looking donor-incentivization schedules that can hedge against future demand variation. Our model also has potential wider applications in supply chains with perishable inventory.Funding: This study was funded by the Singapore Management University through a research [Grant 20-C207-SMU-015] from the Ministry of Education Academic Research Fund Tier 1.Supplemental Material: The online appendix is available at https://doi.org/10.1287/msom.2023.1228 .