Supply Chain Applications Research Articles

AI-Enhanced Blockchain for Scalable IoT-Based Supply Chain

Purpose: The integration of AI with blockchain technology is investigated in this study to address challenges in IoT-based supply chains, specifically focusing on latency, scalability, and data consistency. Background: Despite the potential of blockchain technology, its application in supply chains is hindered by significant limitations such as latency and scalability, which negatively impact data consistency and system reliability. Traditional solutions such as sharding, pruning, and off-chain storage introduce technical complexities and reduce transparency. Methods: This research proposes an AI-enabled blockchain solution, ABISChain, designed to enhance the performance of supply chains. The system utilizes beliefs, desires, and intentions (BDI) agents to manage and prune blockchain data, thus optimizing the blockchain’s performance. A particle swarm optimization method is employed to determine the most efficient dataset for pruning across the network. Results: The AI-driven ABISChain platform demonstrates improved scalability, data consistency, and security, making it a viable solution for supply chain management. Conclusions: The findings provide valuable insights for supply chain managers and technology developers, offering a robust solution that combines AI and blockchain to overcome existing challenges in IoT-based supply chains.

The convergence of IoT and sustainability in global supply chains: Patterns, trends, and future directions

This study investigates the pivotal role of Internet of Things (IoT) applications in advancing sustainable supply chains through a comprehensive analysis of research trends, international collaborations, and thematic evolution. Utilizing data from the Web of Science database, the research employs an innovative methodological framework combining keyword network analysis, country collaboration networks, and thematic mapping. The study reveals seven distinct keyword communities, highlighting the dominance of enabling technologies and the emergence of sector-specific applications. Analysis of international collaborations identifies five country clusters, each with unique research foci and collaborative patterns. The thematic map and evolution analysis uncover critical research gaps, including underexplored areas such as security in IoT-enabled supply chains and the integration of smart city concepts. The study projects future research directions, emphasizing the convergence of artificial intelligence and IoT, and the need for more holistic approaches to sustainability in supply chain management. The findings underscore the potential of IoT to foster sustainable business models that balance economic, social, and environmental benefits. This research contributes to the growing body of knowledge on IoT applications in sustainable supply chains by providing a multifaceted analysis of the field’s structure, evolution, and future trajectory. The insights offered are valuable for researchers, policymakers, and practitioners seeking to leverage IoT for creating more efficient, resilient, and sustainable supply chains in an increasingly globalized and environmentally conscious market.

Supply chain digitalization and firms' green innovation: Evidence from a pilot program

Green innovation is essential for realizing environmental sustainability and achieving long-term ecological balance. This paper investigates the influence of supply chain digitalization on firms' green innovation in the manufacturing industry. China's 2018 Supply Chain Innovation and Application Pilot program as a quasi-natural experiment, we employ a difference-in-differences (DID) estimation approach to reach the following findings: (1) supply chain digitalization significantly enhances the quantity and quality of enterprise green innovation, with a more substantial impact on quality. (2) supply chain financing capacity and firm environmental awareness play a partial mediating role in the relationship supply chain digitalization and the quality and quantity of corporate green innovation. (3) the heterogeneity analysis reveals that the impact of supply chain digitalization on green innovation is notably heightened in firms with managers who have overseas experience, weak internal controls, and poor regional supply chain environments. This study enriches the theoretical understanding of enterprise green innovation from the perspective of supply chain digitalization, providing a theoretical basis and practical insights for green innovation practices in enterprises.

Effects of supply chain innovation and application policy on firm performance: evidence from China

To accelerate the innovation and application of supply chains, China issued the Supply Chain Innovation and Application (SCIA) policy in 2017. We empirically explored the net and dynamic effects of SCIA policy on firm profitability by applying difference in differences and propensity score matching methods. Results show that SCIA policy significantly promoted firm profitability and it had a dynamic and time-lag effect. For firms in developed regions, SCIA played a more obvious role in promoting firm profitability. Notably, SCIA policy has been more supportive for non-state firms’ profitability than state ones. The results enrich the understanding about the net and dynamic effects of supply chain innovation policies on firm profitability. Accordingly, they not only guide firms on how to perform supply chain innovation and application activities under the new policy, but also provide insights to improve the existing SICA policy.

Does Supply Chain Digital Influence Green Innovation of Chinese Manufacturing Enterprises: A Quasi-Natural Experiment Based on Supply Chain Innovation and Application Policy Pilot

Does Supply Chain Digital Influence Green Innovation of Chinese Manufacturing Enterprises: A Quasi-Natural Experiment Based on Supply Chain Innovation and Application Policy Pilot

Large-scale data-driven uniformity analysis and sensory prediction of commercial banana ripening process

Artificial intelligence (AI) and machine learning (ML) have found prominent yet mostly academic applications in the food supply chain specifically to preserve and optimize the quality of fresh produce and achieve uniformity across the various stages of the cold chain. Nevertheless, the practical use of AI/ML for predictive analytics within real and large-scale commercial food processes, such as banana ripening, is sparse. This study proposes a novel data-driven approach tested and validated on two new large-scale datasets to automate and optimize the banana ripening process in refrigerated marine containers by successfully employing ML in uniformity analysis of the peel color and the pulp temperature of bananas based on atmospheric conditions. The results demonstrate high correlations between the gas concentrations and the uniformity of the process, suggesting that the uniformity of the peel color and the pulp temperature of fruit can be achieved by controlling the concentrations of the CO2 and O2 gas levels. Furthermore, this study, for the first time, achieves accurate algorithmic predictions of oxygen levels from other atmospheric variables to provide an alternative approach for continuous, improved and more cost-effective monitoring of the atmospheric conditions during ripening. A wide-range of predictive models are tested and validated where the Long Short Term Memory regression provides the lowest root-mean-square-errors (0.033 and 0.202) with robust R-squared values (0.999 and 0.959) for two datasets.

Analysis of Food Supply Chain Process by Block Chain Based Approach

The developments in technology and the emergence of new technologies have increased the expectations for the supply chain to have a more efficient, traceable and transparent structure. Although the legal regulations and standards of blockchain technology have not been clearly revealed yet, it is thought that it will solve many problems with its application in the food supply chain. In this context, the use of blockchain technology in the food supply chain, its advantages and disadvantages and today's application examples were examined by making a literature review. As a result of the study, it has been seen that blockchain technology provides significant added value to the food supply chain and is a solution to many problems of today's traditional supply chain.

Evaluating risk of IoT adoption in the food supply chain using an integrated interval-valued spherical fuzzy generalised TODIM method

The existing literature has shown that the Internet of Things (IoT) has proven to be beneficial for managing operations in the food supply chain. However, the potential failures resulting from these new technologies have not been thoroughly examined. Therefore, the aim of this study is to create a new framework for analyzing the risks associated with implementing IoT in the food supply chain. To achieve this, we introduce an extended failure mode and effect analysis framework using an integrated generalised Toma de Decisão Iterativa Multicritério (TODIM) method within the interval-valued spherical fuzzy setting. Initially, we developed a risk structure consisting of four dimensions and sixteen sub-factors to facilitate the implementation of IoT technology in the food supply chain. We then introduced an interval-valued spherical fuzzy-weighted Bonferroni mean operator to gather expert opinions and create a group risk matrix considering the interaction between the input risk data. Subsequently, we proposed a newly developed interval-valued spherical fuzzy TODIM method based on the Logarithmic percentage change-driven objective weighting (LOPCOW) method to evaluate and rank the risks associated with IoT application in the food supply chain. This method can capture the bounded rational decision behavior of experts and the uncertain preference of risk parameters. Based on the identified risk factors, we implemented a proposed risk analysis framework in line with the preferences of the experts. The results indicate that "human resource risk" and "uncertainty in technology adoption" are the most and least significant risks, respectively. We also conducted sensitivity and comparison studies to test the reliability and rationality of the proposed framework. The main findings can assist policymakers and managers in mitigating and minimizing the risks connected with IoT applications in the food supply chain. Additionally, these outcomes can aid stakeholders in making well-informed decisions about resource allocation for risk management.

Supply chain digitalization and corporate ESG performance: Evidence from supply chain innovation and application pilot policy

The increasing environmental unpredictability has amplified the strategic significance of contemporary digital supply networks. Gaining a comprehensive understanding of the mechanisms and effects of supply chains enabled by digital technology is essential for implementing reforms that improve the economy's supply side. This study utilizes the Difference-in-Differences (DID) approach to investigate the influence of supply chain digitization on company Environmental, Social, and Governance (ESG) performance, using data from 2011 to 2020. This study demonstrates that supply chain digitalization can significantly enhance corporate ESG performance. The stimulating impacts are mainly attained through enhancing corporate governance, total factor productivity, and mitigating financial limitations.

The interaction of upstream encroachment and blockchain technology application in a shipping supply chain

In shipping practice, ocean shipping (OS) companies launch their own cargo-canvassing channel to diversify revenue streams and further develop blockchain technology to improve their shipping service level. This study employs a game theory model to investigate the value of the OS company (i.e., upstream) encroaching and the application of blockchain technology by engaging in strategic interactions. Through our investigation, we find that it is disadvantageous for the OS company to encroach on the shipping market (apply blockchain technology) when the cost of encroachment (blockchain) is relatively high. Interestingly, by analyzing strategic interactions, we find that the application of blockchain technology influences the encroachment pattern, and the encroachment incentivizes the OS company to apply blockchain technology when the market demand for the encroaching channel is moderate. More interestingly, we innovatively propose the synergy, dysergy and mutual exclusivity effect to demonstrate our analysis. Furthermore, we discuss the impacts of the value of OS company’s encroachment and application of blockchain technology on the freight forwarder, and propose a “Blockchain Technology Compensation” (BTC) coordination mechanism to cope with the “win-lose” situation caused by the application of blockchain technology by the OS company.

A Critical Literature Review on Blockchain Technology Adoption in Supply Chains

Inspired by the discontinuation of the blockchain platform TradeLens, co-developed by IBM and Maersk, due to the lack of the involved supply chain stakeholders’ adoption, a critical literature review on the models of supply chain stakeholders’ adoption of blockchain applications was conducted. This review is significant as it provides insights into the exploration of a more universal approach to investigate which factors really influence blockchain adoption, which is a pre-requisite for the technical sustainability of blockchain technology in supply chains. As observed in the review, the technology acceptance model (TAM), the technology–organization–environment (TOE) framework, and the unified theory of acceptance and use of technology (UTAUT) are frequently used in the literature, but little attention has been paid to whether blockchain technology fits the users’ tasks in understanding blockchain adoption in the supply chain. Among the technology adoption theories, task–technology fit (TTF) considers whether a technology fits the tasks, but only two previous studies involved the use of TTF. This study discusses the suitability of these existing models of technology adoption for blockchain applications in supply chains and comes up with a new unified model, namely TOE-TTF-UTAUT. This review also has implications for a more appropriate conceptual research design using mixed methods.

The supply chain advantages and application strategies of artificial intelligence and the Internet of Things

This work addresses issues such as inadequate teaching methods, a lack of teaching resources, and low proactiveness in current accounting education. It introduces a novel teaching approach termed “Just-in-Time Teaching (JITT) cloud teaching,” which integrates “real-time teaching” with the Internet of Things-based “cloud teaching model” specifically for accounting education. First, the current status of accounting education in secondary vocational schools is investigated through a questionnaire survey. Subsequently, adjustments are made to the traditional teaching model, considering the limitations in teaching media creation channels and challenges in teaching activities. The teaching content of accounting education is designed in terms of mind maps, curriculum type, and problem design. The findings indicate: (1) Almost half of the surveyed teachers have heard of but never used the JITT cloud teaching, and the proportion is the largest. Some teachers have used but disapprove of JITT cloud teaching. (2) The proportion of students using website learning resources is 43.81%, while the proportion using mobile learning applications is 38.34%. (3) There is a significant difference between the traditional teaching mode and the JITT teaching mode in terms of “classroom teaching” and “sense of responsibility”. The average values under the JITT teaching mode have significantly improved compared to the traditional one. (4) The experimental group has a higher proportion of students scoring 90–100, which is 58%, significantly higher than the control group. The above research results indicate that there are still many possibilities for the practical application of the JITT teaching method in the future Moreover, applying the JITT cloud teaching model contributes to enhancing teaching quality and supports students’ learning.

A lightweight practical consensus mechanism for supply chain blockchain

We present a consensus mechanism in this paper that is designed specifically for supply chain blockchains, with a core focus on establishing trust among participating stakeholders through a novel reputation-based approach. The prevailing consensus mechanisms, initially crafted for cryptocurrency applications, prove unsuitable for the unique dynamics of supply chain systems. Unlike the broad inclusivity of cryptocurrency networks, our proposed mechanism insists on stakeholder participation rooted in process-specific quality criteria. The delineation of roles for supply chain participants within the consensus process becomes paramount. While reputation serves as a well-established quality parameter in various domains, its nuanced impact on non-cryptocurrency consensus mechanisms remains uncharted territory. Moreover, recognizing the primary role of efficient block verification in blockchain-enabled supply chains, our work introduces a comprehensive reputation model. This model strategically selects a leader node to orchestrate the entire block mining process within the consensus. Additionally, we innovate with a Schnorr Multisignature-based block verification mechanism seamlessly integrated into our proposed consensus model. Rigorous experiments are conducted to evaluate the performance and feasibility of our pioneering consensus mechanism, contributing valuable insights to the evolving landscape of blockchain technology in supply chain applications.

How does supply chain digitization affect green innovation? Evidence from a quasi-natural experiment in China

Digital technology will profoundly impact green innovation by enabling deep supply chain transformation through cost reduction and efficiency gains. Based on a quasi-natural experiment of China's supply chain innovation and application pilot policy, this paper investigates the impact of supply chain digitization on green innovation using data from 1836 listed companies from 2013 to 2021. The results find that: (1) Supply chain digitization significantly promotes firms' green innovation level, a finding that still holds under placebo and other tests. (2) Mechanism tests indicate that supply chain digitization enhances firms' green innovation level mainly through optimizing innovation resource effect, enhancing internal control effect and expanding external demand effect. (3) The green innovation effect of supply chain digitization is greater in regions with low supply chain concentration, capital-intensive, non-polluting industries, and higher digitization degree. (4) Supply chain digitization significantly improves environmental performance through the green innovation effect.

Evaluation of adaptation strategies for sustainable supply chains: application in medical waste reverse supply chains

Evaluation of adaptation strategies for sustainable supply chains: application in medical waste reverse supply chains

Strengthening Maqāṣid al-Sharī’ah Values in Halal Traceability: Evidence and Analysis of Block Chain Based on Halal Supply Chain Applications

Strengthening Maqāṣid al-Sharī’ah Values in Halal Traceability: Evidence and Analysis of Block Chain Based on Halal Supply Chain Applications

Exploring robotic devices and self-governing mechanisms in food and beverage management: A comprehensive review

Resource use and food security are the major worldwide problems due to an expanding global population. This article explores how robotics and autonomous systems (RAS) might improve supply chain operations. RAS has been used to increase productivity. The adoption and integration of RAS into the food business have been investigated by researchers. All recent research, has concentrated on the technical implications of RAS. The primary focus of this work is on the use of RAS from a supply chain management perspective in the food business. Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method was selected for this study. The outcome of this phase was 74 papers for full-text examination. The process produced a final list of 20 publications for data synthesis and analysis. The application of RAS in connection to five crucial aspects of the food supply chain, food waste, supply chain efficiency, food safety, food quality and supply chain analysis was examined in this study. RAS application in food supply chains is examined in relation to the significance of skill competency, cyber security, data accessibility and financial issues.

The Balanced Score Card as a Predictor of Supply Chain Improvement for ACES Peru, 2022

The application of Balance Score Card (BSC) and Supply Chain (SC) as structured methods of strategic analysis is scarce and controversial in the business management of some companies, except in large industries competing in the globalized market. The objective of the study was to analyze the Balance Score Card as a predictive tool to improve the Supply Chain (SC) of the company ACES Peru, 2022. The study evaluates the BSC variables and the five dimensions of the CS variable: Supply, production, storage, transportation and distribution, a total of 24 items on a Likert scale. A total of 260 responses were collected from those who participated in the survey. Statistically, exploratory factor analysis, Varimax factorial rotation, KMO, CFI, TLI and RSMA tests were used. The results show that BSC was not a significant predictor of CS; however, the BSC variable is a significant predictor of the dimensions of the CS variable. In these circumstances the BSC model assumes the role of evaluator of the management strategies developed in ACES and improver of the predictor level of CS and its dimensions. Received: 1 December 2023 / Accepted: 7 April 2024 / Published: 5 May 2024

A Topic Modeling Approach to Determine Supply Chain Management Priorities Enabled by Digital Twin Technology

Background: This paper examines scientific papers in the field of digital twins to explore the different areas of application in supply chains. Methods: Using a machine learning-based topic modeling approach, this study aims to provide insights into the key areas of supply chain management that benefit from digital twin capabilities. Results: The research findings highlight key priorities in the areas of infrastructure, construction, business, technology, manufacturing, blockchain, and agriculture, providing a comprehensive perspective. Conclusions: Our research findings confirm several recommendations. First, the machine learning-based model identifies new areas that are not addressed in the human review results. Second, while the human review results put more emphasis on practicality, such as management activities, processes, and methods, the machine learning results pay more attention to macro perspectives, such as infrastructure, technology, and business. Third, the machine learning-based model is able to extract more granular information; for example, it identifies core technologies beyond digital twins, including AI/reinforcement learning, picking robots, cybersecurity, 5G networks, the physical internet, additive manufacturing, and cloud manufacturing.

A Bibliometric Analysis of a Genetic Algorithm for Supply Chain Agility

As a famous population-based metaheuristic algorithm, a genetic algorithm can be used to overcome optimization complexities. A genetic algorithm adopts probabilistic transition rules and is suitable for parallelism, which makes this algorithm attractive in many areas, including the logistics and supply chain sector. To obtain a comprehensive understanding of the development in this area, this paper presents a bibliometric analysis on the application of a genetic algorithm in logistics and supply chains using data from 1991 to 2024 from the Web of Science database. The authors found a growing trend in the number of publications and citations over the years. This paper serves as an important reference to researchers by highlighting important research areas, such as multi-objective optimization, metaheuristics, sustainability issues in logistics, and machine learning integration. This bibliometric analysis also underlines the importance of Non-Dominated Sorting Genetic Algorithm II (NSGA-II), sustainability, machine learning, and variable neighborhood search in the application of a genetic algorithm in logistics and supply chains in the near future. The integration of a genetic algorithm with machine learning is also a potential research gap to be filled to overcome the limitations of genetic algorithms, such as the long computational time, difficulties in obtaining optimal solutions, and convergence issues for application in logistics and supply chains.