Supply Chain Environment Research Articles

An Algorithm for Part Input Sequencing of Flexible Manufacturing Systems with Machine Disruption

Because disruption happens unpredictably and generates serious impact in supply chain and production environments in the real world, it is important to develop approaches to handle disruption. This paper investigates disruption handling in part input sequencing of flexible manufacturing systems (FMSs). An algorithm is proposed for FMS part input sequencing to handle machine breakage. Evaluation is performed for the proposed algorithm by simulation experiments and result analyses. Finally, conclusions are summarized with managerial implications discussed and further research works suggested.

Algorithm aversion during disruptions: The case of safety stock

Algorithm aversion occurs when organizations or individuals reject optimal analytical decision support in favour of informal, subjective decisions. This phenomenon has been observed in many practical decision-making scenarios and is generally believed to negatively impact decision quality. However, its existence and effect in volatile supply chain environments has not been empirically tested in the literature. Safety stock buffering demand volatility is an important decision in supply chain management, making it an ideal lens to observe algorithm aversion. In this paper, we empirically investigate algorithm aversion behaviour in the context of safety stock settings. We collect data from a case retail company across a range of stockkeeping units (SKUs), encompassing both pre-disruption and post-disruption time stages with varying levels of volatility. We introduce a simulation model to determine whether algorithm aversion exists for safety stock decisions and to assess how algorithm adoption and adaptation affects performance. Our findings indicate that algorithm aversion occurs during supply chain disruptions, with algorithmic decisions significantly outperforming human judgment. Based on interview results and theories of information systems, we propose a theory to explain and generalize the above findings. This theory attributes algorithm aversion behaviour to reduced sense of fitness among algorithm users and lack of slack resources for both users and developers. It also offers insights into how the adoption and adaptation of algorithms influence decision performance during disruptive events.

B-ERAC: Blockchain-Enabled Role-Based Access Control for Secure IoT Device Communication

Security risks are increasingly concerning as the Internet of Things (IoT) expands. Authentication, access control, and authorization present significant challenges for resource-constrained IoT devices. Traditional authentication methods often require enhancements for these devices, but Blockchain technology presents a potential solution. Decentralized and distributed, Blockchain eliminates a single point of failure and relies on Elliptic Curve Cryptography (ECC) for robust security. We have introduced a cutting-edge solution to fortify communication security within IoT devices across supply chain ecosystems. By harnessing the power of Blockchain technology, our framework incorporates smart contracts, adheres to ES256 encryption standards, and seamlessly integrates with Infura API. These components establish stringent access controls, ensure data integrity, and enhance transparency throughout supply chain processes. The framework’s robust architecture facilitates swift and secure transactions, bolsters traceability efforts, and effectively mitigates potential security risks. With its scalable design and reliable functionality, this framework emerges as a pivotal asset for optimizing IoT device communication within dynamic supply chain environments. The use of ProVerif in our analysis provides a formal guarantee of the correctness of our access control mechanisms.

A critical review of machine learning applications in supply chain risk management

This article examines the transformative role of Machine Learning (ML) in Supply Chain Risk Management (SCRM), emphasizing its ability to enhance risk prediction and mitigation through real-time, data-driven insights. It demonstrates how ML applications like demand forecasting, inventory optimization, supplier risk assessment, and fraud detection improve supply chain resilience by analyzing large datasets to identify patterns, predict risks, and recommend proactive actions. However, the article also highlights key challenges, including data quality, availability, and privacy issues, which limit the effectiveness of ML models. Integrating ML with legacy systems poses additional technical and financial difficulties, particularly for smaller businesses. High implementation costs and scalability constraints further hinder widespread adoption. Ethical concerns, such as data bias and privacy, still need to be explored, raising questions about responsible ML use in supply chains. Future research should address these gaps by improving data governance frameworks for accuracy and privacy, developing scalable ML models suited to various supply chain environments, and exploring synergies with technologies like blockchain and the Internet of Things (IoT). These advancements will help realize ML’s full potential, fostering more agile, transparent, and resilient supply chains capable of navigating complex global risks. By tackling these challenges, ML can shift SCRM from reactive to proactive, ensuring long-term operational continuity and a competitive edge in the evolving global 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.

Understanding the survival strategies: organization resilience and innovative capabilities in post-pandemic East Asia

ABSTRACT The COVID-19 pandemic has significantly and globally impacted and even disrupted corporate operations to a considerable extent, compelling businesses to confront changing supply chain environments, unstable market demands and internal contradictions. We argue that East Asian enterprises that have survived the pandemic have learned from their experiences, transforming these into key strategic capabilities in innovation, operational efficiency, customer relationships and employee management to ensure resilience in volatile and hostile environments. One universal lesson learned is that the survival of businesses integrates resilience, sustainability and a people-oriented perspective. This special issue focuses on the business organizations and innovation capabilities in the post-pandemic East Asian region, compiling the core viewpoints and hypotheses from the collection on topics such as strategic innovation and adaptive changes, diverse teams and leadership and stakeholder relationship management. These findings enrich our understanding of East Asian enterprises’ comprehensive capability enhancement and business model transformation, especially after the COVID-19 pandemic.

Prevalence of Listeria monocytogenes in Milk and Dairy Product Supply Chains: A Global Systematic Review and Meta-analysis.

Listeria monocytogenes, one of the main foodborne pathogens, is commonly found in milk and dairy products. This study aimed to estimate the presence of L. monocytogenes in milk and dairy product supply chains using a meta-analysis based on PubMed, Embase, Web of Science, and Scopus databases. A total of 173 studies were included in this meta-analysis. The pooled prevalence in the supply chain environment was 8.69% (95% confidence interval [CI]: 5.30%-12.78%), which was higher than that in dairy products (4.60%, 95% CI: 1.72%-8.60%) and milk products (2.93%, 95% CI: 2.14%-3.82%). Subgroup analysis showed that L. monocytogenes prevalence in raw milk (3.44%, 95% CI: 2.61%-4.28%) was significantly higher than in pasteurized milk (0.60%, 95% CI: 0.00%-2.06%). The highest prevalence of L. monocytogenes in milk and dairy products was observed in North America (5.27%, 95% CI: 2.19%-8.35%) and South America (13.54%, 95% CI: 3.71%-23.37%). In addition, studies using culture and molecular methods (5.17%, 95% CI: 2.29%-8.06%) had higher prevalence than other detection methods. Serogroup 1/2a and 3a (45.34%, 95% CI: 28.74%-62.37%), serogroup 1/2b and 3b (14.23%, 95% CI: 6.05%-24.24%), and serogroup 4b/4e (13.71%, 95% CI: 6.18%-22.83%) were dominant in these studies. The results of this study provide a better understanding of the prevalence of L. monocytogenes in milk and dairy product supply chains and suggest a potential foodborne pathogen burden.

Multi-population genetic algorithm with crowding-based local search for fuzzy multi-objective supply chain configuration

Supply chain configuration is often fuzzy and involves multiple objectives in real-world scenarios, but existing researches lack the exploration in the fuzzy aspect. Therefore, this paper establishes a fuzzy multi-objective supply chain configuration problem model to minimize the lead time and product cost oriented towards real supply chain environments. To solve the fuzzy problem, the theories of membership and closeness degree in fuzzy mathematics are adopted, and a multi-population genetic algorithm (MPGA) with crowding-based local search method is proposed. The MPGA algorithm uses two populations for optimizing the two objectives separately and effectively, and is characterized by three main innovative aspects. Firstly, a radical-and-radial selection operator is designed to balance the convergence speed and diversity of population. In the early stage of the algorithm, two populations are both optimized towards the ideal knee point, and then are separately optimized towards the two ends of the Pareto front (PF). Secondly, an elitist crossover operator is devised to promote information exchange within two populations. Thirdly, a crowding-based local search is proposed to speed up convergence by improving the crowded solutions, and to enhance diversity by obtaining new solutions around the uncrowded ones. Comprehensive experiments are tested on a fuzzy dataset with different sizes, and the integral of the hypervolume of PF is used for the evaluation of the fuzzy PF. The results show that MPGA achieves the best performance over other comparative algorithms, especially on maximum spread metric, outperforming all others by an average of 39 % across all test instances.

Media Reporting of Environmental Supply Chain Sustainability Risks: Contextual and Moderating Factors

Media Reporting of Environmental Supply Chain Sustainability Risks: Contextual and Moderating Factors

E-procurement optimization in supply chain: A dynamic approach using evolutionary algorithms

The increasing dynamism of global markets, coupled with the occurrence of unpredictable events, has introduced substantial challenges in formulating efficient supply chain strategies. The inherent dynamic nature of logistic networks necessitates a departure from traditional supply chain methodologies. This study proposes an advanced solution for dynamic e-procurement utilizing evolutionary algorithms (EAs). In conventional supply chains involving buyers and suppliers, a critical challenge is identifying cost-efficient suppliers capable of fulfilling consumer demands amidst fluctuating prices and quantities. Traditional optimization techniques often fail to perform effectively under these dynamic conditions. Moreover, detecting changes during the optimization process is an additional hurdle in dynamic optimization problems. Recent advancements have demonstrated the efficacy of EAs in solving a variety of real-world dynamic optimization issues.This research introduces a novel evolutionary algorithmic framework that integrates the Hybrid Multipopulational Reinitialization Strategy (HMRS), with a proposed hybrid change detection mechanism (named Smirnov-based Multi-sensor Detection Mechanism (SMDM)) to address the dynamic e-procurement problems. The proposed framework enhances the algorithm’s adaptability and responsiveness to real-time changes within the e-procurement environment. By effectively detecting and responding to these variations, the framework aims to optimize procurement processes, ensuring efficiency and robustness in managing fluctuating requirements and conditions inherent to dynamic e-procurement scenarios. The empirical analysis presented underscores the superiority of Differential Evolution (DE) variants over Genetic Algorithm (GA) variants within the procurement context. The detailed empirical study validates the effectiveness of the proposed dynamic approach in addressing the challenges associated with dynamic e-procurement. Considering real-world parameter fluctuations, the proposed approach demonstrates significant resilience, positioning it as a robust and efficient solution for optimizing the e-procurement process and adeptly managing the complexities of the supply chain environment.

Enhancing Supply Chain Safety and Security: A Novel AI-Assisted Supplier Selection Method

The "Make or Buy” decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.

Social risks assessment of the supply chain of an aluminium semi-finished profile for window

Abstract Purpose Aluminium is among the most energy-intensive industries in the world and is produced from mining operations in bauxite mines. The effects on the environment of the entire aluminium supply chain, especially mining, also entail social risks. This article aims to understand the potential social risks along the supply chain of aluminium by focusing on a semi-finished frame for windows through the use of the Product Social Impact Life Cycle Assessment (PSILCA) database. Method Social Life Cycle Assessment (S-LCA) was implemented by following the Social Life Cycle Assessment of Products And Organizations 2020, to analyse the potential social risks and opportunities associated with the aluminium sector. Specifically, the PSILCA database, designed for S-LCA, was used to evaluate the background processes of the aluminium supply chain of an Italian company specialized in surface treatment of aluminium semi-finished products used in the production of doors and windows. Results The primary social risks manifest in the background processes. Comparing these findings with the literature review, it is straightforward to attribute them to the mining phase of bauxite, which constitutes the initial stage in aluminium production and occurs in various regions globally. Specifically, the highest social risk is associated with “Corruption in the public sector”, likely linked to Italy. Additionally, the study reveals a positive impact in terms of “Contribution to economic development” as also revealed by the literature review. Conclusions An analysis of the potential social risks within the aluminium supply chain was provided, addressing the research gap between the utilization of S-LCA methodology and its application within the sector. Additionally, the PSILCA database was employed for investigating the background processes in the case study. However, a social performance assessment using primary data would be required to enhance the representation of the evaluated production system.

The impact of blockchain adoption in supply chain systems on corporate performance: a technology–organization–environment framework

The instability of the international political environment and frequent occurrence of unpredictable events have introduced greater uncertainty to the external environment of supply chains. Consequently, supply chain operations and development face increasing challenges. With a decentralized architecture, blockchain technology has emerged as an innovative solution capable of addressing various supply chain management issues. However, existing research on the adoption and impact of blockchain technology on corporate performance is limited, particularly within the technology–organization–environment (TOE) framework. Drawing from relevant studies, this study identifies traceability, information sharing, transparency and decentralization as the four perceived benefits of blockchain technology, and proposes a model to evaluate the impact of blockchain technology on corporate performance. Additionally, it examines the moderating effects of technological uncertainty, corporate social responsibility (CSR) orientation, and market uncertainty on the blockchain adoption within the TOE framework.

Optimizing Supply Chain Network Design Under Uncertainty: A Practical Methodology for Sustainable Value Creation

Evaluating supply chain network (SCN) designs is critical for organizations striving to optimize operations and achieve sustainable value creation. However, conventional models often oversimplify, failing to account for the complexities inherent in real-world supply chain environments. In this study, we propose an advanced approach to SCN evaluation that strikes a balance between practicality and sophistication, leveraging real-world data to inform decision-making. Our methodology aims to bridge the gap between theoretical models and practical implementation, offering a pathway to sustainable value creation in SCN design. By incorporating risk analysis, resilience modeling, and solution methods tailored to uncertainty, our approach provides a comprehensive framework for addressing the challenges of SCN design under uncertainty. Simulation results validate the efficacy of our methodology in facilitating informed decision-making and strategic planning within organizations.

Influence of financial accounting information transparency on supply chain financial decision-making

The study examines the ecological and economic effects of the Chinese environment supply chain financial decision-making recommendation systems from 2009 to 2021. Environment analytics has become essential for organizations because of the rapid growth of digital technology and data. This technology offers exceptional corporate performance and environmental sustainability opportunities. This research uses Spatial Durbin Models and mediation effect analysis to examine how environment adoption affects key company performance measures. It also discusses the differences between industry and business models. Environment technologies improve operating efficiency, customer happiness, and company value. According to findings, environmental technology may streamline operational operations, boost customer happiness, and generate value, improving financial performance. Big data has ecological benefits, according to the findings. Environment technology may reduce a firm's environmental effect by improving operational efficiency and allowing sustainable practices. Research shows significant industry and organizational differences. This highlights the need for ecological plans for each sector's needs. Big data also mediates, showing that the environment may affect other operational aspects and increase their impact. Data ethics and responsibility are crucial. The findings demonstrate that the climate may support sustainable behaviors and meet environmental sustainability goals. To better understand big data's revolutionary power. Enterprises must carefully manage and responsibly use this powerful tool to maximize its benefits and minimize its disadvantages. This research will shape environmental strategies and practices as digital possibilities present themselves to enterprises and society.

Prediction of the process capability for compression rubber part forming in the automotive supply chain

The paper proposes predicting production process capability for the compression rubber part in automotive supply chain management. Delivery of parts to tier 1 and OEM on time is the most important part of supply chain management, together with the delivery of on-quality and on-cost control to maintain the competitiveness of the supply chain. There are many suppliers to produce many automotive parts for tier 1. Therefore, the simulation approach properly predicts and prevents the process from getting into trouble during the actual production time. Production process quality control is critical to ensure that the good quality of the parts purchased can be delivered on time. Rubber parts are used widely in automotive, motorcycles, trucks, and other types of vehicles, with small sizes but in huge quantities to support general OEM brands and specific parts. The rubber part manufacturing process is complex and uncertain with compression moulding and rubber curing conditions. Therefore, good conditions can predict the production process's capability to commission and deliver on schedule.A neuro-fuzzy system is adopted and developed to deal with the uncertain process capability under multi-criteria decision-making.The methodology development can be used in the actual rubber part manufacturing supply chain environment and can predict uncertain problems that might occur in the subcontractor factories.The prediction of the production process capability of the rubber part supply chain might be more effective on the real-time monitoring control system and can be tracking location part progress for further planning both success or rescheduling.The platform can be applied to audit and test the actual industrial supply chain, and problem and research questions are brought about from the real supply chain in the local country.The methodology development was originally created for the particular supply chain in rubber automotive parts that can replace the existing system to obtain a more efficient performance evaluation process.

On the capacity of artificial intelligence techniques and statistical methods to deal with low-quality data in medical supply chain environments

We illustrate the capacity of Artificial Intelligence (AI) and Machine Learning (ML) techniques to preserve consistent categorization abilities whenever the quality of the data decreases, displaying mistakes or mismatches across matrix entries, while standard statistical methods exhibit significant modifications in the value of the corresponding coefficients. We design algorithms of different complexity to generate a series of comparable profiles. These profiles are compared within environments that allow for an immediate identification of the generating algorithms and within increasingly complex settings involving almost identical profiles derived from different algorithms. AI and ML techniques outperform standard statistical methods when distinguishing the algorithms generating the profiles. Building on these results, we perform a retrospective analysis where AI and ML techniques are applied to two empirical scenarios defined by different data series of patients transplanted through the period 2006–2019. The first scenario contains the variables describing the evolution of patients inputted correctly. In the second, we modify the content of the vectors of characteristics defining the evolution of patients by exchanging the values of a subset of realizations from two categorical variables. AI and ML techniques are consistently accurate when categorizing patients correctly within both scenarios, a feature particularly relevant when the quality of the information sources composing the medical chain varies. This latter problem is exacerbated among hospitals located in developing countries, where the quality of the data gathered limits their identification and extrapolation capacities.

A novel adjusted real-time decision-making for dynamic distribution in the grocery supply chain

PurposeThe current research has developed a novel method to update the decisions regarding real-time data, named the dynamic adjusted real-time decision-making (DARDEM), for updating the decisions of a grocery supply chain that avoids both frequent modifications of decisions and apathy. The DARDEM method is an integration of unsupervised machine learning and mathematical modeling. This study aims to propose a dynamic proposed a dynamic distribution structure and developed a bi-objective mixed-integer linear program to make distribution decisions along with supplier selection in the supply chain.Design/methodology/approachThe constantly changing environment of the grocery supply chains shows the necessity for dynamic distribution systems. In addition, new disruptive technologies of Industry 4.0, such as the Internet of Things, provide real-time data availability. Under such conditions, updating decisions has a crucial impact on the continued success of the supply chains. Optimization models have traditionally relied on estimated average input parameters, making it challenging to incorporate real-time data into their framework.FindingsThe proposed dynamic distribution and DARDEM method are studied in an e-grocery supply chain to minimize the total cost and complexity of the supply chain simultaneously. The proposed dynamic structure outperforms traditional distribution structures in a grocery supply chain, particularly when there is higher demand dispersion. The study showed that the DARDEM solution, the online solution, achieved an average difference of 1.54% compared to the offline solution, the optimal solution obtained in the presence of complete information. Moreover, the proposed method reduced the number of changes in downstream and upstream decisions by 30.32% and 40%, respectively, compared to the shortsighted approach.Originality/valueIntroducing a dynamic distribution structure in the supply chain that can effectively manage the challenges posed by real-time demand data, providing a balance between distribution stability and flexibility. The research develops a bi-objective mixed-integer linear program to make distribution decisions and supplier selections in the supply chain simultaneously. This model helps minimize the total cost and complexity of the e-grocery supply chain, providing valuable insights into decision-making processes. Developing a novel method to determine the status of the supply chain and online decision-making in the supply chain based on real-time data, enhancing the adaptability of the system to changing conditions. Implementing and analyzing the proposed MILP model and the developed real-time decision-making method in a case study in a grocery supply chain.

The tone of buyer firms' annual reports and suppliers' green innovation: The spillover effects in the supply chain

AbstractGrounded in signaling theory, this study explores the effect of the tone of buyer firms' annual reports on suppliers' green innovation and investigates the factors influencing such spillover effects. We use a panel data regression method to analyze 748 paired buyer–supplier firm‐year observations of Chinese listed manufacturing firms from 2010 to 2020. We demonstrate that the tone of buyers' annual reports promotes suppliers' green innovation, showing a spillover effect on the supply chain. Furthermore, the signal strength and effectiveness may be affected by the signal environment. Specifically, buyers' power over suppliers (supply chain environment) and suppliers' industry competition (industry environment) positively moderate the spillover effects of green innovation. This study enriches signaling theory and contributes to the growing literature on green innovation in supply chain management and also provides significant implications for managers.

Innovating supply chain management with blockchain applications

Innovating supply chain management with blockchain applications