Supply Chain Management Research Articles

Sustainable supply chain management challenges analysis in local plastic recycling business

Sustainable supply chain management (SSCM) has become the key concept for every industry in managing their supply chain system by focusing on three aspects: economics, social, and environmental. Even though the implementation of SSCM will help the industry increase the efficiency of supply chain management, some challenges make the firms cannot implement the SSCM concept well and unsuccessful. The challenges of SSCM implementation need to be identified and managed to overcome these challenges and increase the efficiency of SSCM management. Therefore, this study aims to identify and analyze the challenges to SSCM implementation in the case of the local plastic recycling business in Thailand. The questionnaire was used to collect data from plastic recycling business experts. The best-worst method was used to analyze the significant challenges and prioritize ranking the SSCM implementation challenges. The results of this research found that management challenges are the first rank which lack of the policy and strategy for SSCM is a critical sub-challenge in the management dimension. The financial challenges are the second rank for SSCM due to the local plastic recycling business still being faced with financial constraints, which is the critical sub-challenges in the financial dimension, and the technology challenges are ranked last, which presented that it is less critical for SSCM in the local scale. Furthermore, the research conclusion and the guideline for the research direction of this research are presented

Building Sustainable Supply Chains: Role of Supply Chain Flexibility in Leveraging Information System Flexibility and Supply Chain Capabilities

In today's interconnected and intricate global environment, achieving sustainable supply chain performance is paramount for organizations. Within this context, the flexibility of information systems as dynamic capability, is a driving force behind effective supply chain management. In addition, there are a multitude of compliances that supply chains have to comply with in order to achieve sustainable outcomes. Against this background we examine how information systems flexibility, knowledge capabilities, supply chain capabilities and supply chain flexibility contributes towards sustainable performance of the supply chains. Using the lenses of Dynamic Capability View (DCV), this paper explores relationships among these constructs using Partial least Squares-Structural Equation Modeling (PLS-SEM). 321 tire industry professionals participated in the survey and provided their feedback. The findings underscore the fact that information system flexibility significantly enhances knowledge and supply chain capabilities, which subsequently facilitate flexibility in supply chains. The results of this study also established the significant influence of supply chain flexibility on sustainable supply chain performance, emphasizes the need to have flexibility in supply chain to achieve sustainability goals. This research provides an important input into academic debates on sustainable supply chain management, as it sheds light on the role of information system to improve Supply chain flexibility and sustainability.

Unveiling the power of enablers in enacting sustainable supply chain management practices

Unveiling the power of enablers in enacting sustainable supply chain management practices

Circular economy: the future nexus for sustainable and resilient supply chains?

Circular Supply Chain Management (CSCM) reduces the environmental impacts of the linear economy by optimizing resources and minimizing waste and it potentially enhances resilience through localization, digitalization, and collaboration. Using Interpretive Structural Modeling (ISM) and MICMAC analysis, this study explores the impact of CSCM practices on sustainability and resilience. Key practices like PMS, product as service, and recycled raw materials should be prioritized. CSCM practices must be implemented holistically for a successful transition. Additionally, social sustainability in circular supply chains needs more attention, as it influences other sustainability and resilience measures.

Impulse synchronization strategy for supply chains considering combined effects and demand saturation

Supply chains often face sudden changes in production, distribution, and consumption as a result of factors such as pandemics, Black Friday, and mass production by manufacturers; this is called the ”impulse phenomenon.” The effect of the impulse phenomenon on the supply chain system can be positive or negative. Understanding how to use the impulse phenomenon to synchronize supply chain systems and avoid chaos is therefore important for supply chain management. Existing studies mainly use continuous synchronization strategies as a means to avoid chaos in supply chain systems; however, a discrete impulse synchronization strategy is easier to implement. Considering combined effects of consumption,distribution,production and demand saturation, this study constructs a supply chain system composed of a manufacturer, distributor, and retailer. We then construct a supply chain system with uncertain parameters and input disturbance. Using Lyapunov stability theory and matrix inequality, we obtain the impulse synchronization strategies of two supply chain systems. Finally, taking China’s supply chain system of corn, wheat, and rice as an example, we compare goodness of fit. The production, distribution, and consumption of rice are controlled using a temporary storage purchase policy, batch ordering, and promotion activities, and the effectiveness of the impulse synchronization strategy is verified. Our results show the following: (1) Considering combined effects and demand saturation, the supply chain system has better goodness of fit and better impulse synchronization. (2) When a chaotic supply chain system deviates from its expected state, if impulse intensity and the time interval satisfy Theorem 1 or 2, the supply chain system can be restored to its expected state; if not, the synchronization effect might not be achieved. (3) Generally, when impulse intensity is low, the time interval is short, if impulse intensity is high, the time interval should be longer.

AI for Optimizing Supply Chain Management

The rapid advancement of Artificial Intelligence (AI) technologies has significantly transformed various sectors, with supply chain management (SCM) being a notable beneficiary. This research paper explores the impact of AI on optimizing supply chain processes, focusing on its applications in predictive analytics, natural language processing, robotics and automation, blockchain, and the Internet of Things (IoT). Through a mixed-method approach involving quantitative surveys, qualitative interviews, and secondary data analysis, the study evaluates how AI-driven solutions enhance key performance indicators (KPIs) such as inventory turnover, lead time, order accuracy, and cost reduction. The results indicate substantial improvements in these KPIs post-AI implementation, highlighting AI's role in achieving greater operational efficiency, accuracy, and cost-effectiveness in SCM. Challenges associated with AI adoption, including data quality, integration with legacy systems, and skill gaps, are also discussed. The paper provides actionable insights and recommendations for organizations aiming to leverage AI technologies to optimize their supply chain operations. This research contributes to the growing body of knowledge on AI in SCM and offers practical guidance for industry practitioners

Ensuring product authenticity and traceability with blockchain in supply chains

This review paper explores the efficacy of blockchain technology in enhancing product authenticity and traceability within supply chains. With the complexity of global supply chains increasing, the risk of counterfeit products and inefficiencies in tracking origins is escalating. This paper synthesizes existing literature to outline the current landscape of blockchain applications in supply chains, focusing on the decentralized and immutable capabilities of the technology that promise to revolutionize traditional practices. The core of the review involves a comprehensive analysis of documented implementations of blockchain in various industries, assessing their impact on enhancing transparency, reducing fraud, and improving overall supply chain integrity. We critically examine case studies and existing research to identify patterns of success and common challenges faced by organizations adopting blockchain solutions. Findings highlight that blockchain technology not only supports better traceability and authentication processes but also fosters a more transparent environment for transactions, significantly deterring the proliferation of counterfeit goods. Additionally, the review points out the technological and organizational hurdles in adopting blockchain, such as the need for standardization and the resistance to change in legacy systems. The paper concludes that blockchain technology holds substantial promise for addressing critical issues in supply chain management. However, achieving its full potential requires overcoming significant adoption challenges and fostering collaborative efforts for standardization across industries. This review calls for ongoing research into scalable blockchain applications and strategic partnerships to drive the technology's integration into mainstream supply chain operations.

Adaptive demand planning models: Utilizing reinforcement learning to address contingencies and dynamic market conditions in supply chains

There are remarkable opportunities in supply chain management associated with using the reinforcement learning (RL) approach in demand planning. As opposed to numerous other techniques, such as modeling and forecasting and applying them over a certain period, RL allows for the making and adapting these decisions in real-time, depending on the current demand and other conditions in the market. Supplementary to this, RL-based models enable supply chains to constantly adapt to shifts since they directly update knowledge from incoming data, making them less susceptible to economic shocks or other supply uncertainties. This flexibility is particularly important for contemporary supply chains in uncertain global environments where conventional, deterministic demand planning techniques cannot address changing needs. In this research, we discover how RL-based models could reduce demand volatility and react to contingencies. As such, it is best suited for industries that are required to respond flexibly and faster. This paper enlightens RL on the benefits of demand planning. It provides live examples that give insight into how it may be used to improve inventory, reduce cost, and improve decision-making. Regarding the specific research questions, this study helps investigate the methodologies, evaluation metrics, and case outcomes that shape RL's potential for changing demand planning and improving supply chain adaptability for future research on adaptive supply chain management.

Sustainable supply chain management in U.S. healthcare: Strategies for reducing environmental impact without compromising access

The U.S. healthcare sector’s supply chain operations are responsible for about 8.5% of all national greenhouse gas (GHG) emissions. In healthcare, traditional supply chain management has focused on cost efficiency to the detriment of environmental considerations leading to higher emissions, waste, and resource depletion. The focus of this study is to investigate strategies for making sustainable supply chain management (SSCM) in the U.S. healthcare system, while reducing environmental impact at minimal cost to patient care quality. The objectives of the study are to evaluate the current state of healthcare supply chains, explore sustainable practices, evaluate emerging technologies, propose an SSCM implementation framework, and explore policy implications. Sustainable supply chain management can be achieved by forming the key strategies: comprehensive waste reduction and recycling programs, adoption of energy efficient transportation and storage solutions, green energy sourcing, and use of data driven inventory management that would help optimize inventory, along with other technologies like blockchain, IoT and artificial intelligence that could achieve transparency and efficiency. The research wouldn't go into specific case studies but would likely look at institutions that have already been able to implement these practices. The findings indicate that considerable environmental footprint reduction of the U.S. healthcare sector is possible without sacrificing access to high quality care through adoption of SSCM practices. But it requires joint efforts from policymakers, healthcare providers and industry partners. The long-term goal is a sustainable, resilient healthcare system that will integrate environmental stewardship with high quality care, that will help mitigate climate change and acknowledge the interdependence of environmental and human health.

Deep Enhancement in Supplychain Management with Adaptive Serial Cascaded Autoencoder with Long Short Term Memory and Multi-layered Perceptron Framework

Deep Enhancement in Supplychain Management with Adaptive Serial Cascaded Autoencoder with Long Short Term Memory and Multi-layered Perceptron Framework

AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics

This paper explores the transformative role of artificial intelligence (AI) and predictive analytics in enhancing operational efficiency within healthcare supply chains. By leveraging AI-driven business analytics, healthcare organizations can optimize inventory management, improve demand forecasting, and streamline supply chain processes. The study presents a comprehensive review of recent advancements, challenges, and opportunities in the integration of AI technologies, focusing on their application in various healthcare contexts. Through systematic analysis of existing literature, the findings emphasize the significance of adopting AI and predictive analytics for effective decision-making, cost reduction, and improved service delivery in healthcare. The research highlights the need for organizations to embrace digital transformation and foster a collaborative approach in the implementation of AI-driven solutions to enhance overall supply chain resilience.

Consumer impatience in the e-commerce home delivery context: a consumer-centric supply chain perspective for time-based competition

PurposeThis paper aims to introduce the concept of consumer impatience, empirically explore how it relates to time-based logistics performance (delivery speed and delivery timeliness) and discuss its impact on consumer satisfaction. This research argues that gaining insights related to delivery performance from a consumer’s perspective can help the development of more effective time-based logistics strategies for e-commerce home deliveries.Design/methodology/approachHypotheses in this study are developed using attribution theory and tested with empirical data collected through an online behavioral consumer experiment. Middle-range theorizing is used to develop an understanding of the mechanisms that impact the relationship between time-based logistics performance and consumer satisfaction.FindingsFindings indicate that consumer impatience with delivery speed and delivery timeliness play an essential role in the relationship between time-based delivery performance and consumer satisfaction. Issues with delivery timeliness are shown to have a more negative impact on consumer satisfaction than issues with delivery speed, while delivery communication is demonstrated to have a positive relationship with consumer satisfaction.Originality/valueThis empirical study adds to existing time-based competition literature by taking a consumer-centric perspective and bringing a largely overlooked but critical concept – consumer impatience – into the logistics and supply chain management setting. Middle-range theorizing allows for a conceptualized understanding of consumers’ delivery experiences that can help companies develop proactive actions in their time-based competition initiatives.

GESTÃO DA CADEIA DE SUPRIMENTOS USANDO IA

Supply Chain Management (GCS) integrates and coordinates all activities from obtaining materials to final delivery, aligning objectives to maximize efficiency, reduce costs and improve quality. Planning in the chain is crucial to guarantee resources, minimize uncertainties and add value to the customer. Logistics is essential in efficient distribution, and inventory management balances supply and demand. Performance indicators (KPIs) and process mapping are used to evaluate and improve operations, while production planning and control (PCP) ensure efficiency in production and delivery. These aspects form the basis of GCS, essential for customer success and satisfaction. Therefore, the objective of this work is to analyze the use of artificial intelligence of supply chain management in a contemporary context, identifying challenges, strategies and impacts for organizations, with a focus on optimizing processes and improving business competitiveness.

An inventory allocation model for cost reduction in the supply chain of a payment means company

The Supply Chain Management (SCM) has been gaining greater relevance in the sector, leading companies to improve the level of customer service and keep costs under control. However, it was identified that SCM is a less explored field in the acquirer market, so this work is an opportunity to contribute to the academic research. It was observed the opportunity to build an inventory model considering transportation as a variable cost, unlike most models found in literature. The objective of the study was to develop an inventory allocation model for the SIMcard chain in a Brazilian company in the acquiring sector, keeping the service level agreed with the customer. Inventory and transport costs were calculated for different supply cycles for all SIMcards operators and transportation hubs, to obtain the lowest total cost configuration in the supply chain. The results show that the costs cannot be considered individually and considering both costs, transport and inventory, there is an opportunity to increase efficiency in the studied operation of this work.

The Adaptive Human-AI Synergy in Logistics (AHASL) Theory

This paper introduces the Adaptive Human-AI Synergy in Logistics (AHASL) theory, which focuses on integrating artificial intelligence (AI) into logistics and supply chain management. The study takes a qualitative approach, using interviews, observations, and reflexive journaling, and finds that while AI significantly improves operational efficiency and decision-making, human oversight is still required to address AI's limitations, such as bias, lack of transparency, and potential skill erosion. The AHASL model prioritizes Full-Spectrum Explainability (FSE) and a Bias Mitigation Framework (BMF) to ensure that AI-driven decisions are transparent and equitable while simultaneously arguing for retaining human expertise to retain adaptability and resilience in logistical operations. The study's findings emphasize the significance of balanced human-AI collaboration. However, they also call for more research into the long-term effects, scalability, and ethical implications of AI integration in logistics.

The Impact of Digital Transformation on Supply Chain Management: A Study of How Firms Adapt

Businesses need to embrace digital if they want to succeed in the cutthroat business climate of today. The goal of supply chain management is to maximise value for stakeholders and customers while also achieving operational efficiency. The planning, carrying out, managing, and verifying of store network exercises do this. The benefits and drawbacks of integrating digital technology into supply chain management practices are examined in this study. This analysis also aims to shed light on the potential advantages of advanced change for developing market organisations that aspire to become global. By using a qualitative methodology and carrying out a thorough analysis of six successful real cases in three different emerging markets—Iran, Bangladesh, and Pakistan—the study also seeks to offer recommendations for businesses in emerging markets on how to use digital technologies to optimise supply chain management processes and facilitate international business. The ultimate goal of this research is to improve knowledge of the role and importance of digital transformation in supply chain management for companies that operate in developing countries and to offer these companies useful advice so they can successfully compete on a global scale.

Digitalization and supply chain management: impact assessment of healthcare technology implementation on service delivery in public hospitals, Lagos, Nigeria

The study examined digitalization and supply chain management: assessing healthcare technology implementation for service delivery in Lagos General Hospital, Nigeria. It employed both qualitative and quantitative techniques. It analysed 293 questionnaires using multiple-regression, and Pearson. It used thematic to analyse data from interview and focused group discussion. The results showed a value of 3.28, with a standard deviation of 1.46, indicating the effectiveness of healthcare technology in general hospital. The H2 (R2 =.078, R = 278, F (2,290) = 12.19; p<.001) revealed digitalization-supply chain management have effect on Service-Delivery. The H3 (t = 3.71; B =.234; p<.001) revealed SCM can have an effect on achieving Sustainable-Development-Goal3. H4 (r =.070; p >.05) Revealed E-Medical Record does not have an effect on death rate in Lagos State, Nigeria. The study recommended, among other things, that all government and private healthcare facilities should harmonise EMR through SCM.

Analyzing the Relationship between Risk Management Strategies and Supply Chain

Purpose: The purpose of this article examined how different risk management strategies affect supply chain resilience in Malaysia. It aims to identify effective strategies, such as supplier diversification and digitalization, that help Malaysian companies mitigate disruptions. The findings offer insights to enhance operational continuity and adaptability within Malaysia's unique risk environment. Methodology: This study adopted a desk methodology. A desk study research design is commonly known as secondary data collection. This is basically collecting data from existing resources preferably because of its low cost advantage as compared to a field research. Our current study looked into already published studies and reports as the data was easily accessed through online journals and libraries. Findings: The study found that effective risk management strategies, like supplier diversification and real-time monitoring, significantly enhance supply chain resilience in Malaysia. Companies adopting these strategies experienced fewer disruptions and quicker recovery times. However, challenges such as high costs and limited technology access still hinder broader adoption among smaller firms. Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV), contingency theory & high reliability theory (HRT) may be used to anchor future studies on the relationship between risk management strategies and supply chain resilience in Malaysia. Practically, companies should implement risk management strategies that are tailored to their specific supply chain contexts and risks. At the policy level, governments and industry bodies should support the adoption of resilience-enhancing technologies by offering financial incentives or subsidies, particularly for small- and medium-sized enterprises.

Supply chain learning and environmental performance: leveraging relational capital and information technology

PurposeWhile the importance of supply chain learning (SCL) is widely recognized by both academia and industry, the mechanisms through which SCL is developed and its effects on environmental performance remain insufficiently understood. This study conceptualizes and empirically investigates the role of relational capital and information technology (IT) in enhancing SCL and improving environmental performance.Design/methodology/approachDrawing on socio-technical system theory (STS) and a knowledge-based view, this research employs structural equation modeling to test the proposed model, utilizing survey data collected from Chinese manufacturing companies.FindingsThe results demonstrate that relational capital with supplier and customer significantly enhances both supplier and customer learning. While IT does not directly impact supplier and customer learning, it exerts an indirect influence through its positive effect on relational capital. In other words, relational capital mediates the relationship between IT and supplier and customer learning. Additionally, this study finds that both supplier and customer learning enhance environmental performance.Practical implicationsThis study provides actionable insights for managers, emphasizing the importance of relational capital in fostering SCL and improving environmental performance. By understanding these relationships, managers can develop more effective strategies for leveraging SCL as a tool for sustainability.Originality/valueThis study adds to the existing body of knowledge in supply chain management by offering a more nuanced and holistic model to explain how relational capital, IT and SCL interact to influence environmental performance, particularly within the context of Chinese manufacturing companies.

The Impact of AI on the Development of Manufacturing, Logistics, and Financial Company from Capital Market Perspectives

Abstract: This paper explores the impact of AI technology on enterprises, especially in finance and capital markets. With the rapid development of AI technology, enterprises hope to improve productivity, financial management, and market competitiveness through AI, and ultimately achieve sustainable development. The article aims to analyze the application cases of AI in head enterprises and their effects and put forward corresponding suggestions and insights. By analyzing case studies of enterprises such as Tesla, Amazon and JP Morgan, it elaborates on the application of AI in various areas such as enterprise manufacturing, supply chain management, automated trading, risk management and customer service. It reveals the great potential of AI in improving enterprise operational efficiency, reducing costs and optimizing decision-making. By analyzing the above cases, it is suggested that enterprises should strengthen data privacy and security management and improve technology transparency and interpretability when introducing AI technology. The study reveals that AI technology has potential but also faces challenges, and enterprises need to keep overcoming obstacles to achieve the wide application and deep integration of AI technology.