Supply Chain Research Articles

Transitioning to a Circular Economy: Business Models and Sector-Specific Practices

This paper explores the concept of circular economy models in business, contrasting them with traditional linear models. It introduces three key players in the circular economy: Product Manufacturers, Problem Solvers, and Facilitators, and identifies six general business models, including Circular Supply Chain, Sharing Platforms, Product as a Service, Product Life Extension, Resource Recovery, and Product Incentivized Return. The text also discusses sector-specific adaptations, highlighting permaculture in agriculture as an example of circular economy principles tailored to a specific industry. The content provides a framework for understanding how circular economy models can be implemented and adapted across various sectors, emphasizing the need for detailed analysis and tailored approaches in future research. Keywords: Circular Economy, Business Model, Sustainability

Assessing Best Practices in Natural Gas Production and Emerging CO2 Capture Techniques to Minimize the Carbon Footprint of Electricity Generation.

Natural gas (NG) is expected to provide a substantial portion of electricity generation in many jurisdictions for the foreseeable future. Postcombustion carbon capture and storage (CCS) effectively abates direct CO2 emissions; however, indirect NG supply chain emissions in most jurisdictions are incompatible with climate change mitigation goals. This life cycle assessment evaluates specific opportunities to reduce the carbon footprint of combined cycle gas turbine (CCGT) generation with CCS using existing low-emission NG production practices, technologies, and processes combined with emerging CCS techniques to achieve high CO2 capture rates and mitigate startup emissions. We find baseload life cycle greenhouse gas (GHG) emission intensity ranges from 22 to 62 kgCO2e/MWh for 95-98.5% CO2 capture, within the range of published estimates for wind and photovoltaic power and considerably below prior estimates of CCGT with CCS. Low-emission NG production practices reduce other environmental impacts, which are dominated by combustion-related air pollution. We also show how interim solvent storage can effectively mitigate emissions from CCGT start/stop cycles. This work highlights the importance of mitigating both CO2 and methane emissions from NG supply chains and proposes a more nuanced discussion regarding the potential contribution of NG to the future energy supply. A surrogate model is provided to estimate life cycle GHG emissions for CCGT with CCS and user-input parameters.

Deep learning for freshness categorisation in sustainable agricultural supply chains: a focus on quality assessment of fruits and vegetables

Deep learning for freshness categorisation in sustainable agricultural supply chains: a focus on quality assessment of fruits and vegetables

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.

Strengthening food supply resilience: Lean and Lean Six Sigma strategies for disruptions

ABSTRACT Recognizing that the food industry faces a myriad of challenges, ranging from natural disasters to geopolitical conflicts and pandemics, this study explores how Lean, and Lean Six Sigma (L&LSS) can fortify supply chains against such diverse disruptions. While the COVID-19 pandemic serves as a contextual backdrop, our focus is on the broader application of L&LSS methodologies in navigating disruptive events. This study analyzes responses from 120 food firms, utilising structural equation modelling to correlate L&LSS practices with adaptive performance during crises. Our findings indicate that L&LSS not only equip firms to maintain operational continuity amidst disruptions but also positions them to anticipate and adapt to future systemic shocks. By elucidating the positive correlation between L&LSS engagement and crisis resilience, this research contributes a strategic framework to the literature, advocating for a pre-emptive embrace of L&LSS practices as essential to navigating the unpredictable nature of global crises.

Enhancing Supply Chain Efficiency with Predictive Analytics: A Machine Learning Approach to Dynamic Lead Time Prediction in a Chemical Industry

We develop and employ a four-step methodological approach for predicting the lead time delays in all echelons of a supply chain (SC). The first step of the methodological approach involves a critical synthesis of academic research efforts for identifying the main sources of delays in all echelons of a supply chain. The second step involves the development of questionnaires for validating the findings of the research through workshops with industry stakeholders. The third step involves the development of a suite of machine learning (ML) models, namely, Random Forest Regression, Decision Tree Regression, and Linear Regression. These models were selected based on their prevalence in the recent literature and their ability to handle linear and nonlinear relationships between multiple variables. The final fourth step involves the implementation of the suite of machine learning models in the real case of a Hellenic chemical manufacturing supply chain. The implementation results reveal that Random Forest Regression exhibits the highest predictive accuracy throughout all stages of the supply chain, achieving the lowest Mean Absolute Percentage Errors (MAPE), ranging from 0.5 to 7% in the examined supply chain echelons.

From SolarWinds to Kaseya: The rise of supply chain attacks in a digital world

Supply chains today rely heavily on information technologies. Such reliance has encouraged attackers to shift their focus on conducting supply chain attacks, which is expected to become the most common type of cyberattack by 2030. Thus, it is crucial for Information Systems practitioners to gain a deeper understanding of supply chain cybersecurity. To that end, this teaching case demonstrates the importance of supply chain cybersecurity in the digital era drawing on two recent attacks with significant impact on supply chains: SolarWinds and Kaseya. We first discuss different dimension of cyberattacks, followed by an introduction of supply chain attacks. We then introduce an analytical tool called cyber kill chain that is widely used for analysing different stages of a cyberattack. In addition, we propose a taxonomy of cyberattacks that can be used as a tool, alongside other tools, to analyse cyberattacks. The taxonomy is especially useful for conducting a lightweight analysis and presenting an overview of cyberattacks to non-technical stakeholders, especially executives and directors.

Examining How Blockchain Implementation Affects Supply Chain Visibility in Ghana

Purpose: This article investigate how blockchain technology enhances visibility in supply chains by enabling real-time, transparent data sharing among stakeholders. It explores blockchain's role in reducing information silos and improving traceability. The study aims to inform both industry practitioners and policymakers on blockchain’s benefits and adoption strategies. 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: Blockchain implementation enhances supply chain visibility by enabling real-time tracking and secure data sharing across stakeholders, reducing fraud and improving traceability. It also helps in verifying product origins, building consumer trust, and streamlining inventory management. However, challenges such as high costs and the need for standardized protocols can limit widespread adoption. Unique Contribution to Theory, Practice and Policy: Transaction cost economics (TCE), resource-based view (RBV) & institutional theory may be used to anchor future studies on the examining how blockchain implementation affects supply chain visibility. For practitioners, the implementation of blockchain technology offers significant potential to improve real-time visibility, data accuracy, and trust within the supply chain. Policymakers should develop standards and regulatory guidelines for blockchain implementation in supply chains to ensure secure and ethical data usage.

Research perspectives of supply chain knowledge management: A bibliometrics study

The purpose of this article is to present a bibliometric analysis of scientific publications on knowledge management (KM) and supply chain (SC), provide an overview of research activities in this field and recognise its most substantial and fundamental aspects. In addition, this study aims to quantitatively analyse KM in SC in other words supply chain knowledge management (SCKM) research trends, forecasts and citations from 1999 to 2021 in Web of Science (WOS). A total of 499 documents related to SCKM research were collected from the following databases: SCI-EXPAND, SSCI, AHCI and ESCI. These documents were carefully reviewed and subjected to bibliometric data analysis techniques. We map the time trend, disciplinary distribution, high-frequency keywords and topics, major authors and influential publications to show emerging topics. Based on the findings of other researchers, many implications emerged that improve one’s understanding of the identity of SCKM as a distinct multi-discipline scientific field, and its publication has grown significantly since 2018. SC papers were published in engineering and Operations Research and Management Science journals, while the coverage of research topics related to KM and SCKM is broader and more interdisciplinary than those of SC papers. High-frequency keywords associated with SCKM research are listed. From the citation of 499 papers with both KM and SC as keywords, we find the most popular one.

Applied Research on the Impact of Big Data, Cloud Computing and Cloud Storage on Smart Manufacturing Supply Chain

The rapid advancements in big data, cloud computing, and cloud storage technologies are profoundly reshaping the operational models of smart manufacturing supply chains. The integration and application of these technologies not only drive the enhanced data collection and analysis capabilities across various supply chain segments but also significantly improve the accuracy of demand forecasting, the rationality of resource allocation, and the scientific nature of decision support. Consequently, the collaborative efficiency and dynamic response capabilities of supply chains have been substantially elevated, providing robust technological backing for enterprises in navigating the intricately volatile market environment. Nonetheless, while reaping the benefits of these technological advancements, challenges such as data security and privacy protection, system integration, and technical selection cannot be overlooked. By judiciously selecting technology platforms, reinforcing data security management, and continuously investing in technological advancements and talent cultivation, the holistic effectiveness of smart manufacturing supply chains will be further optimized.

A systematic review of strategies for enhancing pharmaceutical supply chain resilience in the U.S

This review aims to explore strategies for enhancing pharmaceutical supply chain resilience in the United States by addressing vulnerabilities exposed by recent global events, including the COVID-19 pandemic. The study identifies and analyzes eight key strategies: supplier diversification, increased inventory and safety stock, adoption of advanced technologies, improved forecasting and demand planning, vertical integration, reshoring and nearshoring, contingency planning and risk management, and collaboration and information sharing. Drawing on these strategies in the context of the United States, the review examines the advantages and potential challenges associated with them. The implementation of strategies such as supplier diversification and the adoption of advanced technologies shows positive effects, but these approaches require significant resources and face policy restrictions. This paper also analyzes the policy and industry implications of these strategies, emphasizing the need for collaboration between the government and industry. Various policy implications include the need to encourage supplier diversification, support for strategic stocking, and funding for technology implementation. For the industry, there is a need for operational adjustments such as redesigning supply chain networks with emphasis on enhancing risk management practices. Although implementing these strategies presents certain complexities, they hold great promise for improving the overall resilience of the US pharmaceutical supply systems and should be further explored in the future. Keywords: Supply Chain, Risk Management, Industry Disruption, Inventory Management, Blockchain Technology

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.

Decoding food losses in pulses trade: proposing sustainable solutions from an Indian perspective

ABSTRACT Food losses are a critical concern for economic, social, and environmental sustainability within logistics systems. This paper quantifies food losses in India's pulses trade, examining specific cases and identifying causes and stages of losses in the international food supply chain. Focusing on sustainable development goals, it offers insights into food losses in internationally traded cargo (pulses), which rank second in the FAO-Food Loss Index and are India's second-largest imported food product. Using a descriptive research methodology, the study unravels the complexities of food losses in India's pulse supply chain. Primary data were collected via questionnaires from 130 importing firms and supply chain professionals, supplemented by secondary sources. Purposive sampling guided data collection from April 2023 to May 2024, involving virtual and physical stakeholder interactions. The comprehensive analysis provides strategies to mitigate food losses, laying the groundwork for improvements in India and other developing countries.

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.

Welfare‐Reducing Integration Under Emission Taxes and Endogenous Competition Mode

ABSTRACTThis study examines asymmetric supply chain competition between integrated and non‐integrated supply chains under emission taxes when firms choose competition mode in the final goods market endogenously. We find that higher emission taxes may lead the non‐integrated downstream firm to adopt aggressive pricing to induce its upstream firm to lower prices, leveling competition with an integrated one. When the non‐integrated chain integrates, however, quantity contracts become dominant strategies, which might distort welfare under lower emission taxes if both environmental damage and product substitutability are sufficiently high. Our finding indicates the welfare‐reducing effects of government interventions post‐integration when emission tax policy and endogenous competition mode are relevant.

The Application of Virtual Reality Technology in Clothing Product Development

With the rapid development of modern technology, virtual reality technology is gradually emerging in the clothing industry, which has a profound impact on key aspects such as product design, sample clothing production, production management, and supply chain management. This study delves into the specific application status, efficiency advantages, and complex challenges faced by virtual reality technology in clothing product development. Through system analysis, this article provides a detailed explanation of the technology's implementation of digital clothing design, virtual fitting, and efficient integration of 3D modeling and simulation systems. Meanwhile, this article provides a detailed and in-depth discussion on the application details of virtual reality technology in areas such as sample clothing production, production process management, and supply chain operations. The research results show that virtual reality technology has brought new design and management tools to the clothing industry, effectively improving design efficiency, reducing cost expenditures, enhancing user experience, and driving the entire industry towards more efficient, intelligent, and sustainable development.

Assessing Geopolitical Risks and Their Economic Impact on the USA Using Data Analytics

Understanding geopolitical risks is a paramount aspect of examining the stability and resilience of national economies, specifically in today’s rapidly evolving global surroundings. Advanced analytics in the big data era open unparalleled avenues toward the quantification and comprehension of geopolitical risks on the performance of the economy of the United States. The prime objective of this study was to analyze the impact of geopolitical events on the U.S. economy, to identify key risk factors and their economic implications as well as propose strategies for mitigating adverse effects. Datasets used in this exploration were collected from different reliable sources to assess sources of geopolitical risk data and their economic impact on the U.S. First, data on geopolitical risk were collated from a combination of real-time news reports, government databases, and international organizations involved in monitoring geopolitical events. Key sources for this included GDELT news and sentiment data, official reports from U.S. government agencies such as the Department of State and the Department of Defense about foreign policy, conflict, and security, while major financial news outlets like Bloomberg and Reuters provided moment-by-moment coverage of events in the geopolitical sphere. We applied the Geo-Risk-Regressor model, a form of multimodal design to predict geopolitical threats arising from economic indicators, real-time news sentiment, and government reports on geopolitical events. The Geo-Risk-Regression Model is an integrated set of machine learning algorithms, from time-series and NLP to econometric regression, on structured and unstructured data comprising economic indicators, real-time news sentiment, and government reports on geopolitical events. A rigorous structured procedure was followed in implementing the Geo-Risk-Regressor to analyze the economic impact of geopolitical risks in the U.S. To assess and evaluate the performance of the algorithms, two key performance evaluation metrics were utilized MSE & R-squared. Among all the models, the best performance was that of XG-Boost; it had the lowest MSE and highest R². Thus, XG-Boost is the best model fitted for the prediction of GPRD_THREAT, probably because of its robust optimization and also its capability to capture a lot of complicated patterns in data. The geopolitical threat level perceived using the proposed models will enable business organizations in the USA to identify and manage risks that may affect the operations of the business organizations. Companies can, therefore, understand factors that contribute to risk and develop contingency plans, enabling them to take proactive measures to mitigate negative impacts from geopolitical events. Predictive models will help businesses in America estimate the potential risks to their supply chains and create strategies for mitigating any disruptions that might come through geopolitical events.

The Role of AI in Transforming Local Economies: Exploring How AI Technologies Are Impacting Local Businesses and Labor Markets

Abstract: AI is emerging as a key disruptor of local economies. The impact of AI technologies including machine learning, automation and precision farming in reshaping local business landscape, unlocking innovation, enhancing productivity and creating net new jobs, is yet to be fully appreciated. This paper provides an analysis of the role of AI in revitalising local economies: how AI technologies are enabling small and medium sized enterprises (SME) to improve productivity, how rural economies can overcome labour shortages and supply chain issues and how to realise AIs transformative potential in the near term. It goes on to discuss the impact of AI on labour markets, the creation of net new jobs in data and drone related occupations and need for reskilling and upskilling. It also discusses the role of local governments in encouraging AI adoption through public-private partnerships, tax incentives and infrastructure improvements. The paper concludes with a discussion about how AI shapes the future of work, the need to address the digital divide and the role of AI in fostering sustainable economic development at the local level. This paper analyses the impact of AI on productivity, consumer engagement, supply chain optimisation and workforce transformation.

Assessing the Effect of Green Supply Chain Practices on Corporate Performance in Philippines

Purpose: This article evaluated how environmentally sustainable practices impact corporate outcomes. It explores green supply chain strategies like eco-friendly sourcing and waste reduction, examining their effects on financial performance and brand reputation. The study aims to help businesses understand the benefits and challenges of integrating sustainability into their supply chains. 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: Green supply chain practices positively impact corporate performance by enhancing efficiency, reducing costs, and improving brand reputation. Companies adopting practices like sustainable sourcing and waste reduction report better operational outcomes and increased customer loyalty. Unique Contribution to Theory, Practice and Policy: Resource-based view (RBV), stakeholder theory, institutional theory may be used to anchor future studies on the assessing the effect of green supply chain practices on corporate performance. For practitioners, the findings underscore the importance of implementing GSCM practices as part of a long-term strategic framework. At the policy level, governments should provide incentives and subsidies for companies that adopt GSCM practices, particularly in high-impact sectors like manufacturing and textiles.

A One Health Approach to Reducing Livestock Disease Prevalence in Developing Countries: Advances, Challenges, and Prospects.

Challenges in livestock production in developing countries are often linked to a high disease prevalence and may be related to poor husbandry, feeding, and nutrition practices, as well as to inadequate access to preventive veterinary care. Structural barriers including chronic poverty, gender roles, inadequate supply chains, and limitations in surveillance infrastructure further complicate progress. Despite many challenges, the livestock sector substantially contributes to agricultural GDP, and reducing livestock disease prevalence is a goal for many countries. One Health initiatives that work across disciplines and sectors to reduce livestock diseases are underway around the world and use integrated approaches that consider the connections between humans, animals, and their shared environments. The growing recognition of the role livestock play in sustainability and livelihoods, as well as their involvement in zoonotic disease transmission and global health security, has highlighted the need for disease reduction strategies as described in this review.