Supply Chain Risk Research Articles

Risk Factors of Perishable Product Supply Chain: Literature Review and A Proposed Framework for Future Research

United Nations statistics show that about one-third of globally transported perishable products are annually wasted due to damage. Where in the perishables supply chain is most likely to go wrong and how can this be corrected or prevented? This article aims to review the literature 50 articles related to the risk factors of perishable products supply chain and cluster the risk factors of perishable products supply chains. Identifying 140 risk factors from 235 risk factors through duplication filtering. And the researcher therefore clusters of risk factors with hierarchical clustering (divisive: distance matrix) into 19 clusters by KNIME machine learning. These findings offer valuable insights for future research on orchid’s supply chain risk management for sustainability in Thailand, top global orchid exporter. For the benefit of orchid supply chain stakeholders in Thailand can develop appropriate and sustainable supply chain risk management in the future. Thailand 3rd National Logistics Development Strategy (2017-2021) emphasizes cold chain logistics to elevate agricultural and industrial supply chains to meet standards. Implementing a cold chain system is vital to reduce losses, enhance traceability, and lower production costs.

Dominant risk factors (DRFs) in construction-specific supply chains: a systematic review

PurposeDue to the uniqueness of individual construction projects, identifying the dominant risk factors is needed for risk mitigation in ongoing and future projects. This study aims to identify the dominant construction supply chain risk (CSCR) factors, based on studies conducted between 2002 and 2022.Design/methodology/approachThe study adopts the preferred reporting items for systematic reviews and meta-analysis (PRISMA) procedure to identify, screen and select relevant articles in order to provide a bibliography and annotation of the prevalent risks in the supply chains. A descriptive analysis of the findings then follows.FindingsThe study’s findings have highlighted the three most prevalent risks in the construction supply chain (poor communication across project teams, changes in foreign currency rate, unfavorable climate conditions) as reported in literature, that project teams need to pay closer attention to and take proactive steps to mitigate.Research limitations/implicationsDue to limitations imposed by the chosen research methodology, tools, time frame and article availability, the study was unable to examine all CSCR-related papers.Practical implicationsThe results will serve as a useful roadmap for risk/supply chain managers in the construction industry to take strategically proactive steps towards allocating resources for CSCR mitigation efforts.Social implicationsContext-specific research on the impact of social and cultural risks on the construction supply chain would be beneficial, due to emerging social network risk factors and the complex socio-cultural settings.Originality/valueThere is presently no study that has reviewed extant studies to identify and compile the dominant risk factors (DRFs) associated with the supply chain of construction projects for ranking in the supply chain risk management process.

Optimal Emergency Order Policy for Supply Disruptions in the Semiconductor Industry

Semiconductor materials are strategic materials in global manufacturing. Affected by supply chain risks, semiconductor supply shortages have brought unprecedented pressure on global supply chain security, such as car manufacturers and electrical and electronic production stopping production among others, resulting in economic losses. When supply disruption occurs, retailers typically place emergency orders to reduce unexpected costs caused by the event. This study examines the emergency ordering problem faced by retailers under supply disruptions in the semiconductor industry, using the supply of semiconductor chips from Renesas Electronics to Toyota as an example. For this, we establish an emergency ordering optimization model based on retailers' profit maximization and solve it by modelling analysis. The model's validity and the impact of parameters on emergency ordering policy are verified by numerical experiments. The experimental findings demonstrate that: first, the retailer should consider the impact of consumer demand, alongside market price volatility, and make reasonable arrangements for the replenishment time and quantity. Second, at a higher partial backordering rate, the retailer makes larger adjustments for partial backordering by increasing the backordering quantity and making more profits. Third, in the case of shortages, the greater the shortage penalty cost is, the lower the profits the retailer gains, making it necessary to develop a timely replenishment strategy to prevent losses caused by shortages in semiconductor supply chain systems. Thus, in the event of a disruption, retailers should ensure that their order quantities not only meet market demand but also consider inventory holding costs while judiciously determining the number of emergency orders. This study addresses real-world scenarios in the semiconductor industry's supply disruptions and aids managers in formulating optimal emergency ordering strategies to maximize profits and ensure business continuity.

Adaptive Supply Chain Risk Management Using AI Mitigating Disruptions and Enhancing Resilience in the Post-Pandemic Era.

The COVID-19 pandemic has starkly revealed the vulnerabilities within global supply chains, prompting the urgent need for enhanced risk management strategies. This paper explores the application of Artificial Intelligence (AI) in adaptive supply chain risk management to mitigate disruptions and enhance resilience in the post-pandemic era. By leveraging AI technologies such as machine learning, predictive analytics, and optimization algorithms, organizations can proactively identify, assess, and respond to risks in real-time, thereby fortifying their supply chains against unforeseen disruptions. [1] This paper reviews existing literature on supply chain risk management, AI applications in supply chain management, and post-pandemic supply chain challenges. Furthermore, it presents examples illustrating how AI-driven adaptive risk management approaches have been implemented successfully to navigate disruptions and improve supply chain resilience. Through this analysis, the paper aims to provide insights into the transformative potential of AI-enabled adaptive risk management strategies in building agile and robust supply chains for the future. DOI: https://doi.org/10.52783/tjjpt.v45.i02.6294

Risk management maturity and robustness in the Chinese manufacturing supply chain: a comparison study across organisational cultures

ABSTRACT Chinese manufacturing, a major engine of the world economy, has been at the heart of the challenge of major global supply chain disruptions. This research explores the relationships among different dimensions of supply chain risk management maturity and supply chain robustness in Chinese manufacturing organisations. A structural equation model is developed and tested based on the participation of 296 supply chain managers. Based on the Competing Values Framework, four different culture groups are assessed to determine whether the relationships differ by culture type. While all model relationships were found to be significant and positive across the culture groups, the weights associated with certain relationships were significantly different. The adoption of the proposed framework could bring the benefit of a more comprehensive evaluation of supply chain risk management maturity, as well as the identification of both strengths and areas where improvements may be necessary for achieving supply chain robustness.

Deep learning approaches to identify order status in a complex supply chain

The emergence of artificial intelligence (AI) and its related capabilities has led industries to rethink the existing practices of conventional supply chain management and data analysis. Machine learning (ML), Deep Learning (DL) and their unique ability to predict future data and classify data have led to important research in the supply chain (SC) domain, particularly in identifying and prioritizing supply chain risks. This paper proposes several DL methodologies to exploit the benefit of DL, particularly to identify whether any product will be delivered late due to any unforeseen reason in a complex SC system. Four different DL architectures (Simple-LSTM, Deep-LSTM, 1D-CNN, and TCN-1DSPCNN models) are proposed to extract features, while six variant classifiers: Softmax, random trees (RT), random forest (RF), K-nearest neighbor (KNN), artificial neural network (ANN), and support vector machine (SVM), were used to classify delay or non-delay information. By seamlessly capturing intricate temporal dependencies, these DL models enhance accuracy in robustly identifying supply chain late orders. Leveraging their hierarchical feature learning, these proposed DL models excel in recognizing subtle patterns and correlations, making them ideal for classifying late orders within the supply chain. Their parallel processing prowess facilitates real-time decision support, allowing organizations to address potential delays and allocate resources effectively and proactively. Five-fold cross-validation is presented to avoid over-fitting and to prove the efficiency of the proposed DL models. The total accuracies of the six ML classifiers are 74.03, 75.81, 93.35, 87.72, 93.59, and 95.10, respectively, while the maximum accuracies obtained from four proposed DL methodologies obtained an accuracy of 97.6, 98.63, 100, 100% respectively using the SVM classifier for predicting late orders based on five-fold cross-validation.

Adoption of Blockchain in Critical Minerals Supply Chain Risk Management

This study examines the impact of digital transformation, digital awareness, and supply chain risk management (SCRM) on the intention to employ blockchain technology in the US critical minerals industry. It examines the influence of digital transformation on supply chain resiliency and the adoption of blockchain by analyzing responses from 122 managers and professionals through structural equation modeling and partial least squares approaches. SCRM, perceived usefulness, and usability shape the intention to use blockchain, while digital transformation enhances supply chain resiliency. This research contributes to the existing literature by confirming the relevance of the technology acceptance model in blockchain adoption and by highlighting the importance of digital transformation in supply chain resiliency. Practically, it offers valuable insights for managers, emphasizing the role of SCRM in recognizing blockchain's benefits, the need for digital literacy and transformation to facilitate blockchain adoption, and the potential of blockchain to enhance supply chain operations.

Research of blockchain-embedded agricultural quality credit regulation influencing factors

PurposeThe credit of agricultural product quality and safety reflects the ability of the main actors involved in the supply chain to provide reliable agricultural products to consumers. To fundamentally solve the problem of agricultural product quality and safety, it is worth studying how to make the credit awareness and integrity self-discipline of the supply chain agriculture-related subjects strengthened and the role and value of credit supervision given full play. Starting from the application of blockchain in the agricultural product supply chain, this paper aims to investigate the main factors affecting the credit regulation of agricultural product quality.Design/methodology/approachUsing the DEMATEL-ISM (decision-making trial and evaluation laboratory–interpretative structural modeling) method, we analyze the credit influencing factors of agricultural quality and safety empowered by blockchain technology, find the causal relationship between the crucial influencing factors and deeply explore the hierarchical transmission relationship between the influencing factors. Then, the path analysis in structural equation modeling is utilized to verify and measure the significance and effect value of the transmission relationship among the crucial influencing factors of credit regulation.FindingsThe results show that the quality and safety credit regulation of agricultural products is influenced by a combination of direct and deep influencing factors. Long-term stable cooperative relationship, Quality and safety credit evaluation, Supply chain risk control ability, Quality and safety testing, Constraints of the smart contract are the main influence path of blockchain embedded in agricultural product supply chain quality and safety credit supervision.Originality/valueCredit supervision is an important means to improve the ability and level of social governance and standardize the market order. From the perspective of blockchain embedded in the agricultural supply chain, the regulatory body is transformed from the product body to the supply chain body. Take the credit supervision of supply chain subjects as the basis of agricultural product quality supervision. With the help of blockchain technology to improve the effectiveness of agricultural product quality and safety credit supervision, credit supervision is used to constrain and incentivize the behavior of agricultural subjects.

Supply chain risk in grain trading: Inventories as real options for shipping grain

AbstractIntegrating trading and logistics is an important challenge in commodity trading. Trading and logistics are strategic decisions and are integral to most commodities including grain shipping by rail, in addition to other modes (barges, ocean shipping). There are substantial risks, such as the ordering and placement of rail cars. The other risk is having sufficient grain stocks to load rail cars. Alternatives for managing these risks include holding grain inventories and the strategic use of shipping options. The purpose of this study is to develop a model for determining an optimal grain inventory strategy for shippers. The real option methodology is used to value uncertainty in rail car velocity and determine optimal purchases and inventories. The results show the importance of integrating trading and logistics decisions and illustrate that inventories can be interpreted and valued as real options that are affected by uncertainties. Taken together, the real options indicate how much extra inventory shippers should maintain to mitigate risks and maximize profits.

Inventory Management in Pharmaceutical Industry

This research paper explores the intricacies of managing inventories of medicines in the pharmaceutical industry, focusing on meeting the healthcare needs of both humans and animals. Through a comprehensive review of existing literature and analysis of real-world case studies, this paper identifies key challenges, strategies, and best practices associated with pharmaceutical inventory management. The literature review reveals the complexities of inventory management in the pharmaceutical sector, including demand variability, regulatory compliance, and the need for precise temperature control for perishable medicines. Strategies such as economic order quantity (EOQ) and just-in-time (JIT) inventory management are discussed for managing normal inventories, while specialized approaches like cold chain management are highlighted for managing perishable inventories. Technological innovations, such as RFID technology and predictive analytics, are recognized for their potential to enhance inventory visibility and mitigate supply chain risks. Case studies provide valuable insights into successful inventory management practices within the pharmaceutical industry. Examples demonstrate the importance of collaboration among stakeholders, investment in advanced inventory management systems, and adherence to regulatory compliance measures. Furthermore, case studies underscore the significance of maintaining optimal inventory levels, minimizing stockouts, and ensuring medication safety and efficacy. This research paper contributes to the ongoing discourse on pharmaceutical supply chain optimization, patient-centric care delivery, and the pursuit of sustainable healthcare solutions. By synthesizing insights from literature and real-world examples, stakeholders can develop strategies to address challenges, enhance inventory management practices, and improve patient outcomes in the pharmaceutical industry. KEYWORDS Pharmaceutical Inventory Management, Medicine Inventory Control, Perishable Inventory, Normal Inventory, Supply Chain Optimization, Regulatory Compliance, Demand Variability, Cold Chain Management, RFID Technology, Patient Safety, Supply Chain Efficiency, Case Study Analysis, Healthcare Logistics, Sustainable Healthcare Solutions.

Managing Operations and Supply Chains in Automotive Industry

Profitability, customer satisfaction, and innovation in the cutthroat automobile industry depend on well-managed operations and supply networks. Managing operations and supply chains in the automotive industry is complex, and this paper lays forth a comprehensive framework for doing so. An integral part of effective operations management in the automotive industry is demand forecasting and planning. Using collaborative forecasting methodologies, market trend analysis, and advanced analytics, automotive manufacturers can improve production schedules, minimize inventory costs, and foresee changes in demand. Reducing waste, enhancing process efficiency, and managing low inventory levels are all achieved through the use of lean manufacturing techniques like Just-in-Time (JIT) production and Total Productive Maintenance (TPM). Collaboration with suppliers, uninterrupted material flow, and supply chain innovation can all be achieved through supplier relationship management (SRM). Automakers may enhance product quality, cut costs, and lessen supply chain risks by communicating openly, working together to improve processes, and forming agreements that benefit all parties involved. Automobile companies can't afford to have any product flaws that damage their reputation or the loyalty of their customers, which is why quality management is so important in this industry. Statistical Process Control (SPC), Failure Mode and Effects Analysis (FMEA), and Six Sigma are a few of the rigorous quality control methods that manufacturers use to find and rectify flaws early

Strengthening resilience in supply chains: the role of multi-layer flexibility, supply chain risks and environmental dynamism

PurposeThis study examines the relationship between multi-layer supply chain flexibility (MSCF) and Supply chain resilience (SCR). Further, it looks at the moderating effect of environmental dynamism (ED) and supply chain risks (SCRI) on the relationship between MSCF and SCR.Design/methodology/approachExecutives from the pharmaceutical, agri-food, electronics, automobile and textile industries were invited to complete a self-administered questionnaire. We received feedback from a total of 302 participants. Prior to conducting the primary analysis, we addressed the potential for nonresponse bias and verified the assumptions of homoscedasticity and normal distribution of the data. The reliability and validity of the constructs were established through confirmatory factor analysis. Structural equation modelling is employed for the purpose of conducting hypothesis testing.FindingsThe results demonstrate a notable influence of MSCF on SCR, particularly in settings characterized by high levels of ED and SCRI. The study highlights the importance of flexibility in multiple aspects of the supply chain to build resilience against a range of disruptions and uncertainties.Originality/valueThe study presents the fundamental role of Multi-Layer Flexibility in building up SCR. The results of this study reinforce the existing literature and offers empirical evidence for how ED, SCRI moderates the influence between MSCF to SCR. These results offer valuable information to both supply chain specialists and researchers for building comprehensive strategy to bring resilience in supply chains.

Multi-element integrated design of marine landscape in sustainable smart ports

In the era of economic globalization and heightened international competition, the emergence of free trade zones has significantly increased regional openness, introducing both new opportunities and challenges for ports. This paper proposes the enhancement of intelligent design in the maritime landscapes of smart ports, with a focus on optimizing internal pathways and developing a predictive model to gauge the impact of smart port landscapes. Furthermore, the study delves into the assessment of risks in smart port supply chains, employing an innovative approach that integrates rough set theory and neural networks. This novel model significantly improves the accuracy of risk evaluation in port supply chains, thereby providing crucial decision support for port management. Additionally, the economic efficiency of smart ports is scrutinized using the Data Envelopment Analysis (DEA) Malmquist index method. Through meticulous selection of research subjects, port efficiency indicators, and the DEA Malmquist index, this analysis offers a comprehensive insight into the economic operations of smart ports. This research not only offers guidance for port economic management but also establishes a framework for the sustainable development of smart ports, thereby ensuring their long-term viability.

How do joint ventures and non-equity strategic alliances impact the complexity of the supply chain and thus supply chain performance?

PurposeThis paper aims to explore the significance of resource sharing in business to capture new market opportunities and securing competitive advantages. Firms enter strategic alliances (SAs), especially for designing new products and to overcome challenges in today’s fast changing environment. Research projects have dealt with the creation of SAs, however without concrete referencing the impact on selected supply chain resources. Furthermore, academia rather focused on elaborating the advantages and disadvantages of SAs and how this affects structural changes in the organization than examining the effects on supply chain complexity and performance.Design/methodology/approachThe authors collected and triangulated a multi-industry data set containing primary data coming from more than 200 experts in the field of supply chain management along and secondary data coming from Refinitiv’s joint ventures (JVs) and SA database and IR solutions’ database for annual reports. The data is evaluated in three empirical settings using binomial testing and structural equation modeling.FindingsThe results show that nonequity SAs and JVs have varying degrees of impact on supply chain resources due to differences in the scope of the partnership. This has a negative impact on the complexity of the supply chain, with the creation of a JV leading to greater complexity than the creation of a nonequity SA. Furthermore, the findings prove that complexity negatively impacts overall supply chain performance. In addition, this study elaborates that increased management capabilities are needed to exploit the potentials of SAs and sheds light on hurdles that must be overcome within the supply network when forming a partnership. Finally, the authors give practical implications on how organizations can cope with increasing complexity to lower the risk of poor supply chain performance.Originality/valueThis study investigates occurring challenges when establishing nonequity SAs or JVs and how this affects their supply chain by examining supply networks in terms of complexity and performance.

Role of logistics innovation capabilities in supply chains operations at manufacturing firms

This study aims to examine the impact of Logistics Innovation Capabilities and mitigating the disruption risk in supply chain operations in the manufacturing Industries of Karachi Pakistan. With the fast growth in Pakistan's economy and vision 2025 seeks to enhance the national transport infrastructure and logistics network system. The demand for logistics services in Pakistan has been steadily expanding. Pakistan's Industry focuses more on adopting logistics innovation capabilities to provide their customer with better services. Quantitative research is designed to study the link between the capabilities of dependent and independent variables to minimize the risk. By empirically analyzing survey data SPSS is a popular method to validate the study model. The impact of innovation in logistics technologies has a huge positive influence affect by firm performance, environment uncertainty, technology implementation & customer side. Through Logistics Innovation capabilities, many empirical studies have been conducted to minimize the supply chain risk. This study provides empirical support for dependent and independent variables. Our results can help Pakistan's manufacturing industry develop logistics strategies and adopt innovation capabilities to reduce supply chain risk.

Flexibility in Enhancing Supply Chain Resilience: Developing a Resilience Capability Portfolio in the Event of Severe Disruption

The current study aimed to determine alternative configurations of supply chain resilience strategies for managing supply chain performance (SCP) during a severe disruption. To do this, a multi-method and multi-study approach was adopted. Phase 1 of the study employed a qualitative approach to explore supply chain risk factors and their mitigation strategies during the COVID-19 pandemic. In Phase 2, the quality function deployment technique was used alongside quantitative case studies to determine the most critical risk factors and most crucial resilience strategies. The final phase, Phase 3, used fuzzy set qualitative comparative analysis to determine the alternative portfolio of strategies for SCP to create flexibility when employing resilience strategies. The results revealed that implementing resilience strategies alone is ineffective in improving SCP, while resilience strategies combined with the nullification of risk factors enhance SCP. The study also revealed two alternative configurations of resilience strategies to tackle the relevant risk factors. These findings can be used to guide managers toward identifying the most suitable configuration of resilience strategies to manage severe and unprecedented supply chain risk. The alternative configurations of resilience strategies can also provide flexibility to managers in deciding the best course of action for their firms.

A systematic evaluation of risk in bioenergy supply chains

We introduce a novel method of evaluating risks of disruption to (bio)fuel supply chains. The biofuel landscape is complex, with multiple options for feedstocks and processing routes, but the type, size, and scale of risks are currently not sufficiently appreciated. As a consequence, the uptake of biofuels remains below expectation suggesting the need for comprehensive risk assessment. Our method of risk discovery and evaluation is based on a transparent and robust sustainability assessment framework, and exploits the richness of data and expert analysis available in publications. In a UK case study, we show that biomass (solids) has a similar risk score to coal, biogas being slightly less risky, with bioliquids being less risky still though more risky than wind and solar power. The most important cause of risk, ‘changing policy or regulatory framework’, reflects this fledgling industry's need for policy support. The second most important cause of risk is ‘lack of access to capital’, reflecting the scale of the process engineering required to convert biomass to tightly specified products which could substitute fossil fuels. Levels of optimism bias in the biofuel industry are high, leading to unrealistic expectations from complex technologies and dubious claims about the quantity of resource available. This, together with the wide variety and variability of feedstocks complicates the business case for biofuel investment. Bioenergy policy would benefit from a more nuanced understanding of risks which impede the widespread large-scale deployment of biofuels.

Supply Chain Risk Management in LNG Import Terminals through Proactive and Reactive Operational Strategies

Supply Chain Risk Management in LNG Import Terminals through Proactive and Reactive Operational Strategies

See no evil, hear no evil, speak no evil? Barriers to modern slavery risk management in supply chains: an empirical investigation

This paper seeks to identify the barriers that prohibit the detection, prevention and remedying of modern slavery incidents in supply chains. A conceptual model is developed that draws on literature on supply chain risk management and barriers to modern slavery. 22 semi-structured stakeholder interviews were conducted to examine common challenges. We distinguish between barriers at different levels – macro, supply chain and organisational – and before and after a modern slavery incident happens – pre-incident and post-incident phases. New barriers emerged in the pre-incident phase, including disparities in terminology, a lack of global law, and limited power in the supply chain; and in the post-incident phase, a lack of remediation frameworks emerged. Modern slavery is notoriously difficult to research as companies fear exposure, and we make a novel contribution by collecting primary data from 22 stakeholders engaged in the management of modern slavery.

Weathering the Storm: Supply Chains and Climate Risk

Weathering the Storm: Supply Chains and Climate Risk