Supply Chain Visibility Research Articles

Solving supply chain management issues with AI and Big Data analytics for future operational efficiency

This review paper examines the use of artificial intelligence (AI) and big data analytics in solving supply chain management (SCM) issues and enhancing future operational efficiency. The primary objective is to synthesize existing research and provide a comprehensive overview of how these technologies are revolutionizing SCM. The paper systematically reviews recent literature on AI and big data applications in SCM, focusing on key areas such as demand forecasting, inventory management, and logistics optimization. By analyzing various studies and case examples, it highlights the transformative effects of these technologies on supply chain processes. The review covers a range of AI techniques including machine learning, deep learning, and predictive analytics, as well as the role of big data in capturing and processing large volumes of supply chain-related information. Key findings from the literature indicate significant improvements in supply chain visibility, decision-making accuracy, and operational efficiency. AI and big data analytics enable more precise demand forecasting, better inventory control, and optimized logistics, leading to cost reductions and enhanced responsiveness to market fluctuations. The review also discusses the challenges and considerations for implementing these technologies, such as data quality, integration complexity, and the need for specialized skills. The paper emphasizes the critical role of AI and big data analytics in addressing contemporary SCM issues and fostering future-ready supply chains. It underscores the necessity for organizations to adopt these technologies to stay competitive and achieve long-term operational excellence. The insights provided serve as a valuable resource for researchers and practitioners aiming to leverage AI and big data for supply chain innovation. Keywords: Artificial Intelligence (AI), Big Data Analytics (BDA), Supply Chain Management (SCM), Predictive Analytics, Internet of Things (IoT), Blockchain Technology, Automation, Machine Learning, Inventory Management, Logistics Optimization, Supply Chain Visibility, Real-time Monitoring, Data Quality, System Integration, Risk Management, Operational Efficiency, Strategic Decision-making, Sustainability, Supply Chain Resilience, Emerging Technologies.

The Influence of Governance on Cyber Supply Chain Performance with Mediating Effect from Cyber Supply Chain Visibility

This research aims to analyze the influence of governance on Cyber Supply Chain (CSC) Performance, which is mediated by Cyber Supply Chain (CSC) Visibility to achieve effective and efficient cyber supply chain risk management (CSCRM) practices for the supply chain management (SCM) process. The population of this research is all manufacturing companies on the island of Java, Indonesia, that have used computerized systems in their supply chain processes with qualifications or certifications ISO 27001, ISO 28000, ISO 28001, or other certifications that are management information system standards. This research will reveal the strengths or weaknesses of the mediating role of CSC Visibility on CSC Performance in manufacturing companies. This study also found empirical evidence that having a dedicated governance team consisting of technical and non-technical personnel is important in CSCRM in the manufacturing industry, especially Java Island in Indonesia.

Early Risk Detection and Management Through Tier N Supply Chain Visibility: A Practical Lens

Early Risk Detection and Management Through Tier N Supply Chain Visibility: A Practical Lens

Modelling supply chain visibility: a framework with considerations for manufacturing and business

PurposeSupply chain visibility plays a pivotal role in ensuring stakeholders have access to and share mutually beneficial information - information that is critical to processes, operations, and informed decision-making. This study leverages a framework to explore the influence of four key factors on supply chain visibility: supply chain linkages, supply chain relationships, green absorptive capacity, and information sharing.Design/methodology/approachThis investigation adopted a survey-based research methodology to collect data. A sampling strategy was employed to recruit participants from various industry sectors, with a primary focus on manufacturing and business. A total of 204 useable questionnaires were obtained. Exploratory factor analysis was conducted to identify underlying factors within the data. Confirmatory factor analysis (CFA) was then used to assess the validity and reliability of the identified factors. Finally, structural equation modelling was employed to test the hypothesised relationships between the constructs studied.FindingsThis study's findings, particularly the significant positive correlations observed between information sharing, supply chain relationship, internal linkage, and green absorptive capacity, provide evidence that these factors are key drivers of supply chain visibility. Additionally, the analysis revealed that external linkages with supply chain partners further enhance information sharing within the chain.Originality/valueThis study offers a unique contribution by exploring the interplay between green absorptive capacity, information sharing, internal and external supply chain linkages, and their combined influence on supply chain visibility. Extending prior research that focused primarily on information sharing and traditional supply chain relationships, this study integrates green absorptive capacity and linkages within a novel framework. Our findings suggest that green absorptive capacity enhances information sharing within the supply chain network, ultimately leading to improved visibility. Furthermore, the study distinguishes the influence of internal vs external linkages on visibility.

Port Logistics and Supply Chain Management: An Empirical Review

Ports play a critical role in the global economy, acting as vital links within international transportation networks. Their efficiency and effectiveness directly impact the performance of global supply chains. This paper examines the intersection of port logistics and supply chain management (SCM) through an empirical lens. We review existing research to explore how SCM principles can be applied to enhance port operations and overall supply chain performance. The paper begins by outlining the evolving role of ports in today's globalized trade environment. It then delves into the core concepts of SCM and their relevance to port management. The subsequent sections review empirical studies that investigate the impact of SCM practices on port performance metrics such as cost, efficiency, and service quality. We explore key areas like collaboration, information technology (IT) integration, and infrastructure development, highlighting their contributions to improved supply chain visibility and streamlined logistics processes.The review also identifies critical research gaps and proposes future research directions. We emphasize the need for studies that explore the impact of emerging technologies like block-chain and artificial intelligence (AI) on port logistics and SCM integration. Additionally, the paper underscores the growing importance of sustainability practices within port operations and their role in enhancing overall supply chain resilience.

Effectiveness of Risk Management Strategies in Mitigating Supply Chain Disruptions in South Africa

Purpose: The aim of the study was to examine the effectiveness of risk management strategies in mitigating supply chain disruptions in South Africa 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 effectiveness of risk management strategies in mitigating supply chain disruptions in South Africa has proven to be critical in maintaining operational stability and ensuring business continuity. In an environment characterized by frequent economic, political, and environmental challenges, robust risk management strategies have become indispensable for South African businesses. These strategies, including risk assessment, diversification of suppliers, implementation of advanced technologies, and development of contingency plans, have significantly enhanced the resilience of supply chains. By systematically identifying and evaluating potential risks, businesses have been able to proactively address vulnerabilities and reduce the impact of disruptions. The use of advanced technologies, such as predictive analytics, real-time monitoring systems, and blockchain, has further strengthened risk management efforts. These technologies provide valuable insights into supply chain dynamics, enable early detection of potential disruptions, and facilitate rapid response measures. As a result, businesses can maintain smoother operations and minimize downtime during unforeseen events. Unique Contribution to Theory, Practice and Policy: Resilience Theory, Resource-Based View (RBV) & Contingency Theory may be used to anchor future studies on effectiveness of risk management strategies in mitigating supply chain disruptions in South Africa. Encourage the adoption of advanced technologies such as Internet of Things (IoT), artificial intelligence (AI), and blockchain for real-time monitoring and supply chain visibility. Practical implementation of these technologies can enhance early detection of disruptions and facilitate rapid response strategies. Foster closer collaboration with suppliers to co-develop robust contingency plans and risk-sharing mechanisms. Practices such as dual sourcing, supplier diversification, and joint risk assessments can build resilience across the supply chain network. Advocate for policies that incentivize resilience-building practices within supply chains. This could include tax incentives for investments in resilient infrastructure, as well as mandatory reporting on supply chain risk management practices to enhance transparency and accountability.

Internet of Things (IoT) and its Utilisation in Marine Logistics

The marine logistics industry is the backbone of global trade, responsible for the transport of more than 90% of goods traded worldwide. However, the industry faces challenges in terms of improving efficiency, reducing costs, and ensuring supply chain security and visibility. The Internet of Things (IoT), which refers to a network of physical devices, vehicles, and other objects equipped with sensors, software, and internet connectivity, is emerging as a potential solution to address these issues. This research aims to explore the role and utilization of IoT in improving the performance of the marine logistics industry. The methodology used includes a comprehensive literature review, in-depth interviews with industry experts, and case study analyses of IoT implementations in marine logistics companies. The results revealed several opportunities and benefits from IoT implementation, such as real-time container and cargo tracking, efficient vessel fleet management, route optimization and logistics planning, and improved supply chain security and safety. However, IoT implementation also faces challenges related to data security, interoperability, investment costs, and regulation. This research provides in-depth insights into how IoT can be leveraged to optimize marine logistics operations and improve industry competitiveness, as well as recommended solutions to overcome the challenges faced.

Integration of Technology and Regulations for Safe and Efficient Marine Logistics

Maritime logistics is at the heart of global trade, with nearly 90% of the world's cargo transported by sea. However, the industry faces significant challenges in ensuring safety, operational efficiency, and environmental sustainability amidst increasing trade volumes and demands to reduce carbon emissions. Integration of modern technology with existing regulatory frameworks is key to improving maritime logistics performance while complying with safety and environmental standards. This paper explores various current technologies that can be applied, such as real-time ship tracking systems with the Internet of Things (IoT) and wireless communications, automation of loading and unloading processes using robotics and artificial intelligence (AI), as well as the use of big data and predictive analytics for optimization route and fleet management. These technologies have the potential to increase supply chain visibility, and time efficiency, reduce human error, and optimize resources. On the other hand, international regulatory frameworks such as the Convention on the Safety of Life at Sea (SOLAS) and the Convention for the Prevention of Pollution from Ships (MARPOL) from the International Maritime Organization (IMO), as well as national and regional regulations related to shipping safety, the marine environment, maritime security, and labor, must be adhered to in adopting new technology.

Leveraging supply chain visibility for implementing just-in-case practices: the roles of knowledge and digital resources bundling

PurposeThis study aims to explore how bundling knowledge resources (i.e. knowledge integration mechanisms [KIMs]) and digital resources (i.e. big data-powered artificial intelligence [BDAI]) can enhance supply chain visibility (SCV) capabilities for implementing just-in-case (JIC) practices.Design/methodology/approachAnalysis of survey data from Chinese manufacturers was conducted to test the proposed hypotheses.FindingsThe results reveal a significant positive effect of KIMs on BDAI, as well as positive effects of both BDAI and KIMs on SCV. Furthermore, the results suggest that SCV partially mediates the KIMs–JIC relationship and fully mediates the BDAI–JIC relationship.Originality/valueThis study advances the digital SC and inventory management literature by proposing and empirically testing a digital JIC model that explores how to bundle knowledge and digital resources into SC capabilities for managing JIC inventory in uncertain and digital times.

Modelling supply chain Visibility, digital Technologies, environmental dynamism and healthcare supply chain Resilience: An organisation information processing theory perspective

The healthcare sector is a complex and critical industry that requires a resilient supply chain to ensure patients receive the care they need. However, the pandemic and geopolitical crises have highlighted the need for a more comprehensive understanding of healthcare supply chain resilience. To address this, healthcare providers and policymakers increasingly recognise the importance of digital technology in enhancing their supply chain resilience. Despite this recognition, there is a lack of empirical studies that have explored the relationship between supply chain visibility, digital capabilities, and environmental dynamism in the healthcare sector. Therefore, our study aimed to fill this gap by utilising organisational information processing theory (OIPT) to explain the relationship between these factors. Our analysis of 137 survey responses revealed that supply chain visibility is a foundational resource for digital capabilities. Moreover, digital capabilities are vital in enhancing healthcare supply chain resilience in highly turbulent environments. Our research expands the concept of the OIPT to offer a more comprehensive explanation of how supply chain visibility is a crucial factor for the effective functioning of digital technologies, particularly in highly uncertain environments. Our findings also provide useful insights for healthcare managers and policymakers who wish to enhance their supply chain’s resilience and leverage digital technologies to achieve this objective.

Revolutionizing Land-Based Vehicle Parts Management: Basis for a Unified Digital Platform for Auto Parts Information Systems

The study provides the basis for a much-needed digital platform one that is consumer-friendly by examining current practices, pain points, and stakeholder requirements. This marketplace aims to aggregate the information of all of the autoparts dealers, suppliers, distributors, and manufacturers centrally for collaboration. Our goal is to reduce TCO and time associated with data access/wait times for real-time insights around inventory management, supply chain visibility, and actionable insights. This study is designed to save the face of the land and rarity-based car parts management at the end, foster efficiency in the automotive industry while taking on sustainability. To be a highly effective, secure and scalable "Autohub System" the design is implemented using several strategies and tactics in database domain.In order to reduce redundancy, this entails using a Relational Database Management System (RDBMS) such as MySQL and implementing strategies such table definition, relationship building, and normalization. Indexing helps enhance efficiency while looking for information and to model the autoparts ecosystem we use Entity-Relationship Diagrams (ERD). For stronger security against attacks and availability of data some methods used are concurrency control, adequate back-up and recovery procedures, and encryption of data. Performance tuning and considering scalability are other topics as they focus on the efficiency of work and further increase in the company’s possibilities, whereas data auditing gives them accountability. Altogether these strategies can accurately set the database to cater for the goals that the autoparts management system has to accomplish. It’s the exact same concept with the Autohub System which revolutionizes the conventional autoparts management sector by producing IoT and predictive maintenance, making operations smoother while eliminating waste. It is beneficial for many businesses as they experience reduced costs, less downtime, and an overall increase in productivity. This is true because the indicated system enhances earth-friendly causes since the automotive industry it fosters is more competitive because of worldwide cooperation. The newly introduced system, the Autohub System, has posed a major change in the ways businesses within the autoparts management industry operate today. This is achieved through offering real-time information, supplier control and a clear and user-friendly front-end in order to ensure that it facilitates the tends, procurement and cooperation. Through sustained supplier dynamics, buy order optimization, and analytical proficiency, decision making becomes enhanced tremendously. The long-term feasibility is guaranteed by the very fact of the opportunity to expand on it and adapt to new needs. This technology takes autoparts management to a new level because it has an overall positive impact on the functions of decreasing downtime, improving supplier interaction, and improving the supply chain. Summing up, The Model T exemplifies the milestone in progress toward sustainability and efficiency that shapes the future of the automobile industry.

Enhancing Supply Chain Visibility in Large Enterprises: A Literature Review

Enhancing Supply Chain Visibility in Large Enterprises: A Literature Review

Optimization of cooling rate of Q-P treated 42SiCr steel using AI digital twinning

In the continuously advancing field of mechanical engineering, digitalization is bringing a major transformation, specifically with the concept of digital twins. Digital twins are dynamic digital models of real-world systems and processes, crucial for Industry 4.0 and the emerging Industry 5.0, which are changing how humans and machines work together in manufacturing. This paper explores the combination of physics-based and data-driven modeling using advanced Artificial Intelligence (AI) and Machine Learning (ML) techniques. This approach provides a comprehensive understanding of mechanical systems, improving materials design and manufacturing processes. The focus is on the advanced 42SiCr alloy, where AI-driven digital twinning is used to optimize cooling rates during Quenching and Partitioning (Q-P) treatments. This leads to significant improvements in the mechanical properties of 42SiCr steel. Given its complex properties influenced by various factors, this alloy is perfect for digital twinning. The Q-P heat treatment process not only restores the material's deformability but also gives it advanced high-strength steel (AHSS) properties. The findings show how AI and ML can effectively guide the development of high-strength steels and enhance their treatment processes. Additionally, integrating digital twins with new technologies like the Metaverse offers exciting possibilities for simulated production, remote monitoring, and collaborative design. By establishing a clear workflow from physical to digital twins and presenting empirical results, this paper connects theoretical modeling with practical applications, paving the way for smarter manufacturing solutions in mechanical engineering. Furthermore, this paper analyzes how digital twins can be integrated into advanced technologies like the Metaverse, opening up new possibilities for simulated production, remote monitoring, design collaboration, training simulations, analytics, and complete supply chain visibility. This integration is a crucial step toward realizing the full potential of digitalization in mechanical engineering.

Data-Driven Transformation of Agri-Supply Chain (Ascs): Comprehensive Review

Traditionally, the agricultural supply chains have dealt with a lot of flaws that affect the whole sector. The agricultural industry is undergoing a transformative shift with advanced technologies, particularly Machine Learning. This review depicts the bridging of the gap in the development of agricultural supply chains. ML and AI are found to be powerful tools for making informed decisions regarding challenges like post-harvest losses, price volatility, logistical difficulties, etc. In many review papers, the stated challenges are not addressed completely. The same can be addressed by handling and analyzing the data carefully and properly using ML algorithms to make the system more efficient than the present scenario. We believe these gaps can be bridged with techniques like demand forecasting, optimal resource utilization, supply chain visibility, etc.

A Study on Exploring Digital Transformation in Logistics Service Providers: Challenges, Key Success Factors, and Best Practices with special reference to Aaj Enterprises Pvt Ltd

Digital transformation in logistics Advancements in digital technology are transforming the logistics sector, leading to innovative ways of transporting, handling, and distributing goods globally. This study focuses on the challenges and strategies related to digital transformation among logistics service providers (LSPs). Challenges Facing LSPs. Modern LSPs encounter various challenges in the digital age, including Outdated infrastructure, complex data management, cybersecurity threats, a lack of skilled talent, and regulatory compliance, Key Success Factors: To succeed in digital transformation, LSPs should prioritize Strong and forwardthinking leadership Focusing on customer needs, Collaboration with stakeholders Allocating resources to enhance technology infrastructure. This analysis combines industry knowledge and case studies to pinpoint the most effective strategies for logistics service providers (LSPs) to excel in the digital age. LSPs can significantly enhance operational efficiency, customer experiences, and competitive advantage by implementing these best practices, which include leveraging data analytics, utilizing IoT devices, optimizing delivery routes using AI, and boosting supply chain visibility with blockchain technology. LSPs can adapt to the shifting dynamics of digitalization by comprehending and integrating its principles, opening up fresh prospects, addressing challenges, and establishing themselves for ongoing success in the increasingly digitized logistics environment.

Revolutionizing Supply Chain Management: Real-time Data Processing and Concurrency

In the contemporary business landscape, effective supply chain management (SCM) is paramount for organizations seeking to thrive amidst evolving market dynamics and heightened customer expectations. This research paper presents a pioneering approach to SCM that harnesses cutting-edge technologies, namely Kafka and Akka, to revolutionize data integration and decision-making processes. By leveraging Kafka as a robust distributed event streaming platform and Akka as a versatile toolkit for developing concurrent and distributed applications, our system facilitates seamless communication and coordination across diverse nodes within the supply chain network. This paper elucidates the intricacies of the proposed architecture, detailing the implementation methodology and performance evaluation metrics. Through a comprehensive examination, we demonstrate how our solution enhances supply chain visibility, fosters operational agility, and enables real-time responsiveness to market fluctuations and customer demands. Moreover, practical use cases exemplify the transformative impact of our approach on inventory management optimization, order fulfillment efficiency, and logistics optimization. Furthermore, we delve into the challenges encountered during implementation and deployment, offering insights into potential mitigative strategies. Finally, we outline avenues for future research, exploring emerging trends and opportunities in the realm of SCM empowered by Kafka and Akka technologies.

데이터 분석역량이 공급사슬 역량과 시장성과에 미치는 영향

This study aims to investigate the Effect of data analytics capability (DAC) on supply chain capability (SCC) and market performance (MP). It also examines whether supply chain visibility (SCV) and supply chain agility (SCA) mediate the relationship between DAC and MP. A survey was conducted on ICT technology users in various industries, and the collected data were analyzed using statistical analysis. The results show that data analytics capability has a significant effect on SCV, SCA, and MP. Additionally, data analytics capability was found to have a significant effect on MP through SCV and SCA as mediating variables. This study contributes to the existing literature by providing empirical evidence of the critical role of data analytics capability in enhancing SCV, SCA, and ultimately, MP. It also highlights the importance of SCV and SCA as key mechanisms through which data analytics capability can lead to improved MP.

ENHANCING TRUCK FREIGHT MANAGEMENT VIA DIGITAL TRANSPORT AND LOGISTICS ECOSYSTEMS

The Republic of Kazakhstan is prioritizing the digitalization of its transportation and logistics sector as a strategic approach to sustainable economic growth and enhancing the quality of life for its citizens. Guided by the decrees and messages from the President of Kazakhstan, the nation is focusing on developing its transit potential and incorporating modern technologies to streamline transport and logistics processes. The emphasis is on increasing the efficiency of transportation activities, developing transit and multimodal potential, and ensuring the security and stability of transport connectivity. This initiative aims to raise the transportation and logistics sector's contribution to the GDP and establish Kazakhstan as a central transit hub in the region. Recent advancements in digital ecosystems, which include technologies like the Internet of Things (IoT), artificial intelligence (AI), and blockchain, are set to revolutionize the truck freight transportation industry. These technologies promise enhanced efficiency, improved supply chain visibility, and reduced environmental impacts. Kazakhstan's efforts in digitalizing its transport and logistics complex, including recommendations for implementing digital transport corridors and developing an integrated information and logistics platform, signify a critical step toward realizing these benefits. This approach will not only enhance the transparency and efficiency of management processes but also reduce costs and improve service quality for end-users, contributing significantly to the nation's economic growth and regional integration.

Creating a sustainable future through Industry 4.0 technologies: Untying the role of circular economy practices and supply chain visibility

AbstractThe intersection of Industry 4.0 technology (I4.0‐Tech) with sustainability (SUS) remains largely unexplored, particularly due to its interactions with supply chain visibility (SCV) and circular economy practices (CEP). To fill this gap, our study empirically examines I4.0‐Tech's impact on SUS through SCV and CEP. We utilized structural equation modeling to test the hypothesized relationships using data from a survey of 344 medium and large Chinese firms. The empirical findings support the indirect role of I4.0 through SCV, and CEP. I4.0‐Tech enables real‐time material and product tracking for SCV. Optimizing material utilization, fostering closed‐loop supply chains, and allowing circular product design enhance CEP. Furthermore, our findings show a positive relationship between SCV, CEP, and SUS. This highlights the need for robust supply chain capabilities in enabling I4.0‐Tech to drive SUS. This paradigm emphasizes recalibrating approaches to SCV and CEP‐focused frameworks to easily include and exploit I4.0‐Tech. This study contributes to the literature and gives actionable insights for managers and policymakers wanting to synergize I4.0‐Tech, SCV, and CEP in industrial processes to promote sustainability goals and a sustainable future.

The Impact of Business Continuity on Supply Chain Practices and Resilience Due to COVID-19

Background: Business continuity entails the potential negative consequences of uncertainty on a firm’s ability to achieve strategic objectives. The COVID-19 pandemic significantly impacted business continuity due to lockdowns, travel restrictions, and social distancing measures. Consequently, firms adopted specific supply chain (SC) practices to effectively navigate this global crisis. Methods: This research adopted a stochastic approach based on Bayesian Networks to evaluate the implications of business continuity on firms’ decisions to embrace SC practices, focusing on omnichannel strategies, SC coordination, and technologies such as artificial intelligence systems, big data and machine learning, and mobile applications. Results: Our findings revealed that firms facing disruption in a single performance area can apply specific strategies to maintain resilience. However, multiple areas of underperformance necessitate a varied approach. Conclusions: According to our empirical analysis, omnichannel strategies are critical when disruptions simultaneously impact quality, inventory, sales, and ROI, particularly during major disruptions such as the COVID-19 pandemic. AI and big data become vital when multiple risks coalesce, enhancing areas such as customer service and supply chain visibility. Moreover, supply chain coordination and mobile app adoption are effective against individual performance risks, proving crucial in mitigating disruption impacts across various business aspects. These findings help policy-makers and business owners to have a better understanding of how business continuity based on performance resistance to disruptions pushes companies to adopt different practices including new technologies and supply chain coordination. Accordingly, they can use the outputs of this study to devise strategies for improving resilience considering their supply chain vulnerabilities.