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Journal articles on the topic 'Predictive Analytics in Supply Chain'

Author: Grafiati

Published: 7 June 2025

Last updated: 2 August 2025

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1

Arshad, Nabeila, and Urooj Pasha. "Factors Effecting the Supply Chain Performance in Automotive Industry." Journal of Law & Social Studies 5, no. 1 (2023): 93–107. https://doi.org/10.52279/jlss.05.01.01.93107.

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The market for supply chain analytics is expected to develop at a CAGR of 17.3 percent from 2019 to 2024, more than doubling in size. This data demonstrates how supply chain organizations understand the advantages of being able to forecast what will happen in the future with a decent degree of accuracy. Google, Netflix, and Amazon are among the main corporations that use predictive analytics. According to Gartner research, organizations that use predictive supply chains have a good return on investment. Furthermore, owing to more precise demand forecasting, they may reduce inventory by 20-30%.

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Adya, Mishra. "Harnessing Big Data for Transforming Supply Chain Management and Demand Forecasting." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 9, no. 6 (2021): 1–11. https://doi.org/10.5281/zenodo.14851200.

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Evolution of big data and predictive analytics has initiated a paradigm shift in modern supply chain management. Traditional supply chain design and demand forecasting methods that relied on historical, often static data no longer suffice in an environment characterized by rapid market fluctuations, evolving consumer behaviors, and global complexities. Predictive analytics—powered by large and diverse data sets—enables supply chain stakeholders to effectively anticipate demand changes, optimize resource allocation, and mitigate risks. This review paper provides an in-depth examinat

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Shafeeq, Ur Rahaman. "Leveraging Predictive Analytics in Supply Chain Optimization: A Machine Learning Approach." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 5, no. 3 (2019): 1–9. https://doi.org/10.5281/zenodo.14352538.

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The Predictive analytics, fueled by the rapid changes in supply chain management, is increasingly becoming a tool to bring improved operational efficiency, especially in the sphere of inventory management and cost optimization. This article examines the use of predictive models for performance enhancement in supply chains related to demand fluctuation, fluctuating inventory levels, and operational bottlenecks through prediction. These algorithms identify trending patterns and make pretty accurate predictions based on historical data combined with real-time input, thus facilitating much better

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Godwin Nzeako, Michael Oladipo Akinsanya, Oladapo Adeboye Popoola, Excel G Chukwurah, and Chukwuekem David Okeke. "The role of AI-Driven predictive analytics in optimizing IT industry supply chains." International Journal of Management & Entrepreneurship Research 6, no. 5 (2024): 1489–97. http://dx.doi.org/10.51594/ijmer.v6i5.1096.

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This review paper examines the pivotal role of AI-driven predictive analytics in optimizing supply chain operations within the IT industry. By leveraging machine learning, deep learning, and neural networks, predictive analytics can significantly enhance demand forecasting, inventory management, supplier selection, and risk management. Despite its potential to revolutionize supply chains, the integration of AI faces challenges, including data quality, the need for skilled personnel, and organizational resistance. Strategic implementation approaches are discussed, emphasizing robust data infras

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Stefanovic, Nenad. "Proactive Supply Chain Performance Management with Predictive Analytics." Scientific World Journal 2014 (2014): 1–17. http://dx.doi.org/10.1155/2014/528917.

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Today’s business climate requires supply chains to be proactive rather than reactive, which demands a new approach that incorporates data mining predictive analytics. This paper introduces a predictive supply chain performance management model which combines process modelling, performance measurement, data mining models, and web portal technologies into a unique model. It presents the supply chain modelling approach based on the specialized metamodel which allows modelling of any supply chain configuration and at different level of details. The paper also presents the supply chain semantic bus

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Adedoyin, Tolulope Oyewole, Chinazo Okoye Chinwe, Chrisanctus Ofodile Onyeka, and Ejairu Emuesiri. "Reviewing predictive analytics in supply chain management: Applications and benefits." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 568–74. https://doi.org/10.5281/zenodo.14059632.

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Supply chain management (SCM) is a critical component of modern business operations, and the integration of predictive analytics has emerged as a transformative force in enhancing efficiency, decision-making, and overall performance. This paper presents a comprehensive review of the applications and benefits of predictive analytics in supply chain management, exploring its role in demand forecasting, inventory optimization, and supply chain visibility. The literature review provides a historical perspective on the evolution of predictive analytics in SCM, delving into key concepts and definiti

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Zaychenko, I. M., and M. A. Iakovleva. "Predictive analytics in supply chain management." Scientific bulletin of the Southern Institute of Management, no. 2 (July 24, 2019): 18–22. http://dx.doi.org/10.31775/2305-3100-2019-2-18-22.

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8

Masengu, Reason, Chenjerai Muchenje, and Benson Ruzive. "Leveraging predictive analytics to enhance food safety risk management in supply chains: A conceptual framework." Journal of Infrastructure, Policy and Development 9, no. 1 (2025): 10114. https://doi.org/10.24294/jipd10114.

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Food safety in supply chains remains a critical concern due to the complexity of global distribution networks. This study develops a conceptual framework to evaluate how food safety risks influence supply chain performance through predictive analytics. The framework identifies and minimizes food safety risks before they cause serious problems. The study examines the impact of food safety practices, supply chain transparency, and technological integration on adopting predictive analytics. To illustrate the complex dynamics of food safety and supply chain performance, the study presents supply c

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Christian Chukwuemeka Ike, Adebimpe Bolatito Ige, Sunday Adeola Oladosu, Peter Adeyemo Adepoju, and Adeoye Idowu Afolabi. "Advancing predictive analytics models for supply chain optimization in global trade systems." International Journal of Applied Research in Social Sciences 6, no. 12 (2024): 2929–48. https://doi.org/10.51594/ijarss.v6i12.1769.

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Global trade systems are becoming increasingly complex due to interconnected markets, fluctuating demand, geopolitical uncertainties, and sustainability concerns. Predictive analytics offers transformative potential in optimizing supply chain operations by leveraging data-driven insights for proactive decision-making. This explores the advancements in predictive analytics models tailored for global trade systems, emphasizing their role in enhancing supply chain efficiency, resilience, and agility. By integrating machine learning algorithms, big data analytics, and real-time data feeds, these m

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Motunrayo Oluremi Ibiyemi and David Olanrewaju Olutimehin. "Utilizing predictive analytics to enhance supply chain efficiency and reduce operational costs." International Journal of Engineering Research Updates 7, no. 1 (2024): 001–21. http://dx.doi.org/10.53430/ijeru.2024.7.1.0029.

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This study investigates the application of predictive analytics to enhance supply chain efficiency and reduce operational costs. The primary objective is to understand how predictive analytics can be leveraged to optimize various aspects of supply chain management, including demand forecasting, inventory management, and logistics. The research methodology involved a comprehensive literature review, coupled with a case study analysis of several organizations that have successfully implemented predictive analytics in their supply chain operations. Key findings reveal that predictive analytics si

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Zerine, Ismoth, Younis Ali Biswas, Md Zulkernain Doha, Humayra Mehreen Meghla, and Mohammad Rashed Hasan Polas. "Data-Driven Sustainability: How Predictive Analytics ShapeSupply Chain Performance." Annals of Management and Organization Research 6, no. 4 (2025): 433–46. https://doi.org/10.35912/amor.v6i4.2613.

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Purpose: The integration of predictive analytics into supply chains has emerged as a critical driver of sustainability in the manufacturing sector. This study explores the role of predictive analytics in enhancing sustainable supply chain performance, with a particular focus on manufacturing firms in Dhaka, Bangladesh. Research Methodology: This study adopts a positivist paradigm with a hypothetical deductive approach and employs a cross-sectional design. Data were collected from 211 manufacturing firms using stratified random sampling and structured questionnaire surveys. Results: The finding

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Ekene Cynthia Onukwulu, Mercy Odochi Agho, and Nsisong Louis Eyo-Udo. "Developing a framework for predictive analytics in mitigating energy supply chain risks." International Journal of Scholarly Research and Reviews 2, no. 2 (2023): 135–55. https://doi.org/10.56781/ijsrr.2023.2.2.0042.

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The integration of predictive analytics into energy supply chain management is increasingly recognized as a crucial tool for mitigating risks and ensuring operational efficiency. Energy supply chains face numerous challenges, including supply disruptions, fluctuating demand, price volatility, and environmental concerns, which can impact both short-term operations and long-term sustainability. Predictive analytics, leveraging data-driven insights, machine learning algorithms, and statistical models, offers a proactive approach to addressing these challenges by forecasting potential risks and en

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Adeleke Damilola Adekola and Samuel Ajibola Dada. "Optimizing pharmaceutical supply chain management through AI-driven predictive analytics: A conceptual framework." Computer Science & IT Research Journal 5, no. 11 (2024): 2580–93. http://dx.doi.org/10.51594/csitrj.v5i11.1709.

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The pharmaceutical supply chain is a complex, multi-layered system that faces unique challenges, including fluctuating demand, stringent regulatory requirements, and logistical constraints. This paper explores the role of AI-driven predictive analytics in optimizing pharmaceutical supply chain management, presenting a conceptual framework that enables companies to leverage advanced data analytics for improved decision-making, risk management, and operational efficiency. Key AI techniques, such as machine learning, data mining, and predictive demand forecasting, are discussed as tools for addre

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Adedoyin Tolulope Oyewole, Chinwe Chinazo Okoye, Onyeka Chrisanctus Ofodile, and Emuesiri Ejairu. "Reviewing predictive analytics in supply chain management: Applications and benefits." World Journal of Advanced Research and Reviews 21, no. 3 (2024): 568–74. http://dx.doi.org/10.30574/wjarr.2024.21.3.0673.

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Supply chain management (SCM) is a critical component of modern business operations, and the integration of predictive analytics has emerged as a transformative force in enhancing efficiency, decision-making, and overall performance. This paper presents a comprehensive review of the applications and benefits of predictive analytics in supply chain management, exploring its role in demand forecasting, inventory optimization, and supply chain visibility. The literature review provides a historical perspective on the evolution of predictive analytics in SCM, delving into key concepts and definiti

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15

Farhan Qadir. "Exploring a nexus among the big data Predictive analytics, Supply Chain Performance, and Organizational Performance: A case of an Asian country." Asian Bulletin of Big Data Management 1, no. 1 (2021): 1–10. http://dx.doi.org/10.62019/abbdm.v1i1.14.

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The goal of this study is to look into the relationship between big data predictive analytics, supply chain performance, and organizational performance in an Asian country. The study employs a variety of approaches, including quantitative data analysis and qualitative interviews with experts in the subject. According to the findings, applying predictive analytics to big data has a positive impact on the performance of supply chains, which in turn has a positive impact on the performance of companies. According to the poll, the application of big data analytics in supply chain management is not

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16

Shlash Mohammad, Anber Abraheem, Iyad A. A. Khanfar, Badrea Al Oraini, Asokan Vasudevan, Ibrahim Mohammad Suleiman, and Zhou Fei. "Predictive analytics on artificial intelligence in supply chain optimization." Data and Metadata 3 (January 1, 2024): 395. http://dx.doi.org/10.56294/dm2024395.

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AI-powered predictive analytics is among the most important ways of optimizing supply chains. This paper on AI-powered predictive analytics will address improving the competitiveness and effectiveness of supply chain operations. Nevertheless, current methods are not always scalable or adaptable to complex supply networks and changing market environments. Therefore, this paper posits that Supply Chain Optimization using Artificial Intelligence (SCO-AI) systems can help with these concerns. SCO-AI employs real-time data analysis and advanced machine learning algorithms which results to reduced r

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Teja Reddy Gatla and Sasikanth Reddy Mandati. "OPTIMIZING SUPPLY CHAIN EFFICIENCY THROUGH MACHINE LEARNING-DRIVEN PREDICTIVE ANALYTICS." International Journal of Innovations in Engineering Research and Technology 7, no. 3 (2020): 67–80. https://doi.org/10.26662/ijiert.v7i3.pp67-80.

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The complexities of modern supply chains present significant challenges in maintaining efficiency, reducing operational costs, and adapting to market fluctuations. Traditional supply chain management approaches often struggle to anticipate disruptions and align with dynamic demand patterns, leading to costly inefficiencies and suboptimal decision-making. This paper explores the integration of machine learning-driven predictive analytics as a transformative solution for optimizing supply chain efficiency. By leveraging diverse data sources—such as historical demand, real-time inventory levels,

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18

Puica, Elena. "Predictive Analytics Functionalities in Supply Chain Management." Proceedings of the International Conference on Business Excellence 17, no. 1 (2023): 986–96. http://dx.doi.org/10.2478/picbe-2023-0090.

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Abstract This scientific paper presents a comprehensive analysis of the capabilities of IT solutions for predictive analytics in supply chain management. The study uses a multi-method approach, including a literature review, case studies by applying a machine learning model to technology solutions currently available on the market. The study examines the various software and technology platforms available today and their key features and functionalities, focusing in particular on Scripting, Data Mining, Algorithms, Data Analysis, Modeling, Data Interaction, Data Visualization, Reporting and Da

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19

Kshirsagar, Pranali S., and Avinash M. Pawar. "Predictive Analytics for Cyber Threats to Enhance Security in the Cyber Supply Chain." Research Journal of Computer Systems and Engineering 4, no. 1 (2023): 102–9. http://dx.doi.org/10.52710/rjcse.68.

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Using predictive analytics is a key part of making the computer supply chain safer because it helps companies find and stop threats before they happen. Predictive analytics uses complex algorithms and machine learning to look through huge amounts of data for trends and outliers that could point to cyber dangers. Businesses can stay ahead of cyber attackers and keep their digital assets safe with this method. In the online supply chain, one of the best things about prediction analytics is that it can find new threats before they become full-blown attacks. Predictive analytics looks at past data

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20

Rajesh, Kotha. "Big Data Analytics for Improving supply chain transparency and efficiency." European Journal of Advances in Engineering and Technology 7, no. 12 (2020): 119–24. https://doi.org/10.5281/zenodo.13919466.

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Industry 4.0 is rapidly approaching all over the world. Upcoming industrial revolutions will prioritize functions and services provided by devices that produce or store extensive information. A company’s supply chain is made up of extensive information coming from production, transportation and other operations. The extensive data may be examined using big data analytics (BDA) to make inferences improve the supply chain management. BDA leads to new possibilities. Supply chain elements, including inventory forecasting and prediction, may be assessed using this data. This study examines ho

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Adebunmi Okechukwu Adewusi, Abiola Moshood Komolafe, Emuesiri Ejairu, Iyadunni Adewola Aderotoye, Oluwatosin Oluwatimileyin Abiona, and Oyekunle Claudius Oyeniran. "THE ROLE OF PREDICTIVE ANALYTICS IN OPTIMIZING SUPPLY CHAIN RESILIENCE: A REVIEW OF TECHNIQUES AND CASE STUDIES." International Journal of Management & Entrepreneurship Research 6, no. 3 (2024): 815–37. http://dx.doi.org/10.51594/ijmer.v6i3.938.

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This study investigates the transformative impact of predictive analytics on enhancing supply chain resilience (SCR). Employing a systematic literature review and content analysis, the research aims to explore the integration, challenges, and strategic implications of predictive analytics within the supply chain ecosystem. Focusing on literature from 2014 to 2023, the study synthesizes insights from peer-reviewed articles and conference papers, adhering to stringent inclusion and exclusion criteria to ensure relevance and recency. The findings reveal that predictive analytics significantly con

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Rhoda Adura Adeleye, Oluwaseun Peter Oyeyemi, Onyeka Franca Asuzu, Kehinde Feranmi Awonuga, and Binaebi Gloria Bello. "ADVANCED ANALYTICS IN SUPPLY CHAIN RESILIENCE: A COMPARATIVE REVIEW OF AFRICAN AND USA PRACTICES." International Journal of Management & Entrepreneurship Research 6, no. 2 (2024): 296–306. http://dx.doi.org/10.51594/ijmer.v6i2.771.

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This paper explores the application of advanced analytics in enhancing supply chain resilience, offering a comparative review between African and USA practices. Supply chain resilience has become a critical factor in the global business landscape, particularly in the face of unprecedented disruptions and uncertainties. As organizations strive to mitigate risks and improve responsiveness, advanced analytics emerges as a powerful tool in achieving these objectives. The study begins by providing a comprehensive overview of the key components of supply chain resilience and the role advanced analyt

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Soniya Munir. "The impact of Big Data Predictive Analytics on the performance of Asian firms: Does the supply chain effectiveness matter?" Asian Bulletin of Big Data Management 1, no. 1 (2021): 11–22. http://dx.doi.org/10.62019/abbdm.v1i1.15.

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The purpose of this research is to evaluate the influence that big data predictive analytics have on the performance of Asian companies and to determine whether or not the efficiency of the supply chain has any bearing on this relationship. The use of a quantitative research design allowed for the collection of data from a representative cross-section of Asian businesses. The findings suggest that big data predictive analytics have a beneficial impact on company performance, and the findings also suggest that the relationship between these two factors is increased by the efficiency of the supp

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REDDY VUMMADI, JAYAPAL, and KRISHNA CHAITANYA RAJA HAJARATH. "AI-DRIVEN PREDICTIVE ANALYTICS FOR SUPPLIER LEAD TIME AND PERFORMANCE FORECASTING." Turkish Journal of Computer and Mathematics Education (TURCOMAT) 13, no. 2 (2022): 1201–5. https://doi.org/10.61841/turcomat.v13i2.15245.

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The effective management of supplier lead times and performance is a cornerstone of supply chain efficiency. Traditional methods for forecasting supplier performance often rely on historical data and simplistic models, which fail to account for the complex, dynamic nature of modern supply chains. Artificial Intelligence (AI) and predictive analytics have the potential to significantly improve the accuracy and reliability of supplier lead time predictions and performance forecasting by leveraging real-time data, advanced machine learning algorithms, and statistical models. This research article

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Brundha R, Meenaumadevi M. Guide, and Mrs. K. Sugashini. "Supply Chain Management." International Journal of Management and Humanities 11, no. 6 (2025): 15–18. https://doi.org/10.35940/ijmh.d1768.11060225.

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The Supply Chain Management (SCM) system is designed to enhance the efficiency, transparency, and resilience of supply chain operations. This project integrates advanced data analytics, machine learning, and real-time tracking to overcome the limitations of traditional SCM systems, such as demand volatility, inventory imbalances, and poor response to disruptions. By utilizing diverse data sources—including historical demand, market trends, and consumer insights. The system provides accurate demand forecasting and optimal inventory management. Key features include real-time visibility across th

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Nidhi Shashikumar. "Optimizing supply chain efficiency in healthcare using predictive modeling and data analytics." International Journal of Science and Research Archive 15, no. 1 (2025): 1331–41. https://doi.org/10.30574/ijsra.2025.15.1.1107.

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The increasing complexity of healthcare delivery systems, combined with rising patient expectations and global supply chain vulnerabilities, has amplified the urgency to optimize healthcare supply chain management (SCM). Predictive analytics, with its ability to anticipate demand, manage uncertainties, and inform strategic decisions, presents a transformative opportunity for healthcare logistics. This paper explores the foundational concepts of predictive modeling in healthcare SCM, reviews current applications and case studies from global contexts, and identifies key limitations such as data

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Meenaumadevi, M. Guide. "Supply Chain Management." International Journal of Management and Humanities (IJMH) 11, no. 6 (2025): 15–18. https://doi.org/10.35940/ijmh.D1768.11060225.

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<strong>Abstract: </strong>The Supply Chain Management (SCM) system is designed to enhance the efficiency, transparency, and resilience of supply chain operations. This project integrates advanced data analytics, machine learning, and real-time tracking to overcome the limitations of traditional SCM systems, such as demand volatility, inventory imbalances, and poor response to disruptions. By utilizing diverse data sources—including historical demand, market trends, and consumer insights. The system provides accurate demand forecasting and optimal inventory management. Key features inclu

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Vaitinadin, Manykandaprebou. "Advanced Analytics in Supply Chain Visibility: A Comparative Review of Techniques for Retail and Consumer Goods." International Scientific Journal of Engineering and Management 03, no. 01 (2024): 1–6. https://doi.org/10.55041/isjem01340.

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Supply chain visibility (SCV) has become a critical factor in the efficiency and resilience of the retail and consumer goods industries. With increasing complexity in global supply chains, organizations are leveraging advanced analytics techniques, such as machine learning (ML), big data analytics, and artificial intelligence (AI), to improve visibility and enhance decision-making. This paper provides a comparative review of various advanced analytics techniques and their effectiveness in improving SCV within the retail and consumer goods sectors. It discusses key methodologies, challenges, an

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29

Bankole Ibrahim Ashiwaju, Israel Osejie Okoduwa, Jeremiah Olawumi Arowoogun, Kehinde Feranmi Awonuga, and Jane Osareme Ogugua. "The Impact of COVID-19 on supply chain analytics: A global review." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 013–21. http://dx.doi.org/10.30574/wjarr.2024.21.2.0398.

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The COVID-19 pandemic has triggered unprecedented disruptions across global supply chains, challenging businesses to reassess and optimize their supply chain analytics strategies. This paper delves into the far-reaching impact of the pandemic on supply chain analytics, providing a comprehensive global review of the challenges, innovations, and transformations that have emerged. The pandemic-induced disruptions have exposed vulnerabilities in traditional supply chain models, prompting organizations to reevaluate their analytics frameworks. The sudden fluctuations in demand, supply chain interru

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Bankole, Ibrahim Ashiwaju, Osejie Okoduwa Israel, Olawumi Arowoogun Jeremiah, Feranmi Awonuga Kehinde, and Osareme Ogugua Jane. "The Impact of COVID-19 on supply chain analytics: A global review." World Journal of Advanced Research and Reviews 21, no. 2 (2024): 013–21. https://doi.org/10.5281/zenodo.13992631.

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The COVID-19 pandemic has triggered unprecedented disruptions across global supply chains, challenging businesses to reassess and optimize their supply chain analytics strategies. This paper delves into the far-reaching impact of the pandemic on supply chain analytics, providing a comprehensive global review of the challenges, innovations, and transformations that have emerged. The pandemic-induced disruptions have exposed vulnerabilities in traditional supply chain models, prompting organizations to reevaluate their analytics frameworks. The sudden fluctuations in demand, supply chain interru

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Researcher. "INTELLIGENT SUPPLY CHAIN RISK MITIGATION: A MACHINE LEARNING APPROACH TO NEWS-BASED DISRUPTION FORECASTING." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 304–20. https://doi.org/10.5281/zenodo.13981742.

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Modern supply chains are increasingly complex and vulnerable to a wide array of disruptions, from natural disasters to geopolitical events. This article presents an innovative approach to supply chain risk management through the development of an AI-powered early warning system. By leveraging natural language processing, machine learning, and predictive analytics, our system continuously monitors global news sources, social media, and other relevant data streams to identify potential supply chain disruptions before they occur. The proposed framework integrates real-time data ingestion, sophist

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Alonge, Enoch Oluwabusayo, Nsisong Louis Eyo-Udo, Bright Chibunna Ubanadu, Andrew Ifesinachi Daraojimba, Emmanuel Damilare Balogun, and Kolade Olusola Ogunsola. "Real-Time Data Analytics for Enhancing Supply Chain Efficiency." International Journal of Multidisciplinary Research and Growth Evaluation. 2, no. 1 (2021): 759–71. https://doi.org/10.54660/.ijmrge.2021.2.1.759-771.

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In today's dynamic business environment, real-time data analytics has emerged as a transformative tool for enhancing supply chain efficiency. Traditional supply chain models often suffer from inefficiencies due to delays in data collection, analysis, and decision-making. Real-time data analytics leverages big data, artificial intelligence (AI), and the Internet of Things (IoT) to enable continuous monitoring, predictive insights, and agile decision-making. This paper explores the role of real-time data analytics in optimizing supply chain operations by improving demand forecasting, inventory m

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I. Kala. "IntelliChain - Supply Chain Management Solution." Journal of Information Systems Engineering and Management 10, no. 20s (2025): 169–85. https://doi.org/10.52783/jisem.v10i20s.3028.

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IntelliChain emerges as a game-changing supply chain management system that effortlessly integrates the powers of Blockchain, Internet of Things (IoT), and Artificial Intelligence (AI). IntelliChain places a high priority on real-time tracking, transparency, and predictive analytics in an attempt to completely transform traditional supply chain procedures. By integrating Blockchain technology, a transparent and safe framework is established, guaranteeing data that cannot be manipulated and improving supply chain visibility. The system, enhanced by IoT devices, gathers real-time data from vario

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Razib, Md Nurul Huda, Md Ibrahim, and Imran Hossain Rasel. "PREDICTIVE ANALYTICS AND ITS ROLE IN OPTIMIZING SUSTAINABLE SUPPLY CHAIN PERFORMANCE." International Journal of Business Management and Economic Review 08, no. 01 (2025): 12–27. https://doi.org/10.35409/ijbmer.2025.3640.

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This study explores the role of predictive analytics in optimizing sustainable supply chain performance. With the increasing focus on environmental sustainability, organizations are leveraging advanced data analytics tools to enhance operational efficiency, reduce waste, and minimize their environmental footprint. This research highlights how predictive models improve demand forecasting, transportation optimization, and supplier selection based on sustainability criteria. The study also identifies key challenges to integrating predictive analytics, such as data quality issues, high implementat

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Edith Ebele Agu, Njideka Rita Chiekezie, Angela Omozele Abhulimen, and Anwuli Nkemchor Obiki-Osafiele. "Building sustainable business models with predictive analytics: Case studies from various industries." International Journal of Advanced Economics 6, no. 8 (2024): 394–406. http://dx.doi.org/10.51594/ijae.v6i8.1436.

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Predictive analytics has emerged as a powerful tool for businesses across various industries to build sustainable business models. This review provides insights into the significance of predictive analytics in fostering sustainability and showcases case studies from different sectors where predictive analytics has been effectively employed. Predictive analytics enables businesses to anticipate future trends, identify potential risks, and make data-driven decisions, thereby enhancing operational efficiency, improving customer experiences, and driving growth. By leveraging historical data and ad

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Ramanamuni, Sandeep. "Risk Management in Digital Transformation Projects." International Journal of Multidisciplinary Research and Growth Evaluation. 2, no. 3 (2021): 567–70. https://doi.org/10.54660/.ijmrge.2021.2.3.567-570.

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The COVID-19 pandemic has disturbed global supply chains. It has pushed organizations to speed up their digital transformations toward resilience and continuity. This paper throws due light on the risk management practices in digital transformation projects while focusing on the adoption of advanced supply chain solutions. The paper discusses the key risk factors for supply chains that are operating in the post-pandemic era. This includes supply disruptions, cybersecurity threats, and integration challenges. The study argues that digital technologies, such as blockchain, IoT, AI, predictive an

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Angela Omozele Abhulimen and Onyinye Gift Ejike. "Solving supply chain management issues with AI and Big Data analytics for future operational efficiency." Computer Science & IT Research Journal 5, no. 8 (2024): 1780–805. http://dx.doi.org/10.51594/csitrj.v5i8.1396.

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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

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Ramanamuni, Sandeep. "Personalized supply chains in pharmaceuticals." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 01 (2024): 1–8. https://doi.org/10.55041/ijsrem28111.

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Personalized medicine is the concept of rendering targeted treatment to patients; that is, to tailor medicines as per the specific requirements of a particular patient. It focuses on offering healthcare that meets the unique needs of patients. However, its unique requirements also pose certain challenges to the pharmaceutical supply chains. There is a need for innovative solutions to optimize pharmaceutical logistics and to ensure an efficient delivery of personalized medicines. This paper explores the complexities of the personalized supply chain in pharmaceuticals. It also focuses on the log

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Fardin Sabahat Khan, Abdullah Al Masum, Jamaldeen Adam, Md Rashidul Karim, and Sadia Afrin. "AI in Healthcare Supply Chain Management: Enhancing Efficiency and Reducing Costs with Predictive Analytics." Journal of Computer Science and Technology Studies 6, no. 5 (2024): 85–93. http://dx.doi.org/10.32996/jcsts.2024.6.5.8.

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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 findin

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Byreddy, Maheswar Reddy. "Predictive Analytics and SAP Integration in Pharmaceutical Supply Chain Management: A Comprehensive Analysis." European Journal of Computer Science and Information Technology 13, no. 30 (2025): 104–10. https://doi.org/10.37745/ejcsit.2013/vol13n30104110.

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The pharmaceutical industry faces significant challenges in supply chain management, particularly in maintaining optimal inventory levels and ensuring timely medication delivery. This comprehensive article examines the integration of predictive analytics and SAP systems in pharmaceutical supply chain management, focusing on their transformative impact on operational efficiency and risk management. The article explores the evolution from traditional reactive approaches to modern predictive analytics, analyzing the implementation of SAP's technical framework for demand forecasting and inventory

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Kumar, Suresh Nanda, S. Chandrasekar, Mathew Vizhalil, B. Jeyaprabha, V. Sasirekha, and Ambika Bhatia. "Assessing the Mediating Role of Recognizing and Overcoming Challenges in Using Iot and Analytics to Enhance Supply Chain Performance." Journal of Lifestyle and SDGs Review 5, no. 2 (2025): e05796. https://doi.org/10.47172/2965-730x.sdgsreview.v5.n02.pe05796.

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Objective: This study examines the impact of integrating Internet of Things (IoT) technologies and sophisticated analytics on supply chain efficiency within the automobile sector. It examines the alleviation of issues including data security, difficulties of system integration, and deficiencies in workforce skills to attain best operational results. Through this research we aim to achieve the sustainable development goals (SDGs) of 8 and 9, which are Decent Work and Economic Growth and Industry, Innovation and Infrastructure. It builds resilient infrastructure for the industry using innovative

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Uche Nweje and Moyosore Taiwo. "Leveraging Artificial Intelligence for predictive supply chain management, focus on how AI- driven tools are revolutionizing demand forecasting and inventory optimization." International Journal of Science and Research Archive 14, no. 1 (2025): 230–50. https://doi.org/10.30574/ijsra.2025.14.1.0027.

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The dynamic landscape of global supply chains necessitates innovative solutions to tackle challenges in demand forecasting and inventory optimization. Traditional methods, often constrained by limited adaptability and scalability, struggle to manage the complexities of modern supply chains. Artificial Intelligence (AI) has emerged as a transformative force, enabling predictive supply chain management through advanced data analytics, machine learning algorithms, and real-time decision-making capabilities. By harnessing AI-driven tools, businesses can accurately forecast demand patterns, reduce

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Sornprom, Natapong. "Supply Chain 4.0: Blockchain, Cloud Analytics, and Security Solutions for Real-Time Innovation." European Journal of Engineering and Technology Research 10, no. 3 (2025): 31–35. https://doi.org/10.24018/ejeng.2025.10.3.3247.

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Supply Chain 4.0 represents a transformative shift in the way businesses manage and optimize their supply chains. Powered by technologies such as blockchain, cloud analytics and advance Security solutions will change the way that businesses manage and optimize their supply chain. The key objective of this transformation is to have efficient, transparent and agile supply processes, which can adapt quickly to dynamic market conditions. With blockchain technology, supply chain partners can achieve a high level of trust and transparency, as it enables secure, decentralized, and immutable record-ke

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Surkunde, Arpita Bhausaheb, Dr Rajendra Jarad, Dr Mahendra Yadav, Dr Praveen Suryavanshi, Prof Dhanajay Bhavsar, and Prof Nilambari Moholkar. "Transforming Supply Chain Management with Big Data and IoT Innovations." INTERANTIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 08, no. 12 (2024): 1–7. https://doi.org/10.55041/ijsrem39893.

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The integration of Big Data and the Internet of Things (IoT) is revolutionizing supply chain management, enabling enhanced efficiency, transparency, and adaptability in a rapidly changing global landscape. Big Data analytics empowers organizations to process and interpret vast datasets, delivering actionable insights for demand forecasting, inventory optimization, and risk mitigation. Concurrently, IoT devices facilitate real-time monitoring, tracking, and seamless communication across the supply chain, bridging the gap between physical and digital operations. This paper examines the transform

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Tantawy, Alshaimaa A., Zenat Ahmed, and Mahmoud M. Ali. "Applying Big Data Analytics to Retail for Improved Supply Chain Visibility." American Journal of Business and Operations Research 4, no. 1 (2021): 39–46. http://dx.doi.org/10.54216/ajbor.040104.

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Retail supply chains generate huge volumes of data that can provide valuable insights if analyzed effectively. This paper explores how retailers can leverage Big Data analytics techniques on supply chain data to gain enhanced visibility into their operations. We examine three use cases of data-driven supply chain visibility: (1) predictive replenishment to anticipate future demand and optimize inventory levels; (2) personalized assortment optimization to tailor product selections for local customer segments; and (3) optimized order fulfillment to improve delivery times and reduce transportatio

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Iyadunni Adewola Olaleye, Chukwunweike Mokogwu, Amarachi Queen Olufemi-Phillips, and Titilope Tosin Adewale. "Transforming supply chain resilience: Frameworks and advancements in predictive analytics and data-driven strategies." Open Access Research Journal of Multidisciplinary Studies 8, no. 2 (2024): 085–93. http://dx.doi.org/10.53022/oarjms.2024.8.2.0065.

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Supply chain resilience is critical in maintaining operational stability and competitive advantage in an increasingly volatile global economy. This paper explores the transformative potential of predictive analytics and data-driven strategies in enhancing supply chain resilience. Organizations can achieve real-time monitoring, improved demand forecasting, and robust risk assessment capabilities by integrating advanced technologies such as IoT, big data, and cloud computing. These innovations enable proactive decision-making, agility, and recovery from disruptions across supply chain stages, in

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Onyeka Chrisanctus Ofodile, Adeoluwa Omoyemi Yekeen, Ngodoo Joy Sam-Bulya, and Chikezie PaulMikki Ewim. "Optimizing supply chains with artificial intelligence in the 4IR: A business model perspective." Open Access Research Journal of Multidisciplinary Studies 6, no. 2 (2023): 086–99. http://dx.doi.org/10.53022/oarjms.2023.6.2.0051.

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The Fourth Industrial Revolution (4IR) heralds a transformative era characterized by the integration of advanced technologies such as Artificial Intelligence (AI) into various sectors, notably supply chain management. This paper explores how AI optimizes supply chains, enhancing efficiency, responsiveness, and resilience from a business model perspective. Traditional supply chain models often face challenges such as demand variability, inventory management, and logistical inefficiencies. By harnessing AI technologies, businesses can address these challenges through improved data analytics, pre

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Enoch, Oluwademilade Sodiya, Sonimitiem Jacks Boma, David Ugwuanyi Ejike, et al. "Reviewing the role of AI and machine learning in supply chain analytics." GSC Advanced Research and Reviews 18, no. 2 (2024): 312–20. https://doi.org/10.5281/zenodo.11216488.

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The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learnin

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Ibrahim Adedeji Adeniran, Christianah Pelumi Efunniyi, Olajide Soji Osundare, and Angela Omozele Abhulimen. "Optimizing logistics and supply chain management through advanced analytics: Insights from industries." International Journal of Scholarly Research in Engineering and Technology 4, no. 1 (2024): 052–61. http://dx.doi.org/10.56781/ijsret.2024.4.1.0020.

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This review paper explores the transformative role of advanced analytics in optimizing logistics and supply chain management, offering insights into industry applications, best practices, and future trends. As global supply chains become increasingly complex, integrating advanced analytics—encompassing data mining, predictive analytics, machine learning, and big data—has emerged as a critical driver of efficiency, cost reduction, and enhanced decision-making. The paper discusses how various industries, including manufacturing, retail, healthcare, and transportation, leverage advanced analytics

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Enoch Oluwademilade Sodiya, Boma Sonimitiem Jacks, Ejike David Ugwuanyi, et al. "Reviewing the role of AI and machine learning in supply chain analytics." GSC Advanced Research and Reviews 18, no. 2 (2024): 312–20. http://dx.doi.org/10.30574/gscarr.2024.18.2.0069.

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The integration of Artificial Intelligence (AI) and Machine Learning (ML) in supply chain analytics has emerged as a transformative force in reshaping traditional logistics and operations. This review critically examines the multifaceted role of AI and ML in optimizing supply chain processes, enhancing decision-making capabilities, and fostering agility in an era of dynamic market demands. AI and ML technologies have revolutionized data analytics by enabling the extraction of actionable insights from vast and complex datasets. The application of predictive analytics, powered by machine learnin

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