Relevant bibliographies by topics / Predictive Analytics in Supply Chain

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Books
  4. Book chapters
  5. Conference papers
  6. Reports

Academic literature on the topic 'Predictive Analytics in Supply Chain'

Author: Grafiati
Published: 7 June 2025
Last updated: 2 August 2025

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

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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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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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Dissertations / Theses on the topic "Predictive Analytics in Supply Chain"

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Schmidt, Rachel Marie S. M. Sloan School of Management. "Using predictive analytics to address risk in complex supply chains." Thesis, Massachusetts Institute of Technology, 2018. http://hdl.handle.net/1721.1/117964.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.<br>Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 68-69).<br>Li & Fung (LF) is a global supply chain manager for consumer product brands and retailers. Worldwide, LF contracts with over 13,000 factories. Frequently, these factories ex
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Rodriguez, Pellière Lineth Arelys. "A qualitative analysis to investigate the enablers of big data analytics that impacts sustainable supply chain." Thesis, Ecole centrale de Nantes, 2019. http://www.theses.fr/2019ECDN0019/document.

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Les académiques et les professionnels ont déjà montré que le Big Data et l'analyse prédictive, également connus dans la littérature sous le nom de BDPA, peuvent jouer un rôle fondamental dans la transformation et l'amélioration des fonctions de l'analyse de la chaîne d'approvisionnement durable (SSCA). Cependant, les connaissances sur la meilleure manière d'utiliser la BDPA pour augmenter simultanément les performances sociales, environnementale et financière. Par conséquent, avec les connaissances tirées de la littérature sur la SSCA, il semble que les entreprises peinent encore à mettre en o
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Faber, Andreas D. [Verfasser], Stefan [Gutachter] Spinler, and Arnd [Gutachter] Huchzermeier. "Data analytics in supply chain planning : applications in intermittent demand forecasting, partial defection prediction and price discrimination / Andreas D. Faber ; Gutachter: Stefan Spinler, Arnd Huchzermeier." Vallendar : WHU - Otto Beisheim School of Management, 2021. http://d-nb.info/1240764359/34.

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Bohle, Alexander, and Liam Johnson. "Supply Chain Analytics implications for designing Supply Chain Networks : Linking Descriptive Analytics to operational Supply Chain Analytics applications to derive strategic Supply Chain Network Decisions." Thesis, Internationella Handelshögskolan, Högskolan i Jönköping, IHH, Centre of Logistics and Supply Chain Management (CeLS), 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:hj:diva-44120.

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Today’s dynamic and increasingly competitive market had expanded complexities for global businesses pressuring companies to start leveraging on Big Data solutions in order to sustain the global competitions by becoming more data-driven in managing their supply chains.The main purpose of this study is twofold, 1) to explore the implications of applying analytics designing supply chain networks, 2) to investigate the link between operational and strategic management levels when making strategic decisions using Analytics.Qualitative methods have been applied for this study to gain a greater under
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Zubar, Tymofiy, Тимофій Андрійович Зубар, Olena Volovyk, and Олена Іванівна Воловик. "Big data in logistics: last mile application." Thesis, National Aviation University, 2021. https://er.nau.edu.ua/handle/NAU/50494.

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1. 3PL Survey - https://www.3plstudy.com/ 2. 5 Examples of How Big Data in Logistics Can Transform The Supply Chain - https://www.datapine.com/blog/how-big-data-logistics-transform-supply-chain<br>Big data is revolutionizing many business areas, including logistics and business processes in it. The complexity and dynamics of logistics, coupled with the reliance on many movable parts, can cause bottlenecks at any point in the supply chain, making big data application a vital element of effectiveness in logistical processes design and management. For example, big data logistics can be used to o
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Herden, Tino T. [Verfasser], Frank [Gutachter] Straube, and André [Gutachter] Ludwig. "Managing supply chain analytics - guiding organizations to execute analytics initiatives in logistics and supply chain management / Tino T. Herden ; Gutachter: Frank Straube, André Ludwig." Berlin : Technische Universität Berlin, 2020. http://d-nb.info/1213348587/34.

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Budnichenko, M. "It’s time for a new generation of supply chain analytics!" Thesis, National Aviation University, 2009. https://er.nau.edu.ua/handle/NAU/46700.

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Nearly every enterprise has a high-level strategic initiative focused on analytics—and is applying advanced technologies to measure, manage and ultimately improve its overall performance. The result is an explosion of business-intelligence efforts, with Forrester Research recently estimating that large enterprises may have 5–15 reporting and analysis solutions, with some companies using at least 20 separate products.
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CHALAKKAL, VARGHESE KISHORE. "Application of Model Predictive Control in Supply Chain Processes." Doctoral thesis, Università Politecnica delle Marche, 2020. http://hdl.handle.net/11566/273217.

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Questa tesi esamina la pianificazione del fabbisogno di materiali Material (Requirements Planning MRP) da una diversa prospettiva, quella delle matrici. L'intero processo viene infatti sviluppato utilizzando una serie di matrici che si evolvono nel tempo in un approccio basato sul sistema tempo-variante. Invece di iterare lungo i livelli della distinta base (materiali), calcoleremo pertanto, simultaneamente, tutti i requisiti dei materiali per tutti i prodotti in un dato momento. Il vantaggio principale di questo approccio è la velocità: possiamo calcolare MPS (Piano Principale di Produzione M
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Cao, Lizhong (Lizhong Lilly). "Predictive modeling of fulfillment supply chain for delivery performance improvement." Thesis, Massachusetts Institute of Technology, 2017. http://hdl.handle.net/1721.1/112039.

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Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2017.<br>S.M. in Engineering Systems Massachusetts Institute of Technology, School of Engineering, Institute for Data, Systems, and Society, in conjunction with the Leaders for Global Operations Program at MIT 2017<br>Cataloged from PDF version of thesis.<br>Includes bibliographical references (pages 51-52).<br>NIKE, Inc. Direct-To-Consumer (DTC) represents sales from NIKE-owned retail stores and internet websites. DTC is experiencing tremendo
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Pinheiro, Éder Vasco. "An application of distributed model predictive control to supply chain management." reponame:Repositório Institucional da UFSC, 2017. https://repositorio.ufsc.br/handle/123456789/190880.

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Dissertação (mestrado) - Universidade Federal de Santa Catarina, Centro Tecnológico, Programa de Pós-Graduação em Engenharia de Produção, Florianópolis 2017.<br>Os produtos e serviços não apresentam valor até que eles literalmente estejam no lugar e no instante que os consumidores precisam. O conjunto de princípios e técnicas que busca garantir esses valores baseados no tempo e na localização é atribuído a definição de logística. Portanto, sempre que houver um processo incluem o planejamento, a implementação e o controle de fluxos de informações e de bens ou serviços entre origem ofertantes e
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Books on the topic "Predictive Analytics in Supply Chain"

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Deep, Kusum, Madhu Jain, and Said Salhi, eds. Logistics, Supply Chain and Financial Predictive Analytics. Springer Singapore, 2019. http://dx.doi.org/10.1007/978-981-13-0872-7.

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Robertson, Peter W. Supply Chain Analytics. Routledge, 2020. http://dx.doi.org/10.4324/9781003084020.

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Liu, Kurt Y. Supply Chain Analytics. Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-030-92224-5.

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Biçer, Işık. Supply Chain Analytics. Springer Nature Switzerland, 2023. http://dx.doi.org/10.1007/978-3-031-30347-0.

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Heckmann, Iris. Towards Supply Chain Risk Analytics. Springer Fachmedien Wiesbaden, 2016. http://dx.doi.org/10.1007/978-3-658-14870-6.

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Ivanov, Dmitry. Introduction to Supply Chain Analytics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-51241-4.

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Ibne Hossain, Niamat Ullah, ed. Data Analytics for Supply Chain Networks. Springer International Publishing, 2023. http://dx.doi.org/10.1007/978-3-031-29823-3.

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R, Lummus Rhonda, ed. The supply chain professional: Concepts and analytics. Hercher Publishing Inc., 2015.

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Watson, Michael. Supply chain network design: Applying optimization and analytics to the global supply chain. FT Press, 2013.

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Alharbi, Ibraheem, Chiheb-Eddine Ben Ncir, Bader Alyoubi, and Hajer Ben-Romdhane, eds. Advances in Computational Logistics and Supply Chain Analytics. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-50036-7.

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Book chapters on the topic "Predictive Analytics in Supply Chain"

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Rosenberg, Stuart M. "Predictive analytics and manufacturing." In The Digitalization of the 21st Century Supply Chain. Routledge, 2020. http://dx.doi.org/10.4324/9781003054818-43.

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Araz, Özgür M., and David L. Olson. "Simulating Supply Chain Risks." In Risk and Predictive Analytics in Business with R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003562399-5.

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Rosenberg, Stuart M. "Predictive analytics and mainstream business tools." In The Digitalization of the 21st Century Supply Chain. Routledge, 2020. http://dx.doi.org/10.4324/9781003054818-47.

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Rosenberg, Stuart M. "Predictive analytics and the transportation management system." In The Digitalization of the 21st Century Supply Chain. Routledge, 2020. http://dx.doi.org/10.4324/9781003054818-44.

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Rosenberg, Stuart M. "Using predictive analytics to drive sales and customer service." In The Digitalization of the 21st Century Supply Chain. Routledge, 2020. http://dx.doi.org/10.4324/9781003054818-48.

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Janzen, Sabine, Hannah Stein, and Sebastian Baer. "GRASPER: Leveraging Knowledge Graphs for Predictive Supply Chain Analytics." In Lecture Notes in Computer Science. Springer Nature Switzerland, 2024. http://dx.doi.org/10.1007/978-3-031-75599-6_4.

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Araz, Özgür M., and David L. Olson. "Association Rule Modeling in Supply Chains." In Risk and Predictive Analytics in Business with R. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003562399-4.

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Jain, Bindiya, and Udit Mamodiya. "Quantum machine learning for predictive analytics in consumer electronics." In Quantum Computing and Artificial Intelligence in Logistics and Supply Chain Management. Chapman and Hall/CRC, 2025. https://doi.org/10.1201/9781003498292-4.

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Ningombam, Devarani Devi, and Venkata Sravan Telu. "Supply Chain Management in the Oil and Gas Business." In Understanding Data Analytics and Predictive Modelling in the Oil and Gas Industry. CRC Press, 2023. http://dx.doi.org/10.1201/9781003357872-5.

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Schmitt, Robert H., Daniel Buschmann, Kristof Briele, Peter Schlegel, and Max Ellerich. "Sustainable Production through Predictive Quality and Sustainability Analytics along the Supply Chain." In The Monetization of Technical Data. Springer Berlin Heidelberg, 2023. http://dx.doi.org/10.1007/978-3-662-66509-1_26.

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Conference papers on the topic "Predictive Analytics in Supply Chain"

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A.Sathish, R.G.Kumar, P. Venkata Lakshmi, P. Sathvika, R. Sirisha, and B. Sreedhar. "Enhancing Cyber Supply Chain Security through Predictive Analytics." In 2024 4th Asian Conference on Innovation in Technology (ASIANCON). IEEE, 2024. https://doi.org/10.1109/asiancon62057.2024.10838129.

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Ramu, Murugan, S. Thangamayan, and S. Selvaraju. "Predictive Insights: Real-Time Decision Making in Supply Chain Management." In 2024 8th International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC). IEEE, 2024. http://dx.doi.org/10.1109/i-smac61858.2024.10714831.

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Kalyan Raj Kumar Yesodha, Karthik, Prabhakaran Rajendran, Manoj Bhalerao, Ketan Gupta, and N. Yuvaraj. "Predictive Analytics and Automation in Supply Chain Management with Internet of Things (IoT)." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10968125.

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Cheng, Zhi-Qi, Yifei Dong, Aike Shi, et al. "SHIELD: LLM-Driven Schema Induction for Predictive Analytics in EV Battery Supply Chain Disruptions." In Proceedings of the 2024 Conference on Empirical Methods in Natural Language Processing: Industry Track. Association for Computational Linguistics, 2024. http://dx.doi.org/10.18653/v1/2024.emnlp-industry.24.

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Rajendran, Prabhakaran, Manoj Bhalerao, Karthik Kalyan Raj Kumar Yesodha, Ketan Gupta, and N. Yuvaraj. "Integrating Predictive Analytics and Internet of Things (IoT) to Optimize Supply Chains." In 2025 3rd International Conference on Integrated Circuits and Communication Systems (ICICACS). IEEE, 2025. https://doi.org/10.1109/icicacs65178.2025.10969060.

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Tandon, Pankaj. "Corrosion Data Management with Operational Excellence." In CONFERENCE 2025. AMPP, 2025. https://doi.org/10.5006/c2025-00302.

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Abstract This session presents a flexible data analytics and insights model with risk assessment for pipeline corrosion management with improved safety, and operational efficiency. We present a case study with integration of As-built, pipeline and corrosion and cathodic protection data. The risk model is an informed overlay on data integration with twin benefits of instant customizability and risk management. AI backed software capability provides a framework for actionable risk, with customized data driven assessments, predictive scheduling of remediation, preventative and mitigation methods,
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Srinivasan, Sridhar, and Gerrit Buchheim. "Improved Unit Economics, Crude Blending and Throughput Management Using a Novel High Temperature Corrosion Prediction Framework." In MECC 2023. AMPP, 2023. https://doi.org/10.5006/mecc2023-20054.

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Abstract Margin improvement in crude processing can benefit from new knowledge-based, engineering solutions, designed to offer a framework to harness the immense power of plant and process data to achieve superior process efficiencies, productivity and reliability. The need to work with opportunity crudes in refinery unit operations, a business imperative of crude blending and crude slate optimization, has meant that operators face substantial risk of corrosion damage in the crude distillation unit (CDU), vacuum distillation unit (VDU), associated equipment and side stream piping. Using Total
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Jayalakshmi, D., V. R. Vimal, S. Loganayagi, Lakshmi Kanthan Narayanan, and R. Hemavathi. "Enhancing Supply Chain Efficiency with IoT and Data Analytics." In 2024 International Conference on Recent Advances in Science and Engineering Technology (ICRASET). IEEE, 2024. https://doi.org/10.1109/icraset63057.2024.10894860.

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Popova, Petya, and Veselin Popov. "Artificial Intelligence and Big Data Analytics for Supply Chain Sustainability." In 2024 7th International Conference on Advanced Communication Technologies and Networking (CommNet). IEEE, 2024. https://doi.org/10.1109/commnet63022.2024.10793329.

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Bhoraskar, Aryan, Ayush Sangram Kadam, and Anusha N. "Big Data Analytics and Explainable AI in Supply Chain Management." In 2025 Third International Conference on Augmented Intelligence and Sustainable Systems (ICAISS). IEEE, 2025. https://doi.org/10.1109/icaiss61471.2025.11041879.

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Reports on the topic "Predictive Analytics in Supply Chain"

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Helinski, Ryan, Gio Kao, Jason Hamlet, Joshua Letchford, Philip Campbell, and Benjamin Anthony. Macro Supply Chain Lifecycle Decision Analytics. Office of Scientific and Technical Information (OSTI), 2016. http://dx.doi.org/10.2172/1761979.

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Zhao, Yao. Supply Chain Analytics and Stock Market Intelligence. Instats Inc., 2025. https://doi.org/10.61700/8cx0l3vsfg5ea1342.

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This seminar delves into the intersection of supply chain management and stock market intelligence, highlighting how data analytics can reveal the influence of supply chains on financial and stock market performance. Participants will acquire advanced techniques, data and tools to analyze these dynamics, enhancing their research capabilities and understanding the strategic implications for businesses and investors.
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Gerst, Tyler. Data Analytics in Supply Chain Managment: Linking the World Together for a Brighter Tomorrow. Iowa State University, 2022. http://dx.doi.org/10.31274/cc-20240624-476.

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Pasupuleti, Murali Krishna. Securing AI-driven Infrastructure: Advanced Cybersecurity Frameworks for Cloud and Edge Computing Environments. National Education Services, 2025. https://doi.org/10.62311/nesx/rrv225.

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Abstract: The rapid adoption of artificial intelligence (AI) in cloud and edge computing environments has transformed industries by enabling large-scale automation, real-time analytics, and intelligent decision-making. However, the increasing reliance on AI-powered infrastructures introduces significant cybersecurity challenges, including adversarial attacks, data privacy risks, and vulnerabilities in AI model supply chains. This research explores advanced cybersecurity frameworks tailored to protect AI-driven cloud and edge computing environments. It investigates AI-specific security threats,
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Jahidin, Kamaruzaman Bin. Industry 4.0 Behavioral Insights. Asian Productivity Organization, 2023. http://dx.doi.org/10.61145/pstf6290.

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Applications of behavioral insights (BIs) in manufacturing optimize user experience, predictive maintenance, supply chain management, many other industrial processes, and ultimately overall productivity. Kamaruzaman Bin Jahidin breaks down BI applications in the Industry 4.0 era to benefit manufacturers of all sizes, focusing on SMEs. Concise real-world examples are given to guide enterprises starting BI initiatives
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