Predictive Analytics Functionalities in Supply Chain Management

Puica Elena ()
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Puica Elena: Bucharest University of Economic Studies, Bucharest, Romania Economic Informatics Doctoral School

Proceedings of the International Conference on Business Excellence, 2023, vol. 17, issue 1, 986-996

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 Data Unification. The study also assesses the potential benefits and challenges associated with implementing these solutions in supply chain management. The results of the study provide valuable information for supply chain professionals, IT managers and researchers interested in the application of predictive analytics in this field. In addition, the paper also discusses the current trend and future direction in this field.

Keywords: Predictive Analysis in SCM1; Predictive Analytics Efficiency2; Technology efficiency3; Predictive analytics software4 (search for similar items in EconPapers)
Date: 2023
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Persistent link: https://EconPapers.repec.org/RePEc:vrs:poicbe:v:17:y:2023:i:1:p:986-996:n:36

DOI: 10.2478/picbe-2023-0090

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