From Factory Floor to Forecasting Models: Building AI-Driven Supply Chain Platforms

Contemporary manufacturing organizations confront extraordinary obstacles in administering intricate supply chain networks while preserving competitive positioning within swiftly changing international markets. The technological evolution of production establishments has generated substantial prospects for deploying artificial intelligence systems capable of analyzing extensive operational information to produce implementable strategic guidance. Modern supply chain networks necessitate advanced analytical structures that surpass conventional responsive approaches, adopting forecasting methodologies that predict market variations and manufacturing obstacles before interruptions manifest. The incorporation of Fourth Industrial Revolution technologies, encompassing Internet of Things detection devices, cloud-based computing frameworks, and instantaneous analytical systems, creates fundamental components for thorough AI-powered supply chain solutions. Sophisticated machine learning algorithms exhibit enhanced capabilities in demand prediction, inventory management, and operational effectiveness improvement when compared to traditional statistical techniques. Combined learning methodologies merging various predictive models produce dependable projections that sustain precision across different market circumstances and cyclical changes. Novel product development monitoring systems furnish stakeholders with immediate awareness of product advancement status, manufacturing preparation evaluations, and supply chain readiness assessments. The convergence of production operations with machine learning technologies establishes possibilities for enhancing manufacturing procedures, minimizing waste creation, and advancing overall equipment performance throughout varied manufacturing settings. Deployment obstacles include information quality concerns, integration difficulties, and organizational transformation management needs that require thorough planning and strategic implementation methods.

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Publication Details

Journal
European Modern Studies Journal
Published
2025-08-07
DOI
https://doi.org/10.59573/emsj.9(4).2025.3
Primary Topic
Digital Transformation in Industry
Type
article
Field-Weighted Citation Impact
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article

From Factory Floor to Forecasting Models: Building AI-Driven Supply Chain Platforms

Srikanth Maru
European Modern Studies Journal
Digital Transformation in Industry
article

From Factory Floor to Forecasting Models: Building AI-Driven Supply Chain Platforms

Srikanth Maru
article en

Abstract

Contemporary manufacturing organizations confront extraordinary obstacles in administering intricate supply chain networks while preserving competitive positioning within swiftly changing international markets. The technological evolution of production establishments has generated substantial prospects for deploying artificial intelligence systems capable of analyzing extensive operational information to produce implementable strategic guidance. Modern supply chain networks necessitate advanced analytical structures that surpass conventional responsive approaches, adopting forecasting methodologies that predict market variations and manufacturing obstacles before interruptions manifest. The incorporation of Fourth Industrial Revolution technologies, encompassing Internet of Things detection devices, cloud-based computing frameworks, and instantaneous analytical systems, creates fundamental components for thorough AI-powered supply chain solutions. Sophisticated machine learning algorithms exhibit enhanced capabilities in demand prediction, inventory management, and operational effectiveness improvement when compared to traditional statistical techniques. Combined learning methodologies merging various predictive models produce dependable projections that sustain precision across different market circumstances and cyclical changes. Novel product development monitoring systems furnish stakeholders with immediate awareness of product advancement status, manufacturing preparation evaluations, and supply chain readiness assessments. The convergence of production operations with machine learning technologies establishes possibilities for enhancing manufacturing procedures, minimizing waste creation, and advancing overall equipment performance throughout varied manufacturing settings. Deployment obstacles include information quality concerns, integration difficulties, and organizational transformation management needs that require thorough planning and strategic implementation methods.

European Modern Studies JournalVol. 9(4)
Film Independent (US)
Openalex Percentile: Top 67%
Digital Transformation in Industry
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