Demand Planning and Forecasting: Technical Foundations for Supply Chain Excellence

Demand planning and forecasting represent critical capabilities in modern supply chain management, enabling organizations to anticipate customer needs while optimizing inventory investments and operational resources. This article explores the technical foundations of effective demand planning, including statistical frameworks, computational requirements, and advanced methodologies. Starting with baseline forecasting approaches, the discussion progresses through consensus planning architectures, hierarchical aggregation systems, and cross-functional integration mechanisms. The article then examines quantitative forecasting techniques like machine learning models and causal modeling alongside qualitative forecast integration mechanisms. Technical infrastructure requirements spanning data integration, computational environments, visualization tools, and system scalability considerations are detailed. Performance evaluation metrics and emerging technical trends complete the overview, providing a comprehensive framework for developing sophisticated demand planning capabilities that balance statistical rigor with practical business application.

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Publication Details

Journal
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Published
2025-04-12
DOI
https://doi.org/10.32628/cseit25112812
Citations
1
Primary Topic
Big Data and Business Intelligence
Type
article
Field-Weighted Citation Impact
2.28
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article

Demand Planning and Forecasting: Technical Foundations for Supply Chain Excellence

Tushar Dasgupta
1 citations
International Journal of Scientific Research in Computer Science Engineering and Information Technology
Big Data and Business Intelligence
2.28
article

Demand Planning and Forecasting: Technical Foundations for Supply Chain Excellence

Tushar Dasgupta
article en
1 citations

Abstract

Demand planning and forecasting represent critical capabilities in modern supply chain management, enabling organizations to anticipate customer needs while optimizing inventory investments and operational resources. This article explores the technical foundations of effective demand planning, including statistical frameworks, computational requirements, and advanced methodologies. Starting with baseline forecasting approaches, the discussion progresses through consensus planning architectures, hierarchical aggregation systems, and cross-functional integration mechanisms. The article then examines quantitative forecasting techniques like machine learning models and causal modeling alongside qualitative forecast integration mechanisms. Technical infrastructure requirements spanning data integration, computational environments, visualization tools, and system scalability considerations are detailed. Performance evaluation metrics and emerging technical trends complete the overview, providing a comprehensive framework for developing sophisticated demand planning capabilities that balance statistical rigor with practical business application.

International Journal of Scientific Research in Computer Science Engineering and Information TechnologyVol. 11(2)
Duke University (US)
Openalex Percentile: Top 13%
Big Data and Business Intelligence
2.28
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