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Open Access Journal Article

Time Series Forecasting Models for Demand Planning

by David Taylor 1,*
1
David Taylor
*
Author to whom correspondence should be addressed.
MAD  2023 5(2):46; https://doi.org/10.69610/j.mad.20231230
Received: 20 October 2023 / Accepted: 28 November 2023 / Published Online: 30 December 2023
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Abstract

This paper presents an in-depth analysis of various time series forecasting models that are commonly utilized in the field of demand planning. Demand planning is a crucial aspect of supply chain management, as it involves predicting future customer demand to ensure that products and services are available when needed. The study evaluates the performance and applicability of different time series forecasting techniques, such as ARIMA, exponential smoothing, and machine learning-based models. It discusses their strengths and limitations in the context of demand planning and highlights the importance of selecting the appropriate model based on the specific characteristics of the dataset and the business objectives. Furthermore, the paper explores how these models can be integrated with other demand planning tools and methodologies to improve accuracy and effectiveness. The findings of the study are expected to provide valuable insights for managers and practitioners in the supply chain and logistics industry, assisting them in making informed decisions regarding inventory management, production planning, and resource allocation.

Keywords: Time Series Forecasting; - Demand Planning; - Supply Chain Management; - ARIMA Models; - Exponential Smoothing.;
Copyright: © 2023 by Taylor. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) (Creative Commons Attribution 4.0 International License). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
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APA Style
Taylor, D. (2023). Time Series Forecasting Models for Demand Planning. Management Analytics and Decision, 5(2), 46. doi:10.69610/j.mad.20231230
ACS Style
Taylor, D. Time Series Forecasting Models for Demand Planning. Management Analytics and Decision, 2023, 5, 46. doi:10.69610/j.mad.20231230
AMA Style
Taylor D. Time Series Forecasting Models for Demand Planning. Management Analytics and Decision; 2023, 5(2):46. doi:10.69610/j.mad.20231230
Chicago/Turabian Style
Taylor, David 2023. "Time Series Forecasting Models for Demand Planning" Management Analytics and Decision 5, no.2:46. doi:10.69610/j.mad.20231230

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ACS Style
Taylor, D. Time Series Forecasting Models for Demand Planning. Management Analytics and Decision, 2023, 5, 46. doi:10.69610/j.mad.20231230
AMA Style
Taylor D. Time Series Forecasting Models for Demand Planning. Management Analytics and Decision; 2023, 5(2):46. doi:10.69610/j.mad.20231230
Chicago/Turabian Style
Taylor, David 2023. "Time Series Forecasting Models for Demand Planning" Management Analytics and Decision 5, no.2:46. doi:10.69610/j.mad.20231230
APA style
Taylor, D. (2023). Time Series Forecasting Models for Demand Planning. Management Analytics and Decision, 5(2), 46. doi:10.69610/j.mad.20231230

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