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  Scopus ID: 21100926589

Warehouse Product Demand Forecasting Using Time Series Methods

Sayali Bora and Pushpak Bhonde

Abstract

Forecasting plays an essential role in supply chain operations as a lot of critical decisions are dependent on predicted future factors like product demand and sales. It may be necessary to make forecasts years in advance or only a few minutes. Forecasting is a crucial tool for efficient and productive planning, regardless of the situations or time frames involved. Given the significance of warehouses in supply chains, warehouse operations must be productive and profitable. Accurate projections not only help to reduce discrepancies between actual and anticipated sales, but they can also have an impact on other supply chain issues, such as managing excess and obsolete inventories. This paper showcases a comparative analysis of two time series models- Holt-Winters Smoothing Method and Seasonal ARIMA or SARIMA, for better prediction of fluctuating demand of products which in turn will contribute to the company’s betterment in multiple ways.

Published on: March 07, 2023
doi: 10.17756/nwj.2023-s1-002
Citation: Bora S, Bhonde P. 2023. Warehouse Product Demand Forecasting Using Time Series Methods. NanoWorld J 9(S1): S6-S9.

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