Demand-centric Inventory Forecasting Approach: Comparing Regression Methods
Abstract
With our ever-growing population and the increas- ing demand for goods (raw or finished), companies running manufacturing/production and retail stores managing products have had their plates full while trying to maintain the demand- supply equilibrium. Most of the established enterprises have a separate division focusing on keeping the supply and stocking afloat to meet the ongoing and future demand. To do a parallel study, we aim to get over 9 weeks of sales data from Grupo Bimbo (bakery industry) across Mexico and analyze it for purchase patterns to estimate the demand trend in the coming weeks. Our objective is to solve this problem statement by generating an inventory forecast based on the estimated demand by using machine learning techniques like Multiple Linear Regression, Stochastic Gradient Descent regression, Random forest regres- sion, and Gradient boosting (XGBoost).
How to Cite This Article
Jwalin Thaker (2020). Demand-centric Inventory Forecasting Approach: Comparing Regression Methods . International Journal of Multidisciplinary Research and Growth Evaluation (IJMRGE), 1(3), 82-92. DOI: https://doi.org/10.54660/IJMRGE.2020.1.3.82-92
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