Year: 2024
Type: article
Abstract: The current issue faced by the client involved lost sales and increased holding costs for leftover inventory. Both issues have a direct impact on the economic profits of the firm and are thus of pressing importance to the company. This research aims to build an accurate demand forecast for a group of SKUs that have unusually low performance in certain stores as compared to the majority. We have used historical sales data in our project in order to better understand the patterns in sales which can then give us an idea of future sales. Through this study, we have identified anomalous SKUs based on outlier detection and understanding the statistical significance of each input predictor. We have defined thresholds in sales per store amount to classify each SKU as “pesky”, i.e., underperforming in some stores and overperforming in others, or not. Further, we have attempted to forecast the demand for these pesky SKUs in order to improve the inventory management and sales reporting of the firm. We explored and applied prediction models including linear, random forest and lasso regression. This will not only reduce holding costs and avoid lost sales, but also streamline the supply chain as it gives the client a better understanding of the parts that need to be supplied to each store.
Source: Research Society and Development
Institution: Purdue University West Lafayette
Language: English
Field: Decision Sciences
Domain: Social Sciences
Open Access status: diamond
Funder: Purdue University
OpenAlex ID: W4402681528