DEMAND FORECASTING FOR IMPROVED INVENTORY MANAGEMENT IN SMALL AND MEDIUM-SIZED BUSINESSES

Purnamasari, Dian Indri and Permadi, Vynska Amalia and Saefudin, Asep and Agusdin, Riza Prapascatama (2023) DEMAND FORECASTING FOR IMPROVED INVENTORY MANAGEMENT IN SMALL AND MEDIUM-SIZED BUSINESSES. JANAPATI, 12 (1). pp. 56-66. ISSN 2548-4265

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Abstract

Small and medium-sized businesses are constantly seeking new methods to increase productivity across all service areas in response to increasing consumer demand. Research has shown that inventory management significantly affects regular operations, particularly in providing the best customer relationship management (CRM) service. Demand forecasting is a popular inventory management solution that many businesses are interested in because of its impact on day-to-day operations. However, no single forecasting approach outperforms under all scenarios, so examining the data and its properties first is necessary for modeling the most accurate forecasts. This study provides a preliminary comparative analysis of three different machine learning approaches and two classic projection methods for demand forecasting in small and medium-sized leathercraft businesses. First, using K-means clustering, we attempted to group products into three clusters based on the similarity of product characteristics, using the elbow method's hyperparameter tuning. This step was conducted to summarize the data and represent various products into several categories obtained from the clustering results. Our findings show that machine learning algorithms outperform classic statistical approaches, particularly the ensemble learner XGB, which had the least RMSE and MAPE scores, at 55.77 and 41.18, respectively. In the future, these results can be utilized and tested against real-world business activities to help managers create precise inventory management strategies that can increase productivity across all service areas.

Item Type:	Article
Subjects:	H Social Sciences > H Social Sciences (General)
Divisions:	Faculty of Law, Arts and Social Sciences > School of Social Sciences
Depositing User:	DR PURNAMASARI DIAN INDRI
Date Deposited:	27 Apr 2023 01:49
Last Modified:	27 Apr 2023 01:49
URI:	http://eprints.upnyk.ac.id/id/eprint/34681

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