Demand Forecasting for Improved Inventory Management in Small and Medium-Sized Businesses
نویسندگان
چکیده
Small and medium-sized businesses are constantly seeking new methods to increase productivity across all service areas in response increasing consumer demand. Research has shown that inventory management significantly affects regular operations, particularly providing the best customer relationship (CRM) service. Demand forecasting is a popular solution many interested because of its impact on day-to-day operations. However, no single approach outperforms under scenarios, so examining data properties first necessary for modeling most accurate forecasts. This study provides preliminary comparative analysis three different machine learning approaches two classic projection demand small leathercraft businesses. First, using K-means clustering, we attempted group products into clusters based similarity product characteristics, elbow method's hyperparameter tuning. step was conducted summarize represent various several categories obtained from clustering results. Our findings show algorithms outperform statistical approaches, ensemble learner XGB, which had least RMSE MAPE scores, at 55.77 41.18, respectively. In future, these results can be utilized tested against real-world business activities help managers create precise strategies areas.
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ژورنال
عنوان ژورنال: Jurnal Nasional Pendidikan Teknik Informatika (JANAPATI)
سال: 2023
ISSN: ['2089-8673', '2548-4265']
DOI: https://doi.org/10.23887/janapati.v12i1.57144