The wholesale industry for daily necessities is currently experiencing rapid growth. Maintaining excess inventory can lead to high storage costs and the risk of product quality decreasing, both physically and out of date. By aligning inventory levels with demand, businesses can reduce risk and increase profits. This research focuses on developing a Unilever product sales prediction system using the K-Nearest Neighbor (KNN) method and Value Chain Analysis to assess results and sales. The dataset consists of sales data for twelve months from January 2023 to December 2023, which will be used for one year of Training data and three months of tasking data. RapidMiner will be used for data processing to categorize sales levels as not selling, medium, selling, and very selling.
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Sibelawanti et al. (2024) studied this question.
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