Improvement Model to increase service level by applying clustering k-means and lean warehousing management tools in a pet food company

This study presents an improvement model to increase the level of service in a wholesale pet food company, which faces a technical gap of 13% with respect to the sector in this indicator, a gap mainly attributed to stock breakage caused by inadequate demand planning and inefficient inventory management. As a solution to this problem, a demand forecasting model is developed based on k-means and RFM clustering techniques, leading into categorizing customers according to their purchase level and geographic location. Identifying 4 customer categories and 31 key products. In addition, an ABC analysis is applied together with Lean 5S and Kanban techniques to reorganize the warehouse, achieving a 23.26% reduction in operating times through a pilot test. To avoid stock-outs, EOQ and ROP parameters are introduced to standardize the purchasing process and thus achieve a timely supply of inventory, resulting in an increase in sales equivalent to 1100 bags of feed. The simulation in Arena validates that the set of these techniques together increase the service level by 13.18% and reduce the average inventory by 22.70%. In this way, the project achieves revenue maximization by increasing the units sold and optimizes storage costs. These improvements have a positive economic impact equivalent to USD 72,750 and consolidate a significant improvement in the company's operating efficiency.