Intelligent Inventory Rotation and Revenue Optimization Using Integer Linear Programming: A Coffee Shop Case Study

Amin Haghdadi, Mohammad Hossein Zolfagharnasab, Siavash Damari, Safa Vakili

Abstract: This study employs Integer Linear Programming (ILP) to optimize gross profit for a local coffee shop, addressing challenges in inventory management and sale revenue optimization. A dataset comprising of 40 menu items and 34 ingredients was developed, incorporating constraints such as capital budget, ingredient availability, costs, and sales ratios to simulate monthly revenue. By applying the ILP methodology, the study achieved a gross profit margin of 42.28% of total sales revenue within a single month, underscoring its efficacy in improving profitability. The sensitivity analysis indicated that an increase in budget resulted in a proportional rise in sales revenue and gross profit, while inventory costs escalated at a comparatively slower pace. The research pinpointed high-performing items, including coffee, tea, and cold beverages, as significant contributors to profit, thereby highlighting the necessity for effective inventory management.

External IDs:doi:10.1007/978-3-032-00137-5_3