Coffee roasting represents a rapidly growing market where operational efficiency is essential for sustaining profitability and product quality. Despite its economic significance, the operational-level challenges of roasting and packaging have received limited attention in the literature. To fill the gap, this research presents a mixed-integer linear programming (MILP) model to address the challenges of optimizing coffee production planning. The proposed model determines optimal daily production schedules and best quantities to roast, package, store, and dispatch. The objective is to maximize overall profitability while satisfying key operational constraints, including production capacity, inventory limitations, and demand requirements. In addition, this research includes the shelf-life constraint to reduce waste and guarantee product quality and freshness which is critical in the coffee market. To reflect the real-life case and give the model more flexibility, shortages are allowed and back-ordered later with specific cost. The research validates the model through an extensive real-world case study conducted in a Saudi Arabian coffee roastery. This model will be used by the decision makers to find the optimal production schedules that utilize the resources and increase profitability.