This Capstone developed an optimization-based framework to support production planning decisions in supply networks that operate with both make-to-stock (MTS) and make-to-order (MTO) strategies. Many manufacturing firms face the challenge of deciding which products should be produced in advance and which should be produced only after receiving customer orders. These decisions become more complex in networks with multiple products, factories, and customers, where demand variability, capacity limits, lead times, and transportation distances interact. The proposed model evaluated MTS and MTO decisions at the product–factory–customer level, allowing the same product to be produced to stock for some customers and to order for others, reflecting realistic hybrid production environments. The model maximized total profit by jointly considering revenue, production, transportation, and inventory holding costs. Demand decay captured lost revenue under MTO due to long lead times, while safety stock costs were modeled using pooled demand variability to represent inventory risk under MTS production. The framework was applied to a dataset based on real operational data provided by a company and extended to enable a comprehensive analysis. The network included multiple products, customers, and factories. Results showed that the optimal strategy was inherently hybrid, with MTS and MTO allocations driven by a balance of demand variability, holding cost, customer distance, factory capacity, and lead-time sensitivity rather than any single factor. Sensitivity analyses further demonstrated how changes in demand variability, holding cost, lead time, and demand decay affect the MTS–MTO mix. Overall, hybrid strategies consistently outperformed pure MTS or pure MTO approaches, illustrating the framework’s value as a scalable decision-support tool for improving profitability and supply network resilience.
