Tactical planning in blood supply chain: An integrated demand forecasting and optimization approach

• Introduces Blood-TAC, an integrated tactical planning tool for blood supply chains. • Combines machine learning-based demand forecasting with a rolling-horizon approach. • Balances network costs, unmet demand penalties, and safety stock deviations in planning. • Demonstrates real-world applicability through a case study of the Portuguese Blood Network. Blood Supply Chain management is crucial to ensure blood flow from donors to patients. At the tactical planning level, it is critical to develop strategies that not only guarantee demand fulfillment but also minimize costs and waste. To address this challenge, this research develops a decision support tool, Blood-TAC, that considers demand predictions and the comprehensive tactical planning of the Blood Supply Chain network. This tool comprises a prediction model and a mixed-integer linear programming model within a rolling horizon framework. First, an XGBoost model is used to predict demand for multiple blood products and locations. These predictions serve as input to the second step, which involves an optimization model that simultaneously manages collection, production, storage, and distribution across a network of blood centers. The goal is to provide decision-makers with key insights for setting mid-term targets and strategies that ensure demand fulfillment while minimizing waste and costs. Blood-TAC is tested using the case study of the Portuguese Blood Supply Chain, demonstrating its ability to generate realistic and improved plans and provide valuable insights to decision-makers. Overall, Blood-TAC reduces costs by 16 % when compared to the network’s historical performance while fulfilling demand and reducing waste of blood products to zero.