Control Tower-Driven Multi-Echelon Inventory Positioning in Pharmaceutical Supply Chains

Pharmaceutical supply chains face high shortage risk when demand uncertainty interacts with lead-time variability, while shelf-life limits, cold-chain requirements, and quality-release delays raise the cost of both stockouts and excess inventory. This chapter proposes a control tower–driven decision-support framework for multi-echelon inventory positioning that converts visibility into actionable policies. A multi-echelon optimization module generates candidate target-stock and reorder policies across manufacturing and distribution echelons. Monte Carlo–based scenario analysis tests robustness under demand, lead-time, and disruption uncertainty. An MCDA layer then ranks policies using total cost, service level, shortage probability, expediting intensity, days of supply, and recovery time, with sensitivity analysis when weights are uncertain. The framework provides a signal-to-decision map, event-triggered policy updates, and a compact metric dictionary for shortage-risk management without proprietary field data.