The project sponsor is a reverse logistics company that provides Returnable Transport Items (RTIs) to large manufacturing companies, distributors and retailers. Its business model is characterized by a unique closed-loop supply chain. The seasonal peak demand for RTIs from June to September negatively impacts service levels and costs. The sponsor company seeks an opportunity to level load production and build inventory position, while optimizing the service levels and annual supply chain costs. This capstone project proposes an optimal supply chain plan by analyzing historical data, identifying key cost-service drivers and creating a Scenario Planning Tool (SPT) that demonstrates tangible benefits in terms of cost and service level improvements. The data analysis quantifies the correlation between serviceability (days of coverage), inventory position and supply chain component costs (cost to serve). This correlation is used to model a Scenario Planning Tool (SPT) that recommends the optimal supply plan and directs the inventory policy decisions in order to maximize serviceability, while minimizing the total annual supply chain costs. A key takeaway is that the correlation between inventory policies and supply chain costs provides an opportunity to optimally plan inventory coverage in order to minimize supply chain costs while meeting service level targets.