This paper considers the performance of a production or distribution-scheduling algorithm termed Automatic Pipeline, Inventory and Order Based Production Control System (APIOBPCS) embedded within a Vendor Managed Inventory (VMI) supply chain where the demand profile is deemed to change significantly over time. A dynamic model of the system using causal loop diagrams and difference equations is presented. The APIOBPCS ordering algorithm is placed within a VMI relationship and a near saturated search technique evaluates optimum solutions based on production adaptation cost, system inventory cost and distributors’ inventory costs. The procedure can also cope with supply chains that operate in a localized region (where small, frequent deliveries are possible) or on a global scale, where large batch sizes are needed to gain economies of scale in transport costs. Properties of the optimal systems are highlighted via various Bullwhip, customer service level and inventory cost metrics. Managerial insights are gained and a generic decision support system is presented for ‘tuning’ VMI supply chains. An important feature of the optimization procedure is the ability to generate a number of competing ordering algorithm designs. Final selection of the ‘best’ system is then made via managerial judgement on the basis of the simulated response to typical real-life demands. We finish with a discussion of how the procedure may be used in an industrial context to design and strategically manage VMI supply chains.