Abstract: A manufacturing process is migrated from an existing operation to a configure-to-order (CTO) system. As the CTO operation will eliminate the “machine-type model” (MTM) inventory of the existing operation, the emphasis is shifted to the components, or “building blocks”, which will still follow the build-to-stock scheme, due to their long leadtimes, and hence still require inventory. The solution involves an inventory-service trade-off of the new CTO system, resulting in performance gains, in terms of reduced inventory cost and increased service level. Other benefits include better forecast accuracy through parts commonality and risk-pooling, and increased customer demand, as orders will no longer be confined within a restricted set of pre-configured MTMs.

Abstract: A manufacturing process is migrated from an existing operation to a configure-to-order (CTO) system. As the CTO operation will eliminate the “machine-type model” (MTM) inventory of the existing operation, the emphasis is shifted to the components, or “building blocks”, which will still follow the build-to-stock scheme, due to their long leadtimes, and hence still require inventory. The solution involves an inventory-service trade-off of the new CTO system, resulting in performance gains, in terms of reduced inventory cost and increased service level. Other benefits include better forecast accuracy through parts commonality and risk-pooling, and increased customer demand, as orders will no longer be confined within a restricted set of pre-configured MTMs.

Abstract: A manufacturing process is migrated from an existing operation to a configure-to-order (CTO) system. As the CTO operation will eliminate the “machine-type model” (MTM) inventory of the existing operation, the emphasis is shifted to the components, or “building blocks”, which will still follow the build-to-stock scheme, due to their long leadtimes, and hence still require inventory. The solution involves an inventory-service trade-off of the new CTO system, resulting in performance gains, in terms of reduced inventory cost and increased service level. Other benefits of the method include better forecast accuracy through parts commonality and risk-pooling, and increased customer demand, as orders will no longer be confined within a restricted set of pre-configured MTMs.

Abstract: A manufacturing process is migrated from an existing operation to a configure-to-order (CTO) system. As the CTO operation will eliminate the “machine-type model” (MTM) inventory of the existing operation, the emphasis is shifted to the components, or “building blocks”, which will still follow the build-to-stock scheme, due to their long leadtimes, and hence still require inventory. The solution involves an inventory-service trade-off of the new CTO system, resulting in performance gains, in terms of reduced inventory cost and increased service level. Other benefits include better forecast accuracy through parts commonality and risk-pooling, and increased customer demand, as orders will no longer be confined within a restricted set of pre-configured MTMs.

Abstract: A method suitable for use in a physical distribution network. The method comprises the steps of using a distribution resource planning (DRP) logic for providing estimates of at least one of projected future on-hand inventory and replenishment requirements; and, incorporating within the DRP logic an uncertainty of future demand for estimating at least one of future replenishment requirements and inventory levels in the physical distribution network.

Abstract: A method for providing inventory optimization for levels of products in a complex supply chain network for multiple internal supplier or manufacturer locations and external distributor or retailer locations. The invention constructs a representative supply chain network model to indicate the flow of products between internal and external locations, it determines inventory levels and fill rates to meet the service level requirements, calculates a total inventory cost for all products in the network, and optimizes the fill rates based on estimated gradient information of the total inventory cost.