Abstract: A method for deploying parts is disclosed. Locations that include supply locations and demand locations are defined. A supply location supplies parts to a demand location. A demand is computed for each part at each location. An availability lead-time is estimated for each part at each location. A lead-time demand is computed for each part at each location using the availability lead-times for the part. A stock level is computed for each part at each location. A completely filled demand is determined from the lead-time demands and the stock levels, and a partially filled demand is determined from the lead-time demands and the stock levels. A coverage function for the parts at the locations is generated from the completely filled demand and the partially filled demand.

Abstract: A method of multi-enterprise optimization at a buyer computer includes accessing a forecasted demand for at least one item and generating one or more proposed flexible trade contracts using the forecasted demand for the item. The proposed flexible trade contract is communicated to a seller computer and subsequently executed after acceptance of the proposed flexible trade contract at the seller computer to create a flexible trade contract. Each proposed flexible trade contract may be a forward contract, an option contract, or a flexible forward contract.

Abstract: A system (10) for managing a value chain includes a planning application (36) that receives planning data from a number of entities included in a value chain and automatically generates a plan according to the planning data. At least two of the entities do not directly communicate planning data to one another. The system (10) also includes a manager application (44) that receives the plan and automatically identifies one or more exceptions in the plan, communicates planning data relating to the exceptions to one or more of the entities, receives instructions from one or more of the entities regarding how the exceptions are to resolved, and automatically modifies the planning data in response to the instructions.

Abstract: The present invention is a constraint-based planning tool that specifically addresses the needs of complex discrete and process industries by providing a computationally efficient method and system for fulfillment of customer orders in a supply chain. By comprehensively synchronizing and optimizing the flow of materials through the enterprise, the present invention creates a feasible plan for manufacturing operations that simultaneously optimizes both material and capacity constraints by planning and scheduling multiple customer orders, planning for use the various resources located in remote locations needed to fulfill such orders, and scheduling the used resources for replenishment at appropriate times so that the resources meet the needs of the orders.

Abstract: The present invention is a constraint-based planning tool that specifically addresses the needs of complex discrete and process industries by providing a computationally efficient method and system for fulfillment of customer orders in a supply chain. By comprehensively synchronizing and optimizing the flow of materials through the enterprise, the present invention creates a feasible plan for manufacturing operations that simultaneously optimizes both material and capacity constraints by planning and scheduling multiple customer orders, planning for use the various resources located in remote locations needed to fulfill such orders, and scheduling the used resources for replenishment at appropriate times so that the resources meet the needs of the orders.

Abstract: The present invention is a constraint-based planning tool that specifically addresses the needs of complex discrete and process industries by providing a computationally efficient method and system for fulfillment of customer orders in a supply chain. By comprehensively synchronizing and optimizing the flow of materials through the enterprise, the present invention creates a feasible plan for manufacturing operations that simultaneously optimizes both material and capacity constraints by planning and scheduling multiple customer orders, planning for use the various resources located in remote locations needed to fulfill such orders, and scheduling the used resources for replenishment at appropriate times so that the resources meet the needs of the orders.

Abstract: A statistical system and method for filters sales history to yield two demand series—one that is time insensitive (i.e. function of price and promotional activities only) and another that is price & promotional activities insensitive (i.e. function of temporal factors only). Once this task is completed, then specialized econometric techniques can be used to model the former and specialized time series techniques can be used to model the latter.

Abstract: The present invention is a constraint-based planning tool that specifically addresses the needs of complex discrete and process industries by providing a computationally efficient method and system for fulfillment of customer orders in a supply chain. By comprehensively synchronizing and optimizing the flow of materials through the enterprise, the present invention creates a feasible plan for manufacturing operations that simultaneously optimizes both material and capacity constraints by planning and scheduling multiple customer orders, planning for use the various resources located in remote locations needed to fulfill such orders, and scheduling the used resources for replenishment at appropriate times so that the resources meet the needs of the orders.

Abstract: The present invention is a constraint-based planning tool that specifically addresses the needs of complex discrete and process industries by providing a computationally efficient method and system for fulfillment of customer orders in a supply chain. By comprehensively synchronizing and optimizing the flow of materials through the enterprise, the present invention creates a feasible plan for manufacturing operations that simultaneously optimizes both material and capacity constraints by planning and scheduling multiple customer orders, planning for use the various resources located in remote locations needed to fulfill such orders, and scheduling the used resources for replenishment at appropriate times so that the resources meet the needs of the orders.

Abstract: The present invention divides processing across multiple nodes to escape limitations placed by the processing power of commonly available application server platforms. The solution processing divides the problem into independently processed batches. Coordination of processing between nodes is achieved using a relational database tables and coordination for algorithm startup and initialization may be accomplished through a messaging system. The database table is loaded with rows to represent batches to be processed, and the independent processing nodes are given a message via the messaging system to start processing various jobs at a particular level. The nodes may then signal back when they can no longer find batches to process at that level. Once all nodes have completed a level, a message is sent to the nodes starts on to the next level. In this way, the messaging between the nodes is generally limited to two messages per level per node.

Abstract: The present invention provides a configurable pricing system that allows users to define or modify data used to analyze, evaluate, improve, and design pricing changes according to the user's need. A Graphical user interface or some other type of user interface allows the user to access and review various data to be used during pricing optimization. The user may then modify this data as needed to improve the pricing evaluation, such as defining sales or pricing trends, or relationships between the product of interest and other competing items. The user interface may further display changes in pricing and the effects of the pricing changes, as caused by the user's changes. The interface may also allow the user to modify the mathematical model to be used during price optimization, as well as define variables, constraints, and boundaries to be considered during the price optimization.