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: Determining an inventory target for a node of a supply chain includes calculating a demand stock for satisfying a demand over supply lead time at the node of the supply chain, and calculating a demand variability stock for satisfying a demand variability of the demand over supply lead time at the node. A demand bias of the demand at the node is established. An inventory target for the node is determined based on the demand stock and the demand variability stock in accordance with the demand bias.

Abstract: A system for generating a display of at least one side of a market includes a computer system having one or more computers. The displayed side of the market includes one or more offers from one or more market participants. Each offer includes an offered price and an offered quantity. The computer system receives market data reflecting the one or more offers and generates a display of the received market data. The display includes a price axis, a quantity axis, and one or more offer bars. Each offer bar represents one or more offers including equal offered prices. Placement of an offer bar along the price axis represents the equal offered prices for the one or more offers represented by the offer bar. The height of an offer bar relative to the quantity axis represents the aggregate offered quantity for the one or more offers represented by the offer bar.

Abstract: Generating an optimized supplier allocation plan includes identifying parts and suppliers associated with an allocation problem, where each supplier can supply at least one part. One or more objective functions are selected. Each objective function has part variables, and each part variable represents a quantity of a part to be procured from a supplier. At least one constraint constraining at least one part variable is received. The one or more objective functions are optimized with respect to the at least one constraint to yield a value for each part variable. A quantity of each part to be procured from at least one supplier is determined according to the values to generate the optimized supplier allocation plan.

Abstract: A system for generating a display of at least one side of a market includes a computer system having one or more computers. The displayed side of the market includes one or more offers from one or more market participants. Each offer includes an offered price and an offered quantity. The computer system receives market data reflecting the one or more offers and generates a display of the received market data. The display includes a price axis, a quantity axis, and one or more offer bars. Each offer bar represents one or more offers including equal offered prices. Placement of an offer bar along the price axis represents the equal offered prices for the one or more offers represented by the offer bar. The height of an offer bar relative to the quantity axis represents the aggregate offered quantity for the one or more offers represented by the offer bar.

Abstract: In one embodiment, determining order lead time for a supply chain includes generating probability distribution for expected order lead time options, where each probability distribution for expected order lead time option is associated with a category. A category that corresponds to a supply chain is identified. The supply chain has nodes, including a starting node and an ending node that supplies a customer, and designates a path from the starting node to the ending node. A probability distribution for expected order lead time option associated with the identified category is selected as a probability distribution for expected order lead time for the supply chain. The probability distribution for expected order lead time describes ending node demand for the ending node versus order lead time.

Abstract: Generating a price schedule involves generating a graph having paths that include states with values. The graph is generated by determining the values of a successor state from the values of a predecessor state. An optimal path is selected, and a price schedule is determined from the optimal path. Computing an elasticity curve involves having a demand model, values for demand model, and filter sets that restrict the values. Elasticity curves are determined by filtering the values using filter sets, and calculating the elasticity curve using the demand model. A best-fitting elasticity curve is selected. Adjusting a demand forecast value includes estimating an inventory and a demand at a number of locations. An expected number of unrealized sales at each location is calculated. An sales forecast value is determined according to the expected number.

Abstract: A system (10) for planning and scheduling service events for one or more assets includes a demand optimizer engine (12) that receives information regarding future service requirements for an asset and operating characteristics of the asset. The demand optimizer engine (12) also generates a service plan identifying one or more planned service events during which one or more services are to be performed on the asset and identifying a planned start time and a planned end time for each service event so as to minimize the amount of time the asset is non-operational and the costs associated with each service event. The system (10) also includes a supply optimizer engine (14) that receives the service plan from the demand optimizer engine (12) and generates a service schedule for one or more of the planned service events based on information regarding the flow of one or more parts used in conjunction with the services of each planned service event.

Abstract: A system for demand breakout for a supply chain includes a memory operable to store a plurality of orders for at least one product. Each product may be produced using at least one precursor. The memory is also operable to store a production schedule identifying one or more resources in the supply chain, a quantity of each product and precursor scheduled to be produced by the resources, and a time period associated with production of each product and precursor. The system also includes one or more processors collectively operable to identify one or more particular units of the product that correspond to each product order. The one or more processors are also collectively operable to identify one or more particular units of one or more precursors that correspond to each product order.

Abstract: A system and method for performing large scale supply chain planning is disclosed, which includes at least one database with a plurality of partitions. A large scale, supply chain planning problem is decomposed into a plurality of independent and isolated sub-problems. The data for each sub-problem is maintained in a separate database partition. The sub-problems are preferably solved by separate planning processes operating in parallel in a distributed database processing environment.

Abstract: Generating an optimized supplier allocation plan includes identifying parts and suppliers associated with an allocation problem, where each supplier can supply at least one part. One or more objective functions are selected. Each objective function has part variables, and each part variable represents a quantity of a part to be procured from a supplier. At least one constraint constraining at least one part variable is received. The one or more objective functions are optimized with respect to the at least one constraint to yield a value for each part variable. A quantity of each part to be procured from at least one supplier is determined according to the values to generate the optimized supplier allocation plan.

Abstract: Generating an order plan is disclosed. Data describing priority levels including at least one item request is accessed. Data describing a supply chain network including network components that supply one or more items to satisfy an item request is accessed. For each priority level, and for each item request of a priority level, an order for an item request of a current priority level is planned according to recorded unplannable network components that are unable to satisfy an item request, and the unplannable network components for the current priority level are determined. For each priority level, the unplannable network components are validated for the current priority level, and the validated unplannable network components for the current priority level are recorded. An order plan including the orders planned for the item requests at each priority level is provided.

Abstract: Redistributing parts includes defining locations. An actual inventory of parts is established among the locations, and a desired allocation of the parts is established among the locations. A demand for the parts at each location is determined using the actual inventory and the desired allocation. Paths are determined, where a path transfers an excess part from one location to another location. A transfer function describing a cost of transferring the excess part along the paths is generated. The transfer function is optimized to achieve the desired allocation of the excess parts at a minimum cost.

Abstract: Service parts inventory planning and management are provided for one or more entities in a supply chain (12), preferably within the context of an electronic marketplace (14). Status data reflecting substantially current service parts inventory levels at stocking locations within the supply chain (12) is accessed, these inventory levels indicating service parts excesses or needs at one or more stocking locations. A substantially current service parts inventory plan is generated according to the status data, a service parts demand forecast, and a supply chain model, the inventory plan including target stocking levels for service parts at stocking locations. According to the inventory plan, one or more services are initiated to attempt to resolve service parts excesses or needs at stocking locations through interaction with one or more other entities.

Abstract: In one embodiment, planning repairs in response to demand in a multi-level repair network includes accessing a forecasted demand for serviceable parts at a repair location and, for each inspected unserviceable part at the location that is not repairable at the location: (1) planning a move of the part to an upstream repair location such that the part can be available for repair at the upstream location at an estimated earliest time; (2) estimating a latest time at which a repair of the part can begin at the upstream location to help satisfy the forecasted demand, according to the forecasted demand and the estimated earliest time, and planning a repair at the upstream location at the estimated latest time; and (3) re-planning the move, according to the start time of the repair, by modifying a move delivery time according to a repair start time and modifying a move start time according to the modified delivery time.

Abstract: A system for generating a display of at least one side of a market includes a computer system having one or more computers. The displayed side of the market includes one or more offers from one or more market participants. Each offer includes an offered price and an offered quantity. The computer system receives market data reflecting the one or more offers and generates a display of the received market data. The display includes a price axis, a quantity axis, and one or more offer bars. Each offer bar represents one or more offers including equal offered prices. Placement of an offer bar along the price axis represents the equal offered prices for the one or more offers represented by the offer bar. The height of an offer bar relative to the quantity axis represents the aggregate offered quantity for the one or more offers represented by the offer bar.

Abstract: Optimizing inventory targets for nodes of a supply chain to satisfy a target customer service level may include accessing a supply chain model that has an assumed value for each of a number of inputs. An optimized inventory target is calculated according to the supply chain model to satisfy the target customer service level, and a measured actual customer service level and a measured actual value for each input are accessed. If the measured actual customer service level fails to satisfy the target customer service level, deviations between the measured actual and assumed values for each input are determined. An input for which the deviation is significant is identified to be a root cause of the failure. For a subsequent time period, using the deviation for the identified input as feedback, the assumed value for the identified input is adjusted, and a reoptimized inventory target is calculated to satisfy the target customer service level.

Abstract: In one embodiment, estimating demand for a supply chain includes accessing a probability distribution for expected order lead time of the supply chain. The supply chain has nodes including a starting node and an ending node and a path from the starting node to the ending node. The probability distribution for expected order lead time describes ending node demand for the ending node versus order lead time. The path is divided into order lead time segments, and the order lead time segments are associated with the probability distribution for expected order lead time by associating each order lead home segment with a corresponding order lead time range of the probability distribution for expected order lead time. A demand percentage is estimated for each order lead time segment in accordance with the probability distribution for expected order lead time in order to estimate demand for the supply chain.

Abstract: A system for displaying strikes between bids and asks in a market over time includes a computer system. The computer system receives data for a plurality of strikes reflecting values specified in the strikes for a plurality of strike variables. The computer system generates a display of the received data. The display includes multiple polygons each representing a strike between a bid and an ask. Each polygon includes multiple contiguous parallelograms each associated with one or more different strike variables. One or more dimensions of each parallelogram each reflecting a value specified in the represented strike for a strike variables associated with the parallelogram. The display includes a strike variable axis representing a range of values for an additional strike variable. Each polygon is positioned with respect to the strike variable axis according to the value specified in the represented strike for the additional strike variable.

Abstract: A computer-implemented system (10) for associating target data with a product classification schema includes a data association module (39) that accesses the product classification schema. The schema includes a taxonomy that includes a hierarchy of classes into which products may be categorized. The schema further includes ontologies that are associated with one or more of the classes. Each ontology includes one or more product attributes. The data association module (39) accesses the target data to be associated with the schema and determines one or more classes with which at least a portion of the target data should be associated. This determination is based on a comparison between the target data and the product attributes of the ontologies or between the target data and values for one or more of the product attributes.