An apparatus and method are provided for an interface enabling a user to determine optimum prices of products for sale. The interface includes a scenario/results processor that enables the user to prescribe an optimization scenario, and that presents the optimum prices to the user. The optimum prices are determined by execution of the optimization scenario, where the optimum prices are determined based upon estimated product demand and calculated activity based costs. The scenario/results processor has an input/output processor and a scenario controller. The input/output processor acquires data corresponding to the optimization scenario from the user, and distributes optimization results to the user. The scenario controller is coupled to the input/output processor. The scenario controller controls acquisition of the data and the distribution of the optimization results in accordance with a price optimization procedure.

Interface for merchandise price optimization

The demand aggregation system includes buyer sponsored DealRooms, seller sponsored DealRooms, and multiple buyer and multiple seller sponsored DealRooms. Moreover, the demand aggregation system includes a customer relations management (CRM) package. In the CRM package, information on buyer and prospective buyers are loaded into a database that can include information such as: individual name; company name and address; email address; phone number; cell number; products purchased; volumes; time of purchase. Further the CRM package can be integrated with the demand aggregation system as well as a customer resource planning system (e.g., MRP, ERP).

Demand aggregation system

System and method for asynchronous distributed optimization of an enterprise. The system includes a based optimization system (402) operates in conjunction with an enterprise or process (404) to optimize the process (404) to optimize the process. The computer system (402) executes software programs which receive process data from the process (404).

System and method for real-time enterprise optimization

A system and method for price and promotion response analysis is provided. Such a system is useful for a business to analyze the forecasted lifts associated with changes in price and promotion activity. The system sets the configuration of the response report, which includes price change and promotion change intervals. Promotions include temporary price reductions, displays, ads and multiples. Modeling data is received for the products. Forecasts, both non-cannibalistic and cannibalistic, are generated for the sales of the products dependent upon the price change and promotion change intervals. Forecasts include at least one of product forecasts, demand group forecasts, line level forecasts and category level forecasts. Suspect forecasts below a minimum confidence may be flagged. Confidence matrices may be generated which reflect accuracy of the forecasts. The response report may be generated by collecting the forecasts and the confidence matrices according to the configuration of the response report.

Systems and methods for price and promotion response analysis

An apparatus and method are provided for determining an optimum promotion plan for merchandising of products for sale. The apparatus includes a scenario/results processor (233) than enables a user to prescribe an optimization scenario, and that presents the optimum promotion plan to the user, where the optimum promotion plan is determined by execution of the optimization scenario, and where the optimum promotion plan is determined based upon estimated product demand and calculated activity based costs. The scenario/results processor has an input/output processor (404) and a scenario controller (412). The input/output processor acquires data corresponding to the optimization scenario from the user, and distributes optimization results to the user. The scenario controller is coupled to the input/output processor. The scenario controller controls the acquisition of the data and the distribution of the optimization results in accordance with a promotion plan optimization procedure.

Interface for merchandise promotion optimization

A computer-based system (10) for aggregating and scheduling product batches (50) includes a batch aggregation engine (20) that allocates one or more product demands (40) to one or more product batches (50) having suggested sizes and suggested starting times. The batch aggregation engine (20) communicates to a scheduling engine (30) the suggested batch sizes, the suggested starting times, and feedback (26) relating to the suggested sizes and suggested starting times to assist the scheduling engine (30) in scheduling a start time for each product batch (50). The system (10) may also include a scheduling engine (30) coupled to the batch aggregation engine (20) that receives the suggested sizes, suggested starting times, and feedback (26) from the batch aggregation engine (20). The scheduling engine (30) schedules a starting time for one or more of the batches (50) according to the suggested sizes, suggested starting times, and feedback (26) received from the batch aggregation engine (20). The scheduling engine (30) also communicates the scheduled starting times and feedback (36) relating to the scheduled starting times to the batch aggregation engine (20) to assist the batch aggregation engine (20) in allocating the demands (40) to the batches (50). The batch aggregation engine (20) and the scheduling engine (30) may communicate their respective outputs (22, 32) to each other in an iterative cycle until they have collaboratively reached a sufficiently optimal batch aggregation and scheduling solution or until a predetermined number of iterations has been reached.

System and method for collaborative batch aggregation and scheduling

In one embodiment, a method is provided for collaboratively solving an optimization problem using at least first optimization software and second optimization software each having at least partial information concerning the optimization problem. The method includes: (1) determining a solution to a first sub-problem of the optimization problem using the first optimization software based on the at least partial information concerning the optimization problem known to the first optimization software; (2) communicating from the first optimization software to the second optimization software the solution to the first sub-problem and information concerning one or more penalties for deviating from the solution to the first sub-problem; and (3) determining a solution to a second sub-problem of the optimization problem using the second optimization software based on the at least partial information concerning the optimization problem known to the second optimization software, the communicated solution to the first sub-problem, and the communicated information concerning one or more penalties for deviating from the solution to the first sub-problem.

Collaboratively solving an optimization problem using first and second optimization software each having at least partial information concerning the optimization problem

In one aspect, a computer-implemented method is provided for aggregating and scheduling product batches in a manufacturing environment. Using a batch aggregation engine implementing a mathematical programming strategy, one or more product demands are allocated to one or more product batches having suggested sizes and suggested starting times. The mathematical programming strategy includes evaluating a number of time-based penalties relative to one another in allocating the demands to the batches, the time-based penalties being based on relationships between suggested starting times for batches and times of demands being considered for allocation to batches. The suggested sizes, the suggested starting times, and feedback relating to the suggested sizes and suggested starting times are communicated from the batch aggregation engine to a scheduling engine to assist the scheduling engine in scheduling starting times for the batches.

Joachim Paul Walser

Papers

System and method for collaborative batch aggregation and scheduling

Collaboratively solving an optimization problem using first and second optimization software each having at least partial information concerning the optimization problem

Collaborative batch aggregation and scheduling in a manufacturing environment