Methods and systems for employing dynamic risk-based scheduling to optimize and integrate production with a supply chain

• US 7,941,236 B2
• Filed: 07/06/2007
• Issued: 05/10/2011
• Est. Priority Date: 07/07/2006
• Status: Active Grant

• Current Assignee: Strategic Project Solutions, Inc.
• Original Assignee: Factory Physics Incorporated
• Inventors: Spearman, Mark L.
• Primary Examiner(s): Decady; Albert
• Assistant Examiner(s): Lin; Jason
• Application Number:
  US11/822,564
• Publication Number:
  US 20080015721A1
• Time in Patent Office: 1,404 Days
• Field of Search: 700/95, 700/102, 700/103, 700/99, 705/7, 705/8, 705/28
• US Class Current: 700/95
• CPC Class Codes: G06Q 10/06 Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
• CPC Class Codes: G06Q 10/087 Inventory or stock management, e.g. order filling, procurement or balancing against orders
• CPC Class Codes: G06Q 30/0202 Market predictions or forecasting for commercial activities

Abstract

A production and inventory control for a manufacturing facility is provided that facilitates and coordinates improved planning and execution of such facility in a supply chain with a focus on providing an improved and robust planning, production and inventory control, even in the presence of uncertainty. This may include Optimal Planning that can balance the need for low inventory, low cost (i.e., high utilization of equipment and labor), and efficient on-time delivery. The result of such planning is not a schedule per se but a set of parameters that form a dynamic policy that generates an evolving schedule as conditions (demand, production) materialize. An Optimal Execution applies the dynamic policy resulting in a manufacturing system that is robust enough to accommodate moderate changes in demand and/or capacity without the need to reschedule. Optimal Execution may also involve a “Capacity Trigger” that detects when the assumptions regarding demand and capacity used to determine the dynamic policy are no longer valid. The Capacity Trigger also may provide a Trigger Signal to the planner indicating the need for either more or less capacity.

Claims

1. A system for applying a dynamic policy to a production control system, said system comprising:

a dynamic risk-based scheduling (DRS) computer based system interoperably communicable with at least one of an enterprise resources planning (ERP) system, a manufacturing execution system (MES), and a generic manufacturing database, wherein the DRS system includes;

a computer based optimal planning module that generates a dynamic policy comprising optimized parameters including optimized re-order quantities for balancing inventory levels, utilization of equipment and labor, and on-time delivery, and the optimized parameters used for generating an evolving schedule as changes in demand and production capacity arise; and

a computer based execution module that applies the dynamic policy to accommodate changes in demand or capacity without rescheduling.

2. The system of claim 1, wherein the DRS system is interoperably communicable with both the ERP system and the MES system and wherein:

the optimal planning module receives demand, capacity and cost data from the ERP system and returns optimal parameters to the MES system including at least any one of;

a re-order point (ROP), a re-order quantity (ROQ), a planned lead time (PLT), and a CONWIP level to the ERP system.

3. The system of claim 2, wherein the ERP system releases information related to at least one shop order based on the optimal parameters.

4. The system of claim 2, wherein the MES system provides work-in-process (WIP) location information to the execution module.

5. The system of claim 1, wherein the execution module provides a release signal based at least in part on the optimal parameters.

6. The system of claim 1, wherein the execution module provides a trigger signal based at least in part of the optimal parameters to indicate one of an overcapacity and an under capacity.

7. The system of claim 1, wherein the optimized parameters include any of:

a re-order point (ROP), a re-order quantity (ROQ), planned lead time (PLT) for one or more items, and a CONWIP level for one or more flows.

8. The system of claim 1, wherein the DRS determines a probability of shortage for the demand associated with each of a plurality of orders.

9. The system of claim 1, wherein the dynamic risk-based scheduling (DRS) system interoperably communicates with the at least one of an enterprise resources planning (ERP) system, a manufacturing execution system (MES), and a generic manufacturing data base.

10. The system of claim 1, wherein the dynamic policy includes a set of optimized ERP parameters, including any of:

a re-order point (ROP), a re-order quantity (ROQ), planned lead time (PLT) for one or more items, and a CONWIP level for one or more flows, that operate as a time-phased reorder point system to generate both make-to-order and make-to-stock shop orders thereby pulled into production according to the CONWIP protocol.

11. The system of claim 1 wherein the dynamic policy can accommodate changes in demand and/or capacity without rescheduling by employing the CONWIP protocol which results in both an ability to increase production automatically in response to increasing demand by increasing the rate of release of shop orders, and an ability to decrease production automatically in response to decrease demand and/or decrease in capacity by decreasing the rate of release of shop orders.

12. The system of claim 7, wherein a plurality of optimized lot size (ROQ) values is determined by considering a plurality of available capacities and for each available capacity computing a LaGrange multiplier for each of a plurality of resources such that the product of the LaGrange multiplier and the associated slack values is or is near zero.

13. The system of claim 12, wherein a plurality of optimized reorder point (ROP) values is determined using the determined ROQ values that result in a desired customer service level.

14. The system of claim 7, wherein the optimized CONWIP level is determined by employing a stochastic queuing model to estimate the throughput and cycle time for a plurality of WIP levels resulting in two plots:

throughput versus WIP and cycle time versus WIP.

15. The system of claim 12, wherein the optimized ROQ is further determined by applying the resulting ROQ values to a stochastic queuing and inventory model to evaluate an objective function that includes performance measures such as out-of-pocket costs, raw material, WIP, and finished goods inventory and choosing the set of ROQ values that minimize the objective function.