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

• US 20080015721A1
• Filed: 07/06/2007
• Published: 01/17/2008
• Est. Priority Date: 07/07/2006
• Status: Active Grant

• Current Assignee: Strategic Project Solutions, Inc.
• Original Assignee: Mark Spearman
• Inventors: Spearman, Mark
• Granted Patent: US 7,941,236 B2
• Time in Patent Office: Days
• US Class Current: 700/99
• 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Trigger” that detects when the assumptions regarding demand and capacity used to determine the dynamic policy are no longer valid.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 computer-implemented method for tracking production in a given product flow against demand associated with a plurality of orders in the flow, said method comprising the steps of:

determining a probability of shortage for the demand associated with at least one of the plurality of orders;

determining expected inventory-days for at least one of the plurality of orders in the flow; and

generating output showing the probability of shortage associated with at least one of the plurality of orders and the expected inventory-days for the at least one of the plurality of orders.

2. The computer-implemented method of claim 1, further comprising the step of determining total expected inventory-days for a set of orders in the flow.

3. The computer-implemented method of claim 1, wherein the step of generating output includes displaying a graph showing the overall probability of shortage versus a specified parameter.

4. The computer-implemented method of claim 1, wherein the step of generating output includes displaying a graph showing the total expected inventory-days versus a specified parameter.

5. The computer-implemented method of claim 1, further comprising the step of determining a probability distribution of finish times for all orders in the flow.

6. The computer-implemented method of claim 1, further comprising the steps of determining a probability distribution of associated lateness for each order in the flow.

7. The computer-implemented method of claim 1, further comprising computing an initial re-order quantity (ROQ) for each part in the flow.

8. The computer-implemented method of claim 7, wherein the initial re-order quantity (Q_i) is determined using the relationship of:

$Q_i=\sqrt{\frac{2<br><br>\text{\hspace{1em}<br><br>}<br><br>D_i\left(V_i+\underset{k}{\sum <br><br>}<br><br>{\mathrm{\lambda <br><br>}}_k<br><br>{\mathrm{\alpha <br><br>}}_{k,i}\right)}{h_i<br><br>c_i}}$

where

D_idemand for part i

V_i=OOP cost for part i

λ

_k=LaGrange multiplier for resource k

${\mathrm{\alpha <br><br>}}_{k,i}=\underset{j}{\sum <br><br>}<br><br>f_{i,j}<br><br>S_j<br><br>{\mathrm{\delta <br><br>}}_{k,j}$

f_i,j=part i demand fraction visiting operation j

S_j=setup time for operation j

δ

_k,j=1if k=j and zero, otherwise;

and where the λ

_k values are adjusted so that the products of λ

_k values and the slack time at each constraint are all zero and so that each λ

_k value is greater than or equal to zero.

9. The computer-implemented method of claim 7, further comprising the step of improving upon the initial re-order quantity using global optimization.

11. The computer-implemented method of claim 9, wherein the step for altering capacity includes at least one of:

adding or reducing overtime, adding or reducing hours, adding or reducing additional people.

12. The computer-implemented method of claim 9, wherein the step of altering alters a cost basis.

13. The computer-implemented method of claim 9, wherein the overall probability of shortage is associated with a specified inventory item.

15. The system of claim 13, further comprising:

means for determining overall probability of shortage for a set of associated demands; and

means for determining total expected inventory-days for a set of orders in the flow.

16. The system of claim 13, further comprising:

means for determining expected inventory-days for each of the plurality of orders in the flow; and

means for displaying a graph showing the total expected inventory-days versus a specified parameter.

10. A computer-implemented method to signal insufficient or overabundance of capacity for a given flow operating under a given demand, said method comprising the steps of:

determining the total expected inventory-days and overall probability of shortage for a specified trigger;

detecting when insufficient capacity is present and generating a first electronic signal based on the specified trigger;

detecting when an overabundance of capacity is present and generating a second electronic signal based on the specified trigger; and

altering capacity in a production process according to the first or second electronic signal.

14. A system for tracking production in a given product flow against demand associated with a plurality of orders in the flow, said system comprising:

means for determining a probability distribution of finish times for all orders in the flow;

means for determining a probability distribution of associated demand time for at least one of the plurality of orders in the flow;

means for determining the probability of shortage for the at least one associated demand based at least in part on both probability distributions; and

means for displaying a graph showing the overall probability of shortage for the at least one associated demand versus a specified parameter.

17. A computer program product comprising a computer usable medium having readable program code embodied in the medium, the computer program product including at least one component to cause or perform execution of the following steps:

determining a probability of shortage for the demand associated with each of the plurality of orders;

determining expected inventory-days for each of the plurality of orders in the flow; and

generating output showing the probability of shortage associated with at least one of the plurality of orders and the expected inventory-days for the at least one of the plurality of orders.

18. The computer program product of claim 17, wherein the at least one component further causes or performs execution of determining total expected inventory-days for a set of orders in the flow.

19. The computer program product of claim 17, wherein the at least one component further causes or performs execution of generating output that includes displaying a graph showing the overall probability of shortage versus a specified parameter.

20. The computer program product of claim 17, wherein the at least one component further causes or performs execution of generating output includes displaying a graph showing the total expected inventory-days versus a specified parameter.

21. The computer program product of claim 17, wherein the at least one component further causes or performs execution of determining a probability distribution of finish times for all orders in the flow.

22. The computer program product of claim 17, wherein the at least one component further causes or performs execution of determining a probability distribution of associated demand time for each order in the flow.

23. The computer program product of claim 17, wherein the at least one component further causes or performs execution of computing an initial re-order quantity (ROQ) for reach part in the flow.

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

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

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

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

25. The system of claim 24, 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 MES system and returns optimal parameters of capacity including at least any one of a re-order point (ROP), a re-order quantity (ROQ), and a planned lead time (PLT) to the ERP system.

26. The system of claim 25, wherein the ERP system releases information related to at least one shop order based on the optimal parameters.

27. The system of claim 25, wherein the MES system provides work-in-process (WIP) location information to the execution module.

28. The system of claim 24, wherein the execution module provides a release signal based at least in part on the optimal parameters.

29. The system of claim 24, 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.

30. The system of claim 24, wherein the parameters include any of:

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

31. The system of claim 24, wherein the DRS determines a probability of shortage for the demand associated with each of a plurality of orders.

32. The system of claim 24, 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.