Method to provide common support for multiple types of solvers for matching assets with demand in microelectronics manufacturing

• US 6,041,267 A
• Filed: 09/26/1997
• Issued: 03/21/2000
• Est. Priority Date: 09/26/1997
• Status: Expired due to Fees

• Current Assignee: International Business Machines Corporation
• Original Assignee: International Business Machines Corporation
• Inventors: Dangat, Geetaram S., Orzell, Robert A., Milne, Robert J.
• Primary Examiner(s): Moise, Emmanuel L.
• Assistant Examiner(s): ABRAHAM, ESAW T
• Application Number:
  US08/938,753
• Time in Patent Office: 907 Days
• Field of Search: 364/400, 364/468.01, 364/468.402, 364/463.13, 395/701, 395/919, 700/95, 700/107, 700/97, 700/96, 700/105, 700/106
• US Class Current: 700/107
• CPC Class Codes: G05B 19/41865 characterised by job scheduling, process planning, material flow
• CPC Class Codes: Y02P 90/02 Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]

Abstract

A computer implemented decision support tool serves as a vehicle to enable a user to execute within a common work environment, from common production information files, and at the discretion of the user one of three types of matching between existing assets and demands across multiple manufacturing facilities within boundaries established by manufacturing specifications and process flows and business policies. The tool provides an environment which permits the user to easily gain the advantages of a synergistic relationship between the three types of matching. The tool directly supports three types of matching: (1) material requirements planning (MRP) type of matching, (2) best can do (BCD) type of matching, and (3) projected supply planning (PSP) type of matching.

Claims

1. A computer implemented decision support method which matches assets with demands to determine which demands can be met in what time frame, identifies production activities necessary to meet the demand, and creates a projected supply plan to generate different outputs for use in generating a production plan, comprising the steps of:

establishing a first comprehensive detailed data base to represent and store production specification information that includes manufacturing specifications, asset status, and business guidelines;

establishing a second comprehensive detailed data base to represent and store matching solutions including all required manufacturing activities, their quantity, and their start date, wherein the solutions are produced by iteratively selecting and running any of a set of solvers;

capturing and storing core production specification information from various sources and storing them in said first comprehensive detailed data base in a single format and level of detail;

specifying initial production specification information from said first comprehensive detailed data base;

iteratively selecting and running one or more of said set of solvers based upon examination of previous iteration results, said set of solvers commonly accessing the initially specified or a newly specified production specification information from said first comprehensive detailed data base, said set of solvers including;

Advanced Material Requirements Planning (AMRP) that uses a series of integrated Linear Programming and MRP methods to explode demand backwards through a bill of material and assets across multiple manufacturing facilities, Best Can Do (BCD) that matches demand against manufacturing capability across a bill of material supply chain and indicates when end item customer demand is satisfied, and Projected Supply Planning (PSP) that executes a forward flush of starts and work in progress and compares projected supply with demand;

generating different outputs based on said iteratively selecting and running step for each solver; and

determining a production plan based on one or more of the different outputs generated by said iteratively selecting and running step, wherein the different outputs of said set of solvers and said production plan are stored in common data areas of said second comprehensive detailed data base in a common format that is accessible by other solvers and other applications.

2. The computer implemented decision support method recited in claim 1 wherein the step of selectively running solvers comprises the step of selecting the Advanced Material Requirements Planning (AMRP) solver to explode demands into a build plan for purchasing and manufacturing orders.

3. The computer implemented decision support method recited in claim 1 wherein the step of selectively running solvers comprises the step of selecting the Best Can Do (BCD) solver to match assets with demand.

4. The computer implemented decision support method recited in claim 1 wherein the step of selectively running solvers comprises the step of selecting the Projected Supply Planning (PSP) solver to implode existing work in progress and specified starts into a projected work and supply plan.

5. The computer implemented decision support method recited in claim 1 wherein the step of selectively running solvers comprises the steps of:

selecting the Advanced Material Requirements Planning (AMRP) solver to explode demands into a build plan for purchasing and manufacturing orders; and

selecting the Best Can Do (BCD) solver to match assets with demand.

6. The computer implemented decision support method recited in claim 1 wherein the step of selectively running solvers comprises the steps of:

selecting the Best Can Do (BCD) solver to match assets with demand; and

selecting the Projected Supply Planning (PSP) solver to implode existing work in progress and specified starts into a projected work and supply plan.

7. The computer implemented decision support method recited in claim 1 wherein the step of selectively running solvers comprises the steps of:

selecting the Advanced Material Requirements Planning (AMRP) solver to explode demands into a build plan for purchasing and manufacturing orders; and

selecting the Best Can Do (BCD) solver to match assets with demand; and

selecting the Projected Supply Planning (PSP) solver to implode existing work in progress and specified starts into a projected work and supply plan.

8. The computer implemented decision support method recited in claim 1 wherein the step of selectively running solvers permits a user to mix and match a sequence of solver runs to address a business question of concern.

9. The computer implemented decision support method recited in claim 1 wherein the decision support method is used in a first decision tier, strategic scheduling, a second decision tier, tactical scheduling, and a third decision tier, operational scheduling, and wherein the identical set of solvers is used to support first, second and third decision tier processes.

10. The computer implemented decision support method recited in claim 1 wherein the various sources of core production specification information are from a plurality of facilities using different data bases and wherein the step of capturing and storing core production specification information from the various sources comprises the step of converting production specification information from each of several data bases to an independent data base of common format.

11. The computer implemented decision support method recited in claim 10 wherein the step of selectively running solvers permits a user to mix and match a sequence of solver runs to address a business question of concern which includes all said plurality of facilities.

12. The computer implemented decision support method recited in claim 10 wherein the independent data base common format accommodates differing levels of granularity as appropriate to support each of the first, second and third decision support tiers.