System and Method of Segmented Modelling for Campaign Planning in a Very Large-Scale Supply Chain Network
A system and method are disclosed including a planner having a processor and memory. The planner models a supply chain network over a planning horizon having one or more time buckets and a production line to produce one or more products using one or more campaign operations and one or more campaignable resources. The planner further formulates a supply chain master planning problem comprising a hierarchy of objective functions and one or more constraints and segments a campaign planning problem into three stages. The planner solves a first stage to determine prioritized production demands on campaignable buffers, solves a second stage to determine a timing and a sequence for allocating campaignable resources to campaignable buffers, and solves a third stage to determine a quantity of products to produce on campaignable resources.
1 . A system for performing planning for a campaign, comprising:
a server comprising a processor and memory, the server configured to perform in the memory, without exporting intermediate output to a database:
revert a static structure of a supply chain model back to an original state;
revert a bucket structure to daily, weekly and monthly buckets;
formulate a supply chain problem and solve a linear programming optimization planning objective;
perform lotsizing without campaign planning;
generate a supply chain plan from the solved linear programming optimization planning objective; and
combine the generated supply chain plan with a campaign plan to generate an optimized supply chain plan.
2 . The system of claim 1 , wherein a campaignable resource of the campaign comprises tire manufacturing equipment required for curing tires.
3 . The system of claim 2 , wherein the tire manufacturing equipment may produce one tire SKU at a time and further requires a change over time from a first tire SKU to a second tire SKU.
4 . The system of claim 1 , wherein the campaign plan determines an order and length of time each of one or more campaignable operations should use one or more campaignable resources.
5 . The system of claim 1 , wherein one or more campaign-specific objective levels are removed and replaced with one or more non-campaign objective levels.
6 . The system of claim 1 , wherein the planning for the campaign begins with a solved campaign plan.
7 . The system of claim 1 , wherein one or more business objectives of the campaign are prioritized and modeled as a hierarchy of objective functions.
8 . A method for performing planning for a campaign, comprising:
configuring a server to perform the planning in memory without exporting intermediate output to a database, wherein the server comprises a processor and the memory;
reverting, by the server, a static structure of a supply chain model back to an original state;
reverting, by the server, a bucket structure to daily, weekly and monthly buckets;
formulating, by the server, a supply chain problem and solve a linear programming optimization planning objective;
performing, by the server, lotsizing without campaign planning;
generating, by the server, a supply chain plan from the solved linear programming optimization planning objective; and
combining, by the server, the generated supply chain plan with a campaign plan to generate an optimized supply chain plan.
9 . The method of claim 8 , wherein a campaignable resource of the campaign comprises tire manufacturing equipment required for curing tires.
10 . The method of claim 9 , wherein the tire manufacturing equipment may produce one tire SKU at a time and further requires a change over time from a first tire SKU to a second tire SKU.
11 . The method of claim 8 , wherein the campaign plan determines an order and length of time each of one or more campaignable operations should use one or more campaignable resources.
12 . The method of claim 8 , wherein one or more campaign-specific objective levels are removed and replaced with one or more non-campaign objective levels.
13 . The method of claim 8 , wherein the planning for the campaign begins with a solved campaign plan.
14 . The method of claim 8 , wherein one or more business objectives of the campaign are prioritized and modeled as a hierarchy of objective functions.
15 . A non-transitory computer-readable medium embodied with software for performing planning for a campaign, the software when executed configured to:
configure a server to perform the planning in memory without exporting intermediate output to a database, wherein the server comprises a processor and the memory;
revert a static structure of a supply chain model back to an original state;
revert a bucket structure to daily, weekly and monthly buckets;
formulate a supply chain problem and solve a linear programming optimization planning objective;
perform lotsizing without campaign planning;
generate a supply chain plan from the solved linear programming optimization planning objective; and
combine the generated supply chain plan with a campaign plan to generate an optimized supply chain plan.
16 . The non-transitory computer-readable medium of claim 15 , wherein a campaignable resource of the campaign comprises tire manufacturing equipment required for curing tires.
17 . The non-transitory computer-readable medium of claim 16 , wherein the tire manufacturing equipment may produce one tire SKU at a time and further requires a change over time from a first tire SKU to a second tire SKU.
18 . The non-transitory computer-readable medium of claim 15 , wherein the campaign plan determines an order and length of time each of one or more campaignable operations should use one or more campaignable resources.
19 . The non-transitory computer-readable medium of claim 15 , wherein one or more campaign-specific objective levels are removed and replaced with one or more non-campaign objective levels.
20 . The non-transitory computer-readable medium of claim 15 , wherein the planning for the campaign begins with a solved campaign plan.