Automatic cost generator for use with an automated supply chain optimizer
An automatic cost generation apparatus is provided for automatically converting user supplied definitions/requirements into cost parameters for use by a cost-based supply chain optimizer. In one example, this is achieved by generating a linear programming model that incorporates the requirements/definitions as a set of linear constraints. The linear programming model is automatically solved so as to yield a cost model, from which costs are extracted for use by the cost-based optimizer. By first formulating requirements/definitions as linear constraints within a linear programming model, the solution to the model therefore yields a cost model that satisfies the constraints, i.e. a cost model that satisfies the requirements. Hence, requirements/definitions initially provided by the user are thereby automatically converted into a cost model that incorporates costs that can be used during supply chain optimization. The user can therefore use the cost-based optimizer without first having to try to determine the various costs that correspond to his or her business requirements, which can be difficult.
1 . A method for use with an automated supply chain optimizer that optimizes a supply chain based on costs, the method automatically converting non-cost-based definitions into costs for use by the optimizer, the method comprising:
providing a set of non-cost-based definitions;
generating a linear programming model that incorporates the definitions as a set of linear constraints;
solving the linear programming model to yield a cost model; and
extracting costs from the cost model for use with the optimizer.
2 . The method of claim 1 , wherein the linear programming model is configured to include one or more of: production cost variables (LO), transportation cost variables (LT), procurement cost variables (LP), safety stock penalty variables (LC), storage cost variables (LS), late delivery cost variables (LL), non-delivery cost variables (NLP), maximum location-product supply chain cost (LN) variables, maximum slack supply chain cost (LNS) variables and minimum location-product supply chain cost (LM) variables.
3 . The method of claim 2 , wherein solving the linear programming model to yield a cost model includes:
solving the linear programming model by maximizing a sum over all minimum location-product supply chain cost (LM) variables subject to the set of linear constraints to provide an initial set of values for all cost variables;
modifying the linear programming model by setting storage cost (LS) variables based on the initial set of values;
solving the linear programming model by minimizing a sum over all location-product supply chain cost (LN) variables plus a sum over all maximum slack supply chain cost (LNS) variables having a high coefficient subject to the set of linear constraints to provide a set of values of all cost variables representative of the cost model to be used with the optimizer.
4 . The method of claim 2 , wherein extracting costs from the cost model for use with the optimizer includes extracting costs from the cost variables of the cost model.
5 . The method of claim 2 , further including determining late delivery and non-delivery costs based on demand priorities and location-product priorities.
6 . The method of claim 2 , wherein the set of constraints further include one or more of: production or transportation costs are generated only in response to a predetermined demand; and non-delivery penalties are high enough to trigger production if there is a demand.
7 . The method of claim 1 , further comprising:
providing cost master data;
determining if an automatic generation of the cost model is requested or if else the cost master data are to be used by the optimizer; and
ignoring the cost master data and generating the cost model immediately before calling the optimizer if the automatic generation of the cost model is requested.
8 . The method of claim 1 , further comprising, controlling a bandwidth between the highest cost and the lowest cost in the cost model when generating the cost model.
9 . The method of claim 8 , wherein controlling the bandwidth comprises:
maintaining that bandwidth such that a bandwidth threshold is not exceeded.
10 . The method claim 1 , wherein providing a set of non-cost-based definitions comprises:
inputting business requirements including one or more of: demand priorities; safety stock priorities; product priorities; production priorities; transport priorities; and product values.
11 . The method of claim 1 , wherein extracting costs from the cost model comprises:
extracting one of more of: non-delivery penalty costs; late delivery penalty costs; safety stock penalties; storage costs; production costs; product-specific transport costs; and procurement costs.
12 . A machine-accessible medium containing instructions that when executed cause a machine to:
provide a set of non-cost-based definitions;
generate a linear programming model that incorporates the definitions as a set of linear constraints;
solve the linear programming model to yield a cost model; and
extract costs from the cost model for use with the optimizer.
13 . The machine-accessible medium of claim 7 , further comprising instructions causing the machine to:
provide cost master data;
determine if an automatic generation of the cost model is requested or if else the cost master data are to be used by the optimizer; and
ignore the cost master data and generating the cost model immediately before calling the optimizer if the automatic generation of the cost model is requested.
14 . An automatic cost generation apparatus for use with a supply chain optimizer that optimizes a supply chain based on costs, the apparatus automatically converting non-cost-based definitions into costs for use by the optimizer, the apparatus comprising:
a definitions unit operative to provide a set of non-cost-based definitions;
a model generation unit operative to generate a linear programming model that incorporates the definitions as a set of linear constraints;
a linear programming model solution unit operative to solve the linear programming model to yield a cost model; and
a cost extraction unit operative to extract costs from the cost model for use with the optimizer.