Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Operations research or analysis
Reexamination Certificate
1998-05-21
2001-10-23
Hafiz, Tariq R. (Department: 2163)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Operations research or analysis
C705S002000, C706S013000, C706S016000, C706S019000
Reexamination Certificate
active
06308162
ABSTRACT:
BACKGROUND OF THE INVENTION
1. Field of the Invention
The present invention relates to enterprise planning models, and more particularly, to controlling the optimization of a retail demand model through the application of one or more strategic constraints.
2. Description of Related Art
As technology continues to penetrate into all aspects of the economy, a wealth of data describing each of the millions of interactions that occur every minute is being collected and stored in on-line transaction processing (OLTP) databases, data warehouses, and other data repositories. This information, combined with quantitative research into the behavior of the value chain, allows analysts to develop enterprise models, which can predict how important quantities such as cost, sales, and gross margin will change when certain decisions, corresponding to inputs of the model, are made. These models go beyond simple rules-based approaches, such as those embodied in expert systems, and have the capability of generating a whole range of decisions that would not otherwise be obvious to a designer of rules.
There is however a problem with the use of model-based decision-making tools. As the decision-making process is automated, the operational decisions that are recommended by the model may begin to deviate from broader considerations that are not specifically built into the enterprise planning model. The reason for this is that an economic model can realistically only succeed on either a small scale or large scale, but not both. Incorporating both small scale decisions and large scale decisions in a single enterprise planning model would result in a model of enormous complexity, making the optimization of the enterprise planning model computationally impractical, and economically inefficient.
The importance of this problem can be illustrated with an example from the retail industry. A retailer can use a demand model to accurately forecast each item's unit sales given the item's price and other factors. However, if the demand model is used directly to optimize pricing decisions, it will generate prices that vary greatly from those of a human pricing manager. This is because a demand model has no knowledge of the enterprise's strategic objectives, and therefore generates prices that do not reflect the company's overall pricing policy. This inability to align and optimize an enterprise's operational decisions with its strategic objectives is a huge problem, and results in a billion-dollar inefficiency in the retailing industry alone.
Thus, it would be desirable to exploit the power of enterprise planning models that work well on a small scale, while providing control on a larger scale.
SUMMARY OF THE INVENTION
The present invention provides a computer-implemented method and system for controlling the optimization of an enterprise planning model while simultaneously satisfying at least one strategic constraint not taken into account in the enterprise planning model. In a preferred embodiment, a user is presented with a menu on a display device. Using an input device, the user first selects a primary goal to be realized—e.g., maximize gross profits. The primary goal is represented by a primary objective function which is dependent upon a set of operational variables. Each of the operational variables represents a single operational decision that the user seeks to optimize in order to reach the primary goal. Next, the user selects an auxiliary goal that the user would also like to be realized. The auxiliary goal is represented by a constraint function that is dependent upon a subset of the set of operational variables.
Next, an effective objective function is constructed by combining the primary objective function with the constraint function multiplied by a weighting factor. The resulting effective objective function depends on the same set of operational variables. The effective objective function is then optimized with respect to each of the operational variables, with the enterprise data providing physical constraints on the optimization. As a result of the optimization, optimal values for each of the operational variable is obtained. The optimal values of the operational variables represent a set of operational decisions that should achieve the primary goal and auxiliary goal.
The effective objective function can be optimized through a range of values of the weighting factor, with the results stored in a table. This computed table essentially provides a relationship between different optimized values of the primary goal, the auxiliary goal, and the values for the operational variables. The user can then be provided with a way to specify a target value for the auxiliary goal to attain, and then use the table to interpolate the value for the weighting factor that corresponds to the target value. This interpolated value for the weighting factor is then inserted into the effective objective function. The effective objective function is optimized, yielding the set of operational decisions which optimize the primary objective fuinction while at the same time satisfying the constraint that the auxiliary goal achieve the target value.
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Chaubal Charu V.
Ouimet Kenneth J.
Hafiz Tariq R.
Jeanty Romain
KhiMetrics, Inc.
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