Data processing: financial – business practice – management – or co – Automated electrical financial or business practice or... – Health care management
Reexamination Certificate
1999-10-05
2004-01-27
Hafiz, Tariq R. (Department: 3623)
Data processing: financial, business practice, management, or co
Automated electrical financial or business practice or...
Health care management
C705S007380
Reexamination Certificate
active
06684193
ABSTRACT:
FIELD OF THE INVENTION
The present invention relates generally to a method and apparatus for the multivariate allocation of resources. In particular, the present invention provides a method and apparatus for modeling objects, such as customers and suppliers, and thereafter presents a method for solving a resulting multivariate expected value function as a closed form expression.
BACKGROUND OF THE INVENTION
According to microeconomic theory, a recurring problem of large scale manufacturing processes is the allocation of valuable resources to meet uncertain consumer demand over a large number of products. In the most general scenario, certain resources are shared among many products. As a result, depletion of any one resource by a product demanding a high amount of that resource will preclude the manufacture of all other products requiring that same resource for manufacture.
A simple solution to this problem would be to maintain a large inventory of all relevant resources. This, however, is not an effective solution because resource inventory accrues a cost to the company. Some fast moving, or volatile inventory materials might decrease in value at an exponential rate. Certain types of memory components, for example, are known to depreciate at a rate of approximately one percent per week. If significant inventories are maintained for a long period of time, then such components will lose most their value before being used. Sometimes such components can even become valueless. This adds unnecessary costs to the manufacture of the product, and ultimately the price offered to the consumer. If such costs cannot be passed onto the consumer, as is typical in competitive markets, then such costs will come directly out of a company's profits.
A converse solution would be to maintain low inventories, and then procure the parts from the suppliers on an as-needed basis. This is not an effective solution because procuring scarce parts on a short-term basis often carries added costs, or “penalty costs.” For instance, parts that are ordered during the normal course of business carry a certain cost. Parts that are required on an expedited basis are often priced at higher levels. These costs are usually ratcheted upwards (on a lock-step basis, or otherwise) as the demand for product increases. Hence, if a significant number of parts are needed to complete the manufacture of a series of products, then a significant premium will have to be paid to the suppliers in order to procure sufficient parts. As a worst case, such scarce parts might not be available at any price. If the parts cannot be procured, then the end products cannot be manufactured. This will obviously result in lost sales. Significant lost sales can even lead to overall lost market share and reduced customer loyalty.
Accordingly, the general solution to such problems involves finding the allocation of components (or resources) that maximizes value (i.e. profits, or revenues minus costs) across the set of products (or refinements) to be manufactured. More importantly, the solution must take into account the “horizontal” interaction effects among products, as well as the “vertical” consumption effects between products and components.
Simple prior art solutions to allocation problems include Manufacturing Requirements Primer (MRP) models. The basic principle behind an MRP model is to formulate a “recipe” pertaining to the manufacture of a product, i.e. one microprocessor, two memory modules, and one storage device might be used to make up an end product. An MRP model performs a count of such components and tallies them up across the number (and type) of desired end products. Thereafter the MRP system schedules the allocation and delivery of such components at the factory so that the manufactured products come out on time, and in the proper order. However, such MRP models and solutions do not adequately account for the interactive effects among products and components. Moreover problematic, MRP models and solutions typically assume fixed, known demands on products.
Other prior solutions have been proposed which partially address the horizontal interaction effects and the vertical consumption effects, with the result being an expected value function which must be solved for a given value. The expected value function is generally the expectation of a linear, or polynomial, or exponential function over a multivariate normal (or other type) distribution. The more interactions that occur between the various components of a model, the higher the order of the expected value function. For any model involving a plurality of interactions, the form of this expression usually becomes a very complicated multivariate integral. To solve this function over a plurality of variables, prior solutions must employ significant computer resources. Often the best approach in solving such integrals involves applying a “Monte Carlo” technique, which in the end serves as only an approximation of a result. Monte Carlo techniques also takes massive amounts of computer processing power (i.e. a supercomputer) to solve, and cannot generally be solved in a reasonable period of time.
Given that the solution to such allocation problems often carries significant financial ramifications for a company, it is important to produce a solution which is more than just an estimate. Moreover, an expression is needed which can be solved in a reasonable amount of time, and without super-computer resources. Hence, a modeling technique is needed that will properly account for the horizontal and vertical interactions between certain modeled elements. A solution technique is thereafter needed which will present a closed form expression of the resulting function, wherein it will not be necessary to solve multiple integrals in order to determine a solution. This closed formed expression should also be executable on ordinary computer resources, and in a reasonable period of time, despite the multivariate nature of the problem.
SUMMARY OF THE INVENTION
To achieve the foregoing, and in accordance with the purpose of the present invention, a method and apparatus are disclosed that provides an efficient solution for the multivariate allocation of resources.
The theory and solution generalizes to any model of resource consumption, in relation to producing a “refinement.” The term “refinement,” as used through this document, is generally intended to represent an end result (i.e. product) which might be produced from a set of resources (i.e. components, or the like). Therefore, a typical refinement-resource framework might involve product-component models, wherein certain components are used to comprise certain products. Resources might also include available liquid capital for investment, bonds, stocks, and options. The present system might also be used to consider assets, a portfolio of assets, or consumption of those assets, such as energy (e.g. gas, nuclear, electric), space, real estate, etc. Another example problem includes the allocation of manpower. For instance, in association with manpower problems, a set of resources (i.e. employees) exists that might be used by many different end sources (i.e. work tasks, or jobs). Sharing of such manpower will involve a complex mix of parameters. In order to maximize productivity (or revenue), the assets (or resources) will need to be analyzed, in relation to production (or refinement) goals.
As yet another example, a company might entertain a portfolio of development projects. Each project requires the allocation of capital, work force, and new equipment. Certain factors remain uncertain, including project growth as based upon the success of the venture, market indicators of the interest in the product, uncertain market pressures and demand, and the synergization and cannibalization offered by competing projects. The company desires to know how to best allocate its resources over the various projects in order to maximize revenues in the face of the aforementioned uncertainties.
According to one aspect of the present invention, relevant models a
Chavez Thomas A.
Dagum Paul
Campbell III Samuel G.
Campbell Stephenson Ascolese LLP
Doren Beth Van
Hafiz Tariq R.
Rapt Technologies Corporation
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