Abstract:
A system and method for forecasting the monetary impact resulting from non-predictable events within an enterprise begins by determining one or more monetary impact contributors attributable to the non-predictable events. The monetary impact of the contributors at the occurrence of previous non-predictable events is determined. A modeling function most likely to correspond to the monetary impact of the contributors at the occurrence of the previous non-predictable events is selected and the scaling coefficients for each of the contributors are calculated. The modeling function is then verified and an error function developed by the verifying to a deviation limit is compared to a deviation limit. If the error function exceeds the deviation limit, other modeling functions are selected and tested until the error function does not exceed the deviation limit. Once the deviation limit is not exceeded, a future monetary impact of upon occurrence of the non-predictable event is forecast.

Description:
BACKGROUND OF THE INVENTION 
   1. Field of the Invention 
   This invention relates to systems and methods for determining the monetary impact of a non-predictable but recurring event. More particularly, this invention relates to systems and methods for determining a monetary cost due to a failure event such a loss of a power supply that causes fabrication facility such as a semiconductor fabrication line to cease operation. 
   2. Description of Related Art 
   U.S. Pat. No. 5,450,317 (Lu, et al.) provides a logistics planning method and system for recommending optimal order quantities and timing, choice of vendor locations and storage locations, and transportation modes, for individual items and for product families. The system is designed for use in cooperation with the computer having memory and incorporates item, customer, supplier, and routing information databases. In operation, the item, customer and supplier databases are accessed in order to provide customer and warehouse demand forecasts. The routing and customer databases are similarly accessed to provide transportation cost forecasts necessary to determine optimized routing modes for selected items, customers and suppliers. The demand and transportation costs are processed in accordance with a dynamic programming model to determine stock and non-stock order/shipment solutions for the selected items and customers, including optimized supplier and routing selection, order timing and quantity. 
   U.S. Pat. No. 5,467,265 (Yamada, et al.) teaches a system for determining an effective and practical operation method for thermal source equipments includes a fundamental plan data storage unit, a fundamental plan generating unit for determining a fundamental operation plan of each equipment while minimizing an operation cost by linear programming, an operation knowledge storage unit for storing operation knowledge such as equipment performance characteristics and operation know-how, a fundamental plan evaluating unit for evaluating the fundamental plan, a modifying rule storage unit for storing modifying rules used for modifying the evaluated fundamental plan, and a fundamental plan modifying unit for modifying the fundamental plan in accordance with the modifying rules. 
   U.S. Pat. No. 6,110,214 (Klimasauskas) describes an analyzer for modeling and optimizing maintenance operations. A first model or first analyzer having a series of filters is provided to represent time-varying effects of maintenance events. The first model or analyzer further enhances the selection of derived variables, which are used as inputs to the first analyzer. Additionally, a combination of fuzzy logic and statistical regression analyzers are provided to better model the equipment and the maintenance process. An optimizer with a bi-modal optimization process, which integrates discrete maintenance events with continuous process variables is also provided. The optimizer determines the time and the type of maintenance activities, which are to be executed, as well as the extent to which the maintenance activities can be postponed by changing other process variables. Thus, potential modifications to process variables are determined to improve the current performance of the processing equipment as it drifts out of tolerance. 
   SUMMARY OF THE INVENTION 
   An object of this invention is to provide method for forecasting a monetary impact resulting from non-predictable events within an enterprise. 
   To accomplish this and other objects, a method for forecasting the monetary impact resulting from non-predictable events within an enterprise begins by determining one or more monetary impact contributors attributable to the non-predictable events. The monetary impact of the contributors at the occurrence of previous non-predictable events is determined. A modeling function most likely to correspond to the monetary impact of the contributors at the occurrence of the previous non-predictable events is selected and the scaling coefficients for each of the contributors are calculated. 
   The modeling function is then verified and an error function developed by the verifying to a deviation limit is compared to a deviation limit. If the error function exceeds the deviation limit, other modeling functions are selected and tested until the error function does not exceed the deviation limit. Once the deviation limit is not exceeded, a future monetary impact of upon occurrence of the non-predictable event is forecast. 
   The monetary impact is a cost to the enterprise and the non-predictable event is a power outage resulting in cessation in operation of a fabrication facility within the enterprise. The cessation in operation of the fabrication facility results in the monetary impact from costs that include raw material loss and recovery costs. In the case of a semiconductor fabrication facility the raw material is electronic component substrates and the recovery costs are the costs of removal and repair of the electronic component fabricating equipment processing the substrates. 
   The modeling function may be either linear or nonlinear mathematical functions. The deviation limit is a measure of the adequacy or degree of fit of the modeling function for forecasting the monetary impact when compared to the actual monetary impact of the contributors at the occurrence of previous non-predictable events. 

   
     BRIEF DESCRIPTION OF THE DRAWINGS 
       FIG. 1  is a block diagram of a system for forecasting a monetary impact resulting from non-predictable events within an enterprise of this invention. 
       FIG. 2  is a flow diagram of the method for method for forecasting a monetary impact resulting from non-predictable events within an enterprise of this invention. 
       FIG. 3  is a diagram of the contents of the cost history database of  FIG. 1   
       FIG. 4  is diagram of the contents of the memory of  FIG. 1 . 
   

   DETAILED DESCRIPTION OF THE INVENTION 
   The process of forecasting is well known in the art and is used to predict a future outcome based on prior history. A forecast may be based on an “educated guess” of personnel closely involved with the day-to-day activities of the process being forecast. Alternately, the forecast maybe based on prior historic data of the process being forecast. There are a number of methods for performing the forecast, including a last value of the process used to predict the future value, an average of all past values, a moving average of certain number of past values of the process, exponential smoothing using known curve fitting routines to determine a function for the changes in the values of the process. 
   In general the forecasting problem as cited from  Introduction to Operation Research , Hillier and Lieberman, Holden-Day, Inc. San Francisco, Calif., 1980, pp. 534-539 is:
         “There exists a sequence of random variables X 1 , X 2 , . . . (a stochastic process) having expected values given by E(X 1 ), E(X 2 ), . . . . The distribution of each of these random variables may be the same, or they may be changing (e.g. shifting) according to some pattern. The random variables may be independent. Observations on these random variables X 1 , X 2 , . . . , X t  have been taken, and their values are denoted by x 1 , x 2 , . . . , x t . Based on these previous outcomes, E(X t ) is to be estimated; the estimate, which will be used as the forecast for subsequent periods will be denoted as Ê(X t ).”       

   It is common for the data of previous outcomes of the stochastic process to be stored in a computing system as a database of information describing the variables and the results of the process caused by the variables. There are various programming products such as spreadsheets like EXCEL from Microsoft Corporation, Redmond, Oreg., which are used to calculate the expected values for use to provide the future forecast. 
   In a semiconductor fabrication facility, a major excursion or outage of the main power supply system can cause severe damage to semiconductor substrates being processed to form integrated circuits. A major power excursion or outage of the semiconductor fabrication facility causes a severe impact to the profit and loss statement of the enterprise. Therefore it is desirable to be able to forecast with reasonable accuracy the future impact of the damage. 
   Generally, the main contributing factors during a major power excursion event are the costs of the semiconductor wafers or substrates and the removal and restoration costs to repair any equipment damaged during the excursion. The estimation of these costs previously was primarily manually determined. This required a long process time and had a high degree of inaccuracy. 
   The system and method of this invention provides a model that determines regressively the costs of a major event such as a power outage based on the prior costs of such events. Refer now to  FIG. 1  for a description of a forecasting system for estimating a monetary impact resulting from non-predictable events within an enterprise. The monetary impact being the costs incurred as a result of a major power excursion event (the non-predictable events) within a semiconductor fabrication facility (enterprise). 
   The forecasting system has a forecast execution unit  5 , which is used to identify and determine the significance of each contributory factor that impacts the profit or loss resulting from a particular event excursion. In the preferred embodiment of this invention, the event excursion is a power outage and the contributory factors are the cost factors resulting from the power outage. The previous cost history  12  from prior power outages is transferred to a cost history database  10 . The cost history database  10  is in communication with the cost factor significance analyzer  25 . The cost significance analyzer  25  receives an input of the potential cost factors  27 , provides a statistical analysis of the potential cost factors using the data of the cost history database  10 . 
   Refer to  FIG. 3  for a discussion of the structure of the cost history database  10 . The cost history database  10  details the date and time of occurrence of the event. The fabrication line within the facility identifies the location of the event. The lot identifier identifies the lot of the semiconductor wafers that were damaged during the event. The quantity of the loss is the number of wafers damaged and lost during the event. The unit cost of the material is the basic cost of the wafers and the unit cost of the recovery indicates the cost of removal and restoration of the equipment in preparation for restarting the fabrication line. The raw material cost is the unit cost of the wafer multiplied by the number of wafers and is the total cost for the raw material lost in the event. The recovery cost is the unit cost of the recovery multiplied by the number of wafers to determine the total recovery cost attributable to an event. The total loss is the sum of the raw material cost and the recovery cost. The raw material cost and the recovery cost being the two major contributing factors to the loss resulting from the power outage event. 
   The cost significance analyzer  25  is in communication with a memory  15 . Upon completion of the analysis of the potential cost factors and selection of the appropriate cost factors, the cost significance analyzer  25  transfers the most significant contributory cost factors to the memory  15 . The cost factor coefficient calculator  30  then retrieves the contributory cost factors from the memory  15  and determines a function of the contributory cost factors that describes best the predicted total cost. The cost factor coefficient calculator  30  can have external input to chose which function should have a best fit. The cost factor coefficient calculator  30  then determines the coefficient with the statistical deviation describing the quality of the fit. Alternately, the cost coefficient calculator  30  determines the function having the best fit based on statistical error functions. The cost coefficient calculator  30  transfers the cost factor coefficients and the deviation calculations to the memory  15 . 
   The structure of the contents of the memory  15  is shown in  FIG. 4 . For a system forecasting the monetary impact of a power outage event within a semiconductor fabrication facility, the memory contains the material cost coefficient, the recovery cost coefficient, and the statistical error values. The statistical error values describe the quality of fit for the coefficients. 
   The cost forecast calculator  35  extracts the cost factor coefficients from the memory  15  to calculate an event forecasted cost  40  of a future event. In the case of the semiconductor fabrication facility, the forecasted cost  40  is the total expected cost of a future power outage event. The cost forecast calculator  35  is in communication with unit cost database  20 . The unit cost database  20  contains the current unit costs of the contributing cost factors. These cost factors  22  are provided externally to the unit cost database  20 . 
   In addition to the future event forecast, the cost forecast calculator  35  retrieves the unit costs of the cost factors to determine a “predicted” cost for the previous events. The predicted cost is compared to the actual total cost and the difference or deviation is determined. The cost forecast calculator  35  places these predictions in the memory  15  for review by displays or systems in communication with the forecasting system of this invention. 
   Returning to  FIG. 4 , the memory  15 , as described above, has the history of the unit cost of material and cost of recovery for the prior power outage events as extracted by the cost factor coefficient calculator  30 . The total loss is also extracted from the cost history database  10  by the cost factor coefficient calculator  30  and placed in the memory  15 . The cost forecast calculator  35  forecasts the loss based on the raw material cost and the recovery costs and then calculates the deviation from the actual total loss. 
   While the above functions are described as separate entities and can be constructed as such, in reality the system as described would be a computing system having a magnetic or optical media containing the database information a memory as described, and a central processing unit which when programmed appropriately assumes the functions as described. 
   The structure of the method for forecasting the monetary impact of an event of this invention is shown in  FIG. 2 . The major contributing factors significantly determining the monetary impact are determined (Box  100 ). In the case of the semiconductor facility, the contributing factors are the costs of the wafers and the cost of recovery and restoration of the equipment. Thus the cost impact of each power outage excursion is the sum of the costs of the wafers destroyed and the costs for the recovery. The monetary impact of each of the contributory factors for all previous events is determined (Box  105 ). In this case the cost impact of the previous power outage excursions is recorded for evaluation. 
   The monetary impact for the previous events is examined and a suitable function describing these events is selected (Box  110 ). As described above, the previous event cost could be used for the prediction. Alternately, the absolute average or running average could be employed as a predictor for the forecast. In the alternative and most preferable, a smoothing function could be chosen to describe a mathematical equation describing the contributing factors that determine the final costs. The smoothing function could be a linear mathematical function or non-linear mathematical function and use known curve fitting algorithms to determine the function. In the case where the event is a power outage within a semiconductor fabrication facility, the cost factors are the cost of the wafers and the cost of the recovery and a simple linear least squares fit is generally adequate to forecast the impact of the power outage event. 
   The coefficients of the each of the contributing factors are determined (Box  115 ). Any appropriate curve fitting method can be selected to provide the appropriate coefficients with the measurement of the degree of fit. 
   The modeling function with the determined coefficients is executed (Box  120 ) using the data from the previous event occurrences. The calculated monetary impact as predicted by the modeling function is compared (Box  125 ) to the actual monetary impact. A statistical test such as a Students-t test or an F test is performed (Box  130 ) to determine a quality or level of deviation. An alternate could be just a simple average of the deviations of the previous monetary impacts versus the predicted monetary impact. In the case of the costs of a power outage for a semiconductor fabrication facility, the costs of previous power outages are compared to the predicted cost and the deviation determined. An average of the deviations is determined. 
   The results of the statistical test are compared (Box  135 ) to a deviation limit. If the deviation limit is exceeded, a different function model is selected  110  and validated for fit. However, if the deviation limit is not exceeded the future monetary impact is forecast  140  and published  145 . In the case of the power outage at the semiconductor fabrication facility, the deviation limit is based on the average of the deviations of the predicted costs versus the actual costs. The limit being determined from experience of the supervisory personnel. 
   It is well known in the art that while the above describes a method and system for forecasting a monetary impact resulting from non-predictable events within an enterprise, the method as described is, in fact, implemented as program code for execution on a computing system. The program code is retained in media such as storage nodes of the cluster network of computer systems or a global communication network such as the Internet, or stored on storage media such as a random access memory (RAM), a read only memory (ROM), an electro-optical disk or a magnetic disk. The program code executed by the computing system executes the procedure in the method of  FIG. 2   
   While this invention has been particularly shown and described with reference to the preferred embodiments thereof, it will be understood by those skilled in the art that various changes in form and details may be made without departing from the spirit and scope of the invention.