Abstract:
The present disclosure describes novel methods for estimating the predictability of demand for one or more products. The data may be organized into one or more hierarchies and may contain one or more attributes.

Description:
RELATED APPLICATIONS 
       [0001]    The instant application claims priority to and hereby incorporates by reference in its entirety co-pending U.S. Provisional Patent Application Ser. No. 61/043,332 entitled “Demand Curve Analyzer” filed on 8 Apr. 2008. Furthermore, the instant application is related to and hereby incorporates by reference in its entirety each of the following U.S. patent applications filed concurrently herewith: U.S. patent application Ser. No. ______ entitled “Demand Curve Analysis Method for Analyzing Demand Patterns” [PLA01 011] filed 7 Apr. 2009; and U.S. patent application Ser. No. ______ entitled “Demand Curve Analysis Method for Predicting Forecast Error” [PLA01 013] filed 7 Apr. 2009. 
     
    
     BACKGROUND 
       [0002]    Balancing supply and demand pressures is an increasingly important and difficult task for businesses to manage. Specifically, understanding demand curve behaviors is a critical task that must be closely monitored in order to effectively and efficiently run a business. Unfortunately, demand curve behaviors are dependent on a multitude of parameters any of which may change over different periods of time, such as weekly, monthly, seasonally, annually, etc. Such considerable differences in variability coupled with the large number of demand parameters results in a monumental challenge in predicting a product&#39;s future demand. Furthermore, some of the parameters may be dependent on one another, thereby adding further complexity to the problem. Consequently, accurate demand curve predictions over an appreciable time frame are extremely difficult to obtain. 
         [0003]    Business managers typically lack the necessary understanding of the intricacies of demand curve prediction, such as demand curve variations, the interdependency of demand curve parameters, the variations in parameters over time, the level of accuracy of historical date, etc. Furthermore, managers typically do not have access to the information to help increase their level of understanding or the necessary tools to increase the accuracy of their demand curve predictions. Historically, managers predicted demand curves by only taking into account the gross variations in one or two of the demand factors and/or used a “gut feel” to predict future demand. Not surprisingly, such predictions often do not match actual demand for anything other than the very short term and therefore result in inefficiencies and lost profits for the business. Additionally, prior art systems and methodologies used to assist managers in accurately predicting demand curves also lacked the necessary. 
         [0004]    Accordingly, there is a need for a system and method to increase the accuracy of demand curve predictions. The current disclosure is directed towards systems and methods to overcome the deficiencies in the prior art and to provide for various aspects of demand curve planning. In one aspect, the present disclosure describes novel systems and methods for analyzing demand patterns for one or more products based on time series data for the product(s) such as order history, shipment history, and point of sale history. The data may be organized into one or more hierarchies and may contain one or more attributes. A method for analyzing demand patterns may include gathering and preparing a time series of data and loading the data into a demand curve analysis (“DCA”) tool, setting a plurality of parameters to be used by the DCA tool, processing the time series of data with the DCA tool, and reviewing the output of the DCA tool. 
         [0005]    In another aspect, the present disclosure describes novel systems and methods of demand planning for one or more products. This may include gathering and/or preparing a time series of data for a predetermined time period, generating categorizations of the data, determining the lumpiness of demand, determining seasonal tendencies of demand, determining trend tendencies of demand, testing the hygiene of the data, and determining a forecast of demand based on one or more of the lumpiness, seasonal tendencies, and/or trend tendencies of demand. 
         [0006]    In yet another aspect, the present disclosure further describes other novel systems and method of demand planning including estimating the potential error reduction in a forecast of demand by determining forecast errors based on equivalent past time periods, determining an error threshold having an upper and lower confidence interval, calculating a potential forecast error reduction using a forecast of demand for the confidence intervals, and modifying the forecast with the estimated potential error reduction. 
         [0007]    In still another aspect, the present disclosure further describes other novel systems and methods of demand planning including determining forecast smoothing tendencies, determining forecast bias tendencies, and determining forecast value added measures. 
         [0008]    In yet still another aspect, the present disclosure describes novel systems and methods for estimating the predictability of demand for one or more products. This may include determining a coefficient of variation for a data series for a product and comparing the coefficient of variation to a scale that defines the predictability of demand for the product. 
         [0009]    In a further aspect, the present disclosure describes novel systems and methods for estimating potential forecast error. This may include determining actual forecast error, computing calculated forecast errors, determining a variance of the calculated forecast errors to establish a threshold, and comparing the actual forecast error with the threshold to estimate the potential forecast error. 
         [0010]    In yet a further aspect, the present disclosure describes novel systems and methods for estimating potential forecast error improvement. This may include determining multiple actual forecast errors, computing calculated forecast errors, determining a threshold with an upper confidence interval and a lower confidence interval, and comparing the actual forecast errors with the threshold to estimate the potential forecast error improvement. 
         [0011]    In still a further aspect, the present disclosure describes other novel systems and methods for estimating potential forecast error. This may include making a forecast for a predetermined time period, determining multiple actual forecast errors, calculating a mean squared error, determining an upper confidence interval and a lower confidence interval, and estimating the potential forecast error using the upper confidence interval and the lower confidence interval. 
         [0012]    In yet still a further aspect, the present disclosure describes further novel systems and methods for estimating potential forecast error. This may include determining an actual forecast error, making a forecast for a time period, estimating a potential forecast error for the time period, and comparing the actual forecast error with the potential forecast error. 
         [0013]    The above advantages, as well as many other advantages, of the present disclosure will be readily apparent to one skilled in the art to which the disclosure pertains from a perusal of the claims, the appended drawings, and the following detailed description. 
     
    
     
       BRIEF DESCRIPTION OF THE DRAWINGS 
         [0014]      FIG. 1  is a flow diagram showing relationships between various entities in a supply chain and types of historical data for a method of analyzing demand patterns according to an embodiment of the disclosure. 
           [0015]      FIG. 2  is a flow diagram showing expanded relationships between various entities in a supply chain and types of historical data for a method of analyzing demand patterns according to an embodiment of the disclosure. 
           [0016]      FIG. 3  is a flow diagram for a method of analyzing demand patterns according to an embodiment of the disclosure. 
           [0017]      FIG. 4  is a flow diagram for a method of analyzing demand patterns according to an embodiment of the disclosure. 
           [0018]      FIG. 5  is a more detailed flow diagram for a method of analyzing demand patterns according to an embodiment of the disclosure. 
           [0019]      FIG. 6  shows exemplary parameters for use in methods for analyzing demand patterns according to embodiments of the disclosure. 
           [0020]      FIG. 7  is a flow diagram for a method of estimating the predictability of demand according to an embodiment of the disclosure. 
           [0021]      FIG. 8  is a flow diagram for a method of demand planning according to an embodiment of the disclosure. 
           [0022]      FIG. 9  is a flow diagram for a further method of demand planning according to an embodiment of the disclosure. 
           [0023]      FIG. 10  shows exemplary plural characterizations and exemplary data hygiene testing combinations for use in methods for demand planning according to embodiments of the disclosure. 
           [0024]      FIG. 11  is a flow diagram for a method of estimating potential forecast error according to an embodiment of the disclosure. 
           [0025]      FIG. 12  is a flow diagram for a method of estimating potential forecast error according to an embodiment of the disclosure. 
           [0026]      FIG. 13  is a flow diagram for a method of estimating potential forecast error according to an embodiment of the disclosure. 
           [0027]      FIG. 14  is a flow diagram for another method of estimating potential forecast error according to an embodiment of the disclosure. 
           [0028]      FIG. 15  is a flow diagram for yet another method of estimating potential forecast error according to an embodiment of the disclosure. 
           [0029]      FIG. 16  is a four-quadrant graph illustrating a volume and variability profile for a product according to an embodiment of the disclosure. 
       
    
    
     DETAILED DESCRIPTION 
       [0030]    The current disclosure is directed towards systems and methods to overcome the deficiencies in the prior art and to provide for various aspects of demand curve planning as described herein with reference to the various Figures. Those of skill in the art will readily understand that the present disclosure is not necessarily limited to any actual examples stated herein but will encompass foreseeable variations and equivalents to those examples within the teaching of the spirit of the disclosure. 
         [0031]    With attention directed towards  FIG. 1 , a flow diagram  100  is shown which indicates relationships between various entities in a supply chain and types of historical data for a method of analyzing demand patterns according to an embodiment of the disclosure. In this simplified version of a supply chain, a supplier  110  may supply one or more products to a client  120 . The client  120  may ship the product(s) to a customer  130  typically in response to orders received by the client  120  from the customer  130 . The customer  130  may then ship/transfer the product(s) to a market  140  for further disposition/sale. It shall be readily understood by those of skill in the art that this simplified version of a supply chain may include one or more suppliers  110 , customers  130  and markets  140 . Additionally, there may be additional suppliers upstream of the supplier  110  (e.g., an entity that supplies the product(s) to the supplier  110 ) as well as additional markets downstream of the market  140  (e.g., the market  140  may be a distributor who supplies the product(s) to further markets downstream). For the purposes of the present disclosure, the supply chain depicted in  FIG. 1 , and variations thereof, may be applicable to gathering historical data for analyzing demand patterns for the product(s) of interest. The historical data may be for sales activities associated with the client  120  and/or sales activities associated with a competitor of the client  120 . In an embodiment, the historical data from a competitor may be used, for example, to determine how the competitor&#39;s sales affect the client&#39;s sales. 
         [0032]    One of skill in the art may readily understand that the simplified supply chain of  FIG. 1  may represent the whole or any portion of an actual supply chain from which historical data may be gathered and analyzed, such as in a Demand Curve Analysis (“DCA”) tool. The DCA tool may comprise software, hardware, firmware, a microprocessor, or other similar devices or appliances for analyzing demand patterns and that references herein to “DCA tool” includes such similar software, hardware, firmware, microprocessors, devices, and/or appliances. 
         [0033]      FIG. 1  further includes hierarchies  150  which may be used by the client  120  to distinguish a product for which a demand pattern is being analyzed. The hierarchies  150  may include, but are not limited to, the exemplary hierarchies depicted such as channel of sale  151 , product  152 , and geography  153 . The channel of sale hierarchy  151  may include, but is not limited to, direct sales to customers, such as the customer  130 , retail sales, sales to distributors, and other sales channels. The product hierarchy  152  may include various products for which a demand pattern is to be analyzed. For example, the exemplary product hierarchy  152  may include at a first categorical level for the product(s) of interest, such as “drugs”, as shown, which may be broken down into sub-categories such as “pain killers” which, in turn, may be further broken down into sub-sub-categories such as “brand 1”, “brand 2”, etc. The geographic hierarchy  153  may include various levels of geographic regions such as “Midwest” which may be broken down into cities/towns such as “Chicago” and further broken down into specific depots, for example, such as “warehouse 1” and “warehouse 2” as shown. Those of skill in the art will readily understand that the present disclosure is in no way limited to the exemplary hierarchies and sub-hierarchies listed above but rather is applicable to a wide range of sales channels, products, geographies, and other hierarchies that would be useful in assessing demand patterns. 
         [0034]    The product hierarchy may additionally have associated therewith a number of attributes  160  which may be useful in analyzing a demand pattern for the product(s) of interest. For example, the attributes  160  may include, but are not limited to, information regarding whether one or more product(s) are branded or unbranded, packaged or unpackaged, displayed in an endcap display or on a regular shelf display, whether the product(s) are to be sold as part of a special sale (e.g., Labor Day Sale, President&#39;s Day Sale, etc.) or simply regularly sold, whether there is a particular promotion or advertisement associated with the product(s) or not, a size or type of package in which the product(s) are sold, a location in the store in which the product(s) are to be sold, etc. As will be readily understood by one of skill in the art, the above exemplary attributes are not limiting and not all of the above attributes may be used in conjunction with a particular product. Other attributes may be used with specific products that may not be applicable with other products. Furthermore, attributes may be used with any of the hierarchies  150  and are not limited to the product hierarchy  152 . The attributes chosen may be based solely on the availability and/or quality of historical data associated with those attributes for the hierarchies of interest in a demand pattern analysis as discussed herein. 
         [0035]    The historical data to be analyzed may be collected and evaluated in one or more “time buckets”, i.e., durations of time. For example, the time bucket for the historical data may be based on any convenient time duration, such as daily, weekly, monthly, quarterly, semi-annually, annually, etc. The choice of the size of the time bucket may be dictated by the type and extent of historical data available for the product(s) of interest. Furthermore, the size of the time bucket chosen for the demand pattern analysis may affect the results of the analysis. In one non-limiting embodiment, historical data used for analyzing a demand pattern using a DCA tool may require two or more years of data in order to be able to ascertain historical demand pattern trends. In a further non-limiting embodiment, a demand pattern analysis using a DCA tool may be limited to a total of ten attributes for each of three hierarchies. 
         [0036]    With reference now directed toward  FIG. 2 , a flow diagram  200  is shown depicting expanded relationships between various entities in a supply chain and types of historical data for a method of analyzing demand patterns according to an embodiment of the disclosure. In this more detailed version of a supply chain, a supplier  210  may supply one or more products to a client  220 . The client  220  may ship the product(s) to multiple customers, such as customer  1  ( 230 ), customer  2  ( 231 ), and customer N ( 232 ) typically in response to orders received by the client  220  from the customers  230 ,  231 , and  232 .). Each of the customers  230 ,  231 , and  232  may then ship/transfer the product(s) to one or more markets. As shown in the exemplary supply chain in  FIG. 2 , the customer  230  may ship/transfer the product(s) to market  1  ( 240 ), market  2  ( 241 ), and market  3  ( 242 ). Similarly, the customer  231  may ship/transfer the product(s) to market  4  ( 243 ) which the customer  232  may ship/transfer the product(s) to market  5  ( 244 ) and other markets up to and including market N ( 245 ). It shall be readily understood by those of skill in the art that this more detailed version of a supply chain may further include one or more suppliers  210 . Additionally, there may be additional suppliers upstream of the supplier  210  (e.g., an entity that supplies the product(s) to the supplier  210 ) as well as additional markets downstream of the markets  240 - 245  (e.g., the market  240  may be a distributor who supplies the product(s) to further markets downstream). For the purposes of the present disclosure, the supply chain depicted in  FIG. 2 , and variations thereof, may be applicable to gathering historical data for analyzing demand patterns for the product(s) of interest. Therefore, one of skill in the art may readily understand that the simplified supply chain of  FIG. 2  may represent the whole or any portion of an actual supply chain from which historical data may be gathered and analyzed, such as in a Demand Curve Analysis (“DCA”) tool. The DCA tool may comprise software, hardware, firmware, a microprocessor, or other similar devices or appliances for analyzing demand patterns. 
         [0037]      FIG. 2  also includes hierarchies  250  and attributes  260  which are similar to hierarchies  150  and attributes  160 , respectively, as discussed above with respect to  FIG. 1 . That discussion is incorporate herein with respect to  FIG. 2 . 
         [0038]      FIG. 3  depicts a flow diagram  300  for a method of analyzing demand patterns according to an embodiment of the disclosure. The method may be performed on a DCA tool, as discussed above. At block  310  time series data useful for analyzing demand patterns of one or more products may be gathered and/or prepared for analysis. At block  320  the time series data may be loaded into a DCA tool or other similar software program, hardware device, or similar appliance capable of performing the necessary analysis of the time series data. At block  330  one or more parameters to be used by the DCA tool may be set. These parameters will be discussed in further detail below with respect to  FIG. 6 . At block  340  the time series data may be processed using the DCA tool. 
         [0039]    Furthermore, in another embodiment of the disclosure the time series input data may include sales history time series data for a competitor of the entity for which a demand pattern is being determined/analyzed. For instance, the historical (e.g., time series) data in block  310  may be for sales activities associated with the client  120  in  FIG. 1 , as discussed above. In addition to the client&#39;s historical data, historical data for a competitor in block  312  may also be used in the method for analyzing the client&#39;s demand patterns. 
         [0040]      FIG. 4  depicts a flow diagram  400  for a method of analyzing demand patterns according to an embodiment of the disclosure. Blocks  410 ,  420 ,  430 ,  440 , and  450  are similar to blocks  310 ,  320 ,  330 ,  340 , and  350 , respectively, as discussed above with respect to  FIG. 3 . At block  451  the output of the DCA tool may be reviewed and/or fine tuned. As a non-limiting example, at block  452  the output of the DCA tool may be fine-tuned to account for inaccurate and/or incomplete time series input data to thereby normalize any anomalies in the DCA tool output that cannot be supported by statistical data based on the time series input data. 
         [0041]    With attention now directed towards  FIG. 5 , a more detailed flow diagram  500  is shown for a method of analyzing demand patterns according to an embodiment of the disclosure. In this flow diagram, blocks  510 ,  520 ,  530 ,  540 , and  550  are similar to blocks  310 ,  320 ,  330 ,  340 , and  350 , respectively, as discussed above with respect to  FIG. 3 . At block  543  the processing performed using the DCA tool at block  540  may include running an ABCD algorithm. At block  544  the ABCD algorithm may be used to produce a quadrant graph on the time series data based on variability and volume in the format shown in  FIG. 16 . The details of the quadrant graph will be discussed further below. At block  545 , the processing performed at block  540  may include deriving a Lorenz curve from the time series data. At block  511 , the time series data that may be gathered and prepared in block  510  may include data selected from a particular time bucket, such as daily data, weekly data, biweekly data, monthly data, bimonthly data, quarterly data, semiannual data, annual data, or any other convenient time duration. At block  512  the time series data that may be gathered and prepared in block  510  may include sales history time series data for at least one product. In an embodiment, the sales history time series may include data from at least a twenty-four month period. At block  513 , the sales history time series data may include one or more of order history, shipment history, and point of sale history data. 
         [0042]    At block  517  the sales history time series data may include data for one or more hierarchies, where the hierarchies may be a type of sales channel, a type of product, or a geographic area. At block  518 , the data for the hierarchies in block  517  may include data for one or more attributes, as discussed above with respect to block  160  in  FIG. 1 . The attributes  518  may include, but are not limited to, information regarding whether one or more product(s) are branded or unbranded, packaged or unpackaged, displayed in an endcap display or on a regular shelf display, whether the product(s) are to be sold as part of a special sale or simply regularly sold, whether there is a particular promotion or advertisement associated with the product(s) or not, a size or type of package in which the product(s) are sold, a location in the store in which the product(s) are to be sold, etc. As will be readily understood by one of skill in the art, the above exemplary attributes are not limiting and not all of the above attributes may be used in conjunction with a particular product. Other attributes may be used with specific products that may not be applicable with other products. Furthermore, attributes may be used with any of the hierarchies  517  and are not limited to the product hierarchy. In a particular embodiment, the number of hierarchies may equal three and the total number of attributes may equal ten. 
         [0043]    With reference still directed towards  FIG. 5 , the sales history time series data of block  512  may include at least one of a statistical forecast time series and a consensus forecast time series, as those time series are known in the art. In particular embodiments, either one or both of the statistical forecast time series and the consensus forecast time series may include data from at least a twelve month period, preferably from the most recent twelve month period. 
         [0044]    Now considering  FIG. 6 , exemplary parameters  600  are shown which may be used for methods for analyzing demand patterns according to embodiments of the disclosure. In particular, at block  530  of  FIG. 5 , certain parameters may be set in the DCA tool for the analysis of demand patterns. These parameters may be selected by an operator of the DCA tool and may include one of more of the following exemplary parameters: lumpy demand, seasonality, seasonality weighting, seasonality index, seasonality upper limit, seasonality lower limit, high seasonality upper limit, high seasonality lower limit, Seasonality Autocorrelation factor, High Seasonality Autocorrelation factor, quadrant volume, quadrant variability, high trend differential, low trend differential, RDD (Rapid Declining Demand)/RAD (Rapid Accelerating Demand) percent change, outliers, maximum confidence expectation, minimum confidence expectation, consensus forecast smoothing, and alpha smoothing, bias percent, inclusion/exclusion of consensus forecast time series, and inclusion/exclusion of statistical forecast time series. One of skill in the art will readily understand that other parameters applicable to demand pattern analysis may be set in the DCA tool. 
         [0045]      FIG. 7  is a flow diagram for a method of estimating the predictability of demand for at least one product according to an embodiment of the disclosure. At block  710  a determination may be made for a coefficient of variation for a data series for a predetermined product or products. The coefficient of variation may be defined by at least one of a standard of deviation of the data series for the product(s) and an average of the data series for the product(s). At block  720 , a comparison may be made between the coefficient of variation determined in block  710  and a predetermined scale where the scale may be useful in defining the predictability of demand for the predetermined product(s). Thus, the coefficient of variation can then be measured to thereby estimate the predictability of demand for the predetermined product(s). 
         [0046]    Taking into account  FIG. 8 , flow diagram  800  represents a method of demand planning for at least one product according to an embodiment of the disclosure. In an embodiment, the demand planning may take into account one or more time series data sets for a product or products which may be categorized based on volume and/or variability combinations of the time series data. Additionally, the demand planning may include determinations of lumpiness of demand, seasonal tendencies of demand, and trend tendencies of demand. The data used for the demand planning may be tested for hygiene, as will be discussed further with respect to  FIG. 10 . At block  805  time series data for at least one product for a predetermined period of time may be gathered and/or prepared. The time series data may include upstream data (e.g., data from the supplier  110  to the client  120  in  FIG. 1 ) for the product(s). Furthermore, the time series data may include at least one of historical data, statistical forecast data, and consensus data from one or more past time periods, which may be referred to hereinafter, individually or collectively, as historical data. The time periods may be equivalent. At block  810  one or more characterizations of the historical data may be generated. The characterizations may be based on volume and variability combinations of the historical data and are discussed further below with respect to  FIG. 10 . At block  815  a lumpiness of demand may be determined for the product(s). The lumpiness of demand may be determined based on the data from the one or more past time periods and/or may also take into account the one or more characterizations of the historical data. At block  820  seasonal tendencies of demand may be determined based on the data from the one or more past time periods and/or may also take into account the one or more characterizations of the historical data. At block  825  trend tendencies of demand may be determined based on the data from the one or more past time periods and/or may also take into account the one or more characterizations of the historical data. At block  830 , the historical data may be undergo hygiene testing. At block  835  a forecast of demand may be determined that is based on at least one of the following determinations: lumpiness of demand, seasonal tendencies of demand, and trend tendencies of demand. 
         [0047]    Moreover, another embodiment for demand planning according to the disclosure may include, at block  850 , estimating a potential error reduction in the forecast of demand from block  835 . The estimation of a potential error reduction may include: at block  855 , determining one or more forecast errors which may correspond to one or more of the equivalent past time periods; at block  860 , determining an error threshold for the historical data where the error threshold has an upper confidence interval and a lower confidence interval; and, at block  865 , calculating a potential forecast error reduction for at least one of the determined forecast errors using the forecast of demand and one or both of the upper and lower confidence intervals. At block  870  the forecast of demand may be modified by the calculated forecast error reduction. In another embodiment, the determining of seasonal tendencies of demand may include evaluating the historical data using an auto-correlation function, which may be set to be equal to 0.3. 
         [0048]    Looking now towards  FIG. 9 , flow diagram  900  represents a further method of demand planning according to an embodiment of the disclosure. Blocks  905 ,  910 ,  915 ,  920 ,  925 ,  930 , and  935  are similar to blocks  805 ,  810 ,  815 ,  820 ,  825 ,  830 , and  835  respectively, as discussed above with respect to  FIG. 8 . At block  926  forecast smoothing tendencies for the product(s) may be determined using at least one of historical data, statistical forecast data, and consensus forecast data from the past time periods, where the past time periods may be equivalent. At block  927  forecast bias tendencies for the product(s) may be determined using at least one of historical data, statistical forecast data, and consensus forecast data from the past time periods, where the past time periods may be equivalent. At block  928  forecast value added measures for the product(s) may be determined using at least one of historical data, statistical forecast data, and consensus forecast data from the past time periods, where the past time periods may be equivalent. At block  935  a forecast of demand may be determined that is based on at least one of the following determinations: lumpiness of demand, seasonal tendencies of demand, and trend tendencies of demand, taking into account one or more of the forecast smoothing tendencies, forecast bias tendencies, and forecast value added measures. 
         [0049]      FIG. 10  shows exemplary plural characterizations  1010  and exemplary data hygiene testing combinations  1020  for use in methods for demand planning according to embodiments of the disclosure. The one or more characterizations that may be generated in block  810  in  FIG. 8  may be based on volume and variability combinations of historical data. At block  1010 , these characterizations may include high volume/high variability, low volume/low variability, low volume/high variability, and high volume/low variability, high volume/lumpy demand, low volume/lumpy demand, low volume/none lumpy demand, high volume/none lumpy demand, and outliers. These characterizations may be visualized graphically in the quad graph depicted in  FIG. 16  where the abscissa is a measure of product volume, such as, for example, the current year volume, and the ordinate is a measure of the coefficient of variation of the product which may be measured in, for example, a percentage. For example, Class A products would be those products that have a high volume and a low coefficient of variation. Class B products would be those products that have a high volume and a high coefficient of variation. Class C products would be those products that have a low volume and a low coefficient of variation. Class D products would be those products that have a low volume and a high coefficient of variation. 
         [0050]    With attention now back to  FIG. 10 , testing the hygiene of the historical data that may be performed in block  830  in  FIG. 8  may include, at block  1020 , identifying one or more combinations such as, but not limited to, active combinations, new combinations, obsolete combinations, zero instances, invalid combinations, combinations where there is misalignment between said historical data and a forecast for said past time period associated with said historical data, and combinations thereof. 
         [0051]    In  FIG. 11  a flow diagram  1100  is shown representing a method of estimating potential forecast error according to an embodiment of the disclosure. At block  1110  an actual forecast error may be determined based on historical data for one or more products. At block  1120  one or more forecast errors for the product(s) may be computed based on the results of one or more error forecasting algorithms. At block  1130  a variance of the forecast errors may be determined to thereby establish a threshold value. At block  1140  the actual forecast error may be compared to the threshold value to thereby estimate a potential forecast error for the product(s). 
         [0052]      FIG. 12  shows a flow diagram  1200  for a method of estimating potential forecast error according to an embodiment of the disclosure. Blocks  1210 ,  1220 ,  1230 , and  1240  are similar to blocks  1110 ,  1120 ,  1130 , and  1140  respectively, as discussed above with respect to  FIG. 11 . At block  1211 , the historical data used for determining an actual forecast error in block  1210  may be collected and evaluated in one or more “time buckets”, i.e., durations of time, as discussed above. For example, the time bucket for the historical data may be based on any convenient time duration, such as daily, weekly, biweekly, monthly, bimonthly, quarterly, semi-annually, annually, etc. At block  1221  the error forecasting algorithms used in block  1220  for computing forecast errors may include a mean average deviation, a root square mean error, or a combination of the two. Those of skill in the art will readily understand that other error forecasting algorithms are contemplated herein. 
         [0053]    With respect to  FIG. 13 , a flow diagram  1300  is shown for a method of estimating potential forecast error according to an embodiment of the disclosure. At block  1310  one or more actual forecast errors may be determined based on at least one of historical data for one or more products, statistical forecast time series data, and consensus forecast time series data. At block  1320  one or more calculated forecast errors may be computed for the product(s) using one or more error forecasting algorithms. At block  1330  a threshold may be determined for the historical data where the threshold has an upper confidence interval and a lower confidence interval. At block  1340  the one or more actual forecast errors may be compared with the threshold to thereby estimate a potential forecast error improvement for the product(s). At block  1311 , the historical data used for determining the one or more actual forecast errors in block  1310  may be collected and evaluated in one or more “time buckets”, i.e., durations of time, as discussed above. For example, the time bucket for the historical data may be based on any convenient time duration, such as daily, weekly, biweekly, monthly, bimonthly, quarterly, semi-annually, annually, etc. At block  1321  the error forecasting algorithms used in block  1320  for computing forecast errors may include a mean average deviation, a root square mean error, or a combination of the two. Those of skill in the art will readily understand that other error forecasting algorithms are contemplated herein. 
         [0054]    Considering  FIG. 14 , is a flow diagram  1400  is shown representing another method of estimating potential forecast error according to an embodiment of the disclosure. At block  1410  a forecast may be made for a predetermined time period using historical data for one or more products for past time periods, where the past time periods may be equivalent. At block  1420  one or more forecast errors may be determined for the product(s) and the forecast errors may each correspond to one of the past time periods. At block  1430  a mean square error may be calculated using the one or more forecast errors. At block  1440  an upper confidence interval and a lower confidence interval may be determined using the mean square error. At block  1450  a potential forecast error may be estimated for the product(s) based on the forecast determined in block  1410  and the upper and lower confidence intervals determined in block  1440 . 
         [0055]    With attention now directed towards  FIG. 15 , a flow diagram  1500  is shown for yet another method of estimating potential forecast error according to an embodiment of the disclosure. At block  1510  an actual forecast error may be determined based on historical data for one or more products. At block  1520  a forecast may be made for a predetermined time period using the historical data for the product(s) from past time periods, where the past time periods may be equivalent. At block  1530  a potential forecast error may be estimated using the forecast determined in block  1520 . At block  1540  the actual forecast error determined in block  1510  may be compared with the potential forecast error estimated in block  1530 . 
         [0056]    While preferred embodiments of the present disclosure have been described, it is to be understood that the embodiments described are illustrative only and that the scope of the invention is to be defined solely by the appended claims when accorded a full range of equivalents, many variations and modifications naturally occurring to those of skill in the art from a perusal hereof.