Sample Store Forecasting Process And System
A method and system of predicting market information includes the steps of receiving first data, forecasting further data based on the first data, receiving second data and comparing the further data with the second data, and creating an adjustment factor to account for any difference between the further data and the second data.
1 - 4 . (canceled)
5 . A method for forecasting un-reported prescription/product transactions or transactions that are not timely reported in a subject time interval to a sample store or outlet in a universe of product stores, the universe of product stores comprising sample stores and non-sample stores in market channels such as retail, mail order, and long term care, the sample stores generally reporting prescription transaction data to a history database, the method comprising the steps of:
identifying new products that have been launched in a number of recent weeks based on analysis of prescription transactions stored in the database;
assigning products to product groups;
for each product group,
generating data files containing projected national prescription count information by product for each of the three channels, retail, mail order, and long term care, and
supplementing the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks and also an estimate of national current week volume;
identifying outlets as normal volume or low volume outlets;
for low volume outlets, using a 4 week average by product group as a forecast for the current week volume;
for normal volume outlets, when the product is not new, using a moving four-week average of outlet/product raw prescription counts to forecast the current week volume based on outlet/product raw prescription counts for the prior number of weeks and projected national prescription counts for both the current week and the prior number of weeks; and
for normal volume outlets, when the product is new, using a national ratio of product prescription counts to product group prescription counts applied at outlet level to forecast a new product volume for the current week.
6 . The method of claim 5 wherein using a moving four-week average of outlet/product raw prescription counts to forecast the current week volume comprises using an Autoregressive Integrated Moving Average (ARIMA) model.
7 . The method of claim 5 wherein identifying new products that have been launched in a number of recent weeks comprises identifying new products that have been launched in a the last thirteen weeks and supplementing the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks comprises raw prescription data for twenty five prior weeks.
8 . The method of claim 5 wherein assigning products to product groups comprises identifying a number of leading products based on analysis of national prescription counts information and assigning such number of products to its own product group.
9 . The method of claim 5 wherein assigning products to product groups comprises grouping products by therapy class.
10 . A system for forecasting un-reported prescription/product transactions or transactions that are not timely reported in a subject time interval to a sample store or outlet in a universe of product stores, the universe of product stores comprising sample stores and non-sample stores in market channels such as retail, mail order, and long term care, the sample stores generally reporting prescription transaction data to a history database, the system comprising a processing arrangement configured to perform the steps comprising:
identify new products that have been launched in a number of recent weeks based on analysis of prescription transactions stored in the database;
assign products to product groups;
for each product group,
generate data files containing projected national prescription count information by product for each of the three channels, retail, mail order, and long term care, and
supplement the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks and also an estimate of national current week volume;
identify outlets as normal volume or low volume outlets;
for normal outlets, use a 4 week average by product group as a forecast for the current week volume;
for low volume outlets, when the product is not new, use a moving four-week average of outlet/product raw prescription counts to forecast the current week volume based on outlet/product raw prescription counts for the prior number of weeks and projected national prescription counts for both the current week and the prior number of weeks; and
for low volume outlets, when the product is new, use a national ratio of product prescription counts to product group prescription counts applied at outlet level to forecast new product volume for the current week.
11 . The system of claim 10 further comprising wherein using a moving four-week average of outlet/product raw prescription counts to forecast the current week volume comprises using an Autoregressive Integrated Moving Average (ARIMA) model.
12 . A computer-readable medium for forecasting un-reported prescription/product transactions or transactions that are not timely reported in a subject time interval to a sample store or outlet in a universe of product stores, the universe of product stores comprising sample stores and non-sample stores in market channels such as retail, mail order, and long term care, the sample stores generally reporting prescription transaction data to a history database, the computer-readable medium having a set of instructions operable to direct a processing system to perform the steps comprising:
identifying new products that have been launched in a number of recent weeks based on analysis of prescription transactions stored in the database;
assigning products to product groups;
for each product group,
generating data files containing projected national prescription count information by product for each of the three channels, retail, mail order, and long term care, and
supplementing the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks and also an estimate of national current week volume;
identifying outlets as normal volume or low volume outlets;
for normal outlets, using a 4 week average by product group as a forecast for the current week volume;
for low volume outlets, when the product is not new, using a moving four-week average of outlet/product raw prescription counts to forecast the current week volume based on outlet/product raw prescription counts for the prior number of weeks and projected national prescription counts for both the current week and the prior number of weeks; and
for low volume outlets, when the product is new, using a national ratio of product prescription counts to product group prescription counts applied at outlet level to forecast a new product volume for the current week.
13 - 14 . (canceled)
15 . The method of claim 5 wherein identifying outlets as normal volume or low volume outlets is based on average prescriptions per week, number of missing weeks, or maximum prescriptions per week data.
16 . The method of claim 5 further comprising:
combining the forecasts for normal and low volume outlets into a single forecast.
17 . The system of claim 10 wherein identifying new products that have been launched in a number of recent weeks comprises identifying new products that have been launched in a the last thirteen weeks and supplementing the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks comprises raw prescription data for twenty five prior weeks.
18 . The system of claim 10 wherein assigning products to product groups comprises identifying a number of leading products based on analysis of national prescription counts information and assigning such number of products to its own product group.
19 . The system of claim 10 wherein assigning products to product groups comprises grouping products by therapy class.
20 . The system of claim 10 wherein identifying outlets as normal volume or low volume outlets is based on average prescriptions per week, number of missing weeks, or maximum prescriptions per week data.
21 . The system of claim 10 further comprising:
combining the forecasts for normal and low volume outlets into a single forecast.
22 . The computer-readable medium of claim 12 wherein using a moving four-week average of outlet/product raw prescription counts to forecast the current week volume comprises using an Autoregressive Integrated Moving Average (ARIMA) model.
23 . The computer-readable medium of claim 12 wherein identifying new products that have been launched in a number of recent weeks comprises identifying new products that have been launched in a the last thirteen weeks and supplementing the data files with historical raw prescription data at the outlet/product level covering a prior number of weeks comprises raw prescription data for twenty five prior weeks.
24 . The computer-readable medium of claim 12 wherein assigning products to product groups comprises identifying a number of leading products based on analysis of national prescription counts information and assigning such number of products to its own product group.
25 . The computer-readable medium of claim 12 wherein assigning products to product groups comprises grouping products by therapy class.
26 . The computer-readable medium of claim 12 wherein identifying outlets as normal volume or low volume outlets is based on average prescriptions per week, number of missing weeks, or maximum prescriptions per week data.
27 . The computer-readable medium of claim 12 further comprising:
combining the forecasts for normal and low volume outlets into a single forecast.