Companies regularly create short-term, monthly forecasts for all marketed products. A forecast for a market product usually consists of two main components: the product's trend, or momentum, and all other events that are not reflected in the trend.
A trend is a naïve mathematical extrapolation of historical data. Often, information about future changes in demand is known to forecasters, but of course is not reflected in the trend. For example, a new marketing program, a new competitor, a price increase, or customer buying patters will cause sales to be higher or lower than the base trend. These events must be incorporated into the analysis to create a representative forecast.
Companies frequently use some form of short-term forecasting model and basic trending approaches.
LIMITATIONS OF THE EXISTING SOLUTION
• Basic trending approaches may be good, but not good enough. There may be better trending methods available to use for a given situation.
• Many forecasters use third-party software to calculate product trends. This requires experience with another software program and is not automated, so the process can be tedious.
• Typical forecast models may not be analytically sound. To accurately capture and model all the forces affecting a product, forecasters need to understand how future events will affect the market and the product in question. Proper modeling of the interrelationships is frequently beyond the ability of typical forecast models.
• Some forecasters attempt to use noisy data, or data with confounding variability, to create their trends. A better approach is to use cleaner data, or to preprocess the noisy data to remove much of the noise.
• As all corporate forecasters know, an inaccurate or unsupportable short-term forecast will draw scrutiny from senior management.
OBJECTIVE INSIGHTS' SOLUTION
Objective Insights has extensive experience developing and using short-term forecast models. The Objective Insights Short-Term Forecaster (STF) is a tool designed to forecast near-term revenues based on historical market data. STF projects customer demand trends based on the well-known and tested exponential smoothing and Box-Jenkins methods. If desired, the Objective Insights Trend Explorer, a separate tool offered by Objective Insights to select the best trending approach, may be used to generate the trend used by STF.
STF starts with customer demand, which is the cleanest, truest measure of demand, and builds an ex-factory revenue forecast. We have found that preprocessing the data produces better trends. STF may employ one or more of the following preprocessing techniques:
• Data conversion from sales per month to sales per day
• Nearest-neighbor smoothing
STF handles products that have many subproducts (strengths, vial sizes, formulations, etc.) and indications. STF is sophisticated and analytically correct, allowing you to spend your time thinking about the input data and the results, not the underlying calculations.
STF also allows you to enter future events (assumptions) that are not reflected in the historical data. All assumptions are clearly documented. If there are no assumptions changes from month to month, you can produce and updated forecast in minutes.
STF is easy-to-use, offering one-click trend generation and simple importation of product and market data. The model automatically creates as many trends as are needed. For example, total product units may be trended along with each subproduct’s share.
Objective Insights’ STF is built in Microsoft Excel, allowing you to start working in STF immediately rather than having to learn yet another software program. STF uses no costly third-part software that can cause installation and running problems, or require you to perform extra steps
Objective Insights customizes Short-Term Forecaster for your specific situation, and provides comprehensive training and indefinite support.
Contact Objective Insights for a fully functional demonstration version of Short-Term Forecaster.
For more information, please download the STF Overview (pdf).
These inputs are required by subproduct (tablet or capsule strength or vial size):
• Historical sales units per month (tablets, grams, vials, units, etc.)
• Historical gross and net sales revenues per month
• Historical list price per unit
• Historical gross and net revenues
• Preprocessing qualities such as number of shipping or billing days per month
• Historical pipeline information (ex-factory verses wholesaler shipments verses patient usage)
• Future pipeline information (wholesaler / distributer / pharmacy inventory fluctuations, or buying patters) in month's supply of inventory or units (grams, vials, etc.)
• Future price increases
• New competitive product launches
• New product line launches
• New indications and claims
• New marketing and sales programs
• Historical market shares
• Historical sales for competitive products
• Returns and allowances
• Not-for-sale product for starter kits, clinical trials, and uninsured or indigent patient programs
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