Predicting the Future – Forecasting Initial Sales Revenue For Novel Medical Device Technologies

Developing accurate forecasts is important to medical technology companies for both the financial and operational aspects of the business. Forecasts assist the finance group as they develop revenue plans, determine appropriate expense levels, and forecast the profitability of the company. The operations group uses forecasts to develop a production schedule, to make component buying decisions, and to plan for any required capacity changes needed to meet demand.

Developing a sales forecast for existing products can easily be arrived at by conducting a statistical analysis of historical sales data and then combining this information with anticipated changes in market dynamics, sales organization structure and pricing. Forecasting sales revenue and product utilization for novel medical technologies becomes much more difficult due in part to the lack of historical sales data and the unknowns associated with a new product in the marketplace.

Developing an accurate forecast for new medical technologies is both an art and a science. Using input from market based assumptions and company related parameters, a spreadsheet-based model can be built which allows the user to more accurately forecast sales revenue and product demand. With these models, users can determine the effect that changes to baseline assumptions can have on the forecast.

Market Factors

While spreadsheet-based forecasting models can be used to predict sales revenue and product demand, numerous market related factors can substantially influence the accuracy of forecasting models for new medical technologies. Potential market factors include the competitive environment for the product, pricing sensitivity within the target market, and the ease of gaining hospital committee or buying group approval. Broader market factors include the economic conditions within the marketplace, patient-related factors which affect their access to the product, and seasonality of the business. Understanding the influence market-related factors may have on the adoption curve for a new technology and factoring these into the assumptions for the forecasting model is imperative.

Company-related Factors

There are also a variety of company-related factors which can affect forecasting for new medical technologies. The timing of product availability and the ability to build sufficient inventory to meet product demand are critical factors towards determining the timing of a proper product launch. If the new technology is a product line extension, there is the potential for the new product to cannibalize current business. If the product is a planned add-on to the product line which is anticipated to expand applications and use for the technology, the ability to leverage existing business is a key factor to consider when building a forecasting model. Sales history associated with the company’s introduction of previous new products can also be used as a guide to developing assumptions.

The type of product the new technology represents can also influence the assumptions used when forecasting since differing product types have their own unique market dynamics. If the technology is stand alone capital equipment, the customer access to working capital and the timing of start of a new fiscal year are important considerations. The availability of alternative capital placement programs can also influence forecasting since these may expand the ability for hospitals to access the technology. If the technology requires capital equipment and a disposable component, the hospital might also have the ability to bundle disposable purchases in order to obtain the capital equipment. It is important that capital equipment that is not captured in a revenue model is accounted for when a build forecast is developed to insure adequate supply to meet customer’s demands. Forecasts for disposable devices which require a capital equipment component should also include assumptions for the number of disposables which will be utilized over a given time period for each unit of capital equipment available in the field. Assessing the productivity of capital units for generating disposables sales revenue is an excellent method for arriving at metrics which can be used in the future to adjust a forecasting model.

For implants which require specialized instrumentation sets, forecasts should take into account the number of sets which will be available in the field when projecting sales. If a limited number of instrument sets are available at launch due to production capacity of budgetary constraints, assumptions for the revenue model should be adjusted accordingly. The product adoption curve can be accelerated as the number of instrument sets available increases over time. Similar to a capital equipment/ disposable device model, assumptions for the likely number of implant procedures per available instrument set over a given time period is an excellent metric to develop and track following product launch. Since instrument sets are often loaned to customers on a consignment basis and may not be associated with direct sales revenue, there is a need to account for these sets separately as a part of the build forecast.

The structure and makeup of the sales organization is another important company-related factor which can significantly affect the sales ramp for a new medical technology. The use of a direct vs. distributor sales force, the number of products the sales force is promoting, and previous experience the sales representatives have with the introduction of new products are all important elements to consider when developing sales projections. The impact of differential financial incentives to sales representatives associated with selling the newer technology compared to other products should also be considered.

Forecasting for new medical technologies can be further complicated if the strategic plan includes the launch of the product in differing geographic markets. Differences in the timing of introduction into these markets, the use of alternative sales channels, and differences in both market dynamics and pricing structures create the need for more complex models and the ability to create multiple assumptions and modeling scenarios.

A company’s long-term pricing strategy should also be considered when developing revenue forecasts especially if the forecasts will be utilized as a part of a 3 to 5 year strategic planning process. Anticipated future incremental or year over year pricing increases should be included in the model to insure any increased sales revenue resulting from increased pricing is accounted for.

Conclusion

Developing an accurate sales forecast for a new technology requires a thorough understanding of both market and company-related factors which can influence the adoption curve for the product. The development of a forecasting model which has variable inputs that can be modified in order to assess the impact of changes to the basic assumptions used for the model can be useful.

Validating the results of the forecasting by conducting a reality check of the modeled productivity metrics can help to insure the accuracy of the model. Revenue forecasting and product build models should be assessed periodically and adjusted to reflect additional insights and changes to market dynamics which have occurred since product launch.