How Dynamic Forecasting Reduced Risk in a Project with Long Development to Launch Timeline

Certain projects simply take more time than others to development and launch. Injection molded plastic medical device products, for example, can easily take a year or longer to bring to market. These long development times are due to the processes related to designing and testing the safety and efficacy of the device, and to ensure that reliable Six-Sigma production results can be achieved in the final product before investing in costly molds.

In such cases of long lead-times, product and market management must be able to regularly update plans based on market dynamics and when the product will be available for launch. Dynamic forecasting can enable regular adjustments to plans which are critical in achieving desired project outcomes.

A Real-World Problem

An international leader of medical devices embarked on a new injection molded product that would replace one of its flagship lines.  A 2-year development schedule made the timing of production conversion from legacy to new product both critical and challenging. A key objective was to ensure adequate inventory of the new product at launch while avoiding excessive inventory of the older product.

Solution

A survey of available information on the project timeline was augmented by discussions with sales, engineering and production personnel.  During the development period, regular assessments of supply and demand were conducted using the in-house customer database, input from sales channels, engineering timelines and delays, and production planning.
projection-diagram

A dynamic modeling tool was used to capture the multiple inputs and their interlocking relationships.  Information from multiple sources was programmed into the model, relating cause to effect.

The historical sales relationship between old and new products during launch was analyzed to assess uptake and inventory exposure. Estimates of new product adoption were based on proposed pricing strategies and historical seasonal sales cycles.  These relationships and other insights were used to simulate possible 3-year outcomes in units, revenues, and profits, based on when the new product would be available to the market and how it would diffuse into the market, replacing inventory of and demand for the older product.

Results

The dynamic nature of the software modeling tool provided defined causal relationships that resisted wishful thinking by stakeholders regarding desired outcomes.  Rapid product and market updates were possible in response to delays in the development schedule, the availability of product from offshore facilities, and the seasonal shifts in orders. The dynamic model enabled regular and frank discussion among stakeholders that helped set and maintain expectations from the C-suite to the customer for a more controlled and risk-reduced launch.

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