Demand planning in Life Science: today, tomorrow and years ahead
Many life science companies are searching for the best in class solutions to organize the demand planning function and improve performance. As a critical part of the demand planning function companies are also looking for ways to continuously improve their forecasting process. On March 15th, EyeOn hosted the 5th Life Science network meeting at Croy Castle. During this day various topics in the area of demand planning and forecasting were discussed among the network members.
Macro-economic, industry, and sector-specific trends are shaping the playfield for life science companies and impacting the demand planning function. Cost and margin pressure has dominated the industry in the last couple of years and will continue to do so. Increased competition from niche & generic producers, combined with a growing product portfolio through a broader range of sales channels is expected to drive the need for supply chain differentiation. Plan uncertainty, in a more volatile market where – amongst other – tendering is more common, was acknowledged as a new challenge.
In order to be successful the maturity of demand planning processes in life science needs to grow. Room for improvements is perceived in following areas:
- Use of statistics in the forecasting process
- Mid-term trend analysis and scenario planning
- Alignment with F&A and one number planning
- Embedding of continuous improvement cycles in processes and organisation
Today demand planners in the life science industry are often consumed in a process of constant forecast adjustments and re-planning with little added value. Well structured and deployed demand management together with an improved forecasting process brings companies to the next level. A differentiation approach in forecasting enables demand planners to concentrate more on where the value is. Outsourcing of forecasting processes is considered as one of the means to facilitate this approach.
The active use of social media for consumer centric demand planning and forecasting in life science is perceived by most supply chain practitioners as years ahead. Nevertheless the topic raises relevant questions as “will end customers shape demand in future markets, and can social media be used to track and predict their behavior?”