The Paradox of Data in R&D Labs

R&D labs are in the business of generating, analyzing, reviewing and validating data. Data rules the decisions on how projects move forward through the development phases. However, we generally find that R&D labs have little or no data on their own capacity and performance.

Without sufficient statistical data, the labs cannot meaningfully analyze past performance and identify opportunities to improve future performance. Lab managers or supervisors will have views on how the lab is performing and areas for potential improvements but such opinions are rarely supported by rigorous number crunching. The lack of supporting data can create uncertainty in budgetary/resourcing discussions with upper management. Data should always be the basis of these discussions and therefore it is vital to develop structures to record and analyze the lab workloads and performance.

When designing a method to capture the data there are some key concepts to keep in mind. The system should have:

• Ease of day to day use
• Low effort for upkeep
• Simple to understand outputs
• Useful information for short term decisions
• Provide data for long term planning
• Been designed alongside lab personnel

By building a system to routinely collect relevant information labs can move towards making data driven decisions on how best to improve throughput times and productivity to better serve the business and customers.