The skills and expertise of scientists drive what the lab can accomplish. Ensuring the right balance of skills, knowledge, and capacity is a key responsibility for lab managers. Finding that right balance can be challenging. It is important to map the existing levels of skill across the lab, and then predict how that distribution might need to change to serve the needs of the lab moving forward. Using available people and operational data, a skills analysis can be completed in a straightforward way.
Here are four tips to improve your approach to completing effective skills analyses and planning.
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Analyze skill levels across the lab
It is very helpful to generate skill level assessments that can be applied equally across the lab. One set that works well is a three-level system:
- Operator – responsible for generating high-quality data
- Practitioner – responsible for generating technical outcomes and writing reports
- Subject matter experts – key problem-solvers and innovators
Analyze all of the key lab activities based on these three levels. Assign fractional skill levels based on how much each person works in each activity and at what level. This will provide a clear picture of the skill levels available to the lab for all of the main deliverables.
Understand skills and knowledge risk
Analyze how critical the skills and knowledge are to deliver the current work, and how likely it is for the lab to lose the staff involved. A useful way to approach this process is to map potential skill and knowledge loss into a graph:
- The x-axis is the criticality of the knowledge ranging from generally known and easy to replace to critical tacit knowledge that may be irreplaceable
- The y-axis is the best guess when the individual scientist will leave the lab, ranging from within six years to less than two years
The risk of key staff leaving the lab involves a combination of factors revolving around retirement and dissatisfaction. Evaluating these risks is usually imprecise, but the directions are important. This analysis will provide cross-training targets to help the lab retain critical knowledge and skills.
Analyze workloads across the lab
Using lab operations data, often located in a laboratory information management system, analyze the relative workload for each of the main lab activities. This analysis can contain elements like samples analyzed, hours required to conduct the work, and the revenue or funding attached to these activities.
Combining the workload analysis with how the lab is funded will show how different lab activities contribute to revenue for the lab. This can indicate lab activities that require additional investment and areas that can no longer support existing staff.
Predict the skill needs of the future
Combine the skills, workload, and revenue analyses to predict the needs of the lab in the future. Lab activities with growing workloads and increasing contributions to funding are places to invest with more staff and higher skill levels. Areas with low workloads but significant contributions to funding are places with growth potential. Lab activities with declining workloads and reduced contributions to funding are places to deprioritize. There may be opportunity to cross-train and shift some staff to areas with greater potential.
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