A case for lab employee retention
With the ever-growing focus on improving lab efficiency and service levels, it’s, now more than ever, crucial not to lose sight of your most valuable asset: your people.
A high turnover is particularly frustrating in labs because it takes quite some time to get new people up to speed: it’s not uncommon it takes +6months before junior analysts are trained to execute certain test methods according to the correct execution standards.
This means that seniority of your workforce, and thus keeping your people close, pays-off:
- The initial ‘training phase’ of junior lab analysts requires a significant time investment. Making sure these people stay with you as long as possible is thus pivotal to safeguard the return on that investment.
- The more widespread the competencies in the workforce (decentralized seniority), the more versatile you become in dealing with unexpected events, times of high workload,…
The rule of thumb: people are not machines
The rule of thumb to preserve employee retention is straightforward: amid optimizations, lean lab exercises, increased lab efficiency, better service levels, … keep reminding everyone involved: our people are not machines.
Although this might sound self-explanatory, it’s often challenging to find the right balance in a lab context: how can we optimize our ways of working, within the boundaries of what is possible and comfortable for our people?
Here are the 4 guidelines to keep in mind when answering that question:
Guideline 1: people like to know what’s ahead
Imagine how frustrating it must be for a lab analyst to see that their schedule completely changed when they arrive in the lab in the morning. Knowing what they can expect increases stability and reduces stress. Aim to implement processes to stabilize the schedule as much as possible (next two weeks) and to avoid last-minute changes.
Guideline 2: people like a variety of work
In theory, if you give the same set of tasks to the same people until the end of times, they’d become extremely skilled in it, which would increase efficiency levels. In reality, though, no one likes to do the same thing over and over again, day in day out. Therefore, this guideline is simple: you want to make sure your people can work on different projects and tasks. Two quick examples:
- Make sure junior lab analysts move on from doing a small number of test methods over and over (think water testing) once they start to get a hang of a wider variety of test methods.
- Make sure your more senior lab analysts are given the chance to spend sufficient time in the lab and are not grinding their days doing only double-checks or complex administration. In other words, make sure they can still do what they are most passionate about.
Guideline 3: people like to be treated equally
We all have experience with those inevitable periods where everybody is in ‘fire-fighting mode’. There is an unexpected workload peak, therefore throughput needs to increase, and you count on your more senior analysts to help put out the fire (above average campaign sizes, minimize idle time, above average utilization,…). And that’s ok, as long as these periods of increased pressure are limited in time and discussed with the people in question beforehand.
Just be careful not to take the wrong lesson: after the imminent danger passed, it might be tempting to structurally link the throughput of your people to their seniority (higher seniority = higher throughput). Be mindful of this because:
- Your more senior people will notice they consistently need to work harder than other analysts. This could leave them feeling unequally treated and the constant pressure could result in burn-out.
- As your senior people would already be working as efficiently as possible, you’d lose your ‘buffer’ to respond to the next unexpected workload peak.
Guideline 4: people don’t like surprises
Although you’ll never be able to completely avoid ‘fire-fighting’ every once and a while, it’s worth thinking of ways to reduce it to a minimum – to make sure you are not putting too much strain on your people, too frequently.
The key question you could ask yourself: do we have the ability to forecast:
- When workload will peak?
- Which of those peaks will put significant strain on our capacity (people & equipment).
- Based on the answer to the two questions above: what can we do today to avoid or better deal with those workload peaks (think to upskill people, hire and train new analysts, level workload, …).
How our algorithm is built around these 4 guidelines
Of course, we wouldn’t be talking about these guidelines without taking them to heart. People are not machines, and that’s also the idea around which Binocs is built.
Curious to understand better how Binocs takes these guidelines to heart? Request your demo and let us discuss it in more detail!