Subscribe to our blog
Posted 25 September 2020 by
Mathias Lasoen
Head of Growth Marketing

What QC labs can learn from soccer players to increase planning adherence!

“Yes, we lost the game… but I scored!” is not an uncommon expression among soccer players. While soccer trainers incentivize players on an individual level, it’s important that, in doing so, the players do not lose sight of the overall objective: winning the game. What does this tell us about ways to improve QC planning adherence? Well, aiming to “win the game’ (or “win as many games as we can”) is, of course, something that all companies want, and life science organizations are no different. That’s where Smart FIFO comes in.

But first….

A bit of soccer history

Edgar Babayan, an Armenian soccer player, could not stop smiling while being interviewed after the European Championship qualifier game between Armenia and Italy last year. And why would he? He scored a goal! A beautiful long-distance shot, in the 67th min of the game, simply unstoppable for Italy’s goalkeeper.

As any soccer player would do, after scoring, he ran around ecstatic with his arms in the air, smiling and visibly relieved: his day could not get any better than this!

One small detail, though: Armenia was already seven goals behind at this point. Italy continued, after Edgar his goal, to achieve its biggest win in more than 70 years, in the end, beating Armenia 9 to 1.

One could question: was Edgar’s ecstatic celebration rational? Why celebrate if the team already lost (or better, endured a devastating blow)? In the end, what’s the difference between losing 9-1 or 9-0; you lost.

We think in KPIs

Deep-diving in Edgar’s thinking that day, reveals his 22nd goals for the Armenian national team, which made him the best scoring Armenian soccer player in history. For him, it was a beautiful day: his personal KPI improved drastically.

The celebration was caused by the fact that Edgar prioritized his personal KPI above his team’s KPI. “Yes, we lost, but… I SCORED,” while in the end, as soccer is a team sport, it should have been “Yes, I scored, BUT WE LOST.”

The link between soccer & analytical labs

Compare the soccer match with a production batch and the soccer players with the different functional teams involved in releasing the production batch.

In the below report, you can see the planning adherence per test (or % of all tests released on-time) increased significantly over time. In week 31, for example, there are hardly any late tests left. In other words: all teams are releasing their tests on-time (or all players in the team are playing very well!)


Now looking at the planning adherence on batch level
(or % of all batches released on-time), you can see a similar trend: planning adherence increases over time. However, looking at week 31 again, although almost all tests are being released on time, a significant proportion of our batches are released late. In other words, although our players in the team are playing well, we are not winning as many games as we should.

You work towards what you measure

‘You work towards what you measure’ means that if you measure only the first KPI (planning adherence per test), that’s also what the different teams will work towards. Or, in other words, as we focus solely on a KPI that measures the number of tests each team releases on-time, it inevitably rewards teams to think in silos – while the actual performance (on-time release of the entire batch) is a result of the on-time release of multiple functional teams.

Often, the big hurdle is that there is a lack of visibility across the teams. Team 1 is pushing to get tests done for a batch, while team 2 has to postpone testing for the same batch. That would be the equivalent of Edgar scoring a goal, while he has absolutely no idea of what the score of the game is.

Exploring a better approach

Ideally, Edgar would have been able to say: I’m going to withdraw my goal as we’ve lost anyway, and instead, score it in another game where it can make the difference between winning and losing.

The laboratory equivalent is that it’s a waste of resources for one team to push to get tests done, while the production batch will be late anyway. What if you could redirect these ‘resources’ to other production batches, where they can make a difference between on-time or late release?

How to tackle

The solution is relatively straightforward: you need full demand visibility across all teams and all batches. You want this visibility at all times, and you want to gather that information with zero input.  For example, in the Batch tracker dashboard, you can see, per production batch, if a batch will be:

  • Late (red color)
  • Late but you can still reprioritize task to make sure it’s released on time (orange),
  • On-time, planned for today (blue)
  • On-time, planned in the future (green)

This is your finger on the pulse, an overview of all the games you are playing in parallel. The colors indicate which games are lost anyway (red), which games you are winning (blue and green) and which games need an extra goal to win (orange).

Planners can then further deep-dive in each production batch, looking at where each sample within the batch finds itself in the process and where delays are expected. This information then causes planners to take appropriate action (decision-support).


That’s what we often call SMART FIFO, where Binocs can schedule across labs (instead of on a team-by-team basis) and, by doing so, supports labs in releasing more batches at the same lab throughput.

Or to close with a final soccer analogy: as you are playing multiple games at once, you are given the ability to choose when and in which games to score, optimizing the total number of games won (while scoring the same number of goals).

Do you want to discover how Binocs can schedule across labs and supporting labs in releasing more batch with the same lab throughput?

Talk to our expert, or request your free demo today!

  • Binocs case study: more batch releases with smart FIFO in a QC lab

    CASE STUDY: More batch releases with smart FIFO in a QC lab

    Our chemical and microbiology lab teams with a headcount of 140 analysts perform release and stability testing for multiple products that are manufactured in our site. The teams are…
    Read more
  • Critical

    Critical Chain Analysis: how project management concepts influence resource planning

    Project management concepts are ubiquitous in modern planning, including in resource capacity planning systems. In this article, Adam Lester-George explores critical path analysis and critical chain analysis, and their…
    Read more
  • Laboratory staff in white coats discuss data displayed on a tablet device, perhaps looking at 1 of the 4 Quality Control KPIs discussed in this blog post

    4 KPI dashboards that every QC lab should use

    KPI dashboards in QC are crucial as they enable lab personnel to see relationships between different metrics and make informed decisions about how to optimize lab performance. Based on…
    Read more

Mathias Lasoen

Mathias is the Head of Growth Marketing for Binocs.