So the hiatus on this blog was longer than expected, but it was useful as I have recently earned my MBA and now have time once again to pursue my passion of helping scientists improve how they do work. What better place to pick up from than to finally complete talking about how the different Lean wastes apply to laboratories. If you haven’t read part one, or would just like a refresher, please find the post here.

This post will focus on the final 3 wastes as well as one bonus waste, skills.

Over Production

Have you ever worked on an experiment, meticulously taken notes in your notebook, and spent hours assembling data for a project update only for the data to be shown once at an internal lab meeting and never mentioned again? If so, you too have experienced overproduction, where you are doing more work than what is called for.

Over production is a trickier subject for those who work in science, as pushing the boundaries of what we do is how scientists spend their days. If the experiment could have pointed you towards a new opportunity it would not be considered overproduction, as the point of overproduction is that the work does not add any kind of value. Even filling in your laboratory notebook is often seen as overproduction, because rather than ensuring that someone in the lab has the time to perform documentation that is never reviewed, a primary investigator will likely push to start the next test or experiment instead even though proper note taking is critical to working in science.

The trickiest part of over production in the lab, and why we have such a habit of performing more than we need to, is that you never know what bit of information will prove useful in the final publication to reinforce your point. Sure, an extra plate of cells doesn’t sound like a burden to make and maintain, but all of the maintenance of unused lines turns into a waste of time that is better spent on activities directly related to key research projects.

I do want to point out that sometimes learning and training can feel like over production, in that the time taken in practicing a skill or learning something new feels like it takes far longer than it is worth. However, having that skill and being in practice is valuable, so don’t cut your personal growth short. Over production is focused on things that don’t add value, not things where the value may pay off over time.

Over Processing

If over production can be simplified to did you need to do it, then overprocessing can be simplified to did you need to do it to the degree you did? Purification is my go to example for this waste as it always seems there are one thousand and one ways to purify a sample to get a wide variety of results. The purification method should be chosen based on the purity you need to achieve for the next step, if the product is supposed to go into human medicine then the purification standard should be extremely high but if the product is going to be put into a western gel for further analysis the standard of purification necessary is much, much lower. Over processing waste occurs when work is performed beyond the degree or to a standard that is not necessary.

Another good example of overprocessing can occur in experimental design, where more conditions are being run than strictly necessary. If you’re looking to understand how different cells react to exposure to a protein, then clearly the conditions need to include a negative control without protein and a positive test with protein. Overprocessing occurs when multiple different forms of the protein are added to the experiment that are unlikely to add scientific value such as multiple scrambles, isoforms, or testing multiple means of transfections. Complicated experiments are sometimes called for, but understanding the reasoning behind individual conditions can prevent experiments from turning into over processed messes.

When considering overprocessing, take a moment to step back and analyze the minimum standard that needs to be met. Next, consider what is available to you in the lab and how much time you have before you need to do the task. For frequently performed tasks, it can be a good practice to have both a simple and high end methodologies available and ready for use in the lab. If an experiment is planned that involves a new procedure, figure out what standard you need to reach and find what will accomplish your needs rather than immediately purchasing either the cheapest or most expensive option.

Defects

A defect is defined as a shortcoming, imperfection, or lack. What we all know it to mean is that when something is produced that isn’t up to standard. In the lab this could be a sample that isn’t pure enough, cells that are contaminated, and even a PCR result with multiple, and somehow all incorrect bands. One of the critical things about a defect isn’t that it doesn’t have to cause something to be totally non-functional, defects just have to be enough that you need to continue working when if the process had given a normal result you would already be done.

When performing science, defects sometimes happen. The most important part of a defect is understanding first what happened, followed by why it happened. For instance, if a centrifuge has a problem and locks your temperature sensitive samples inside for a prolonged period it is easy to know why the samples may not function properly further on. In this example, an unforeseen circumstance, the centrifuge locking, led to sample degradation, samples may not perform as expected if used in any other experiments or analysis.

Understanding the why or root cause of this problem means looking at the centrifuge, is something damaged in the centrifuge that needs to be replaced, has maintenance not been performed on the equipment that could have prevented the problem, was the sample not balanced inside the centrifuge that caused the error? Zeroing in on the actual cause of the error can help you acknowledge what happened as well as find ways to prevent the defect from recurring.

Skills

While not included in the original TPS wastes, skills or talent waste was added when people in management positions started realizing how underutilized staff members could be. This waste is not focused on ensuring that every person is being useful 100% of their work time, but instead skills/talent waste is focused on does the work a person is doing match the skills that they have. Every job has some basic, even tedious tasks that people have to do. Skills waste is focused around the level of work that a person is performing, for instance a PhD student focused on molecular biology work shouldn’t be the one in the laboratory trying to organize birthday celebrations. There is often a culture that no work should be beneath anyone, what skills waste asks is if the person being assigned to the job is really the right fit.

Once established, skills waste can be difficult to undo. In the lab we are quick to let someone perform all of a task if they’re particularly good at it, or at minimum be the go-to person if someone is having difficulties with a PCR or a western blot. Skills waste asks if that is the right choice on how that person should spend their time. If this is a critical experiment that could get a paper into Nature rather than another journal, sure; if this is a frequent experiment that could help improve another lab mate’s skill while the main person designs an experiment, reassigning the task isn’t the right choice.

Another way to look at this is to reframe the question from “Who is most likely to have success at X experiment” to “Is there a more complicated task person Y could take care of instead?” which shifts the focus from the task being done to a high standard to ensuring the staff is taking best advantage of their skills.

• Concepts