Scientists from academia are often puzzled by the emphasis on standard operating procedures (SOPs) in industry. In one of my early R&D jobs, it felt like an obsession – as soon as I got a new result, my managers wanted to know about the process, and whether I had standardized it. I thought it must be due to their background in manufacturing, where everything has to be replicated from one factory to another. But with more experience, I’ve seen this interest in standardized procedures everywhere – manufacturing, R&D, medical devices, pharmaceuticals… everywhere, that is, except the university research lab. For scientists trained to answer questions by designing new experiments, question of process might not make sense, and even seem a waste of time: If standard procedures are so important, how do we explain the many successful research groups that never bother with them?

The truth is that academic researchers, even grad students, do care about procedures. It’s why we only trust research assistants that we train ourselves. It’s why research groups without good transfer of knowledge between senior to junior students seem to go around in endless circles of inconclusive experiments. We see the value in techniques and procedures done “the right way.” What we don’t care about (and often resent) is the idea that a process can or should be standardized, and give the same results in anyone’s hands. The reasons for this are complex (and a topic of a future essay), but the outcome is simple: Our experiments are less reproducible than we think. And that’s exactly what drives people in industry crazy about scientists from academia.

Anyone who has spent time in a research group will know this scenario: A team of scientists studying an experimental result, while the lead scientist describes what the data would have shown if the right controls were done. The missing piece didn’t seem important at the time (it’s only a negative control, after all…), but because something is missing, the results are inconclusive. So the experiment is run again, with all the controls – only this time the results are inconsistent with the first results. So the experiment is run again, and so on. In an academic group, this is inconvenient – the experiment needs to be repeated (or that result is omitted from the paper) and time is lost. The problem could be fixed by something as simple as a checklist, but that’s not a top priority: new discoveries are more valuable than time.

To be sure, the above scenario is not uncommon in industry. But for a company trying to get a product to market, it can mean the loss of time, revenue, even entire products – and certainly jobs. Here’s a real-world example from one company to illustrate the point: Two scientists independently evaluated the quality of reagents from a supplier. One used whatever materials were on hand, took no notes, but qualitatively judged the reagents to be bad. The second recorded the experiment in a notebook, noted details like time and temperature, manufacturer and lot number of all materials used – and got the opposite result. When the team evaluated the results, what was the outcome? An inconclusive experiment – no decision could be made.

The reason is that there was no standard procedure to evaluate the quality of reagents. What materials should be used? What experimental conditions? What is the criteria for declaring a reagent good enough? A standard operating procedure would spell out these details, the steps involved, and have prior agreement by the team ultimately making the decision. And if you think the weight of data from the more “scientific” of the two studies should cancel out the more qualitative one – that’s not the way it works. It’s very easy for bad data to dilute good data and cloud judgement – remember, many of the decision-makers may not understand the details of the experiments. All that emerges is a tangle of conflicting data, and no clear picture of what to do next.

Now picture yourself as a fresh graduate in a job interview. When asked about standardized operating procedures, what if instead of giving a blank look you could say, “I’ve written SOPs, trained others using them, and implemented them as standard practice.”? What this requires is for you to take some initiative in your research group, and do so without harming any delicate academic feelings. So it will be an exercise in process, as well as politics – the perfect training for the future professional. To do this well, you will need to pick a process where you are the expert (or will become the expert), and where the outcome truly matters.

In the next segment of this two-part series, I’ll propose a plan to get in-depth experience in writing and implementing standard operating procedures. Until then, look around at your own work – how many of the techniques you use every day were standardized by accident, and how many were actually designed to give the best outcome? If you apply your scientific mind to this question, you’ll find plenty of room for improvement.