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Sometimes Success Means We Break the Rules, Part 1

Not every solution fits within the process improvement methodologies we know. Sometimes the best solution to the challenge comes from doing what our methodologies would not.

By ALAN NICOL, Executive Member, AlanNicolSolutions LLC

Rules are important, especially when it comes to governance, but they cannot possibly anticipate or address every single scenario that might come along. Sometimes the best or right thing to do is to break the rules.

Here is an example to set the argument to rest. Suppose you are stopped at a traffic light and an emergency paramedic vehicle needs to get through. It may not be your right-of-way to move into the intersection, but you are blocking that emergency vehicle’s path. The best thing to do is to verify that cross traffic has stopped and to enter the intersection to let the emergency vehicle through. It’s against the normal rule, but right for the immediate need.

Every once in a while process solutions will break the rules of the best methodologies as well. I’d like to share two examples that I ran into this week. From these, I’d like us to remember for the future that when we run into a difficult process improvement, or when our best ideas don’t seem to take our metrics in the right direction, the best solution may be to break the normal rules.

I was sharing experiences with a friend and peer who told me a story that is not necessarily unique, but rings of numerous other challenges in my own experience. His story takes place in a blood bank.

At the time, he was a line supervisor for the blood processing function on the night shift. His team’s job was to process recently collected blood and prepare it for distribution and use. The rule was that every member of the team was to have three 10-minute breaks and one 30-minute “lunch” during the course of the shift.

The breaks were staggered so that during any one person’s break, the rest of the team could be operating the process. This to ensured that the process perpetuated without any stops, and maximized the manpower at any given time throughout the shift.

On paper it makes perfect sense. However, my friend broke the rules and reset the break schedules for his shift and team. Without going into detail about the process, it is important to understand that once certain phases of the process have begun, they cannot be halted until the entire process phase is complete (another driver for the staggered breaks). However, between two phases, there is a distinct switch from one process system to another. It is a change point between processes and a safe place to allow a pause in the movement of product.

My friend reset the break schedule so that the entire team took one 35-minute lunch break when their product run had completed the first phase, and a 25-minute break after the entire run had completed the second phase. The night shift’s productivity consistently exceeded that of the day shift, which ran a larger team.

Within a few weeks, word got around to the management that something was up. Complaints were made that the night shift wasn’t following the rules. Some of it was curiosity concerning how the smallest shift was processing the most product. Management decided to investigate.

After asking my friend why he thought it was OK to break the rules, and he referred them to the numbers, some members of management decided to stay and watch the night shift work one night. Afterward he was free to proceed as he saw best, no more arguments.

Any of us could sit down with a scratch paper and draft a process productivity model that shows how an un-halted process running 80 to 100 percent manpower throughout the entire shift would produce the most productivity, so why did my friend’s solution work better? By the way, he had no formal process improvement training of any kind at the time; he made the decision based on leadership intuition.

I have seen the same phenomenon numerous times in other scenarios, and I believe that I have some insight to answer the question as to why. I welcome comments from others if anyone would like to share.

I believe that the reason that the model with the shared breaks and the pauses in the process works better, is because people are not machines. As living, thinking, feeling, organisms, our energy levels and productivity depend a great deal on our mood and attitudes, affected by morale. Also, our system reset may not happen according to the schedule convenient to the process.

A 10-minute break, alone, to use the restroom and get a drink, may not effectively refresh our energy levels for the next push through the process. However, taking a break twice as long, with work friends or colleagues, with a little time to sit down and share a laugh, might refresh us better. Working assembly-line-style shifts is more like running a distance race than running a set of sprints.

So, a 10-minute break is not something we much look forward to, or at least not as much as a longer break that is more meaningful. So, when we are motivated to get to that break, our energy level for the production process is greater and we are naturally inspired to drive a little harder to get to the break a little sooner. It’s like a basic reward scenario.

Similarly, the process is less stressful and requires less energy when 100 percent of the team is working it at the same time (assuming it is properly resourced). This means that the team members can maintain a higher energy level and better morale for a longer period of time and the productivity of the process is higher, for longer. In this example, that difference was enough for the intermittent model to exceed the continuous model, even in spite of the breaks.

I think there is more, but the point can be made. When productivity hinges on the performance of people, we are often better off designing the process around maximizing the morale and energy of the people, than we are modeling the process as if people are machines with a consistent, infallible output. We are not machines.

Unfortunately, because we are not machines, simple manpower calculations are invariably flawed, and our models will rarely predict actual performance. The best way to find the highest efficiency process, that is sustainable over time, is to experiment. Let your intuition be your guide rather than calculations and models, as it was for my friend. Chase the pain and get rid of it and chances are the process improves.

Please tune into the Chemical Equipment Daily for part two of this two-part series. What’s your take? Please feel free to comment below! For more information, please visit