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Overshoot

July 26, 2017

OvershootGeek Out

<Geek Speak> Warning, a bit of geek speak here. Don’t panic, it’s not going to be that painful!

Back during the times of my undergraduate education, we often talked about the theoretical issue with a stimulus causing overshoot in a second order system. The image on the right shows that phenomenon in a rough form. The image below is a differential equation for a standard second order system.

Second Order Systems

Differential Equation, Second Order Systems

In our electronic control systems, we were concerned about what would happen when a step input (an on or off switch for example) was given. We designed the system to minimize the overshoot and settling time in most cases. We wanted the system to quickly get to the target value for the new inputs. In the above equation, tau is the natural period of oscillation, K is the gain of the system and zeta is the Damping factor.</Geek Speak> Okay, back to business.

Organizational Overshoot

What I have observed is that our human organizational systems exhibit the same second order system characteristics when either internal or external stimuli perturbates the system. The change often causes the organization to over-correct and then we have to bring things back a bit. I’ve often heard this expressed as the “pendulum has swung too far and we have to bring it back toward center.” That’s an apt description.

As an analogy, in our organizations, tau might be the natural period of human adjustment to new habits and procedures (they oscillate between the old and the new trying to figure things out). K is the leverage gained in the system by instituting a new procedure and zeta is sometimes the passive aggressive or natural resistance to change. The settling period is how long it takes to get the new process and/or procedure to be habit.

Obviously, an organization cannot grow without change. And change is only rarely smooth and easy with an asymptotic approach to the desired end state. What I’ve come to understand is that while sometimes we do need to allow fast change and overshoot, many times we do not need to change the system so quickly.

Incremental Change

Even when we undertake large change initiatives, we can break things into bite size chunks for the organization. Perhaps a good example is implementing a new Enterprise Resource Planning (ERP) system. For many organizations, such a project will be long, difficult and expensive with much disruption.

Yet, it can be broken into smaller and more manageable change projects. One might start with simply documenting, accurately, what is being done today. It is surprising how frequently what is actually being done doesn’t adhere to the written documentation. This procedure will likely result in small changes being made now to “fix” processes. Analyzing where the organization wants to go and what procedures need to change to be more efficient is a bit more difficult. Still the changes can be somewhat incremental in many instances. All this will lead up to a full ERP implementation which can almost be guaranteed to not work properly at first. So adjustments will be made.

Constantly Changing

It is now accepted fact that organizations must change constantly and quickly these days. I see that need exacerbating the problem of overshoot as we either try to do too much at once or do not let one change reach the settling time before piling on another change. As leaders, we need to be managing the pace of change. Allowing for settling time is critical to avoiding change burnout.

Market or Product disruption is the external (or if we’re smart, internal) step function change that perturbs our system. How we respond to that depends on our skills at leading the organization through the required changes.

Conclusion

I believe that a leader really has only one responsibility and that is to actively manage the organization’s culture. Since culture is “how we do things around here,” then managing change is surely part of that responsibility. Minimizing the over-correction to required changes will maintain productivity during the implementation time.

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