What You Can Predict, You Can Impact


What You Can Predict, You Can Impact  

Unlocking the value in your organization requires having an impact on key performance metrics. As an organization, you have to be able to effect measures that characterize your business performance and drive business value. One of the first steps effecting a metric is trying to predict it. The theory is if you can predict it, you will understand how to impact it.

There are two key benefits to predicting a specific metric:  1.) it demonstrates your understanding of the metric and the variables effecting it; and 2.) it creates a learning environment.

Understanding the Metric

The unemployment rate is a key measure of economic performance of a country. It is defined as the number of persons unemployed divided by the total labor force. The total labor force is defined as the adult population actively looking for work.

If we challenge ourselves to predict the unemployment rate for next year, we will have to start by understanding these variables. How do we determine the total number of people unemployed? Is it a phone survey? What questions do they ask?  How do they determine the total work force? Is it based on the latest census? How do they determine if someone is actively looking for work?

We would argue the same questions will rise out of asking to predict basic business measures. How is productivity calculated?  Who are the employees counted as part of actual hours worked? Where do the standard hours earned come from? How does the engineer determine labor hours required per part? These questions all rise out of the challenge to predict the productivity measure for the shift before the shift starts.

Predicting the metric drives understanding of how the metric works and that leads to understanding of how to impact it. If you can accurately predict a measure, you are in the best position to impact it.

Creates a Learning Environment

Predicting a metric puts the organization in a position to learn. Not only do they have to understand the metric to predict it, but they also have to make assumptions and expectations for the variables that make up the metric. The predictions for the variables form a hypothesis and the scientific method begins.

If we think of predicting the unemployment rate, we would need to form a hypothesis about how many people will say they are actively looking for work. We might look into plant closures or seasonal changes in the service industry’s hiring practices. Using this data, a hypothesis can be formed and when the results come in, we learn about the correlation between unemployment rate and plant closures or seasonal changes in the service industry.

The same occurs in our business when we have to predict – such as who will work on the product and how many standard hours do we predict they will complete. Then as the results occur, we learn what it takes to impact in detail the metrics.

Predicting metrics is like everything else in operational excellence – it is rarely complicated, but often hard word. Help your organization put the effort into learning how to predict and impact the measures that effect business performance.

Learn more in Patrick’s book, “Facilitating Effective Change,” available online through Amazon and Barnes & Noble.  

Patrick Putorti

Patrick Putorti

Patrick Putorti

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