Measure What Is Likely to Happen, Not What Has Already Happened

Originally titled 'The Importance of Lead Measures'

Instead of measuring what has already happened, measure what is likely to happen.

Measures or metrics in evaluating organizational effectiveness are important because “what gets measured gets managed.” However, most of the measures we select reflect what has already happened. These are classified as “lag measures,” examples of which include sales revenue per month, lost-time accidents, customer returns, on-time delivery rates, machine downtime hours per time period, inventory turns and more. Although valuable in determining how a company has already performed, lag measures are limited in their usefulness in predicting an organization’s needs going forward. For this reason, companies must adopt “lead measures,” which are actions that can predict future performance, though they cannot guarantee that performance. It is important to note that these actions can be controlled by those responsible for specific performance results.

A common dilemma associated with lead measures is that they are difficult to develop because they often requiring a great deal of thought and discussion. Sometimes there is even confusion as to what a lead measure really is because it might actually be a lag measure in disguise. For example, it would be incorrect to think that the number of machines run on a given day could predict the number of jobs completed. Because we will not know the number of machines that ran until the end of that day, this is more of a lag measure of machine performance. It may not adequately predict the number of jobs actually completed during the day.

Here are some lead measure examples for specific performance objectives you may wish to consider for your organization:

Objective

Lead Measure

Why It Is a Lead Measure

Reduce average lead time from six to three weeks.

Percent of jobs not started during the first week.

Past practice shows that a job not started during the first week is highly unlikely to be finished within three weeks. Those responsible for reducing lead time can influence this lead measure by starting any job within the first week it is received. 

     

Increase customer satisfaction from 75 to 90 percent based on positive responses on customer surveys.

Percent of satisfied customer service representatives as measured by their score on an employee engagement questionnaire.

It is predictive because research has shown that a satisfied employee will be more motivated to provide excellent service to customers. Customer service representatives can influence this lead measure because they can control how they interact with customers during each engagement.

     

Reduce number of lost time accidents by 10 percent.

Full compliance on employees wearing personal protective equipment (PPE).

This measure is predictive because employees who wear the appropriate PPE, such as safety glasses, safety shoes, hard hats and gloves are less likely to get injured on the job. Employees can influence this lead measure because they make the decision on whether to wear PPE.

     

Increase the first-piece inspection acceptance rate from 92 to 98 percent.

Number of machine operators who can accurately inspect their own work.

It is predictive because an operator who is able to inspect his or her work should not bring a part to the first-piece inspector if it is not machined “to print.” Once trained, the operator can influence this lead measure by checking the part to confirm it is correct before going to first-piece inspection.

     

Reduce unplanned machine downtime from 15 to 5 percent.

Percent of preventive maintenance activities performed on schedule.

This measure is predictive as maintenance records have shown that preventive maintenance routines increase machine uptime, while extending the useful life of the machine. The lead measure can be influenced because manufacturing controls the availability of machines scheduled for preventive maintenance.

     

Increase assembled-product output by 10 percent each month.

Percent of employees trained to assemble all products.

This measure is predictive as it has been shown an increase in available resources leads to an increase in assembled product output. Employees trained to produce all products increases the pool of resources available at any time. Departments can influence this lead measure by assigning the trained employees to assemble any of the products that are scheduled.

     

Increase sales revenue by 10 percent this year.

Percent of quotes completed and sent to customers same day.

It is predictive as fast quote turnaround yields more orders, which produces higher sales revenue. Employees can influence this lead measure by completing the quote and sending it to a customer the day it is received.