Labor Productivity vs. Information Effectiveness

Productivity is a popular measure of process performance.  It can be misleading if too much attention is paid just to it.  The Agile and Lean Glossary defines productivity as a ratio of the customer value produced per unit of labor.  When labor productivity is the primary focus information effectiveness suffers and the result is higher costs, poorer quality, and lower customer satisfaction.

The emphasis on productivity causes too much attention to be placed on the labor cost denominator of the productivity calculation rather than the customer value numerator.  Reducing the number of hours or cost per hour to produce customer value increases productivity.  This then becomes the goal.

For example, investments in information technologies are typically justified by increasing labor productivity.  If it takes 8 labor hours to create a unit of customer value and a tool can be created to reduce this to 4 hours; the result is a 50% increase in productivity. 

Productivity in our Blood

The use of a tool to increase productivity is something people understand.  It is in our DNA.  We have been prodigious tool users for a very long time.  But too much of a good thing leads to an unbalance. 

To say that the only measure for process improvement is productivity is the same as saying the only tool needed to build a house is a hammer.  Certainly conceivable to do, but add a few more tools to the tool box and the power of possibilities increase.

Lada’s Laws define Service Time and the Lean Ratio as two additional measures that help identify new possibilities for process improvement.  

The calculation of service time is divided into two components:

·         work time

·         wait time

Productivity measures the effectiveness of work time.  Reduce work time and productivity increases.  But reducing work times may not have an impact on wait time.  In fact increasing productivity by reducing work times could cause wait times to stay the same or even increase.  Longer wait times lead to increased cost, more errors, and lower customer satisfaction.

Information Effectiveness

If work time is an indicator of labor productivity, wait time is an indicator of information effectiveness.  What is waiting during that wait time is information.  People are busy.  At any point in time they are applying their labor somewhere. 

In the typical process information is lazy.  It spends a lot of time waiting around for someone to pay attention. And if information is waiting then so is a customer.

Two Perspectives of Improvement

Take the case of customers waiting for their service requests to be completed.  Let’s say that the average service time for those requests is 4 weeks and that the total work time to complete the service request is 16 hours.

If we were able to increase the labor productivity associated with the service request by 50% our work time would be reduced to 8 hours from 16.  Most organizations would consider a 50% increase in productivity a huge win and a significant cost savings. 

From the customer point of view, what used to take 4 weeks would now only take 3 weeks and 4 days.  Would the customer even notice the difference?  An organization focusing only on productivity may not even notice the problem, but their customers would.  Under this scenario you could conceivably reduce the work time to zero and still take 3 weeks and 3 days.

This is a hard lesson to learn.  Time after time, organizations attempt to reduce service times by solely measuring and increasing productivity.  Paying attention to the wait time component of service time, in addition to the work time, provides a more balanced view that leads to increases in both labor productivity and information effectiveness.

Case Study: Measuring Process Performance

Service time is defined by the Agile and Lean Glossary as the wall clock time necessary to provide a service from start to finish.  It is measured by noting the start time and the end time of a unit of customer value as it flows through a process.  Service time can be averaged over a period of time to determine how long customers must wait to receive a product or service.  But averages can be misleading, especially if there is a wide variation in delivered service times.

Figure #1 is a histogram of the service times of an organization’s customer service requests over a four month period prior to implementing a Lean process improvement project.  The histogram was created by counting the number of service requests that completed for each time bucket across the bottom axis.  Those that completed from 0 to 10 days were counted in the “10” bar on the chart.  Those that were completed from 11 to 20 days were counted in the “20” bar, etc..  The total service requests for each month are shown using a different color segment on each bar.

Figure #1

The distribution of service requests shows a peak around 40 days with a long tail out beyond 300 days.  Building to an initial peak followed by a long tail is a common distribution curve of service times.  In this case, the tail was unusually long, with almost 20% of the requests taking over a year to complete.

Averages can be Misleading

Prior to development of these service time histograms, the organization simply calculated the average service time of their service requests.  Although many improvements made as part of a Lean process improvement project, the average service time remained stubbornly flat at 150 days.  Simply calculating the average service time did not reflect the impacts from the improvements being made.

Figure #2 is a histogram of service times for the same organization two years later.

Figure #2

Comparing the histograms of Figure #1 and Figure #2, the latter shows a tighter distribution of fulfilled requests at a peak of around 30 days.  Then a rapid fall off at 60 days to a long flat tail.  The histogram of Figure #2 shows progress over Figure #1 by reducing the customer service time for the majority of customers.  Even though the average service time each month was still around 150 days.

Average service time was never an accurate measure of performance because of the length of the distribution tail.  The average service time only rose or fell in a given month based on the number of 300+ day service requests that were delivered in that month.  It did not take many 300+ day service requests to increase the average service time calculation; even with service times in the 30’s. 

One of the efforts of the Lean initiative was to reduce the work-in-process (WIP) and close out old service requests.  During those months the average service time was worse than 150 days.

Why the long tail?
In this case study root causes were:

1.   Customers learned to put projects in the queue long before they were needed just to insure they would be completed on time.

2.   There was a mix of customer requests; some fully ready for execution and others that first required complex analysis. There was only one process to handle both alternatives.  The second peak at 130 days in Figure #2 represented complex projects.

3.   There was a lot of WIP with no priority to which work should be completed first; causing some work to flow through quickly and other work to literally get lost on someone’s desk

Process Predictability
The lesson learned from this case study is that average service time is not a sufficient to measure process performance.  Average service time is easy to calculate.  Little’s Law defines the average service time (total cycle time) as ST = WIP / Throughput.  But this equation does not consider which element of WIP is throughput next.  

Charting the distribution of service time provides insight into the predictability of a process.  Providing a customer a service ready date based on an average calculation can cause dissatisfaction if their actual service ready date can vary greatly from the target.  A tight service time distribution with a Lean Ratio of 2 or less is an indicator of a high performing Lean process that delivers maximum value for minimum cost.