How much productivity is there in performance?
Productivity is a measure of the efficiency of production, expressed as the ratio of output to inputs used. Performance is defined as the accomplishment of a given task measured against preset standards of achievement, such as accuracy, completeness, cost and speed.
In the wider context of performance management, productivity is measured against productivity KPIs. In their simplest form, productivity KPIs, such as # Units per man-hour, stand at the basis of both modern and older performance evaluation systems. However, it is only but natural that we ask ourselves the following question: How much productivity is there left to both measure and reflect on performance?
In her book, The Measurement Nightmare: How the Theory of Constraints Can Resolve Conflicting Strategies, Policies, and Measures (1999), Debra Smith talks to her readers about a real-life situation, based on one of the most common productivity KPIs in use: # Units per man-hour. And it all starts with defining the KPI. According to her, # Units per man-hour is a “summary of standard costing’s use of standard labor hours and standard labor rates, resulting in labor variance analysis and decisions designed to improve”.
There is not one productivity indicator that does not reflect on performance. And there is not one neglected faction of performance that does not impact productivity in one way or the other.
From here on, Debra Smith describes this particular situation in which, on an intuitive basis, some executive manager from a manufacturing company decides to increase # Units per man-hour by cutting labor costs with highly automated machines. So, instead of 6 loom operators, 4 were assigned to tend to one loom per shift.
And the effect was as expected…at first. # Units per man-hour had increased at the loom. However, because of the downtime of the looms which now increased, the total output of the looms had decreased.
Due to a lack of attending operators, the downtime of the machines escalated up to a point where it impaired all subsequent processes. When that happened, all downstream processes began to suffer from starvation. % On-time delivery of products declined, $ Labor costs went up due to # Overtime and, instead of going up, $ Net profit went down.
Debra Smith’s account of the negative side effects one productivity measure can propagate, when taken out of the context of performance, stand to show that there is more to productivity in performance than counting outputs per unit of input. And this is more visible when dealing with the most popular dimension, which is labor productivity.
In the context of performance management, labor productivity can be translated through individual KPIs. When dealing with employee performance, individual productivity KPIs become part of a more complex performance evaluation system. The overall individual performance index simulates an average between the score of the individual performance scorecard, the individual competencies score, and the employee behaviors score.
Where do KPIs fit into this equation? Productivity KPIs are mindfully incorporated into the individual performance scorecard, to best reflect the quantitative aspects of employee performance. And this is where everything gets tricky and we start asking ourselves: How much of one employee’s performance should be measured in terms of quantity?
Let’s take, for example, the automotive industry. With automotive manufacturing, productivity is a key performance indicator that measures the total production volume of the actual manpower, while taking into consideration the effective days officially scheduled for each automobile.
The core performance indicator of the automotive industry is # Hours per unit or # HPU, and it reveals the number of hours required to build a car. However, at its basis, this # HPU cannot be measured outside # Available manpower, # Effective working time, and # Individual production volume. Let’s add % Absenteeism rate to this reasoning.
When dealing with target production volumes it is important that the plant works at its full throttle to achieve those targets. Given this requirement, % Absenteeism rates should not be overlooked, as they have a major impact on the # Effective working time, which here on, impacts the # Production volume, and ultimately, the # HPU.
However quantifiable, % Absenteeism rates also reflect on less quantifiable variables. This further takes us to the issue of % Employee engagement: a roughly quantifiable, uncontrollable driver of not only productivity but of performance as well.
So, how much productivity is there left, to both measure and reflect on performance? A great deal. And maybe the best way to look at it is by envisioning this revolving cartwheel…this continuous circle, which turns productivity into performance and vice versa.
All things considered, there is not one productivity indicator that does not reflect on performance. And there is not one neglected faction of performance that does not impact the former in one way or the other.