One standard practice to improve warehouse efficiency and productivity is to use performance standards for non-exempt employees. Performance standards management will help ensure that the employees are productive. But you can’t just “turn the knob to 11.” The standards have to be reasonable and attainable.
This implies a trade-off: higher standards mean more turnover, which is costly. Let’s explore this tradeoff and think about where high standards cost more than they’re worth.
Types of Performance Standards
In summary, engineered standards are created by someone timing each step of a process, applying a utilization factor, and using that number as the target performance rate.
Benchmarked standards are used by analyzing group performance to figure out what the number should be, usually set at a percentile of group performance.
Then these standards are used in performance management programs.
Sites use the standards to run their performance management programs. The nature of standards means that any standard will result in turnover. Another way to say this is that if there is a performance bar, some percentage of the people held to the standard will fail it.
A corollary to this is that more stringent standards result in more turnover. All things equal, a rate requirement of 100 picks-per-hour will result in more production from able employees but also more turnover than a rate requirement of 75 picks-per-hour.
Why is this important? Turnover is costly. It’s also notoriously hard to quantify. Common estimates of the total cost of turning over an employee at 10% to 30% of annual salary, to 6 months salary, to checklist-based calculations. Whatever the number, it includes:
- Marketing the position
- Recruiter time screening and processing
- Manager time interviewing
- Lost production over the role’s learning curve
- Training costs
- other various costs
Sources of turnover include attendance, safety and behavioral violations, and performance. Attendance and performance tend to be the biggest factors causing terminations. Performance standards are often directly controllable by the site. So let’s examine that as the primary controllable factor in turnover.
For our estimate, we’ll use 20% of annual salary as the cost. Considering 50% turnover at a site, this means that .5 * .2 = .1, or 10% of annual payroll equivalent is going into turnover costs. So this is a big amount of money which might be saved if turnover goes down.
That’s a total cost estimate. How much of that cost is production impact? And what does that mean for the overall contribution margin of the site? This is very tough to quantify, but let’s take a swing.
Estimating the Trade-off
So let’s look at what having different performance requirements does to turnover and then to productivity.
We’ll make a few simplifying assumptions:
- The new warehouse worker hire produces at an average of 75% efficiency during the learning curve
- The example will be for a benchmarked standard with the standard set at a percentile of the population performance. (This could easily be translated to an engineered standard so long as the impact on turnover can be measured.)
- The turnover from the benchmarked standard is less than the performance percentile cutoff.
- Everyone meeting the goal is achieving efficiency of 100% (not strictly true but enough for illustrating the case).
- The system is in steady-state.
Now let’s look at what happens at different levels of performance standards:
In table 1 we see mock data for a set of increasing performance standards. The first row indicates a situation where the performance standard is set at the 10th percentile of group performance, resulting in performance-related turnover of 3%. This leaves 97% experienced employees with efficiency rates of 100%.
(Again, this wouldn’t be the strict case in real life. The actual correlation to standards and turnover could be analyzed from performance data and terminations causes. But this is close enough for our needs. )
Then we assume that the new hires complete their onboarding and learning curve with an *average* efficiency of 75%. Think of a case where week 1 they achieve 40%, week 2 they achieve 70%, week 3 they achieve 90%, and week 4 they achieve 100%. This creates an average efficiency of 75% over the 4 weeks onboarding.
Now the magic happens. The “Weighted average efficiency” takes a weighted average of the efficiencies of the turnover employees and the experienced employees. So more turnover means more less-experienced employees with lower efficiencies. This reduces the average efficiency of the whole group. In a graph, we see:
This shows that as the performance standard gets higher (x-axis), turnover increases (“Turnover at performance %ile”). As turnover increases, the average weighted efficiency of the group decreases (“Average efficiency”).
Now keep in mind that these are hypotheticals. The actual turnover from production performance rates would vary from site to site and from role to role.
What about actual production and turnover cost?
Let’s apply this to a employee population of pickers. The pickers picking ability is 100 picks per hour with a standard deviation of 15 picks per hour. The group productivity given the performance standards and turnover discussed would look like this:
We see that increasing standards improves the nominal production up to a point. But with standards climbing we see that the actual productivity gains don’t climb nearly as fast as the standards.
The average productivity actually gets lower at the inflection point with the standard at 80% (in this example) of group performance. Remember, this is because of the increasing turnover rates at higher performance standards.
There are a couple points to take away from this.
Managers and employees are under a lot of pressure to perform. But raising the standard doesn’t always bring the best results. In fact, raising the standards too high will result in more turnover. At some point productivity begins dropping because so many employees are inexperienced and can’t perform as well.
The lesson is clear. It’s worth being careful of how high the bar is set in performance standards. Setting productivity metrics should be done keeping in mind achievability and then checking on turnover. Calculate turnover impacts on net productivity and adjust performance standards for overall performance.
And let’s not forget, achievable standards and less turnover creates a better job and better place to work, too. These are how to serve the customer and your team the best way possible.