Most warehouse professionals understand how to calculate required labor hours but few do it while taking quality rates into account. Let’s look at an example.
Picking Labor Example
A typical calculation for required headcount for picking looks at the direct picker rates against the volume, then lays on an indirect labor percentage and an absenteeism or PTO rate. The direct rate is what actually gets the required picking done. The Indirect labor allowance percentage accounts for time lost during the day, such as during startup meetings, getting equipment, moving between work areas, and so on. The absenteeism & PTO rate accounts for the headcount required to make up for days off.
Sometimes there is a separate factor applied for turnover, new hires, and learning. But this is more complicated, it may be easier to add a percentage point or two to the Indirect allowance.
This all might look like the below:
This is pretty good for starting planning for a single process. But there is a key assumption built into one of the numbers. Let’s take a look.
Quality Impacts on Productivity
The typical labor calculation above doesn’t mention Quality. Quality is a key factor affecting productivity but is usually not included in labor hours calculations. Why not?
There are a few possible reasons:
- First, that the picker’s rate already includes the time spent on mis-picks, pick shorts, processing damage, or what-not. This implies that the rate is a benchmarked standard, not an engineered standard.
- Second, that the indirect allowance of 15% includes time spend on rework or error-handling as the cost of doing business. I have never heard this explicitly included in the allowance, but it could be an assumed value.
- Last, the operation assumes that the quality and rework labor goes into the Inventory labor calculation.
The third response is most common with the first response coming in after it. But it is not common for operations to call out the effects of quality on their production rates.
But quality must have an effect. It is hugely important and fixing mistakes has a big cost. So what would the extra labor look like if a quality factor were included?
Picking Labor With Quality Factor
Now we explore a scenario where Picking has an error rate of 1%.
It doesn’t matter whether the errors are pick shorts, picking an incorrect item, or damages. Those things could originate from other sources like receiving, or putaway, or replenishment functions. What matters is that there is an error that someone then has to take time to research and correct.
The corrections could be inventory adjustments, additional replenishments, damage rework, and/or sending a picker to complete another pick. In any case they are much more time consuming and costly than just doing the pick right the first time. We use a multiplier of 5x as the time required to fix an error found in the picking process.
For a modest 1% error rate–not hard to imagine in a facility with a lot of SKUs, replenishment, high bin utilization, and volume–we see an increase of 3.1 headcount on top of the 62.5 originally calculated. That is almost a 5% increase in headcount for 1% error rate. It is a function of the rework multiplier, so while we make an assumption there, we think it’s a fair one and that even up to 10x could be reasonable. If you’ve ever tried to fix a mispick to a tote, you know it takes a lot more time than just doing the pick!
Implications and What To Do
When a staffing model calculates a certain number of headcount, it is assuming a certain defect and rework rate in each process. It does this either in the rates themselves or in the number of headcount identified to process and fix the rework.
Where many facilities get surprised is in not specifying the quality and rework assumptions, leading to overspending on labor to fix quality issues. And the hard part is that the direct rates may be met, but the rework & indirect labor spend may still be over budget, leading to hard-to-answer questions about why the labor spend is so high.
So what do we do about it?
When building the model, the key bit is to list the quality or accuracy assumptions that the model is built on. If the facility standard is a 99.5% picking error rate, then list that. Then calculate the number of implied errors to fix from that error rate, then assign fixing throughput rate assumptions to the inventory or rework staffing.
If for any reason picking error rates later exceed the assumption, it’s now very clear where the additional headcount requirement came from.
Conclusion
“All models are wrong, some are useful” goes the saying. Staffing models are no exception. One reason for this is not understanding the impacts of quality defects on the overall facility throughput. To adjust for this and develop more accurate (or at least explainable) models, build in the quality assumptions for each process. Include the rework generated and the labor required to do it, and you’ll find that your models are now more accurate and traceable.