The Overtime Graph
A warehouse supervisor, Priya, is reviewing a chart of overtime hours for her 40-person team. The chart shows monthly overtime rising from 60 hours in January to 210 hours in June. She concludes that her team is overworked and drafts a request for two additional staff.
Before sending it, she checks the underlying records. Total units shipped also rose over the same period, from 8,000 in January to 28,000 in June. Overtime hours per 1,000 units fell from 7.5 in January to 7.5 in June - actually unchanged, because both figures roughly tripled. Two of the six months included a temporary contract crew of 6 people who were paid through the same overtime code by mistake. A new conveyor system went live in April, and the April–June figures include 40 hours of training time logged as overtime.
1. Evaluate Priya's conclusion that the team is overworked. What does the evidence actually support?
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Priya's conclusion is not supported as stated. Total overtime rose, but so did output - from 8,000 to 28,000 units, a 3.5-fold increase. Overtime rose from 60 to 210 hours, also 3.5-fold. On a per-unit basis the ratio is flat, so the team is not working proportionally harder per unit shipped; it is handling far more volume.
Two data problems weaken the raw total further:
- Misclassified contract labor. Six temporary workers' hours were coded as overtime. Those hours belong to a separate cost line and inflate the team's apparent burden.
- Training time. Forty hours of April–June overtime were training on the new conveyor, a one-time investment, not recurring workload.
What the evidence does support is that volume grew sharply and that the team absorbed it. Whether staffing is adequate depends on capacity limits, not on the raw overtime trend. Priya should recompute overtime excluding the contract crew and training, then compare that figure to output before drawing any conclusion.
2. Priya still believes more staff may be needed. Describe what additional evidence would make the case credible, and how she should present it.
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A credible case would rest on capacity measures, not raw hours.
Useful evidence to gather:
- Adjusted overtime per unit, with contract labor and training removed, tracked monthly. If it is flat or falling, the argument for headcount weakens; if it is climbing, it strengthens.
- Peak-period data. Average figures hide crunch weeks. Weekly overtime in the busiest periods shows whether the team is hitting a ceiling.
- Error, injury, and turnover rates. Rising mistakes or injuries during high-volume weeks are direct evidence of strain.
- Backlog and missed shipments. If orders are slipping, capacity is genuinely short.
- Forecast volume. If 28,000 units is the new normal or a floor, staffing should be sized to it.
How to present it: lead with the question ("can we sustain this volume?"), show the adjusted trend, acknowledge that productivity per unit held steady, and frame the request around forecast volume rather than a scary-looking total. A request built on corrected numbers is far more persuasive than one a finance reviewer can dismantle in five minutes.