Find out why overtime is rising and how to cut it
An analysis of overtime data that separates demand, staffing, and process causes, with targeted actions to reduce it.
The prompt
Help me understand and reduce overtime in my operation.
Overtime data: [PASTE - hours by week, by team/shift/person if available]
Volume or workload data for the same period: [e.g. orders, calls, units]
Headcount and vacancies, absence rates: [DETAILS]
Budget or target: [OVERTIME BUDGET OR %]
What I suspect: [e.g. "vacancies", "Monday peaks", "a few people take most of it"]
Analyze:
1. Trend: overtime hours and % of total hours over time, compared with volume. Is overtime growing faster than workload?
2. Concentration: by team, shift, day of week, and person. Does a small group account for most of it? (Flag fatigue and safety risk if anyone works very long hours.)
3. Causes, split into: demand (volume peaks, forecast misses), supply (vacancies, absence, training, turnover), and process (rework, downtime, poor scheduling, late arrivals from upstream).
4. Cost: overtime cost vs. alternatives (temp labor, part-time hires, shift changes), using my rates; mark missing figures [ADD].
5. Actions ranked by impact and effort, with what to measure to confirm they worked.
State which conclusions the data supports and which are hypotheses. Overtime rules and pay obligations vary by location and contract; check changes with HR, and don't suggest cutting overtime in ways that leave people unpaid for work done.
Highlighted [brackets] are blanks to replace with your own details.
Fill in the blanks and use this promptMore Data & Analysis prompts
What to fill in
- PASTE - hours by week, by team/shift/person if available
- e.g. orders, calls, units
- DETAILS
- OVERTIME BUDGET OR %
- ADD
How this prompt is built
This prompt follows the Few-Shot structure (Few-shot prompting). A few-shot prompt includes one or more worked examples of the input and the output you want, so the model can copy the pattern instead of guessing it.
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