Business Operations & Consulting5.0 · 0 ratings

Capacity Planning & Resource Model

Builds a demand-vs-capacity model to expose over/under-staffing and recommends hiring, reallocation, or automation moves.

Role-BasedStep-by-StepStructured-Output

Prompt

ROLE: You are a workforce and capacity planning consultant for operational teams.

CONTEXT: The team is [TEAM/FUNCTION] with [HEADCOUNT] people. The work intake: [DESCRIBE DEMAND — tickets/projects/units per period, with volumes if known]. Average effort per unit of work: [TIME ESTIMATES]. Productive hours per person per period after meetings/admin: [EFFECTIVE HOURS]. Expected demand change: [GROWTH/SEASONALITY].

TASK:
1. Estimate total demand in hours per period from volumes and effort estimates.
2. Estimate true available capacity, accounting for utilization realities (PTO, meetings, ramp time, context-switching).
3. Compare demand vs. capacity to reveal the gap (surplus or deficit) per period.
4. Model 3 scenarios: baseline, +[X]% demand growth, and a scenario with [automation/process change] applied.
5. Recommend the capacity strategy: hire, reallocate, outsource, automate, or defer — with the breakeven point for each.

OUTPUT FORMAT:
- Demand calculation (show the math)
- Capacity calculation (show the math and utilization assumptions)
- Gap analysis table by scenario
- Recommendation with breakeven reasoning
- Watch-metrics to re-plan when they trip

CONSTRAINTS: Do not assume 100% utilization — name a realistic figure and justify it. Show every formula so I can change inputs. If my data is too thin to model a scenario, state exactly what to measure. Avoid recommending headcount as the default answer; weigh it against process and automation.

How to use this prompt

  1. 1

    Copy the prompt above and paste it into ChatGPT, Claude, or Gemini — or open it in the visual Studio to edit each part on a canvas and run it with your own key.

  2. 2

    Replace any bracketed placeholders with your specifics. The more concrete your context and constraints, the sharper the result — see the 5-part prompt structure.

  3. 3

    Run it, then refine. Ask the model to critique and improve its own answer with self-critique prompting.

Techniques in this prompt

Role-Based

Assigns the model an expert persona so it adopts the right vocabulary, depth, and standards for the task.

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Step-by-Step

Forces explicit intermediate reasoning instead of jumping to a conclusion, which improves accuracy on hard tasks.

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Structured Output

Pins the response to a defined structure so it drops straight into your workflow.

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Build on this prompt

Open it in the visual Studio to wire it into a full workflow with your own API key — or learn the craft behind prompts like this.

Open in Studio How to prompt AI correctly

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