AI Costing Prompts · By task
Quantify the cost of unused capacity
Give an AI model your capacity and cost figures and get the rate per hour at practical capacity, the cost of the capacity you actually used, and the cost of the capacity that sat idle. The prompt also explains why spreading idle cost across products quietly destroys your product costing.
In short
Provide the machine count, theoretical hours, a practical capacity factor, total monthly cost and actual productive hours. The model computes theoretical and practical capacity, sets the cost rate per hour at practical capacity, then splits total cost into used and unused. The unused figure is the cost of idle capacity, and it should be reported as a period cost, not buried in product cost where it triggers a death spiral.
What the prompt is doing
This is the capacity logic that sits underneath time-driven activity-based costing. The crucial move is to set the cost rate using practical capacity, the hours you can realistically run after maintenance, setups and normal downtime, rather than theoretical capacity or last month's actual hours. Practical capacity is usually around 80 to 85% of theoretical, and using it gives a stable rate that does not lurch every time volume changes. See capacity costing for the full method.
The reason this matters is the death spiral. If you divide total cost by only the hours you happened to use, the rate rises whenever volume falls. That higher rate inflates product cost, which makes products look unprofitable, which leads to dropping them, which lowers volume further, which raises the rate again. The fix is to cost products at the practical-capacity rate and report the cost of unused capacity separately, as a visible management number rather than a hidden tax on the products that remain. This is the same discipline you build into a TDABC model.
The prompt
You are a cost accountant. Work only from the data I give you. Do not invent any numbers. Flag any assumptions clearly. Production cell: Machines: 4 Available hours per machine per month (theoretical): 360 Practical capacity factor: 85% Total monthly cost: 48,000 EUR Actual productive hours last month: 980 Do the following: 1. Calculate theoretical capacity in hours. 2. Calculate practical capacity in hours. 3. Calculate the capacity cost rate per hour (at practical capacity). 4. Calculate the cost of used capacity. 5. Calculate the cost of unused capacity and unused capacity as a % of practical capacity. 6. Explain why spreading the unused cost across products distorts product cost. 7. List any assumptions. Output a summary table.
A worked example
| Measure | Value |
|---|---|
| Theoretical capacity | 4 x 360 = 1,440 h |
| Practical capacity | 1,440 x 0.85 = 1,224 h |
| Capacity cost rate | 48,000 / 1,224 = 39.22 EUR/h |
| Used capacity cost | 980 x 39.22 = 38,431 EUR |
| Unused capacity cost | 48,000 - 38,431 = 9,569 EUR |
| Unused capacity | 244 h = 19.9% of practical |
| Utilisation | 80.1% of practical |
Product cost should be built at the practical-capacity rate of 39.22 EUR/h, with the 9,569 EUR of unused capacity reported as a separate period item.
If instead you spread the full 48,000 over only the 980 productive hours, the rate jumps to 48.98 EUR/h, a 24.9% inflation. That is the death spiral in numbers: higher unit cost makes a product look unprofitable, you drop it, volume falls, and the rate rises again on what is left. Keeping the rate anchored to practical capacity breaks the loop and turns idle cost into a visible management decision rather than a hidden distortion.
Assumption flagged: total cost is not split into fixed and variable here. If part of the 48,000 is genuinely variable, it falls when hours fall, so the true cost of idle capacity is lower. Treat the 9,569 EUR as an upper bound until the fixed and variable split is known.
What it costs you to run
The input runs to about 280 tokens. A full run with the calculations, the summary table and the death-spiral explanation costs a fraction of a cent on any current model. Re-running it for several cells or for different utilisation levels stays trivially cheap.
Token figures are approximate and vary by model and tokeniser.
The guardrail that matters
Work only from the data I give you. Do not invent any numbers. Flag any assumptions clearly.
Capacity prompts go wrong when a model assumes a practical capacity factor or a fixed-versus-variable split that you did not give it. This line keeps it inside your numbers and makes it state the upper-bound caveat instead of presenting one figure as certain. See how to stop AI inventing numbers.
Decide what to do with the idle capacity
The prompt above prices the idle capacity and explains why you should not bury it in product cost. This next one takes that number and lays out the three real options, grow demand into it, reduce it, or repurpose it, with the arithmetic and the trade-off behind each. Run it once you have the cost of unused capacity from the prompt above.
Three options for the cost of unused capacity
You are a cost accountant advising on capacity. Work only from the data I give you. Do not invent any numbers; flag every assumption clearly. Where a figure is missing, ask once before you continue. I have a cost of unused capacity already. I want to weigh the three honest options for it, with the numbers behind each. Step 1. Restate the position. Take the theoretical and practical capacity, the capacity cost rate at practical capacity, the used and unused hours, and the cost of unused capacity in money. Confirm the utilisation as a percentage of practical capacity. If total cost is not split into fixed and variable, treat the idle cost as an upper bound and say so. Step 2. Option A, grow demand into it. Compute how much additional volume (in hours, and in units if I give a time per unit) would fill the idle capacity, and the contribution that volume would earn at the practical-capacity rate. State the commercial condition that would have to be true (orders won, a customer added) for this to be real, without inventing that it will happen. Step 3. Option B, reduce it. Identify what could be removed to shrink practical capacity toward actual use (hours, a shift, a machine, a contract), and the cost that would come out. Note the cost of getting it wrong: reduced flexibility, and the higher rate that lands on the remaining products if volume later recovers. Step 4. Option C, repurpose it. Identify work the idle capacity could absorb (insourcing something currently bought, a new line, internal projects), and the cost avoided or value created. Keep it conditional; do not assume the work exists. Step 5. Lay the three side by side: option | the money in play | what has to be true | the main risk. Recommend which to explore first given the utilisation level, and name the one number that would most change the recommendation. Show every formula, show your working as tables, and end with the full assumption list so I can correct it and have you rerun.
Idle capacity is a management decision, not an accounting entry. The value of pricing it separately is precisely that it forces this choice into the open instead of hiding it inside unit costs.
Make it a deliverable you can edit and show
The prompts on this page produce a capacity analysis in the chat. This add-on turns it into a file you can edit and put in front of an operations or finance lead, an adjustable Excel model, a clean PDF, a short deck, or a diagram. Paste it after the capacity prompt once the numbers have run.
Now package this capacity analysis into a deliverable I can edit and show colleagues. Ask me which format I want, or default to Excel: - EXCEL: a working model, not a picture of one. Put the inputs (machine count, theoretical hours, practical capacity factor, total cost, actual productive hours) on one clearly marked input sheet, and drive theoretical and practical capacity, the cost rate, used cost and unused cost with live formulas that reference those inputs, so when I change the practical factor or the hours the whole model updates. Add a summary sheet with the capacity table and, if the tool allows, a chart (used versus unused capacity, in money or in hours). Label every assumption cell. - PDF: a clean, board-readable report, title, one-paragraph summary of utilisation and the cost of idle capacity, the capacity table, one chart, and an assumptions appendix. No chat formatting. - PPT: 5 to 7 slides, the question, the practical-capacity method in one slide, the used-versus-idle split, the death-spiral point (why not to spread idle cost across products), the three options for the idle, next steps. - DIAGRAM: a single clear figure of the capacity logic (theoretical to practical capacity, cost divided by practical capacity to the rate, then split into used and unused cost), as an editable vector or a described layout I can rebuild. Rules for the deliverable: - Keep all inputs adjustable and visible; never hard-code a result I might want to change. - Carry through every assumption and label illustrative figures as illustrative. - Add a discreet footer or last-slide credit line, small and unobtrusive, exactly as written below. Credit line to embed (use verbatim, in the document footer or final slide): "Model scaffolding based on the Cost of unused capacity prompt from costandprofitability.com/ai-costing-prompts/capacity-cost" Keep it to one small line; it should read as a quiet source note, not an advertisement.
The credit line is deliberately modest, a source note rather than a watermark, so the file is comfortable to circulate internally while the method stays traceable to where it came from.
When you need the real model
One cell is straightforward. A real plant has many cost pools, shared resources and time equations that route cost to the work that consumes it. Building that properly, so capacity is costed consistently across the whole operation, is the work behind a defensible TDABC model.
Related prompts
Proof
A distributor in New Zealand. €1.335M of cost-to-serve made visible, then halved, and 830 loss-making customers brought down to 295.
Read the case study →Who you would be talking to
Miguel Guimarães, Founding Partner
Cost and profitability practitioner for 25+ years. Presented the Damco cost-to-serve case at Managing for Profit (Amsterdam RAI, December 2009), on the same programme as Robert S. Kaplan.
Call +351 910 313 731
