capacity-and-demand-planning
Matches operational capacity to expected demand — forecasting load, sizing teams and systems, managing queues, and deciding when to add capacity. Use this to plan staffing for expected volume, diagnose a queue that keeps growing, size support or fulfillment capacity, or decide wh
Install
npx skills add https://github.com/cbrock84/headcount/tree/main/plugins/operations/skills/capacity-and-demand-planning
claude plugin marketplace add https://llmmart.ai/marketplace.json && claude plugin install cbrock84-headcount@llmmart
git clone https://github.com/cbrock84/headcount.git
The skills CLI installs just this skill, for any of its supported agents. Claude Code installs the whole cbrock84/headcount collection as a plugin from our marketplace. Git is the plain clone.
Skill manifest
Capacity and demand planning
This is operational throughput — how much work the organization can absorb. Allocating people
across projects is portfolio work, handled in pmo:portfolio-governance.
Forecast demand honestly
Separate the three components, because they need different treatment:
- Baseline — the steady rate, best estimated from your own history rather than from a plan.
- Trend — the direction, measured over enough periods to distinguish it from noise.
- Spikes — launches, seasonality, campaigns, incidents. Known spikes are a planning input; unknown ones are what headroom is for.
Forecast in the unit the work actually arrives in — tickets, orders, shipments, minutes of handling — not in revenue. Revenue divided by an average is a forecast of an average, and averages are where capacity planning goes to die.
Capacity is not headcount
Usable capacity is people multiplied by available hours multiplied by the fraction spent on the work in question. The last term is the one everyone omits and it is rarely above 70%: meetings, training, holiday, and the interruptions that come with the job are real.
Plan against realistic effective capacity. Planning at 100% guarantees the plan fails on its first ordinary week.
Queues tell you before the dashboard does
Utilization above roughly 80% makes wait times rise sharply and non-linearly — a system at 95% is not slightly slower than one at 85%, it is qualitatively worse. This is why "we have spare capacity on paper" coexists with a queue that never clears.
Watch the trend in queue age, not the queue length. A stable-length queue whose oldest item keeps getting older is a queue that is quietly failing its slowest customers.
Add capacity or fix flow
Before adding capacity, establish which it is:
- Genuine capacity shortfall — arrival rate exceeds service rate at reasonable utilization. Add capacity.
- Flow problem — rework, handoffs, waiting on another team, batching. Adding capacity here adds
cost and often makes throughput worse by increasing coordination. Send this to
operations:process-design.
The tell: if work spends most of its life waiting rather than being worked, it is a flow problem.
Sources
references/sources.md in this skill lists the outside authorities that settle the questions
here — what each one is authoritative for, and what you may do with it. Check them before
answering on anything they cover, and cite what you used. Most are free to read and not free
to reproduce; the use note on each is binding.
Never
- Plan against nominal headcount rather than effective capacity.
- Run a critical queue at sustained high utilization and treat the wait times as a mystery.
- Add capacity to a process you have not measured.
- Forecast in aggregate currency when work arrives in discrete units.
Files (headcount)
-
references
-
sources.md 929 B
# Sources — `operations:capacity-and-demand-planning` <!-- Generated by scripts/build-sources.py from sources/*.toml. Do not edit. --> Check these before answering on anything they cover, and cite what you used. The use note on each one is binding: most of what a professional cites is free to read and not free to reproduce. ## 49 CFR Part 395 — hours of service of drivers Federal Motor Carrier Safety Administration · US · public domain (US government) — quote freely <https://www.ecfr.gov/current/title-49/subtitle-B/chapter-III/subchapter-B/part-395> **Authoritative for:** How many hours a commercial driver may lawfully drive and work, and what records prove it. A hard cap that any realistic transit or capacity plan has to respect. --- Sources are maintained in `sources/` upstream, not here. If one is wrong, out of date, or missing, fix it there — this file is regenerated and an edit to it is lost.
-
-
SKILL.md 3.2 KB
--- name: capacity-and-demand-planning description: Matches operational capacity to expected demand — forecasting load, sizing teams and systems, managing queues, and deciding when to add capacity. Use this to plan staffing for expected volume, diagnose a queue that keeps growing, size support or fulfillment capacity, or decide whether a bottleneck needs more capacity or better flow. --- # Capacity and demand planning This is operational throughput — how much work the organization can absorb. Allocating people across projects is portfolio work, handled in `pmo:portfolio-governance`. ## Forecast demand honestly Separate the three components, because they need different treatment: - **Baseline** — the steady rate, best estimated from your own history rather than from a plan. - **Trend** — the direction, measured over enough periods to distinguish it from noise. - **Spikes** — launches, seasonality, campaigns, incidents. Known spikes are a planning input; unknown ones are what headroom is for. Forecast in the unit the work actually arrives in — tickets, orders, shipments, minutes of handling — not in revenue. Revenue divided by an average is a forecast of an average, and averages are where capacity planning goes to die. ## Capacity is not headcount Usable capacity is people multiplied by available hours multiplied by the fraction spent on the work in question. The last term is the one everyone omits and it is rarely above 70%: meetings, training, holiday, and the interruptions that come with the job are real. Plan against realistic effective capacity. Planning at 100% guarantees the plan fails on its first ordinary week. ## Queues tell you before the dashboard does Utilization above roughly 80% makes wait times rise sharply and non-linearly — a system at 95% is not slightly slower than one at 85%, it is qualitatively worse. This is why "we have spare capacity on paper" coexists with a queue that never clears. Watch the **trend in queue age**, not the queue length. A stable-length queue whose oldest item keeps getting older is a queue that is quietly failing its slowest customers. ## Add capacity or fix flow Before adding capacity, establish which it is: - **Genuine capacity shortfall** — arrival rate exceeds service rate at reasonable utilization. Add capacity. - **Flow problem** — rework, handoffs, waiting on another team, batching. Adding capacity here adds cost and often makes throughput worse by increasing coordination. Send this to `operations:process-design`. The tell: if work spends most of its life waiting rather than being worked, it is a flow problem. ## Sources `references/sources.md` in this skill lists the outside authorities that settle the questions here — what each one is authoritative for, and what you may do with it. Check them before answering on anything they cover, and cite what you used. Most are free to read and not free to reproduce; the use note on each is binding. ## Never - Plan against nominal headcount rather than effective capacity. - Run a critical queue at sustained high utilization and treat the wait times as a mystery. - Add capacity to a process you have not measured. - Forecast in aggregate currency when work arrives in discrete units.
Comments (0)
Sign in to join the conversation.
Reviews (0)
No reviews yet.
No comments yet.