An interactive planning tool for an engineering organisation that is growing faster than its lab space. It connects hiring growth to lab capacity, then to the build projects, budgets, risks and approvals needed to close the gap.
Engineering organisations hire faster than they can build lab space. The warning signs sit in different places: headcount plans in HR, seat and bench counts in facilities, build projects in a project tracker, budgets in finance, and risks and approvals in someone's inbox.
So the shortfall shows up late, when engineers are already waiting for benches. At that point the only fixes are expensive ones. The problem isn't missing data. Nobody connects it early enough to act.
Not a prettier spreadsheet. A repeatable way of deciding that links demand signals to projects, budget, approvals and leadership decisions, so a planning number turns into an action with an owner and a date.
| Step | What the model does |
|---|---|
| 1 · Forecast demand | Starts from current headcount per team, annual growth, seats per engineer, specialised bench needs and a planning buffer. Projects monthly seat demand 24 months out. |
| 2 · Model real capacity | Discounts nominal capacity for downtime, and counts new capacity only from the month its build project is due to finish. Seats and benches are tracked separately. |
| 3 · Drive decisions | Links each shortfall to the projects that would close it, then to their budget, risks, approvals and dependencies. Red items go to executive triage with an owner. |
A common planning mistake is counting a lab expansion as capacity the day it's approved. Here, new capacity only counts from the month its project is due to finish. So if that project slips, the chart shows the shortfall right away. That ties the project schedule directly to capacity risk.
Not every project deserves leadership time. Projects sit in three review tiers (Executive Triage, Weekly Watch, Standard Cadence), so the recurring review spends its time on the few items that can actually change the outcome.
Which labs are close to their buffer or already short? Which milestones or dependencies threaten capacity delivery? Which investments need funding or escalated approval? Who owns each mitigation, and by what date?
Every lab, budget, schedule and risk is synthetic. That keeps the model reproducible and shareable while keeping the shape of a real engineering portfolio.