When Capacity Planning becomes the Loch Ness Monster
Capacity planning… we all know how it’s supposed to work.Except… it never really stays simple.
It starts well… then reality hits
At first, everything looks clean. Then little by little:
- People get pulled into multiple projects
- Priorities change (again… and again)
- The same experts are needed everywhere
- “Quick tasks” and meetings start eating half the week
And suddenly, your nice plan doesn’t reflect reality anymore.
Because the truth is "Not all capacity is equal". Someone focused on one project delivers way more than someone split across three. And experience, context, and dependencies matter a lot more than we like to admit.
Where things go wrong
The real issue I see in most organizations is this: We use capacity planning to try to fit everything in. Instead of using it to decide what not to do.
So we adjust numbers, we stay optimistic, we say “yes”…And then we end up with:
- Overloaded teams
- Slipping deadlines
- Constant rework
At that point, the plan becomes more of a story than a tool.
What actually helps (from experience)
No magic solution here. But a few things make a big difference.
Keep it alive
A plan you build once and never revisit is useless. Things change so the plan should too. Even simple regular updates can improve things a lot.
Focus on skills, not just people
Your constraint is rarely “number of people”. It’s:
- The one expert everyone needs
- The specific skill that’s hard to find
If you don’t see that, your plan will always be off.
Don’t overcomplicate the tools
You don’t need a huge system to get started. A simple setup can work:
- Some form of time or activity tracking
- A shared view of allocations
- A dashboard to make things visible
What matters most is that everyone looks at the same data.
Make the impact visible (what worked for us)
One thing that made a real difference was structuring capacity planning as a data-driven decision system:
| Step | Objective | What is done | Outcome |
|---|---|---|---|
| 1. Data Sources Integration | Centralize delivery data | Connect Azure DevOps / MS Project (planned activities & workload) with Power BI | Single source of truth for workload |
| 2. Workforce Data Enrichment | Reflect real availability | Integrate HR data: • Static (roles, contracts, teams) • Dynamic (holidays, sick leave, trainings) | Accurate view of actual capacity |
| 3. Capacity Modeling | Structure capacity meaningfully | Model resources by: • Skills (FE Dev, BE Dev, BA, BI, etc.) • Seniority (junior, medior, senior) | Realistic capacity representation |
| 4. Analytical Layer | Generate actionable insights | Build unified dashboards to: • Compare workload vs capacity • Identify over/under allocation • Detect skill bottlenecks | Clear visibility on gaps & risks |
| 5. Decision Support & Governance | Enable fact-based decisions | Managers: • Review insights before governance • Adjust allocations dynamically • Prepare arbitration requests | Better prioritization & faster decisions |
The real shift was this: capacity planning stopped being a debate… and became a shared, visual truth.
The takeaway
Capacity planning becomes a “monster” when we expect it to absorb everything. But if we use it for what it really is, a way to make trade-offs visible, it becomes incredibly powerful.
Not perfect. Not exact. But useful.