Demand against capacity
- Now 480 vs capacity 504; in 12 weeks 528; shortfall 24; extra engineers 4.
Reporting and analytics
Enterprise analytics across the platform
Each function counts things differently, so the leadership team argues about the numbers instead of the decisions.
Demonstration: Dunmore Building Services (fictional) · Construction services. Fictional data and a scripted answer.
The situation
- Organisation
- Dunmore Building Services (fictional)
- Who is asking
- Chief operating officer
- Industry
- Construction services
- What happened
- Demand for repair visits is rising and the COO wants one view that joins demand, staffing and productivity to show where a backlog will build.
The question asked
If demand keeps rising, when will our repair backlog start to build, and how many engineers would stop it?
What it reads, and who can see the result
- Demand history exportRequests per week.
- Staffing registerEngineers and visits per engineer.
- Reads
- Weekly request counts, headcount and visit rates.
- Does not read
- Customer details or individual engineers' performance.
- Permission
- Operations analytics access.
- Who sees it
- The COO and the regional managers.
Try it
Change the assumptions. Every result and the answer below are recomputed from them, and a warning appears when a result crosses a stated limit.
- Visits the team can do per week
- 504
- Requests per week in 12 weeks
- 528
- Shortfall per week in 12 weeks
- 24
- Engineers needed in 12 weeks
- 88
- Extra engineers needed
- 4
In twelve weeks demand exceeds capacity, so a backlog builds and grows each week thereafter.
The answer
Simulated answer. Written for this fictional case, not produced by a live model. Only the figures in it are computed, from what you decided above.
In twelve weeks demand reaches 528 a week against capacity of 504: 24 short.
Joining demand, staffing and visit rate shows what no single report shows: today the team keeps up with 480 requests, but with growth of 4 a week the gap opens [S1][S2]. Hiring 4 more engineers would close it at this rate.
The figure is a projection from a straight line. Seasonality, sickness and the time it takes to recruit and train an engineer all make the real picture less tidy.
Relies on: S1 · Repair requests per week, last yearS2 · Engineer headcount and visit rate
No approval is needed for this result: it is read-only insight, and nothing consequential happens because of it.
What it produces
For the COO
- Begin recruitment before the gap opens, not when the backlog appears.
Limits of this example
- A straight-line projection of growth.
- Ignores sickness, seasonality and training time.
- This is a scripted demonstration on fictional data. No model was called and no real organisation, person or result is shown.