Use CasesAI applications
Demand & Capacity Forecasting
Use business data to support more informed staffing and workload plans.
Illustrative scenario
Plan around changing demand.
An example of a possible engagement. The workflow and measures do not represent achieved client results.
The business problem
Demand changes from week to week, but staffing and workload plans rely on a static spreadsheet or last month’s pattern. Managers see a capacity gap only after it becomes a problem.
The work in practice
From friction to a useful change.
Current friction
- Repeat last period’s planRecent changes are easy to miss
- React to surprisesDemand and available capacity diverge
- Adjust at the last minuteOvertime, idle time, or delayed work
Proposed improvement
- Prepare the historyUsable demand and capacity records
- Compare forecast optionsTest simple baselines and ML where useful
- Plan & adjustReview a range of demand scenarios
People stay part of the process
Where judgment matters.
Managers review assumptions, known events, and forecast uncertainty before changing plans. Machine learning is considered when the available history is suitable and testing shows an advantage over simpler methods.
What we would establish and track
Forecast errorCapacity gapsOvertimeService delays
Agree a baseline and success criteria before introducing the change.