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Capacity planning and forecasting

Demand forecasting from historical data, team sizing and schedule validation. So the operation stops scheduling by the average and finding out it is short-staffed only once the queue has formed.

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What usually improves

  • Schedules that follow real demand, not the average
  • Less overtime to cover predictable gaps
  • Less idle time during low-demand hours
  • Headcount decisions backed by data
  • A repeatable process that does not depend on a single person

How much each point improves depends on the environment: volume, structure and data maturity. That is why no percentage is promised here. The assessment measures the current situation and defines, before any work starts, how the gain will be measured.

Diagnosis

Signs this solves your problem

  • The schedule is built on the daily average, and each hour’s peak goes uncovered.
  • Overtime has become routine to fill gaps that were predictable.
  • There are too many people at some hours and too few at others.
  • Team sizing depends on one person’s experience and is not written down anywhere.
  • Days off, breaks and working-hour rules are checked by eye.

Delivery

What gets done

  • Clean historical baseline

    Months of volume by day and by hour, with atypical days identified and handled before any forecast.

  • Demand forecast

    A forecast for each time slot, with the weight of each hour calculated from historical data.

  • Capacity sizing

    How many people each time slot requires, kept separate from the forecast and the schedule so each step can be reviewed.

  • Schedule validation

    The schedule checked against real need, taking into account coverage, days off, breaks and the operation’s rules.

Most-used tools

  • Excel
  • Power BI
  • Python
  • Time series
  • Workforce management

Example

How the problem shows up in the data

The daily average hides the peak. With the need calculated hour by hour, it becomes clear where people are missing and where there are too many.

Illustration with fictitious data

Hourly need versus a schedule built on the average

  • Need (forecast)
  • Coverage gap
  • Average-based schedule (10 people)

Hover over the hours or move through them with the keyboard to see the numbers.

The average-based schedule adds up to 120 person-hours and the need to 123: in total, it almost matches. But 6 hours are left uncovered at the peak (17 person-hours short) and 14 are left over at the edges of the day. An hourly forecast shows where to move people before hiring or paying overtime.
View the data as a table
HourNeedScheduledDifference
8am610+4
9am910+1
10am1310-3
11am1510-5
12pm1110-1
1pm10100
2pm1210-2
3pm1410-4
4pm1210-2
5pm910+1
6pm710+3
7pm510+5

Projects

Related project

Described anonymously: context, approach and outcome, without exposing the client or the implementation.

Capacity planning and schedule validation

Context
The operation needed to size its team in line with real demand, without relying only on gut feeling or a simple average.
Approach
Analysis of months of volume by day and by hour, handling of atypical days, calculation of each slot’s weight, forecasting, sizing and schedule validation.
Outcome
Planning that can be justified and repeated, with forecast, capacity and schedule kept separate and the criteria documented.
See how the problem shows up
  • Excel
  • Power BI
  • Python
  • Time series
  • Workforce management

Contact

Recognize any of these signs?

Describe how the problem shows up day to day. The first conversation is for understanding the situation and saying honestly whether and how help is possible.

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