Demand-based planning does not require a data science team. It requires a simple idea: demand changes by time slot and day, and coverage should follow it. When it does not, overtime, queues, stress, and poor customer experience appear.
1) The mistake of fixed schedules: they optimise for the average
A fixed schedule tends to be 'fine' on average and wrong during peaks. In shifts, peaks are what matter. Demand-based planning aims to move hours where they are needed, not to blindly add hours.
Example: if between 18:00 and 20:00 there is twice the volume, reinforcing that time slot can improve service without increasing the total weekly hours.
2) Minimum data to start (without overcomplicating it)
Start with data you already have: sales by hour, calls by time slot, orders by day, tickets, etc. Cross that with your time records and you will see where there is undercoverage (overtime) and overcoverage (idle time).
A practical example: a call centre uses incoming calls by time slot and sees that the actual peak is 9:00–11:00. They adjust shifts to start earlier instead of requesting daily extensions.
3) Adjust with simple levers: overlaps and short reinforcements
You do not need to redesign the entire workforce. Use 1–2 hour overlaps, partial reinforcements, and position rotation. Small, well-directed changes usually reduce many emergencies.
Example: adding a 3-hour reinforcement during a peak time slot can save 10 overtime hours spread across the team during the week.
4) Close the loop: measure the impact and repeat
After adjusting, measure: overtime, relief delays, last-minute changes, and customer satisfaction. If they decrease, you are on track. If not, review your assumptions: perhaps the peak has shifted or a critical skill is missing.
Demand-based planning is iterative. It is not about 'making the perfect schedule'; it is about improving the schedule every month with evidence.
5) Win-win: fewer emergencies, more predictability
For the company, intelligent coverage reduces costs and improves service. For the worker, it reduces overload and last-minute changes.
When the schedule aligns with actual demand, everyone's stress decreases: Operations, HR, and the team. That is operational win-win.