Service improvement simulator
Change three practices and see their effect on support effort.
Where does the effort go?
The example releases 164.7 staff hours per month after upkeep.
One request, one path
- 01 · Prevention144 prevented
30% preventable × 40% eliminated.
- 02 · Self-service211.2 resolved
40% of remaining requests eligible × 50% successful adoption.
- 03 · Agent work844.8 remaining
Better routing avoids 101.4 extra handoffs, while the underlying requests still require handling.
| Work | Baseline | Scenario |
|---|---|---|
| Request handling | 500 | 352 |
| Extra handoff effort | 40 | 11.3 |
| Improvement upkeep | 0 | 12 |
| Total workload | 540 | 375.3 |
What an IT leader would validate
Check ticket categories, actual handling effort and eligible self-service demand before a pilot. Track resolution quality and reopened requests alongside hours, then compare the result with a measured baseline.
Capacity is time available for other work. It is not cash savings, a staffing recommendation or a promised service-level improvement.
Discuss service improvementHow this example is calculated
Prevention is applied first. Self-service applies only to requests left after prevention. Routing reduces extra handoff effort on the remaining agent requests; it does not remove those requests. This sequence avoids counting the same request twice.
Workload = requests × handling minutes ÷ 60 + misrouted requests × extra handoff minutes ÷ 60 + upkeep hours. Net released capacity = baseline workload − scenario workload. Baseline upkeep is zero. Fractional counts represent a monthly average; calculations use full precision and display one decimal place.
The model holds handling time and request mix constant. It excludes setup effort, queueing, peaks, end-user self-service effort and changes in service quality. All defaults are original fictional examples, not Ricky’s historical results or industry benchmarks.