This report provides a detailed breakdown of how does our capacity variance look (planned vs actual)? for managed service providers.
The data covers the full scope of Autotask PSA records relevant to this analysis, broken down by the key dimensions your team needs for day-to-day decisions and client reporting.
Who should use this: Operations managers, service delivery leads, and MSP owners managing capacity
How often: Weekly for scheduling, monthly for utilization reviews, quarterly for staffing decisions
The 6.4% utilization rate from Autotask reflects scheduled hours against total available capacity across all resources. For demo data with fixed historical periods, this figure is expected to be low. The more operationally useful number is the variance trend over time — shown in section 2 — which tracks whether your planning is becoming more or less accurate month over month.
EVALUATE
ROW(
"Capacity Planned Hours", [Capacity Planned Hours (Proxuma)],
"Capacity Total Min", [Capacity Total (Autotask)],
"Capacity Variance Min", [Capacity Variance (Autotask)],
"Capacity Variance Proxuma", [Capacity Variance (Proxuma)],
"Capacity Utilization Rate", [Capacity Utilization Rate (Autotask)]
)The pattern is clear. H1 2025 (Jan–Jun) showed consistent under-delivery, with January standing out at -58 hours — the largest monthly gap of the year. H2 2025 (Jul–Dec) recovered, with four of the six months showing positive variance. This shift suggests planning processes or staffing alignment improved around mid-year. December's near-flat result (+3.9h) indicates well-calibrated planning at year end.
EVALUATE ROW("TotalHours", [Tickets - Hours Worked], "BillableHours", SUM('BI_Autotask_Time_Entries'[Billable Hours]), "NonBillableHours", SUM('BI_Autotask_Time_Entries'[Non billable Hours]), "Employees", [Total Employees], "HoursPerEmployee", DIVIDE([Tickets - Hours Worked], [Total Employees]))The top 10 resources account for the highest concentration of hours logged. Dr. Amber Ayala DVM leads at 2,400 hours, followed by James Li (2,136h), Kevin Allen (2,060h), and Maxwell Reed (2,050h). The team average sits at 659 hours per resource across 77 active staff. The gap between the top and the mean suggests a group of high-volume contributors carrying disproportionate load — worth reviewing against their capacity targets.
EVALUATE
TOPN(10,
SUMMARIZECOLUMNS(
'BI_Autotask_Time_Entries'[resource_name],
"Hours Logged", SUM('BI_Autotask_Time_Entries'[hours_worked])
),
[Hours Logged], DESC
)EVALUATE
ROW(
"Total Hours Logged", SUM('BI_Autotask_Time_Entries'[hours_worked]),
"Resource Count", DISTINCTCOUNT('BI_Autotask_Time_Entries'[resource_name]),
"Avg Hours Per Resource", DIVIDE(
SUM('BI_Autotask_Time_Entries'[hours_worked]),
DISTINCTCOUNT('BI_Autotask_Time_Entries'[resource_name])
)
)Capacity variance is the difference between planned hours (how much work you committed to) and actual hours (how much work was logged). A negative variance means actual delivery came in below plan. A positive variance means more was delivered than planned. Tracking this monthly shows whether your forecasting process is improving.
The Autotask utilization rate compares scheduled time against total available capacity for all defined resources. In demo data with fixed historical time ranges, most resource-capacity records show as available but unscheduled, pulling this rate down. In a live environment with active scheduling, this figure is typically 60–85% for a healthy MSP team.
Autotask variance uses the total scheduled capacity from Autotask resources as the baseline. Proxuma variance uses hours planned in Proxuma project milestones and compares against Autotask actuals. Proxuma variance is more granular — it tracks per-project and per-period accuracy, while Autotask variance shows overall team utilization.
Three practices reduce variance consistently: (1) set realistic planned hours based on historical actuals rather than ideal estimates, (2) review variance weekly and adjust outstanding planned hours early, (3) ensure all engineers log time accurately and promptly. The 2025 H2 improvement pattern in this report shows that variance reduction is achievable within a single quarter when planning habits change.
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