Below 50% — needs a conversation50–70% — watch70%+ — on track (target: 85%)
Team Trend: Direct vs. Indirect Utilization %
Weekly trend since the Week of Jul 6 (first week of true data capture), dashed line marks the 85% target
Job Dimension Split (Direct Hours)
Direct hours by job dimension, by week since the Week of Jul 6
Top 20 Job Codes by Direct Hours — per Job Dimension
Direct Utilization % Trend — All Employees
"Turn the guys into lines" — each employee's direct-labor utilization % by week since the Week of Jul 6. Click a name below to isolate just that line.
Employee Detail (printable per-person view for quarterly reviews)
Click a name — this view is designed to be printed/handed to each employee
Data Gaps / Roadmap
Flagged on the 7/14 team call — not yet buildable from the current Rippling export
Rental asset / equipment utilization — Richard asked to track utilization of rental fleet as "inventory," not just people. No asset-level dimension exists in the current time-clock export; needs an asset ID field (e.g. a new job dimension or a separate rental-tracking feed) before this can be added.
Travel hours by employee — Richard wants travel broken out (e.g. Scott's 63 hrs/week driving trend). Not currently tagged as its own dimension in Rippling clock-ins; would need a "Travel" job code or a location flag.
Revenue vs. labor-hours correlation — Brandon and Amir agreed to compare rolling 4-week recognized revenue to rolling 4-week direct labor hours (and eventually job-by-job, once clocking is reliably tied to specific unit/PO numbers). Revenue data isn't in this workbook; needs a feed from the finance system. As of this run, the BigQuery finance connector failed to connect (incompatible auth) and the Slack finance connector still needs authorization — Brandon, reconnect BigQuery and authorize Slack before revenue can be pulled in automatically.
Data accuracy ramp / history window — The Week of Jul 6, 2026 is the first week of true data capture, so all trend charts and employee histories start there; earlier weeks in the Rippling export (effectively 100% indirect because nobody was clocking to units yet) are excluded from comparisons. Field tablets went live the week of 7/13/2026, so the first few weeks may still undercount Direct hours. That ramp-up window has now passed (10+ weeks of tablet data as of this run), so a department that stays below target now likely has a real utilization gap, not onboarding noise.