Team Overview
March 2026 · 6 developers · 22 working days
Total Tasks
589
Across 6 members
Completed
406
↑ 68.9% rate
Est. Hours
793h
Planned effort
Actual Hours
950h
▲ +156.6h over
Efficiency
83%
Est ÷ Actual hrs
Overrun Tasks
185
31.6% of tasks
Multi-Day Tasks
94
Continued next day
Over-Estimated
85
Finished < 50% of est.
Status Breakdown
Done — 406
In Progress — 81
In Review — 48
Pending — 42
Query — 4
Dependent — 1
Task Status Distribution
Planned vs Unplanned Tasks
Team — Estimated vs Actual Hours
Team Insights
Automated findings from March 2026 data including multi-day task and estimation analysis
🔴 Critical Alerts
🚨
260.7 hours lost to time overruns — 185 of 585 tasks took longer than planned. The team spent ~33 extra working days beyond estimates in a single month.
🔁
94 tasks spanned multiple days — many tasks estimated at 1h actually took 8–32h across days. The biggest: Kashish's invoice testing task was estimated 0.5h but accumulated 32.1h over 17 days. Tasks should be tracked as continuing, not re-entered each day.
🔥
Kashish Luhar highest overrun risk — 54 overrun tasks, 75.6h extra, avg 119.6% over estimate. Multi-day task analysis shows even more hidden overruns not visible in single-day view.
⚠️
Jay Gajjar's PR review tasks severely underestimated — March 16 Ex Grip ERP task: estimated 2h → took 8.5h (+325%). Same pattern Mar 17 (2h → 8.25h). This is a systematic underestimation of PR review complexity.
🟡 Estimation Quality Warnings
📊
85 tasks were over-estimated — finished in less than 50% of the time allocated. Total wasted buffer: ~80h of padding that could have been used for planning. Krina accounts for 28% of over-estimations.
📅
Ex Grip ERP module has systemic estimation failure — accounts for 156.5h of overruns AND 8 of the top 20 over-estimations. Tasks in this module need calibrated benchmarks, not fresh estimates each time.
🕐
Half-day occurrences detected 4 times — Krina (Mar 04, Mar 09), Chitra (Mar 10), Jay (Mar 11) logged under 5.5h on-site. These days show significantly fewer completed tasks and may indicate health, personal, or external blockers.
⏰
5 late arrivals recorded — Kashish (Mar 02: 10:31), Chitra (Mar 16: 10:20), Sanskar (Mar 16: 10:30, Mar 27: 11:00), Krina (Mar 04: 12:30). Sanskar's Mar 27 arrival at 11:00 is the latest of the month.
🟢 Positive Highlights
🏆
Kartavya Gohil leads completion at 88.8% — 95 of 107 tasks done with only 18 overruns. Best on-time delivery in the team despite complex VAS module work.
⚡
Krina Patel is the only member ahead of estimates — 102.8% efficiency. Completes tasks in less time than planned on average, indicating accurate self-assessment.
📈
Mar 17 was the best day — 32 of 35 tasks completed (91.4%). Team also stayed late on Mar 06 (Jay: 21:00), Mar 16 (Jay: 20:20, Kashish: 20:16), and Mar 17 (Chitra: 20:05) to push deliverables.
💡 Recommendations
🎯
Introduce "task continuation" tagging — when a task is not completed same day, mark it as continuing (not create a new row). This will give accurate total effort per task and expose real overruns hidden by day-by-day entries.
📋
Add 3× buffer to Ex Grip ERP tasks — PDF generation, repeater UI, and PR review tasks in this module consistently take 2–5× estimated time. Create a module-level estimation template.
📉
Review over-estimated tasks quarterly — 85 tasks finished in under half the allocated time. These inflated estimates are masking capacity and making sprint velocity unreliable. Use actuals to recalibrate future estimates.
📌
Reduce unplanned work below 25% — currently at 39.4%. Introduce a buffer sprint slot to absorb reactive tasks without disrupting planned capacity.
Daily Trends
Day-by-day task and hours performance across March
Tasks Assigned vs Completed Per Day
Daily Hours Logged
Daily Completion Rate %
Team Members
Individual performance for March 2026
Tasks Done vs Total
Est. vs Actual Hours
Member-Wise Insights
Strengths, concerns, and actions for each team member
Multi-Day Task Analysis
Tasks that continued across consecutive days — showing true total hours vs day-1 estimate
📌 How This Works
ℹ️
Since tasks are assigned for completion same-day, any task appearing on multiple days means it was not completed as planned. The real overrun = Total actual hours across all days − Day 1 estimate. Single-day analysis misses these hidden overruns.
🚨
Top hidden overrun: Kashish's invoice testing task — estimated 0.5h on Day 1, accumulated 32.1h across 17 days (Mar 25 → Mar 30). A 6,320% real overrun that would look like a 0.1h overrun when viewed day-by-day.
⚠️
Jay's streaming issue (anycall) took 28.5h across 8 days — estimated at 1h. The task is still In Progress. This single task represents a significant unresolved technical blocker.
Multi-Day Tasks
94
Tasks spanning 2+ days
Biggest Real Overrun
+31.6h
Kashish · invoice testing
Still Open
26
Multi-day tasks unresolved
Max Days Span
17
Days for single task
Most Affected
Chitra
Most multi-day tasks
Avg Days/Task
3.2
Days per multi-day task
All Multi-Day Tasks — Real Effort vs Day-1 Estimate
| Member | Dates (from → to) | Days | Module | Task | Day-1 Est | Total Actual | Real Overrun ↓ | Status |
|---|
Time Overrun Analysis
185 single-day tasks exceeded their estimates in March 2026
🔴 Overrun Insights
📌
Ex Grip ERP caused 156.5h of overruns — 60% of all overrun hours. PDF generation, currency sign fixes, and repeater UI tasks average 2.8× their estimate.
🔁
PR reviews are the #1 underestimated task type — Jay's PR review tasks estimated 2h but consistently ran 8+ hours. Codebase complexity is not being factored into review estimates.
💡
Chitra Rathod has the most controlled overruns — only 17.4h across 31 tasks (avg +0.56h/task). Her overruns are small and manageable.
Overrun Tasks
185
31.6% of total
Hours Lost
260.7h
Extra beyond estimates
Worst Task
+6.5h
Jay · Ex Grip ERP
Max % Over
530%
Krina · SPR Roadway
Most Affected
Kashish
54 tasks · 75.6h
On-Time Tasks
400
68.4% on time
Member Risk Summary
Overrun Hours by Member
Overrun vs On-Time Tasks
Top Modules by Overrun Hours
Severity Distribution
All Overrun Tasks
| Sev | Member | Date | Module | Task | Est | Actual | Overrun ↓ | % Over | Status |
|---|
Over-Estimated Tasks
85 tasks finished in less than 50% of their allocated time — estimation needs calibration
📉 What This Means
📊
85 tasks had actual time less than 50% of their estimate — these tasks were allocated far more time than needed. While this looks good on surface, over-estimation inflates capacity buffers, makes sprint velocity unreliable, and suggests developers may be padding estimates.
💡
Possible reasons: Tasks were interrupted and resumed (time gaps), task was simpler than expected, developer underestimated own speed, or tasks with 0 actual hours were listed as done without time tracking.
✅
Positive view: Krina Patel's 102.8% efficiency is partly explained by her strong task completion in under estimated time — she's genuinely fast, not just under-reporting.
🚨
17 tasks logged 0 actual hours — but are marked Done or In Progress. These need to be investigated: were they forgotten, batched, or mis-logged? Zero-hour completions skew efficiency metrics significantly.
Over-Est Tasks
85
Finished < 50% of est.
Zero-Hour Tasks
17
0h logged, marked done
Hours Saved
~80h
Buffer not needed
Most Over-Est
Krina Patel
28% of over-estimates
All Over-Estimated Tasks (Actual < 50% of Estimate)
| Member | Date | Module | Task | Estimate | Actual | % Used | Saved | Status |
|---|
Clock-In / Clock-Out Report
Daily attendance, punctuality, and on-site hours for March 2026
📋 Reporting Summary
⏰
5 late arrivals recorded — Kashish (Mar 02: 10:31), Chitra (Mar 16: 10:20), Sanskar (Mar 16: 10:30), Krina (Mar 04: 12:30), Sanskar (Mar 27: 11:00). Late defined as after 10:05 AM.
🏠
4 potential half-day instances — Krina (Mar 04: 1h on-site, Mar 09: 5.35h), Chitra (Mar 10: 4.55h), Jay (Mar 11: 3.05h). On-site hours under 5.5h flagged as possible half-day.
💪
Extended hours noted — Jay (Mar 06: stayed until 21:00), Jay+Kashish (Mar 16 until 20:20/20:16), Chitra (Mar 17 until 20:05). Team shows commitment during deadline periods.
📊
Average on-site time — team average is 9.3h/day. Standard reporting window appears to be ~09:40 to 19:15. Members reporting before 09:30 include Krina (earliest: 09:05 on Mar 03).
Late Arrivals
5
After 10:05 AM
Half Days
4
Under 5.5h on-site
Avg On-Site
9.3h
Per member per day
Extended Days
5
Stayed past 20:00
Member Reporting Summary
Full Clock-In / Clock-Out Log
| Date | Member | Clock In | Clock Out | On-Site Hrs | Late? | Early Out? | Half Day? |
|---|
Module Breakdown
Project and module-wise task distribution
Actual Hours by Module (Top 10)
Tasks — Done vs Total
Module Detail
| Module | Tasks | Done | Completion | Actual Hrs |
|---|
Leave & Breaks
Leave days and break hours for March 2026
Break Hours by Member
Leave Days by Member
📋 Notes
📅
Sanskar Bagh took 3 leave days — highest in the team. Despite this, delivered 63 completed tasks (64.9% rate).
☕
Break hours range from 16.25h (Sanskar) to 21.45h (Kartavya) — averaging ~0.74–0.98h/day. Consistent across the team. All break and leave data excluded from efficiency metrics.