technician-productivity-and-first-time-fix

name	technician-productivity-and-first-time-fix
description	Diagnose and improve technician utilization, first-time-fix rate, MTTR, and callback rate — including root-cause segmentation by parts/skill/information/diagnosis, the utilization waterfall, and the coaching framework to address each failure category.

Technician Productivity and First-Time-Fix

Purpose: identify what is actually reducing first-time-fix rate and technician utilization, segment root causes by category, and route each category to the right fix — training, parts, scheduling, or data quality.

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Steps

1. Establish the metric baseline

Before diagnosing, measure and validate the four core metrics using scripts/fsm_calc.py:

Metric	Formula	Healthy range (context-dependent)
Technician utilization	billable_hours / available_hours	70–80% (field service typical)
First-time-fix rate	jobs_resolved_first_visit / total_jobs	≥ 75–85% (varies by equipment type)
MTTR	sum(resolution_times) / count(jobs)	Benchmark by equipment type
Callback rate	callbacks_within_30_days / jobs_completed	< 5% target; > 10% is a flag

Data-quality check first: before drawing conclusions, verify that technicians are logging actual start/end times (not dispatcher bulk-close), parts used, and failure codes on every job. Unreliable data produces misleading metrics — fix the data before the metric.

2. Build the utilization waterfall

Decompose available hours into categories:

Available hours (paid hours on clock)
  └─ Scheduled hours (assigned to jobs)
       └─ Travel time (drive to/between jobs)
       └─ Admin time (job notes, parts ordering, team meetings)
       └─ Wait time (waiting for access, parts, customer)
       └─ Billable hours (productive on-site work)
  └─ Unscheduled time (no job assigned — scheduling gap or downtime)

Each drag category has a different owner and fix:

Drag	Fix owner
Excessive travel	dispatch-and-scheduling-engineer (territory redesign)
Admin time > 15%	Workflow/mobile-app improvement; job-note templates
Wait-for-parts	parts-and-inventory-analyst (truck-stock or pre-pull)
Scheduling gaps	dispatch-and-scheduling-engineer (fill-rate / buffer optimization)
Data-entry errors	Dispatcher audit; mobile app field validation

3. Segment first-time-fix failures by root cause

For each first-time-fix miss, classify the failure:

Root cause	Diagnostic question	Fix
Parts unavailable	Was the part not on the truck and not pre-pulled?	parts-and-inventory-analyst
Skill/cert mismatch	Was the technician dispatched outside their skill authorization?	dispatch-and-scheduling-engineer + training
Misdiagnosis	Did the technician assess wrong and fix the wrong component?	Training; diagnostic checklist
Incomplete job information	Was the symptom description or equipment history missing at dispatch?	Mobile data capture; job-intake triage
Customer expectation	Did the customer define the job differently than the technician completed it?	Job-scope confirmation at booking

Calculate the % of first-time-fix misses in each category. The category with the highest share gets the first improvement action.

4. Analyze callback rate by segment

A callback is a job where the customer called back within 30 days for the same or related issue.

Segment callbacks by:

• Technician: is one tech generating a disproportionate share of callbacks? → coaching.
• Job type / equipment model: is a specific job type generating callbacks? → training, parts, or diagnostic-procedure issue.
• Failure mode: is the repeat failure the same component? → parts quality or repair method.
• Time-to-callback: callbacks within 7 days often indicate an incomplete fix; callbacks in 15–30 days may indicate parts failure or an unrelated second failure.

5. Design the coaching framework

For technicians with below-threshold performance on first-time-fix or callbacks:

1. Diagnose first. Is the root cause parts (not their fault), scheduling (dispatched out of skill), or genuine skill/judgment gaps?
2. Only coach what the technician controls. Don't hold a technician accountable for parts- delay failures or skill-mismatch dispatches.
3. Structured 1:1: review the technician's last 30 days of metrics — utilization, first-time- fix rate, MTTR, callbacks. Walk through the specific miss events together.
4. Training-to-gap mapping: identify the 1–2 highest-impact skill gaps from the first-time-fix segment analysis. Design targeted training, not a generic refresher.
5. 30-day improvement check: set a specific, measurable target for the gap (e.g., "first-time- fix on refrigerant jobs from 55% to 75% in 30 days") and check progress at the next 1:1.

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Anti-patterns

• Drawing conclusions from unvalidated job-completion data (bulk dispatcher close, missing failure codes).
• A coaching program that doesn't distinguish parts/scheduling failures from skill failures.
• Treating a systemic failure (all technicians miss on one job type) as individual underperformance.
• Utilization targets that ignore travel-time geography.
• Callback programs that don't segment by technician, job type, and failure mode.

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Output

A productivity diagnostic report with: the metric baseline (utilization waterfall, first-time-fix segment breakdown, MTTR and callback analysis), the root-cause categories ranked by impact, the routing of each non-skill root cause to the right specialist, and the coaching plan for the skill-gap portion. Cite the job-count basis for each calculation.