TransX Reefer Analytics

Fleet-wide analytics platform for reefer shutdown risk prediction, cost modeling, maintenance optimization, and repair pattern analysis.

Quick Stats

Fleet Units --

Avg Fleet Risk --

Shutdown Rate --

Avg Cost/Engine-Hr --

PM Overdue --

Unit Selection

Reefer Unit

Trailer Number

Trip Details

Route

Season

Service Type

Leg Miles

Temperature (set)

Maintenance History

Auto-filled from unit data

Days Since PM

Late PM Count

PM Compliance %

Repair Cost ($)

Repair Count

Unique Repairs

Select a unit and trip details,
then click Calculate

Frozen Model — Training Context

The model is trained once (2025 trip history) and frozen; monthly refreshes update each unit’s inputs, never these numbers. They change only at a retrain.

Training base rate3.34%

Training trips (2025)23,473

Shutdowns in training785

Model AUC0.84

Features used40

Risk Tiers

LOW — <2.0% shutdown probability

MODERATE — 2.0–4.7%

HIGH — 4.7–12.9%

CRITICAL — >12.9%

Trip Risk Calculator

Estimate shutdown risk for any trip without a unit's history — pick a reefer model, set its condition, and enter the trip. Use it for new equipment, planning, or what-if scenarios.

Equipment

Reefer Model

Unit Condition

Trip Details

Route (optional)

Season

Service Type

Leg Miles

Set Temp (°C)

Ambient weather en route is taken from the season. Override the coldest leg under Advanced for a specific winter route.

Pick a model and condition,
enter the trip, then Calculate

How this works

No unit history is required. The condition preset fills the unit's accumulated maintenance/alarm profile (New = clean, Typical = fleet median, Worn = heavy), and the trip inputs drive the rest. The same frozen model and calibration as the live fleet scores are used, so tiers are directly comparable.

Risk Tiers

LOW — <2.0% shutdown probability

MODERATE — 2.0–4.7%

HIGH — 4.7–12.9%

CRITICAL — >12.9%

Filters

Score Month

Location

Lane

Service Type

Model Year

Tier

Make

Search Unit

Units

Avg Risk

Critical

High

Shutdowns

SD Rate

What-If Scenario changeable metrics

Change operational levers and see how the frozen model re-scores the fleet, live. Equipment facts (model year, make) are not levers. Export the affected ranking as CSV or Excel.

Maintenance actions

PM overdue cleared
unit serviced now; current overdue spell ends (history stays) — no effect on units already on schedule Open defects closed
DOR cleared; recent repair churn settled Shutdown/alarm issue fixed
90 days clean of shutdowns & alarms

Trip context

Ambient temp (°C)

Leg miles

Month

Temperature re-derives cold-weather flags; month re-derives season.

Apply to

No selection = whole fleet. Selection uses the official tiers shown on this dashboard.

Single unit (reverse mode)

With a unit set, all three actions are evaluated individually and ranked — “biggest risk reduction: do PM”.

Affected fleet ranking (by adjusted risk)

#	Unit	Baseline	Tier	Adjusted	Tier	Δ

Risk Tier Distribution

fleet baseline below

Group By

-- | Sort:

Click a row to add it as a filter. Bars show tier mix; orange/red tints flag elevated avg risk vs the current slice.

Repair & Maintenance Pattern (current slice vs fleet baseline)

per-unit averages

Metric	Slice Avg	Fleet Avg	Δ vs Fleet	Distribution

PM Delay vs Risk & Shutdown Rate (current slice)

PM Status	Units	Avg Risk %	Shutdown Rate %

Fleet Risk Ranking

click column header to sort

#	Unit	Container	Model · Yr	Make	Risk %	Tier	Trips	SDs · Rate	SDs 90d	Last SD	Last Repair	Cum. PM Overdue	Total Repair $	CPH $/hr	CPM $/mi	Fuel L/hr

New units — not yet scored

New-build reefers seen in uploaded data but newer than the frozen model’s training fleet. Treated as provisional: low risk (freshly serviced) until they are folded in at the next retrain. They are excluded from the ranking and fleet averages above.

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Make × Model × Year × Season

Risk is re-scored for each unit under each of the 4 seasons (other features held constant). Highlights which cohorts are most weather-sensitive.

Make

Model

Year

Season Highlight

Fleet Seasonal Baseline (avg risk if every unit ran in that season)

Cohort × Season Heatmap

Sort by:

Make	Model	Year	Units	Winter	Spring	Summer	Fall	Swing max−min

Cells are color-scaled by risk magnitude. Highlight a season above to outline that column. Swing column shows how weather-sensitive the cohort is.

Most Weather-Sensitive Cohorts (largest gap between worst and best season)

Cohort	Units	Worst Season	Worst Risk	Best Season	Best Risk	Swing

Reefer Model Deep-Dive

Per-model history: shutdown rates, age cohorts, component patterns, and action items. Uses observed historical trip data.

Worst Performing Cohorts

Model + year combinations ranked by shutdown rate — identifies specific vintages to target

Model	Year	Age	Units	SD Rate %	Avg Risk %	Cost/Trip	Elevated+	Top Component Issue

Component Vulnerability by Model

Avg repairs per unit — darker = more repairs. Identifies which components fail on which models.

Risk by Model × Year

Full breakdown showing age effect per model

Model	Year	Age	Units	SD Rate %	Avg Risk %	Cost/Trip

Actionable Recommendations by Model

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Baseline Failure Rate

All trips

With Repair in 14d

Without Repair in 14d

Diag-Only (No Fix)

Time window:

Component Group Failure Rates

Trips with this component group repaired in the pre-trip window — sorted by failure rate. Lift = how many times riskier vs baseline.

Component Group	Trips	Shutdowns	Failure Rate	Lift	Risk Bar

Repair-to-Trip Turnaround

How soon after the last repair the unit was dispatched — shorter gaps = higher risk

Diagnosis Resolution

Was the diagnosis followed by an actual component fix before the trip?

Failure Rate by Repair Reason

Was the pre-trip shop visit a PM, general repair, warranty, etc.?

Reason	Trips	Failure Rate	Lift

Description Keywords

Keywords in repair descriptions (14d window) and their associated failure rates

Keyword	Trips	Failure Rate	Lift

Dangerous Repair Combinations

Two component groups repaired within 14 days before a trip — combinations with highest failure rates

Component A	Component B	Trips	Shutdowns	Failure Rate	Lift	Risk Bar

Repair Sequences (Order Matters)

Last two work orders before a trip, in chronological order. The sequence reveals whether issues escalated or went unresolved.

First Repair		Second Repair	Trips	Shutdowns	Failure Rate	Lift

Repeat Repair Impact

Same component group repaired 2+ times in 30 days before a trip vs single occurrence — repeat = unresolved issue

Component Group	Pattern	Trips	Shutdowns	Failure Rate	Lift

Detailed Comp Code Breakdown

Individual repair codes with 5+ occurrences in the pre-trip window

Code	Group	Trips	Shutdowns	Failure Rate	Lift

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Cost Per Hour (CPH)

Three models for every reefer: PM $/hr (scheduled maintenance), Repair $/hr (GENREP), and the money-pit flag (sell / keep). Start in Triage.

Shop

Make

Money pits only Top 20% repair $/hr

Annual engine-hours (per reefer)

Fleet triage — what to act on this week

One queue combining all three models: scheduled-maintenance $/hr, repair $/hr, and the money-pit flag. Sorted by predicted annual maintenance $.

Reefers

Money pits

Avg PM $/hr

Avg repair $/hr

Fleet maint $/yr

Triage queue

Scheduled maintenance — PM CPH model

Predicts PM cost per engine-hour by COMPCODE, then sums to a per-reefer PM $/hr. Use for budget planning (more stable than repairs).

Avg PM $/hr

PM budget $/yr (slice)

Top reefers by predicted PM annual $

Unit	Make	Model	Shop	PM $/hr	PM $/yr

Fleet PM $ by COMPCODE

This is the schedule forecast — annual $ per service code across the filtered slice

Code	Service	Reefers	Avg $/hr	Total $/yr

Drill into one reefer’s PM schedule

Code	Service	PM $/hr	PM $/yr

Repairs — GENREP CPH model

Predicts general-repair cost per engine-hour. Harder than PM (surprise failures) — treat as a budget range, and lean on the money-pit flag for replace-vs-repair decisions.

Avg repair $/hr

Median

P80 (watchlist cut)

Repair budget $/yr

Top 20% repair watchlist

Unit	Make	Model	Age	Money pit	Reactive PMs	Repair $/hr	Repair $/yr

Repair $/hr distribution

Avg repair $/hr by age band

Money-pit classifier

Flags chronic high-maintenance reefers (~98% accuracy). Use for sell vs keep and before approving $2,500+ repairs. Not a dollar predictor.

Flagged money pits

Median repair $/hr — pit

Median repair $/hr — normal

Pit / normal multiple

How a unit becomes a money pit

Rule 1 — ≥ 5 total maintenance visits

Rule 2 — ≥ 3 reactive PMs (catch-up after repair)

Rule 3 — ≥ 3 overdue / delay events

Reactive PM is usually the dominant trigger (~97% of flags in training).

Sell vs keep

Unit	Make	Model	Age	MY	Reactive PMs	Repair $/hr	Repair $/yr	Decision

All money-pit flags in this slice

Unit	Make	Model	Age	Shop	Reactive PMs	Maint visits	Double-bill ratio	Repair $/hr	Maint $/yr

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Cost Per Mile (CPM)

Lifetime maintenance-cost diagnostic · 857 ThermoKing intermodal containers · GENREP + PM split. Use it to rank units and understand what drives cost — not to forecast next-quarter dollars.

Avg GENREP CPM

Avg PM CPM

Avg Total CPM

Fleet Units

Select Unit

Pred GENREP

Pred PM

Pred Total

Total Miles

Rail Miles

Highway Miles

Year

Age

Type

Reefer

Dominant Shop

# Shops

SHAP Cost Drivers

Per-unit SHAP values in CPM units. Positive = increases cost, negative = reduces cost.

GENREP Drivers

PM Drivers

What-If Scenario Exploration

Adjust feature inputs to explore how lifetime CPM would shift under different conditions. Exploratory scenario tool — not a forecast of actual next-period cost.

Use selected Unit Lookup values as baseline

total_miles

rail_share

pm_orders_per_1k_mi

genrep_orders_per_1k_mi

reactive_pm_share

pm_delay_mean_days

pm_delay_pos_rate

pm_delay_neg_rate

pm_order_count

consignee

Field definitions

total_miles: planned/expected total miles for this scenario.

rail_share: fraction of miles on rail. Example 0.90 = 90% rail, 10% highway.

pm_orders_per_1k_mi: PM order frequency per 1,000 miles.

genrep_orders_per_1k_mi: GENREP order frequency per 1,000 miles.

reactive_pm_share: share of PM done in mixed (repair+PM) context, 0 to 1.

pm_delay_mean_days: average PM timing delay in days (positive = late, negative = early).

pm_delay_pos_rate: fraction of PM cycles that are late beyond threshold.

pm_delay_neg_rate: fraction of PM cycles that are early beyond threshold.

pm_order_count: expected PM order count for the period/profile.

consignee: customer/consignee profile context.

Total Units

High CPM Units

Low CPM Units

Avg Pred Total CPM

CPM Ranking (Lowest to Highest)

Fleet-wide CPM ranking with HIGH/MEDIUM/LOW bands (P20/P80 thresholds)

#	Unit	Pred CPM	Pred Cost	Total Miles	Rail Miles	Hwy Miles	Band

Global SHAP importance shows average CPM contribution magnitude by feature across the fleet. Higher mean |SHAP| = stronger average influence on CPM prediction.

GENREP Driver Pattern

PM Driver Pattern

Feature Glossary

Feature	Description

1 · Choose a file

Drop a monthly export (.xlsx / .csv). The file is parsed in your browser, matched against the agreed template, and checked against the database before anything is saved.

Drop file here or click to browse

Reefer repairs · Container repairs · Meter readings · TK alarm summary

Data type

Uploaded by (audit trail)

2 · Review & confirm

Nothing is written until you confirm. Duplicates are skipped automatically, so re-sending overlapping months is safe.

Validation report appears here

Units registered

Profile incomplete

new units awaiting asset details

Scores as of

Last refresh

Data coverage by source

Source	From	Through	Rows

Upload history

When	Type	File	By	Rows	Inserted	Duplicates	Rejected	Period

Risk score runs

Every fleet scoring run, including backdated month-ends. Download any month as CSV or Excel.

Data as of	Label	Units	Tier mix	Ran	Download

Compare two runs

Baseline run

Compare to

Unit	Score (baseline)	Score (compare)	Δ	Tier change
Pick two runs to see the biggest movers

Dataset

Unit / vehicle contains

From

—

Pick a dataset and press Apply

Read-only view of the live database. CSV download is capped at 20,000 rows — narrow the filters for full extracts of large ranges.

Risk Movers

Reefers whose risk tier changed between the two most recent monthly score runs — an earlier signal than any single snapshot.

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Unit	Previous tier	Current tier	Δ Tier	Prev risk	Curr risk	Δ Risk
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Repair vs Retire

Every reefer placed on a risk × cost grid. High-risk units that are also cost outliers are retirement candidates — fixing them rarely pays back.

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Unit	Model	Tier	Risk %	Maint $/hr	CPM $/mi	Recommendation
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TransX Risk Predictor

TransX Reefer Analytics

Since last refresh

Trip Risk Predictor

Fleet Dashboard

Model Comparison

Repair Patterns

Cost Per Hour

Cost Per Mile

Quick Stats

Unit Selection

Trip Details

Maintenance History

Frozen Model — Training Context

Risk Tiers

Trip Risk Calculator

Equipment

Trip Details

How this works

Risk Tiers

Filters

What-If Scenario changeable metrics

Affected fleet ranking (by adjusted risk)

Risk Tier Distribution

Group By

Repair & Maintenance Pattern (current slice vs fleet baseline)

PM Delay vs Risk & Shutdown Rate (current slice)

Fleet Risk Ranking

New units — not yet scored

Make × Model × Year × Season

Fleet Seasonal Baseline (avg risk if every unit ran in that season)

Cohort × Season Heatmap

Most Weather-Sensitive Cohorts (largest gap between worst and best season)

Reefer Model Deep-Dive

Worst Performing Cohorts

Component Vulnerability by Model

Risk by Model × Year

Actionable Recommendations by Model

Component Group Failure Rates

Repair-to-Trip Turnaround

Diagnosis Resolution

Failure Rate by Repair Reason

Description Keywords

Dangerous Repair Combinations

Repair Sequences (Order Matters)

Repeat Repair Impact

Detailed Comp Code Breakdown

Cost Per Hour (CPH)

Fleet triage — what to act on this week

Triage queue

Scheduled maintenance — PM CPH model

Top reefers by predicted PM annual $

Fleet PM $ by COMPCODE

Drill into one reefer’s PM schedule

Repairs — GENREP CPH model

Top 20% repair watchlist

Repair $/hr distribution

Avg repair $/hr by age band

Money-pit classifier

How a unit becomes a money pit

Sell vs keep

All money-pit flags in this slice

Unit detail — all three models on one card

Select Reefer

Drivers / health signals

PM schedule by COMPCODE

Scenario Planner (New Reefer)

Asset Details

Maintenance Plan

Expected Usage

Cost Per Mile (CPM)

Select Unit

SHAP Cost Drivers

What-If Scenario Exploration

CPM Ranking (Lowest to Highest)

GENREP Driver Pattern

PM Driver Pattern

Feature Glossary

1 · Choose a file

2 · Review & confirm