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Restart Reliability Index · Refreshed quarterly

Which refurb SKUs actually last.

Every unit we ship is tracked. Every failure is logged. We publish the resulting model-by-model reliability data so you can standardise on the SKUs our field data says will hold up — not the ones we have the most of. No refurb competitor publishes this. We think that’s a mistake.

Filtered fleet

44,280

units in sample

Weighted failure rate

3.3%

12-month, all grades blended

Avg battery health

84%

at intake, battery-powered devices only

ModelUnits shipped12-mo failure rateBattery at intakeAvg return gradeMedian RMAOur take

UltraSharp U2723QE

Dell · Monitor

2,6401.3%n/aA2.6 dStrong

MacBook Air 13" M3

Apple · Laptop

2,4501.6%
96%
A2.4 dStrong

EliteBook 845 G10

HP · Laptop

6401.9%
94%
A2.8 dStrong

iPad Air 11" M2

Apple · Tablet

1,4202.0%
92%
A2.9 dStrong

MacBook Pro 14" M2 Pro

Apple · Laptop

1,8202.1%
89%
A3.0 dStrong

OptiPlex 7090 SFF

Dell · Desktop

1,7802.2%n/aB3.1 dStrong

ThinkCentre M75q Tiny

Lenovo · Desktop

1,1402.5%n/aB3.3 dStrong

ThinkPad T14 Gen 3

Lenovo · Laptop

4,8202.8%
87%
A3.2 dStrong

500e Chromebook

Lenovo · Chromebook

2,6802.9%
84%
B3.5 dStrong

ThinkPad T14s Gen 2

Lenovo · Laptop

3,1403.1%
84%
A3.6 dStrong

Chromebook 714

Acer · Chromebook

4,2103.2%
81%
B3.9 dStandard

Latitude 5430

Dell · Laptop

6,2103.4%
82%
B4.1 dStrong

UltraFine 27UP850

LG · Monitor

8203.6%n/aB4.1 dStandard

XPS 13 9315

Dell · Laptop

1,2403.8%
81%
B4.3 dStandard

ThinkPad X1 Carbon Gen 9

Lenovo · Laptop

1,9804.1%
80%
B4.1 dStandard

Latitude 7420

Dell · Laptop

2,4904.8%
79%
B4.6 dStandard

EliteBook 840 G8

HP · Laptop

2,8805.2%
78%
B5.0 dStandard

Surface Pro 8

Microsoft · Tablet

3806.3%
74%
C5.8 dUse with care

Surface Laptop 4

Microsoft · Laptop

5607.1%
75%
C5.9 dUse with care

MacBook Pro 16" Intel 2019

Apple · Laptop

9808.4%
71%
C6.2 dUse with care

Sample reflects units shipped and tracked over the rolling 12 months ending this quarter. “Our take” blends failure rate, battery health, return-grade drift, and RMA speed into a single guidance label — details on the methodology below.

Methodology

How the numbers get made.

  1. 01

    Every unit tagged at intake

    Serial + SKU + battery-health reading + QA result are recorded the moment a unit lands on our floor. No sampling, no aggregation bias.

  2. 02

    Failure defined narrowly

    We count warranty claims + out-of-warranty RMAs + field DOAs. Cosmetic complaints and user error do not count; we surface those separately.

  3. 03

    Weighted by units shipped

    A 20-unit SKU and a 5,000-unit SKU don’t get equal say. Aggregates use unit-weighted averages, published with the sample size alongside.

  4. 04

    Refreshed quarterly

    Rolling 12-month window. Models with < 100 units shipped are held out until the sample is statistically meaningful.

Fair questions

Three objections we get.

Why publish this at all?

Because the honest answer to "which refurb model should I standardise on?" lives in our ops data — and keeping it internal is the industry default we want to break.

What about models we stop recommending?

They stay on the index with a "Use with care" label and a short note on the failure mode. Removing them would make the index a marketing artefact.

Can I audit the underlying data?

Enterprise customers (250+ units/yr) can request an auditor-readable CSV of the sample under NDA. We publish aggregates; customers get the raw.

Standardise on the strong column.

Send us the models rated Strong with a target fleet size and we’ll reserve matched batches from the best-performing SKUs for your next refresh.