How do you calculate a refurbish-vs-replace score?

Multiply each input by its weight and sum: repair cost impact times 0.40, repeat-failure likelihood times 0.35, and final-test confidence times 0.25. Scores of 6, 4, and 3 give 2.4 + 1.4 + 0.75 = 4.55.

Why is repair cost weighted highest?

At 0.40 it is the largest weight because repair cost is the most direct economic driver of whether refurbishing beats replacing. Repeat-failure risk follows at 0.35 and test confidence at 0.25.

What score means replace instead of refurbish?

That depends on the thresholds you set for your rubric, applied consistently across product families. The 4.55 result here lands mid-range; you compare it against your repair, replace, harvest, and scrap cut-offs to make the call.

How should I score each input?

Define a fixed scale (for example 1-10) and a rubric for what each level means per input, then train technicians so a '6' on repair cost means the same thing every time. Consistency is what makes the composite trustworthy.

Can I use this across different product families?

Yes — that is the point. Applying the same weights and thresholds lets you compare disposition decisions across families on one scale, as long as the underlying scoring rubric is held constant.

Refurbish-vs-replace score vs simple repair-cost ratio?

A repair-cost ratio only looks at money. This score also weights repeat-failure risk and test confidence, so it catches cheap-to-repair units that will fail again — a case pure cost comparison misses.

Electronics Repair, Refurbishment & Depot Operations calculator

Refurbish vs Replace Decision Score Calculator

The refurbish-vs-replace decision score is a weighted disposition metric that tells a depot whether a returned unit is worth repairing, replacing, harvesting for parts, or scrapping. It blends three judgments depot engineers make on every unit: how costly the repair is, how likely the unit is to come back, and how confident final test is that the fix will hold. Disposition engineers and depot leads use it to apply a consistent rule across product families instead of relying on each technician's gut. A consistent score keeps repair spend off units that should be replaced and protects customers from units that will fail again.

What this calculator does

Score whether a returned device, module, or board should be refurbished, repaired, harvested, scrapped, or replaced based on cost, risk, and test confidence.
Use it when refurbish vs replace decision in electronics repair, refurbishment and depot operations needs a defensible ranking against other electronics repair, refurbishment and depot operations risks for the next review.
It combines repair cost impact, repeat-failure likelihood, and final-test confidence into one weighted disposition score using fixed weights of 0.40, 0.35, and 0.25.

Formula used

Refurbish vs replace decision score = repair cost impact score × 0.40 + repeat failure likelihood score × 0.35 + final test confidence score × 0.25
Compare scores across product families using the same repair, replace, harvest, and scrap thresholds.

Inputs explained

Repair cost impact score:
Repeat failure likelihood score:
Final test confidence score:

How to use the result

Use it at the disposition step after diagnosis to decide repair, replace, harvest, or scrap, especially when standardizing the call across product families.
It is only as good as the scoring rubric behind each input; inconsistent 0-to-N scoring between technicians makes the composite score unreliable, so the rubric must be defined and trained.

Current U.S. benchmarks

The producer price index for copper and brass mill shapes stands at 559.593 (BLS, May 2026), up 76.8% from a year earlier. Quotes priced off last quarter's material cost miss this move. Global copper trades at $13,484 per tonne (IMF via FRED, May 2026).
U.S. manufacturing runs at 75.6% of capacity (Federal Reserve, May 2026). New factory orders are up 2.3% year over year (Census).
The U.S. has 11,261 computer and electronic products establishments employing about 815,443 workers (Census County Business Patterns, 2023).

Common questions

How do you calculate a refurbish-vs-replace score? Multiply each input by its weight and sum: repair cost impact times 0.40, repeat-failure likelihood times 0.35, and final-test confidence times 0.25. Scores of 6, 4, and 3 give 2.4 + 1.4 + 0.75 = 4.55.
Why is repair cost weighted highest? At 0.40 it is the largest weight because repair cost is the most direct economic driver of whether refurbishing beats replacing. Repeat-failure risk follows at 0.35 and test confidence at 0.25.
What score means replace instead of refurbish? That depends on the thresholds you set for your rubric, applied consistently across product families. The 4.55 result here lands mid-range; you compare it against your repair, replace, harvest, and scrap cut-offs to make the call.
How should I score each input? Define a fixed scale (for example 1-10) and a rubric for what each level means per input, then train technicians so a '6' on repair cost means the same thing every time. Consistency is what makes the composite trustworthy.
Can I use this across different product families? Yes — that is the point. Applying the same weights and thresholds lets you compare disposition decisions across families on one scale, as long as the underlying scoring rubric is held constant.

Last reviewed 2026-05-12.