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Predictive Maintenance Yield Loss Estimator

This estimator quantifies yield loss in a production period where predictive-maintenance performance is being reviewed. It turns total output and defective output into a yield percentage, a loss percentage, and—when a unit value is entered—an estimated value of the lost units.

The calculator is useful for checking whether changes in machine condition or maintenance timing coincide with a change in quality loss. It does not prove that defects were caused by equipment condition; raw material, setup, operator, process, and inspection factors can produce the same pattern. Use a consistent definition of a defective unit and compare equivalent products or process conditions when evaluating trends.

Yield inputs

units
units
USD
Result
—
yield loss rate
Good yield—
Lost units—
Estimated loss value—

1. Enter total production
Use the total number of units made during the period being evaluated.

2. Count defective units
Enter units that fail the quality definition used for this analysis. Do not mix first-pass defects with a different final-inspection convention.

3. Add a unit value if useful
Enter a representative value per lost unit to estimate direct loss value; use 0 if you only need the yield metrics.

4. Review loss and yield
The main result is the defective share of output. Good yield is its complement, while the detail rows show lost units and estimated value.

5. Compare maintenance windows
Use the same defect definition before and after a predictive-maintenance change so the comparison is meaningful.

Yield loss rate = Defective units ÷ Total units × 100 Good yield = (Total units − Defective units) ÷ Total units × 100 Estimated loss value = Defective units × Value per unit

The model treats each defective unit as a full lost unit at the entered value. If some units are reworked or recovered, their net economic loss can be lower and should be modeled separately.

What the result means

The yield loss rate is the percentage of produced units classified as defective under the entered counting rule.

A correlation between maintenance events and yield loss does not establish root cause. Use defect codes, process data, and maintenance history for diagnosis.

Given: 2,400 total units, 72 defective units, and a value of $14.00 per unit.

Calculation: Yield loss = 72 ÷ 2,400 × 100 = 3.00%. Good yield = 97.00%. Estimated loss value = 72 × $14.00 = $1,008.00.

Result: The period has a 3.00% yield loss, equal to 72 units and $1,008 at the entered unit value.

Interpretation: Compare this result with similar runs to determine whether the quality loss changed around maintenance interventions or machine-condition alerts.

Should I count rework as defective output?

Use the definition that matches your quality objective. For first-pass yield, units requiring rework are normally treated as not-good on the first pass; for final yield, the treatment may differ.

What unit value should I enter?

Use the value that matches the decision you are making, such as material cost, conversion cost, or another internally defined loss basis. The calculator does not add disposal, rework, or opportunity costs unless they are built into that value.

Can this show whether predictive maintenance caused the improvement?

No. It can quantify the change in yield loss, but cause-and-effect requires additional evidence such as fault history, process conditions, defect modes, and controlled comparisons.

What happens if there are no defective units?

The yield loss is 0% and good yield is 100% for the entered period. A zero-defect period does not imply future production will remain defect-free.

How is yield loss different from OEE quality?

They use complementary views of the same count relationship: OEE quality is good units divided by total units, while yield loss is defective units divided by total units.