Measure and improve estimating accuracy

The problem

Estimating skill and standards rarely get feedback. Without a structured comparison to actuals, the same misses repeat, and good estimators can't show that their quotes hold up.

What to measure

Accuracy ratio = actual ÷ estimated cost. Track the median and the spread per part family and category each month. A median drifting above 1.0 means standards are too low; a wide spread means inconsistent execution or data.

How MarginGuard helps

MarginGuard computes accuracy for every imported job and groups it by part family, customer, and category, so estimating can see which standards to change and confirm the change worked on later jobs.

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How to get started

1. Step 1
   Import a quarter of closed jobs

   Estimated and actual costs, by category if available.

2. Step 2
   Find the standards that miss

   Sort part families by accuracy ratio and dollars.

3. Step 3
   Update and re-measure

   Change one standard at a time and watch the next month's jobs.

A monthly estimating review

Fifteen minutes: the three part families with the worst accuracy in dollars, a proposed standard change for each, and last month's changes checked against new jobs.

Which estimating inputs miss most often

In job shops the usual suspects are setup hours on short runs and new parts, cycle times for parts that move to a different machine, burden rates that haven't been updated since the last rate study, material prices quoted from an old vendor price, and outside-processing costs with no expedite allowance. Checking accuracy by category points straight at which of these inputs needs work.

What good looks like

Perfect estimates are not the goal. A healthy estimating process has a median accuracy ratio close to 1.0 for each major part family, a narrowing spread over time, and no category that is consistently on one side. When a standard changes, the next month's jobs should show the ratio moving toward 1.0. If it doesn't, the change addressed the wrong driver.