How to Track First-Time Fix Rate
Impacted by Parts in Excel

Learn how to measure the impact that missing parts and stockouts can have on your service performance. In this lesson, you’ll see how to organize your data, compare lost revenue against total opportunity, and build a clear visual that helps you monitor whether inventory issues are hurting your first-time fix results.

Download the Excel file used in this tutorial:

First-Time Fix Rate Impacted by Parts %

1. Identify the required columns in your dataset

  • Confirm your dataset includes:
    • Date
    • Potential ticket revenue
    • Revenue lost (stockouts or shortages)
  • Keep only what you need visible so the build stays clean.

2. Extract Month and Year from the Date column

  • Create a Month field using the TEXT function:
    • Use the date cell as the input
    • Format code for month uses four M characters
  • Create a Year field using the same TEXT function:
    • Use the same date cell
    • Format code for year uses four Y characters
  • Once those helper fields exist, you can delete any duplicate test cells and keep the final Month and Year columns.

3. Create a unique, sequential list of Month and Year

  • Select your Month and Year columns and generate a unique list so your summary table has one row per month.
  • Make sure the output is sequential by date:
    • If your dataset is not sorted by date, sort it first so the month list appears in the correct order.
  • Copy the unique list and paste as values using Ctrl + Shift + V.

4. Set up the summary table headers

Create columns for:

  • Potential ticket revenue
  • Revenue loss
  • Percent
  • Target

This becomes your monthly KPI table.

5. Calculate monthly Potential Ticket Revenue using SUMIFS

  • Use SUMIFS to total potential ticket revenue for each month and year:
    • Sum range: Potential ticket revenue column
    • Criteria range 1: Month column
    • Criteria 1: The month in your summary row
    • Criteria range 2: Year column
    • Criteria 2: The year in your summary row
  • Fill the formula down for all months.

6. Calculate monthly Revenue Loss using SUMIFS

  • Repeat the exact same SUMIFS structure:
    • Only change the sum range to the Revenue lost to stockouts column
  • Fill the formula down for all months.

7. Calculate the percentage

  • In the Percent column:
    • Divide Revenue loss by Potential ticket revenue
  • Format as percent using Ctrl + Shift + 5.
  • Fill down for all months.

8. Add a target percent for comparison

  • Enter a target value (example used in the video: 2%).
  • Fill the target down the column so each month has the same benchmark.

9. Create a combined Month-Year label for charting

  • Build a chart-friendly date label by combining:
    • Month
    • Year
  • Use a simple concatenation approach so the x-axis displays clearly.

10. Build the combo chart with secondary axis

  • Select the Month-Year label plus:
    • Revenue loss
    • Percent
    • Target
  • Insert chart:
    • Insert → Recommended Charts → All Charts → Combo
  • Set chart types and axes:
    • Revenue loss as columns on the primary axis
    • Percent as a line on the secondary axis
    • Target as a line on the secondary axis
  • Confirm the secondary axis is enabled so the percent and target are visible.

11. Clean up the chart formatting

  • Make the target line thinner so it does not dominate the chart.
  • Adjust styling so the KPI line and target line are easy to distinguish.
  • Add data labels only to the line series:
    • Click the line, then add labels for that series only
    • Avoid labeling everything so the chart stays readable
  • If labels are hard to see:
    • Use the fill option on the label background to increase contrast.

12. Add totals for annual rollup

  • At the bottom of your table:
    • Sum Potential ticket revenue
    • Sum Revenue loss
  • Calculate overall percent:
    • Total revenue loss divided by total potential ticket revenue
  • This gives you the full-period KPI number for the dataset.

13. Final check before reuse

  • The hardest part is not the formulas, it is the data structure:
    • Make sure your stockout or shortage events are actually being captured consistently in your system
    • If the inputs are incomplete, the KPI will underreport the true impact

Tracking First-Time Fix Rate Impacted by Parts % in Excel Dashboards

Q1. What does First-Time Fix Rate Impacted by Parts % mean?
This KPI shows how often inventory shortages or missing parts interfere with completing a job successfully on the first visit. It helps HVAC companies understand whether parts availability is reducing service efficiency and affecting customer satisfaction.

Q2. Why is this KPI important for inventory management?
Tracking this metric helps you see whether stockouts are creating service delays, repeat visits, or lost revenue opportunities. It connects your inventory performance directly to operational results, which makes it one of the most valuable inventory KPIs for HVAC businesses.

Q3. How can this analysis help an HVAC company?
By reviewing this KPI month by month, you can identify seasonal pressure points, measure how much revenue is being affected by parts shortages, and determine whether your current inventory strategy is supporting strong field performance during busy periods.

Q4. What should be included in the data for this KPI?
To track this metric properly, you’ll need service dates, potential revenue, and the estimated revenue lost when a job could not be completed because a required part was unavailable. Clean, structured data is essential for building an accurate Excel dashboard for inventory analysis.

Q5. What is the best way to visualize this KPI?
A combo chart works especially well because it allows you to compare revenue impact alongside the percentage and target over time. This makes it easier to spot when stockouts are becoming a bigger operational problem.

Q6. Can this same process be used for other inventory KPIs?
Yes. The same dashboard approach can be adapted for metrics such as stockout rate, dead stock percentage, inventory accuracy, or revenue lost to stockouts, giving you a more complete view of inventory performance.

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