Learn how to spot where your truck replenishment process is breaking down. In this lesson, you’ll build a simple month-by-month heat map that highlights which part categories are driving the most shortfills, set a clear performance target, and quickly see what needs attention to support higher first-time fix rates and fewer return visits.
Download the Excel file used in this tutorial:
Make sure your dataset includes:
Once your data is structured correctly, the rest of the model builds from these four fields.
This groups all requests into clean monthly reporting periods.
This becomes the vertical axis of your heat map.
Now your matrix layout is structured:
Months down the left, Parts Categories across the top.
This shows how much demand was fulfilled for each combination.
This produces the Internal Parts Shortfill Rate % for each month and part category.
This ensures calculations stay aligned as you expand the grid.
Create three rollups using the same logic:
Overall by Part
Overall by Month
Overall for Entire Dataset
Format all summary cells as percentages.
Any value above the target will automatically highlight.
This allows the cell to display something like “Target: 6.0%” while still functioning as a numeric value for conditional formatting.
Q1. What is Internal Parts Shortfill Rate % (Truck Replenishment)?
It’s a KPI that shows how often technicians leave the warehouse without all the parts they requested for their trucks. A higher shortfill rate means your inventory operation is slowing down the field.
Q2. Why does shortfill rate matter for first-time fix rate (FTFR)?
When techs leave without what they need, jobs get delayed, return visits increase, and first-time fix rate drops. Reducing shortfills helps stabilize the schedule and improves customer experience.
Q3. What will this heat map help me see quickly?
It makes it easy to identify patterns by month and by part category, so you can instantly spot spikes (example: one category jumping in a specific month) and prioritize what to fix first.
Q4. What data do I need to follow this lesson?
You’ll need four fields: request date, parts category, requested quantity, and filled quantity. With those columns, you can recreate the same analysis structure shown in the video.
Q5. How do targets help in this analysis?
A target gives your team a clear definition of “acceptable.” In the lesson, anything above the target is flagged so problems stand out immediately and your heat map stays actionable.
Q6. Can I share this with my team as a recurring report?
Yes. This setup works well as a monthly check-in view for operations and inventory teams, because it summarizes performance trends and highlights the categories that need attention without digging through raw rows.