Order Promise Date Slip Tracker Worksheet
Customers forgive a late order faster than a surprise. This worksheet shows which lines have slipped, by how much and for what reason, and whether the customer has been told. It is meant for the daily stand-up between customer service and planning.
| Order / line | Customer | Part number | Line value | Original promise (days from order) (days) | Revised promise (days from order) (days) | Slip | Value slipped | Cause | Customer informed | |
|---|---|---|---|---|---|---|---|---|---|---|
14 days | $6,200 | |||||||||
0 days | $0 | |||||||||
7 days | $18,900 | |||||||||
5 days | $2,400 |
Enter promise dates as days from order date to keep the math simple. Sort by Customer informed, then by slip, so the lines you must call about today are first.
Promise date history
Optional: see how promise changes and at-risk lines could be reported from your ERP.
Optional. The tool and every export work without this. No spam.
How to use the tracker
List each open line that has moved or is at risk. Enter the original and revised promise as days from the order date, and the line value. The sheet computes slip in days and the value of lines that moved. Mark whether the customer has been informed. Before each stand-up, filter for lines marked No and make those calls first.
What the numbers tell you
Share of value slipped shows how much of your open book has moved. Average slip tells you whether delays are a few days or a few weeks. Group by cause to see the pattern: mostly Material points at purchasing and supplier performance, Capacity points at scheduling and quoting, Quality points at the shop floor.
- Material causes: review supplier lead times and safety stock
- Capacity causes: check promise logic against finite capacity
- Customer change causes: tighten the change request process
- Uninformed customers: fix the notification habit first
Linking to the ERP
Your ERP holds the promise date, but it rarely keeps a history of how many times it moved. Capture the original promise here, or in a user-defined field, so you can measure slippage. A repeating pattern of two or three promise changes per line is a sign that promise dates are guesses.
Common mistakes
Resetting the original promise every time it moves, which hides the real slip. Not recording the cause, which makes the data useless for improvement. Only tracking lines already past due, instead of lines that will be late.
Frequently Asked Questions
Should the original promise ever change?
No. Keep the first confirmed promise fixed and record every revision against it. If you overwrite it, the slip always looks like zero and you lose the signal that something is wrong with how dates are set.
How is this different from an on-time delivery report?
On-time delivery measures against the final date after shipment. This tracker works earlier, on open lines, so you can warn customers before the date is missed.
What counts as a slip?
Any revision that moves delivery later than the date the customer was given. Customer-requested moves are logged with the cause Customer change, so they do not count against your own performance in review.
Can I use dates instead of days?
Yes, but the worksheet formulas expect numbers. Convert dates to days from order date, or compute the difference in Excel after export and keep the days columns for the summary.
Netray can read promise date history and open order lines from your ERP, so this tracker is filled from data and the at-risk list is ready each morning.
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