Track orders from purchase to delivery with comprehensive status monitoring
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Fulfilment problems are usually visible in the timestamps before a customer complains. This template records each stage separately so you can see which one is slipping rather than only that the order was late.
The actual columns, with sample rows
| A | B | C | D | E | F | G |
|---|---|---|---|---|---|---|
| 1 | Order | Received | Picked | Packed | Shipped | Cycle (h) |
| 2 | #10241 | 07-02 09:14 | 07-02 11:40 | 07-02 12:05 | 07-02 16:30 | 7.3 |
| 3 | #10242 | 07-02 10:02 | 07-03 08:15 | 07-03 09:40 | 07-03 16:30 | 30.5 |
| 4 | #10243 | 07-02 14:55 | 07-02 15:20 | 07-02 15:38 | 07-02 16:30 | 1.6 |
| 5 | #10244 | 07-03 08:30 | 07-03 09:05 | 07-03 14:50 | 07-04 16:30 | 32.0 |
Why each one is written the way it is
=([@Shipped]-[@Received])*24
Total cycle time in hours. Stage timestamps make it decomposable, which a single "days to ship" column never is.
=([@Packed]-[@Picked])*24
Pack stage duration. Comparing the three stage durations is how you find where the queue actually forms — it is rarely where people assume.
=COUNTIFS(Orders[Shipped],">"&[@Promised])/COUNTIFS(Orders[Order],"<>")
Late-shipment rate against the promised date, the number that matches the customer's experience.
=PERCENTILE.INC(Orders[Cycle (h)],0.95)
The 95th percentile cycle time. This is what to quote as a delivery promise; the average will be missed for one order in two.
A worked example with real numbers
A distributor added a second picker to fix late shipments and the late rate barely moved. Stage timestamps showed picking averaging 40 minutes and packing averaging 5 hours — orders were waiting for a single packing bench. Moving one person from picking to packing halved the P95 cycle time at no extra cost.
Tab by tab
| Tab | Contents |
|---|---|
| Orders | One row per order with a timestamp per stage. |
| Stages | Stage definitions and target durations. |
| Exceptions | Orders held, split or cancelled, with reasons. |
| Dashboard | Cycle time percentiles and late rate by week. |
| README | Where each timestamp is captured and by whom. |
What is included, and what to look at next
Specific to this workbook, not generic
Four is usually enough: received, picked, packed, shipped. More stages produce more precise data and less of it, because people stop recording them.
Because a delivery promise based on the average is broken half the time. Quoting the P95 means you hit it 19 times out of 20.
Log each shipment as its own row against the same order id. Overwriting the original loses the fact that the customer waited twice.
"This fulfillment tracker has streamlined our entire order processing workflow. We've reduced processing time by 40% and improved our customer satisfaction scores significantly."