01 Overview
Order to invoice at a major FMCG manufacturer
One designed process, 294 ways of running it
A single global order to invoice process, executed across dozens of markets and hundreds of plants and distribution centres. The design was common to all of them. The execution was not.
02 The problem
A common process that behaved differently everywhere
The manufacturer ran one designed order to invoice process across more than forty markets, over a hundred plants and three hundred distribution centres. Leadership could see that outcomes varied by region. Nobody could show why.
Standard SAP reporting confirmed that orders were shipping and invoices were being raised. It could not show how an order line actually travelled, which steps were repeated, or why two lines with the same design finished ten days apart. Four things stayed invisible.
Variation without explanation
Outcomes differed by market, plant and customer, with no way to attribute the difference to execution.
Rework never counted
Repeated header and line changes sat inside change documents, recorded for audit but never measured as effort.
Billing time unaccounted for
Nearly half of elapsed time accumulated after fulfilment, in a phase that standard reporting treated as instantaneous.
Value at risk unseen
Delayed and rejected order value was visible only in aggregate, never traced to the execution that produced it.
03 How it worked
Four weeks, from raw tables to findings
The manufacturer provided read-only extracts from its production SAP instance and a short period of subject-matter support. RE-ViVE handled the data modeling, execution reconstruction and analysis from records the business was already keeping.
Read the source evidence as it existed
Sales orders, deliveries, billing documents, change records and credit master data were distributed across SAP tables — VBAK and VBAP, LIKP and LIPS, VBRK and VBRP, CDHDR and CDPOS, KNKK and KNA1. The evidence was not delivered as ready-made process flows organised by market or product.
Create a common execution foundation
RE-ViVE mapped those records into a common Execution Data Model, linking each order line to its deliveries, invoices and subsequent changes so that markets, plants and customers could be reconstructed and compared consistently.
Reconstruct and analyse execution
RE-ViVE reconstructed the observed paths and measured variants, cycle time, rework loops and delay contribution — producing process views for every variant, each drillable to the evidence behind a single order line.
| 02 Apr 09:14 | Sales order created | |
| 02 Apr 09:16 | Credit check cleared | |
| 04 Apr 11:02 | Sales order header change | change 1 |
| 06 Apr 08:40 | Delivery created | |
| 09 Apr 15:27 | Delivery header change | change 2 |
| 11 Apr 16:22 | Goods issue | |
| 15 Apr 10:05 | Invoice created |
The two highlighted rows are recorded in SAP as ordinary change documents. Reconstructed as execution, they are six days of rework on a line that was designed to take three.
04 What we found
What the evidence showed
A quarter of order lines took five times longer than the rest
Across 2.47M order lines the average order to invoice time was 7.48 days. Inside that average sat two very different populations.
618,000 lines in the bottom quartile averaged 18.61 days. The remaining lines completed in 3.77. The gap was execution behaviour, not workload — the same process, the same system, the same order types.
Two activities carried most of the rework
9.69M rework days accumulated in the quarter, representing roughly seventy per cent of all process days consumed.
Sales order header change and delivery header change together accounted for 8.6M of them. Ninety-nine per cent of that rework was manual — changes made by users, not corrections generated by the system.
8.6Mof 9.69M rework days sat in just two activities
A rounding error in volume, a quarter of the value
Rejected order lines were 0.47% of volume. They represented 25% of the $7.03B of order value in the period.
Separately, $238M of value was delayed reaching customers, with a single market accounting for ninety per cent of it, and two plants of eleven driving half of both volume and value.
$1.77Bof the $7.03B ordered in the period was rejected — from just 0.47% of order lines. Order splitting was the dominant reason, concentrated in one sales organisation.
The wait for cash was longer than the process itself
Order to invoice was measured in days. Invoice-to-cash was measured in weeks, and the two barely tracked each other.
The market with the fastest process — 3.57 days to invoice — waited 33.9 days beyond it for payment. The market with the slowest process collected fastest of all.
The collection gap ranged from 33.9 days to 17.7 across seven markets — working capital held outside the process being optimised.
05 What we recommended
Where the time comes back
Five plays, in the order we would take them. Every figure is drawn from the manufacturer's own records and describes opportunity identified during the engagement.
Standardise onto the paths that already work
Retire the bottom-quartile variants behind 618,000 order lines and hold new orders to the proven flows.
Automate the two manual change activities
Sales order and delivery header changes carry the bulk of manual rework across every market.
Streamline the credit release path
Reset limits for customers released repeatedly, and fast-track the two thirds already approved same day.
Work the collection gap
Target the slowest-paying accounts and the payment variant averaging forty days on its own.
Keep the execution views current
Rework, delay and value at risk surface as they happen rather than in the next quarterly review.
06 Next steps
The opportunity was already in the data
Every number on this page came from records the manufacturer had been keeping all along. RE-ViVE made them measurable, and then made them actionable.
This is Execution Intelligence: reconstructing how work actually executed from the evidence already produced by the enterprise, then exposing where time, effort and complexity accumulate.
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