What a new head of procurement actually inherits

What a categorisation vacuum looks like

What does a new head of procurement typically walk into? A spend list. Sometimes several, one per system, not agreeing with each other. Categories that were set up years ago for a different reporting need and never revisited. Suppliers duplicated under different names. Ask "what do we actually spend on cleaning consumables across all our sites" and you get a shrug, or three different answers depending on who you ask.

• The ERP has one version of the category list.
• The CAFM, if there is one, has another.
• Finance has a chart of accounts that predates both and answers to nobody.

Is this rare, or is it the normal starting point? It is the normal starting point. The Hackett Group's benchmarking puts world-class organisations at roughly 97 percent of direct spend under management, and other organisations closer to 70 percent. That gap, the difference between 97 and 70, is not a small rounding error. It is nearly a third of spend that nobody can currently see clearly, classify consistently, or negotiate on with confidence.

What does it feel like from the inside, in the first week? You ask a simple question in a meeting, how much do we spend with our top ten suppliers, and get a number with a caveat attached. Then another number, from another system, with a different caveat. Both people are being honest. Neither is wrong. The data underneath both of them was never built to agree.

Why nobody flagged this before you

Why does a gap like this go unflagged for years? Because the cost is diffuse, not sudden. Nobody gets a single alarming invoice that says "your spend categorisation is three years out of date." It shows up instead as a slightly longer month-end close, a slightly less confident tender submission, repeated often enough that it becomes the accepted baseline rather than a problem anyone escalates. A predecessor who managed around it for years is not the same as a predecessor who fixed it.

Does a missing baseline say anything about the people who came before? It says something about the process. Categorisation is not one person's job in most organisations. It touches procurement, finance, and whoever runs the ERP or CAFM, and something that belongs to three departments at once tends to belong to none of them in practice. A predecessor inheriting the same gap would have found the same gap, whatever their own competence.

What tends to make the gap worse right before a handover? Turnover itself. Whatever informal knowledge existed about which supplier records were duplicates, or which category a specific type of spend actually belonged in, usually lived in one person's head. When that person leaves, the workaround leaves with them, and whoever arrives next starts from less than the previous person had, not more.

How to find your real spend baseline

What is the fastest way to establish a real baseline? Pull every spend line from every system you have, and classify it to a consistent standard in one pass, rather than trusting whatever categorisation is already sitting in each system separately. Standard first-90-days guidance for procurement leaders puts this diagnosis in the first 30 days for a reason: you cannot set a credible target before you know the real starting number.

• Export spend from every system that touches it, ERP, CAFM, finance.
• Match suppliers across those exports so the same supplier counts once, not three times.
• Classify every line to one consistent taxonomy, not whichever category each system happened to assign it.
• Write down the real percentage of spend under management, even if it is uncomfortable.

How long should this actually take? Weeks, not months, if the classification work is done properly rather than by hand in a spreadsheet. Pearstop classifies spend to commodity level across a full portfolio in weeks, for a head of procurement who needs a real baseline before their first board update, not a guess dressed up as one.

What happens if you skip this and set targets on the old, unclassified data instead? The targets are built on the same shrug the meeting gave you in week one. Every report after that inherits the same uncertainty, and it gets harder to challenge once it has been presented as fact a few times.

What to prioritise in the first month

What should get attention before anything else? The baseline audit, before any savings target, any supplier negotiation, and any technology decision. Every one of those depends on knowing what you are actually working with, and doing them first on unreliable data means redoing them later on reliable data.

Is it worth acting on individual supplier issues while the audit is happening? Fix what is obviously broken as you see it, a clearly duplicated supplier, a glaring off-contract spend, but do not build a full strategy on individual fixes before the audit gives you the full picture. Chasing the visible problems first can feel like progress while the bigger gap underneath stays exactly as wide.

What is the one thing worth saying to your own leadership in week one? That you are establishing a real baseline before committing to a number, and why. A credible target built on a properly classified starting point holds up under scrutiny. A target guessed from three disagreeing systems does not, and it is far more expensive to walk back a missed target in month nine than to ask for a few weeks up front.

How do you know when the baseline is actually solid enough to build on? When the same supplier, asked about from three different systems, gives you the same number. Test it directly: pick five suppliers you know are significant, and check their total spend in the ERP, the CAFM, and finance's own records. If all three agree, you have a baseline. If they do not, you have found exactly where the classification work still needs to go, which is a useful result in its own right, not a failed test.

Stephanie Wiechers

CEO & Co-founder, Pearstop

Stephanie leads Pearstop's go-to-market and strategic direction. She works directly with procurement and FM leaders across Europe to understand how data quality affects margins, contracts, and AI readiness.

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