Inventory is often the largest asset on a distributor's balance sheet, and the provisions against it are a judgement call. More and more, the people who sign off on those judgements want to see the method, not just the output.
For most manufacturers and distributors, inventory is the biggest single line on the balance sheet, and the write-downs taken against slow, excess and obsolete stock move the profit. That makes it a focus for whoever audits the accounts. A statutory auditor or a commissaire aux comptes is not satisfied by a total; they test how provisions were set, whether the policy was applied consistently, and whether the same facts would produce the same answer. Under internal-control regimes such as SOX, management has to be able to demonstrate that the process behind a material estimate is designed and operating as intended.
Here is the quiet problem: in a lot of companies the real forecasting and buying logic lives in a spreadsheet only one person understands, or in a planning tool that returns a number without a readable reason. When the auditor asks how the obsolescence provision was built, the honest answer is often a shrug and a reference to experience. That is exactly the kind of estimate auditors are trained to challenge.
Governance is converging on a single demand: show your work. The EU AI Act requires high-risk AI systems to be built with automatic logging so their use can be traced and reconstructed, with record-keeping obligations that regulators can inspect; the high-risk provisions are slated to take effect in 2026, with the exact timing still being finalised in Brussels. Whatever the final date, the direction is set: if an automated system influences a consequential decision, you will be expected to keep a trail of how it did so.
Analysts are watching the same gap from the operational side. Gartner warned in 2026 that enterprises are discovering governance gaps in autonomous AI only after a production incident, and predicts a wave of agents being demoted or decommissioned once those gaps surface. The lesson for planning is blunt: a system that cannot be governed cannot be relied on for a number that ends up in audited accounts.
It is tempting to treat audit-readiness as a compliance cost and cash performance as the real prize, but they are the same muscle. The reason a provision is hard to defend is usually the reason the cash is trapped: the logic for how much to hold, when to reorder and when to write down is not explicit. Make that logic transparent and repeatable and two things happen at once. The auditor gets a method they can test, and the business gets reorder points and safety stock set on measured demand error instead of habit.
That is where the money is. When buffers are set on real variability rather than on a flat rule of thumb, the stock that was quietly over-held comes down without hurting service, and the cash it was tying up is freed. Bad inventory costs twice, in the holding and in the stockouts; a method you can audit is also a method you can tune, and tuning is what releases working capital.
The practical move is to treat auditability as a buying requirement, not a feature to admire later. Before choosing or keeping a planning approach, put it through the test an auditor would. Can it reproduce a prior result exactly on the same data? Can it show, for any figure, the inputs and the steps? Does it keep a durable, time-stamped record that outlives the person who ran it? Can a non-technical reviewer read why the number is what it is?
A tool that passes those tests does more than keep you out of trouble. It turns the annual audit from a scramble into a report you can hand over, it makes the S&OP meeting an argument about the business rather than about whose spreadsheet is right, and it quietly lowers the cost of the capital tied up in stock. The number you can explain is the number you can trust, and increasingly it is the only number the people who sign the accounts will accept.
It means the forecast and the inventory decisions built on it can be reproduced and explained: the same data returns the same number, every figure can be traced to its inputs and steps, the record is time-stamped and durable, and a non-technical reviewer can read why the number is what it is. That is what a statutory auditor or an internal-control regime needs to test an estimate.
The Act requires high-risk AI systems to keep automatic logs so their operation can be traced and reconstructed, with inspectable record-keeping obligations. The high-risk provisions are taking effect in 2026, with final timing still being set. The broader signal is that automated systems influencing consequential decisions will be expected to maintain an audit trail.
Because auditors test the method, not just the total. A spreadsheet only one person understands, or a tool that returns a number without a readable reason, cannot demonstrate that the provision was set consistently and would produce the same answer on the same facts. That is precisely the kind of estimate auditors are trained to challenge.
No. The logic that makes a provision defensible is the same logic that frees cash: explicit rules for how much to hold, when to reorder and when to write down. Make it transparent and repeatable and you get both a method the auditor can test and buffers set on real demand error, which releases trapped working capital.
The number you can explain is the number you can trust; and increasingly it is the only number the people who sign the accounts will accept.
Picture your next S&OP meeting: one number on screen that nobody argues with, and a trail behind it that an auditor could follow. See it free, on your own data, in 45 minutes; and if it does not hold up, we part as friends.