Transparent & Controllable AI

Nadii does not stop at a recommendation such as “order / do not order” or “reallocate / do not reallocate.” Users can see what drives the decision: projected demand, uncertainty, cost of stockout, cost of excess inventory, and the impact on budget, transport, warehouse operations, and service level. This matters most in environments where one decision affects several functions at once and teams need to understand not only the outcome, but also the reasoning behind it.

Not every decision requires manual intervention, and not every intervention improves the result. Nadii automates repetitive decisions and involves people where a real exception, uncertainty, or need for clarification appears. When the system detects lines that require attention, it flags them clearly with comments such as “check”, so users can focus on the few decisions that need review instead of manually screening everything. Operational teams can also define lists or groups of products, channels, or situations they want to manage in semi-automatic mode — for example, always reviewing Nadii’s proposal before approval. This keeps human control where it genuinely adds value, while the rest of the process runs automatically and consistently within the same decision logic.

Reports in Nadii do not stop at showing that performance is below plan. Their role is to explain what the result is made of and which operational factors created it: whether the issue comes from forecast error, supplier delay, an oversized buffer, an incorrect reservation, a warehouse constraint, a manual user change, or a deliberate buying decision such as smart buying. The system also shows the structure of inventory and its cost — how much of the stock comes from display stock, minimum availability rules, safety buffers, channel reservations, campaigns, or purchases made intentionally to secure better margin. This means users do not only see that inventory is high; they can also see whether it is true excess stock or a conscious business choice. Reports also show the impact of these decisions on margin, P&L, cash flow, and availability, and allow before/after comparison, so reporting becomes a decision tool rather than another KPI table that still needs interpretation outside the system.

In many organisations, procurement, sales, logistics, and finance optimise different targets and end up pulling in different directions. Nadii brings these decisions into one logic: the same system weighs availability, inventory, margin, transport cost, operating cost, and cash impact at the same time. This means teams do not need to reconcile conflicting priorities after the fact — they work from the same decision model and the same version of reality.

A large share of operational problems starts not with a bad decision, but with bad data: missing values, duplicates, anomalies, one-off spikes, wrong lead times, or events that should not be treated as normal demand. Nadii automatically cleans and interprets data before using it in forecasts and recommendations. This reduces manual preparation work and allows decision models to run on a more stable and reliable signal.

Every change in Nadii leaves a trace: who changed the decision, when, and why. This is important not only for control, but also for organisational learning. Teams can compare outcomes before and after a policy change, a user override, or a parameter update and see whether the intervention actually improved availability, inventory, margin, or operational load. This is especially valuable in environments where decisions are frequent, distributed, and hard to reconstruct later.