本頁尚無中文版本，因此下方內文為英文。選單、表單與展示預約皆為中文，展示也可以用中文進行。
Platform
A control tower you can audit line by line.
Most supply chain software asks you to trust a dashboard. SIAARU shows you the query. Every KPI, every exception and every forecast carries its inputs, its row count and its date, because a number a planner cannot defend in a meeting is a number they will not act on.
01 — Connect
Read-only, into what you already run.
SIAARU reads. It does not write into your ERP, and that is a deliberate line rather than a missing feature: a system that can place a purchase order in SAP is a system your IT department must treat as production infrastructure, and that turns a four-week deployment into a nine-month one.
Every credential is encrypted with AES-256-GCM before it reaches the database, is never returned to a browser, and is redacted in every audit row. Every outbound URL — including each page cursor a remote hands back — is re-checked before it is fetched.
| System | Protocol | Reads |
|---|---|---|
| SAP S/4HANA, ECC | OData v2 | Materials, suppliers, POs, sales orders, stock, production, BOM |
| Oracle Fusion SCM | Fusion REST | Items, suppliers, POs, on-hand, shipments, work orders |
| Dynamics 365 SCM | OData v4 | Products, vendors, customers, order lines, on-hand, BOM |
| WMS | REST or OData | Stock positions and movements |
| TMS | REST or OData | Shipment milestones and freight |
| PostgreSQL | SQL, read-only | Any query you approve |
| Files | CSV, Excel | Anything, mapped once |
02 — Reconcile
Bad data is quarantined, not quietly averaged.
Every import is scored before it lands. Below your threshold, the rows are held and named rather than written — and they never reach the AI layer.
Units and currency
Kilograms against tonnes, dollars against rupees. Converted on a stated rate with the rate recorded, so a figure can be re-derived later.
Duplicates and gaps
The same material under two codes, a purchase order with no lines, a month with no movements. Each is reported as itself.
A quality score
One number per import with the reasons behind it. You set the threshold; the default is 60.
03 — Measure
The number, then the arithmetic.
Fill rate, OTIF, days inventory outstanding, inventory turns, supplier on-time performance, quality rejection rate, freight cost per tonne, capacity utilisation, forecast accuracy and bias.
All of them are SQL. The engine is architecturally forbidden from importing a model client, the database layer, the network or the clock — and a test enforces it, so the guarantee survives the next person who edits the file.
Why this matters. A language model asked to compute a fill rate will produce a plausible number. Plausible is worse than wrong: wrong gets caught.
Order fill rate
94.2%
÷ lines ordered 9,463
= 94.2%
period: Jul 2026 · 9,463 rows
Click any figure in the product and this is what opens.
04 — Forecast
Ten models compete. The winner is chosen on data it never saw.
Ridge regression on lag and calendar features, gradient-boosted trees, Holt-Winters with a damped trend, Croston and SBA for intermittent demand, moving averages, and seasonal-naive as the benchmark.
Selection is by rolling-origin backtest: the series is cut at several points, each model forecasts forward, and the errors are measured only on periods after the cut. A model that cannot beat seasonal-naive by a stated margin does not get used — the benchmark wins instead, and the product says so.
Where there is not enough history to test honestly, the forecast is marked unvalidated rather than being given a confident-looking accuracy.
| Model | Suits |
|---|---|
| Seasonal naive | The benchmark everything must beat |
| Moving average, damped drift | Short or noisy history |
| Holt-Winters | Trend with a repeating season |
| Croston, SBA | Intermittent demand — spares, slow movers |
| Ridge on lags and calendar | Stable series with a clear signal |
| Gradient-boosted trees | Non-linear response, long history |
05 — Act
Ranked by rupees, approved by a person.
Exceptions with impact
Not an alert list. Each exception carries the money at stake, so the order to work through them is obvious.
Recommendations, cited
A recommended action with the records it was drawn from. The AI explains and drafts; it never computes the number.
Approvals and autonomy
Five autonomy levels, from observe-only to acting on low-risk items. Today the product ships up to level 3: nothing executes without a human approval.
After an action is taken, a verification job re-checks the condition. If it has gone, the exception closes with evidence. If it has not, it re-escalates — which is the part most tools leave out.
06 — Anywhere
The approval is on the floor, not at the desk.
The person who has to approve a purchase order is walking a line, standing at a dock or in a car. If approving means finding a laptop, the exception waits until evening — and a stockout does not.
The whole product, on a phone
Not a cut-down companion app with three of the screens. The same URL, the same permissions, the same audit trail — laid out for a 360px screen.
Nothing to install
No app store, no MDM ticket, no version that is three releases behind because somebody skipped an update. Sign in and it is current.
Built for a bad signal
The screens people use all day make no model call and render in under 300 ms, so they still open on a plant Wi-Fi or a patchy 4G bar.
Tablets get the desktop layout, which is the right call — a warehouse manager on an iPad wants the full control tower, not a phone screen stretched to fill it.