AI Demand & Cash Forecasting

The problem

Most NetSuite forecasting in practice is a straight line drawn through last year. Item demand planning offers a small set of methods, cash forecasting is largely a manual roll-forward of AR and AP ageing, and neither takes account of the variables that actually move your numbers — promotions, price changes, seasonality that is not a clean twelve-month cycle, customer concentration, lead-time shifts, or the fact that one channel behaves nothing like another.

So the forecast lives in a spreadsheet. It is rebuilt by one person each month, its assumptions are undocumented, nobody can explain why last quarter's number was wrong, and by the time it is finished it is describing a month that has already started.

What ECOSIRE builds

We build a forecasting layer inside your NetSuite account: a feature pipeline over your own transaction history, a set of statistical and machine-learning models fitted to that history, and the records, screens and saved searches to review, override and consume the output. The SuiteScript and custom records ship to you as an SDF project or unmanaged bundle you own.

Feature pipeline

A Map/Reduce script assembles the training set from your existing data — Sales Order and Invoice lines, Item Fulfilments, Credit Memos, Customer and Item records, price levels, promotion and campaign fields, and any custom segment you use to classify demand. Series are built at the grain you plan at: item, item-location, item-customer, item-channel, or a custom segment rollup. Calendar features cover day of week, month, fiscal period, holiday and shutdown calendars, and configurable event windows so a known promotion or trade show is a variable rather than an anomaly to be smoothed away.

Outliers are flagged rather than silently deleted, and every excluded point is written to an audit record so a planner can see what the model chose to ignore and reverse it.

Models

We fit multiple candidate models per series and select on out-of-sample error, not on how good the fit looks in-sample. That set typically includes seasonal exponential smoothing, seasonal ARIMA-style methods, a gradient-boosted regression on the driver variables, and simple robust baselines for sparse or intermittent items where sophisticated models are worse than useless. Intermittent demand gets methods appropriate to it rather than a trend line through a mostly-zero series.

Backtesting is built in: a rolling-origin evaluation writes MAPE, MAE and bias per series and per model to a custom Forecast Accuracy record, so the model choice is evidence you can inspect. Where the numbers are computed outside NetSuite, a RESTlet returns scored results and the payload and model version are recorded against the forecast, so no number appears without a traceable origin.

Cash forecasting

The same pipeline drives a direct cash forecast. Expected receipts are modelled from open Invoices weighted by each customer's own observed payment behaviour — not blanket terms — combined with forecast future sales converted through your historical order-to-cash timing. Expected disbursements come from open Vendor Bills and Purchase Orders, payroll and recurring commitments held on a custom record, and forecast purchasing generated from the demand forecast. Output is a period-by-period cash position by subsidiary and bank account, with the driver rows behind every bucket clickable.

Review, override and consumption

Forecasts land in a custom Forecast Version record, immutable once published, so this month's number can be compared to what was said three months ago. A Suitelet review screen lets planners see the statistical number, apply a documented override with a reason code, and see the aggregate effect of all overrides. A User Event script stamps the override author and timestamp; overrides are tracked for accuracy separately from the model, which is usually the most uncomfortable and most useful report in the whole build.

Published forecasts write into NetSuite's own Forecast records or into item demand plans where you want native downstream planning to consume them, and are exposed via SuiteTalk REST for any external system that needs them. A Scheduled Script runs the whole cycle on your calendar and alerts on failure.

Who this is for

Companies with at least a couple of years of clean transaction history in NetSuite, enough SKU or customer volume that manual forecasting has stopped scaling, and real seasonality or driver effects. If you sell a handful of items on steady repeat orders, a saved search will serve you better than a model.

How delivery works

1. Scoping call. We look at your actual history — depth, grain, data quality, how many series are intermittent — and agree what is realistically forecastable. If your data will not support a model at the grain you want, we say so before you pay. 2. Fixed quote. Written scope, fixed price, delivery window. Typical build is two to four weeks from signed scope. 3. Build. Developed against your sandbox with your real history. 4. Install in test. Sandbox install plus a backtest over a period you already know the answer to, so you can judge accuracy against reality rather than against a claim. 5. Production install. SDF project or unmanaged bundle, with roles, permissions and the run schedule configured with your team. 6. Support. A defined post-go-live support window stated in the quote, covering defect fixes and model retuning.

We make no promise about what accuracy your data will support. The backtest in step 4 tells you that, before you commit the forecast to a planning process.