How accurate will the forecast be?

Accuracy depends on the product group and how regular the data is. That is why, before promising anything, we back-test on your history and show the model’s error margin side by side with your current method. The decision is made on those numbers.

What about new products with no history?

We use the launch curve of similar products. As real sales from the first weeks arrive the forecast corrects itself quickly, and until then we work with a wider confidence interval.

How do we actually use the output?

We do not leave the forecast as a report; it becomes order suggestions, safety-stock levels and alerts on the screen your team already uses. If you want, it can also write draft purchase orders into your ERP.

How long does the project take?

For a limited product group, data preparation, modelling and back-testing typically take 4–6 weeks. Covering the whole catalogue with dashboards and ERP integration can extend it to 8–12 weeks.

AI Solutions

Forecasting

Demand Forecasting & Business Analytics

Plan stock, sales and capacity on forecasts — not on gut feeling

Excess stock ties up cash on the shelf; missing stock hands the customer to a competitor. By modelling past sales, seasonality, campaigns and lead times together, we show in advance how much of which product will be needed, where and when.

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Overview

How much better is a model than the average in your spreadsheet?

“The last three months’ average” looks at a single signal: past sales. A forecasting model weighs dozens at once — seasonality, day of week, holidays, campaign periods, price changes, weather, lead times and out-of-stock days. It also produces a range and a confidence level rather than one number, which is what actually answers “how much safety stock for this critical item?”. Accuracy is always measured: we back-test the model on past periods and compare it against your current method.

Demand forecasts by product and location

Safety-stock and reorder-point suggestions

Campaign and season effects modelled

Cash-flow and sales-target projections

Back-testing against historical periods

Decision dashboards and automated reports

How it works

How does it work?

Data preparation

Sales, stock, price and campaign history are merged, and missing or corrupt records are cleaned.

Model building

A suitable forecasting method is chosen per product group, with seasonality and holiday effects added.

Accuracy testing

The model is back-tested on past periods and compared with your current method, with the error margin reported.

Decision screen

Forecasts turn into order suggestions and reach the team as dashboards and scheduled reports.

Scope

What the solution includes

Demand forecasting

Produces weekly and monthly demand projections by product, category and location.

Stock optimisation

Calculates safety stock and reorder points from lead times and target service levels.

Scenario comparison

Shows the impact of scenarios such as “what if we raise price 10%” or “what if we run a campaign”.

Cash-flow projection

Projects the coming period’s cash position from collection and payment history.

Management dashboards

Forecast, actuals and variance on one screen, broken down by product and region.

Automatic refresh

Forecasts refresh nightly with new sales data and model performance is monitored continuously.

Industry Use Cases

How is it used, and in which industries?

What gets forecast changes by industry — case counts here, occupancy rates there. The scenarios we work on most:

Retail & E-commerce

Product-level demand and stock planning

Sell-through, season and campaign effects are modelled per SKU, suggesting how much to reorder and when.

Dead stock and stock-outs fall at the same time.

B2B Distribution & Wholesale

Dealer-level sales forecasting

Each dealer’s ordering rhythm is learned, flagging dealers whose expected order is late and regions with growth headroom.

The field team knows which dealer to call and why.

Tourism & Hospitality

Occupancy forecasting and rate planning

Occupancy is projected from the booking curve, cancellation rates and seasonality, informing rate and allotment decisions.

Revenue lost to empty rooms and underpricing goes down.

Manufacturing

Production planning and raw-material needs

Order forecasts are translated into production plans and material requirements, prioritising long-lead-time items.

Line stoppages and expedited-purchase costs decline.

Logistics

Shipment volume and capacity planning

Shipment volumes are forecast by region and day so vehicle, warehouse and staffing capacity can be planned to match.

Fewer capacity gaps on peak days and fewer idle vehicles on quiet ones.

Finance & Management

Cash-flow and budget projection

Collection behaviour and sales forecasts are combined to project the cash position for the next 3–6 months.

Financing needs stop arriving as a surprise.

Typical Gains

%15–30

Typical improvement in forecast error

%10–25

Potential reduction in excess stock

Gecelik

Automatic forecast refresh cadence

12–24 ay

History needed for a solid model

Figures show typical ranges from comparable projects; actual results depend on your data quality and processes.

Integration

It works alongside your existing systems

ERP (Logo, Netsis, SAP)E-ticaret platformlarıPOS & satış verisiWMS / depo sistemleriExcel & CSVSQL veritabanlarıPower BI / MetabaseMuhasebe sistemleri

Let's talk about the right solution for you

We listen to your needs and map out a plan tailored to you. The first call is free.

FAQ

Frequently Asked Questions

That is exactly how most projects start. The first step is consolidating and cleaning the data — which already improves your reporting. Spreadsheets are a fine starting point; we move to an automated feed afterwards.

Anomaly Detection Sales & Customer Assistant API & System Integration All AI Solutions

Let’s test a forecast on your own history

A back-test shows precisely how much better the model is than your current method — before you commit.