Predictive Analytics and AI in the Supply Chain

The supply chain is where a small forecast error turns into money: either an empty shelf and a lost sale, or frozen stock that gets written off. Manual planning rests on the buyer’s experience and works while the range is small. As the number of SKUs and sales channels grows, that approach stops coping, and this is where predictive analytics comes in.

What the model actually forecasts

What data you need

• A sales history by SKU and by day: at least one or two full seasons.
• A calendar: holidays, sales, campaigns, exhibitions, staff holidays.
• Stock levels and turnover: how much product is sitting there and how fast it sells.
• Supplier lead times and terms: lead time, minimum order quantity, schedule.
• External factors, where available: weather, exchange rates, tariff changes.
• Returns and write-offs, so the forecast does not turn out optimistically high.

Why classical methods are not enough

Simple tricks such as the average for the previous period or last year’s season work with steady sales. In a real business there are campaigns, price changes, weather, competitor promotions and new SKUs. A machine learning model accounts for these factors at the same time and retrains on fresh data, which noticeably improves accuracy on the "restless" categories.

How the solution is built

1. A data audit: completeness of the history, quality of stock records, comparability of SKUs.
2. A pilot on one product group with a clear share of turnover.
3. Building the forecast and measuring quality over a historical period.
4. Comparing with current planning: on how many SKUs and how much money the model is better.
5. Embedding it in the work: a report for the buyer, recommendations for the supplier order.
6. Regular recalculation and quality control of the forecast against actual sales.

How the effect is measured

What it costs and how long it takes

A demand forecasting pilot is usually estimated at $4,400 - $13,200 and takes from six weeks to three months. A full supply chain optimisation solution with procurement and logistics integration starts at $13,200. The exact estimate depends on the number of SKUs, the quality of the data and which systems need to receive the recommendations.

Common questions

What if data is scarce or poor quality

Start with an audit: part of the problem is solved without any models, simply by getting stock and sales records straight. For new SKUs with no history, approaches that use similarity to existing products and the buyer’s expert judgement as one of the model inputs are used.

Do you need to change your accounting system

No. The solution works on top of your system: it takes the data as an export or through an integration and returns the forecast and recommendations in a familiar form, a report, a spreadsheet or an entry in the system.

Who makes the purchasing decision

The decision stays with the buyer. The model gives a well-founded forecast and scenario options, while responsibility for choosing the order stays in the company. In practice the model changes the reasoning behind the decision more often than the decision itself.

A good forecast does not guess the future; it shows a range of probabilities and the price of the error in each of them.

Where to start

Start with one product group that accounts for a noticeable share of turnover and write-offs. On a free consultation we assess the quality of your data and show what the forecast will deliver on your own numbers.