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Solution: Forecasting dashboards

You guess at stock levels and pay for it both ways

We build forecasting dashboards from your own sales history that show what you are likely to sell, what to reorder and when, with the uncertainty shown honestly.

Sounds familiar?

Signs this is costing you time

Too much stock ties up cash and space. Too little loses sales and customers. Most small and mid-sized businesses manage this balance with experience, a spreadsheet and a lot of guessing. That works until the product range grows, sales channels multiply or the person who knows the numbers is away.

Forecasting does not need to be complicated to be useful. A clear view of trends, seasonality and lead times already beats guesswork.

The goal is not to hand buying decisions to software. It is to give the person who buys a clear, current picture every week, so their experience is spent on judgment calls instead of adding up numbers.

  • Best sellers run out while slow items fill the shelves.

  • Cash is tied up in stock that will not sell for months.

  • Seasonal peaks catch you unprepared every year.

  • Reorder decisions live in one person's spreadsheet.

  • Online and in-store stock are managed separately.

Before and after

How it works today, and how it works after

Before

How it works today

  1. Stock is checked by hand or in several systems.

  2. Reorders are based on gut feel and last year.

  3. Supplier lead times are remembered, not tracked.

  4. Stockouts are discovered by customers.

  5. Slow stock is noticed at the annual count.

After

How it works after

  1. Sales and stock from all channels flow into one place daily.

  2. Forecasts show expected demand per item, with a range.

  3. Reorder suggestions account for lead times and safety stock.

  4. Alerts flag items likely to run out soon.

  5. Slow movers are highlighted for promotions or clearance.

What we build

What we build

Sales and stock connections

Your store, point of sale and inventory systems connected through APIs.

Clean data store

A Database that combines channels and handles returns, bundles and variants.

Forecast models

Statistical and machine learning models chosen for your data, from simple seasonal methods to more advanced ones.

Reorder logic

Reorder points and quantities based on forecasts, lead times and your rules.

Dashboard

Clear views by item, category, location and supplier.

Alerts

Notifications for likely stockouts and overstock.

In practice

What it looks like in practice

An illustrative walk-through, not a client story.

An online retailer with a physical shop sells around eight hundred products. The owner reorders by checking the store dashboard every few days and remembering which suppliers are slow.

The new dashboard combines online and in-store sales and shows, for each product, expected sales for the next eight weeks with a likely range. Products that will run out before the next delivery could arrive are flagged in red, with a suggested order quantity. Twenty slow-moving items are listed for a promotion. The owner reviews the suggestions each Monday, adjusts a few based on upcoming events the data cannot know about, and places orders.

Models and tools

AI models and MCP servers that usually fit

We choose models by task, data sensitivity and cost, and test them on your real examples before we commit. These are common starting points, not a fixed recipe.

How we build it

The services behind this solution

Most solutions combine two or three of our services. These are the ones this one usually needs.

Dashboards and reporting

Dashboards that pull numbers from the systems you already use and show what matters without a spreadsheet.

Database design

Data models that stay fast and correct as your business grows, with backups and access rules in place.

API integration

Make two systems share data reliably, with retries, logging and alerts when something goes wrong.

Industries where it fits best

Honest limits

Limits and human checks

Forecasts are estimates, not promises. They work best for products with enough sales history and stable patterns. New products, one-off events and sudden market changes are hard for any model to predict. We show forecast ranges, not single numbers, and make it easy for people to adjust suggestions using what they know.

Data quality matters more than model choice. Returns, stock corrections and missing sales records can distort forecasts, so the first part of the project is usually cleaning and combining data. AI language models play a supporting role, such as explaining changes in plain language; the forecasts themselves come from models suited to numbers.

Keep exploring

FAQ

Questions about forecasting dashboards

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