12Retail & Supermarket · AI solutions

Reorder suggestions from sales history for a retail chain

Purchasing suggestions based on what actually sells, by branch and season.

  • Per-branch demand analysis
  • Seasonality handling
  • Lead-time aware suggestions
  • Buyer approval queue
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The problem

Reorder levels were set once and rarely revisited, so fast lines ran out and slow lines accumulated across every branch equally.

What we built

We generate reorder suggestions from each branch's own sales history and lead times, presented for a buyer to approve rather than ordered automatically.

Modules delivered

  • Per-branch demand analysis
  • Seasonality handling
  • Lead-time aware suggestions
  • Buyer approval queue
  • Overstock identification
  • Suggestion accuracy tracking

What changed

What the delivered system does differently from the process it replaced.

  • Suggestions reflect each branch's own sales pattern
  • A buyer approves before anything is ordered
  • Overstocked lines are identified explicitly

Built with

The stack chosen for this build.

  • Go
  • PostgreSQL
  • REST API
  • Python

Why this customer is not named

Most of our customers allow the work to be described but not their name published. Where a customer has given permission, their name and testimonial appear here.

Independent accounts of our work are published by customers themselves on our Facebook reviews page.

Supplier invoice reading for a hardware importer Supplier invoices with long line lists, read into the system instead of typed.Supermarket checkout lanes with shared stock and weighing scales Multiple lanes billing against one live stock position, with scale-weighed items priced correctly.Shelf-life and near-expiry management for a grocery chain Stock nearing expiry surfaced while it can still be sold rather than written off.Promotions and multi-buy pricing across a retail chain Promotional rules applied consistently at every till without staff remembering them.

Does this look like your problem?

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Reorder suggestions from sales history for a retail chain | Case Study | Sydon Tech