A Singapore-based retailer with three physical stores and a Shopify site was spending more time reporting than acting. Every Monday, the operations manager pulled sales data from the POS, web orders from Shopify, ad spend from Meta and Google, and inventory from a warehouse sheet. The process took 15 hours a week, and the report was usually out of date before anyone read it.
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The business did not need a new ERP. It needed the existing data to flow into one place and update itself. We started with a simple question: if you could see only three numbers every morning, what would they be? The answer was revenue, gross margin, and stock cover.
We connected Shopify, the POS, and the ad platforms to a central data model. Each system kept doing what it did well. A small automation runs every morning, pulls the previous day's transactions, standardizes the product names, calculates the three key numbers, and posts a dashboard the team checks before their morning huddle.
The AI layer came after the data was clean. A lightweight forecast now predicts stock cover by SKU for the next two weeks and flags items likely to run out. A simple alert goes to the buyer when a product is trending above forecast. Nothing fancy — just the right signal at the right time.
The result: reporting time dropped from 15 hours a week to under six. The team now spends those nine hours on buying, pricing, and customer experience. The ROI was not from replacing people; it was from redirecting their attention.
Why this worked
The project worked because we did not start with AI. We started with the report the team already trusted, automated the boring parts, and only then added prediction. Most AI projects fail because they try to predict before the data is reliable. This one did the opposite.
Want to reclaim your reporting hours?