Real numbers from a live retail franchise operation
Less Food Waste
AI-optimised order quantities
Stockout Rate
Shelves stay full
Saved Daily
Ordering takes minutes, not hours
Departments Managed
Hundreds of perishable SKUs
Our client operates a franchise store for one of South Africa's largest national retailers - a household name with thousands of locations across the country. Their specific challenge? They're a convenience-format store specialising in fresh, perishable food: sandwiches, salads, ready meals, dairy, baked goods, and fresh produce - all with shelf lives measured in days, not weeks.
Every single day, the store manager needs to place an order for the next delivery. Order too much, and food expires on the shelf - pure waste that eats directly into margins. Order too little, and customers walk away empty-handed. For a franchise store with tight targets, this daily balancing act was consuming hours and still getting it wrong.
The core problem: With hundreds of perishable products across 60+ departments, each with different shelf lives, sales patterns, and seasonal trends - no human can consistently make optimal ordering decisions using gut feel and a spreadsheet. The store was running at 5-7% waste when the target was under 3%.
The manager would review the retailer's suggested quantities, manually adjust each one based on memory and gut feel, and hope for the best. A single bad call on chicken or sandwiches could mean R2,000+ in waste.
How much macaroni and cheese do you sell on a Wednesday vs. a Friday? What happens to salad sales when it rains? Nobody knew - there was no historical data analysis, just experience and intuition.
Ordered 10 cases, received 7 - but nobody knew until a customer complained. Short deliveries were common, but without tracking, the store couldn't hold anyone accountable or plan around the gaps.
Products approaching expiry were simply written off as waste. There was no system to discount and sell them to regular customers who'd happily buy at a lower price - money literally thrown in the bin.
The legacy point-of-sale system stored valuable sales history in old-format database files that nobody could read. Years of usage data sitting unused.
A complete ordering intelligence platform built from the ground up
We didn't just build a prettier spreadsheet. We built a full-stack ordering intelligence system with two AI engines working in parallel - one rule-based for speed and transparency, one machine learning model for pattern discovery - that integrates directly with the retailer's existing ordering API.
The system runs two independent prediction engines and presents both results side-by-side, letting the manager choose or blend recommendations:
Imports months of sales data from the POS system and tracks every unit sold, wasted, or delivered - broken down by day of week.
Exponential smoothing identifies day-of-week trends. The ML model (Facebook Prophet) detects seasonality, holidays, and weather impact.
South African payday periods (25th-28th), public holidays, weather forecasts, and active promotions all modify the baseline prediction.
Every delivery is fed back into the model. Over/under predictions are tracked, and the AI adjusts its confidence and suggestions automatically.
The smart part: The AI distinguishes between controllable waste (overstock, expiry) and uncontrollable waste (damaged goods, supplier quality issues). It only reduces order quantities for waste that's actually within the store's control - so you don't get penalised for a bad batch from the supplier.
Every feature solves a real problem the store faced daily
The core workflow. Syncs the upcoming week's delivery schedule directly from the retailer's API, shows AI-adjusted quantities alongside the retailer's own suggestions, and lets staff submit the final order with one click. Products are grouped by department with inline barcode scanning to quickly find any item.
A fast, transparent rule-based engine (exponential smoothing, shelf-life capping, trend detection) runs alongside a Python ML backend powered by Facebook Prophet with weather and holiday regressors. Both provide confidence levels and human-readable reasoning for every suggestion.
When a delivery arrives, staff scan and record what was actually received. The system automatically flags shorts (ordered but not delivered) and repicks (substitutions). Order snapshots preserve the original quantities so nothing gets lost when the retailer's system overwrites data.
Near-expiry items are listed for discounted sale to regular customers via WhatsApp. Staff paste the WhatsApp conversation, and a fuzzy-matching parser automatically allocates products to customers fairly. The system learns name aliases over time (e.g. "Mac n Cheese" = "Macaroni & Cheese") and tracks customer preferences.
Interactive dashboards covering sales trends, waste analysis by category and reason, order vs. delivery fulfilment rates, and AI prediction accuracy. The manager can see at a glance if waste is trending up, which departments need attention, and how the AI's suggestions compare to actual results.
A custom parser reads the retailer's legacy dBase III/IV files (.DBF format) - product catalogues, transaction history, waste records, and delivery logs. Drag-and-drop upload with automatic deduplication. This unlocked years of historical data that was previously inaccessible.
The transformation in daily operations
Modern, scalable, and purpose-built for real-time retail operations
Frontend SPA
Build tooling
Database & Auth
ML Backend
Time-series ML
Hosting & API
Camera scanning
Data visualisation
Architecture note: The system integrates directly with the retailer's proprietary ordering API via serverless proxy functions. This means orders placed in our system are submitted directly to the retailer's backend - no double data entry, no copy-paste, no manual syncing. The staff member reviews the AI suggestions, adjusts if needed, and hits submit.
The ordering AI isn't a black box. Every suggestion comes with a confidence rating (high, medium, or low) and a plain-English explanation of why it's recommending that quantity. Here's what it considers:
One of the most impactful features we built wasn't about preventing waste - it was about recovering value from waste that was already going to happen. The store had a loyal WhatsApp group of local customers who loved buying near-expiry items at a discount. But managing it was chaos.
Products approaching expiry are scanned in via barcode or selected from the catalogue. Available quantities are tracked in real-time.
Regular customers send messages like "I'd like 2x mac and cheese and a chicken salad." The requests come in via a WhatsApp group.
Staff paste the entire WhatsApp conversation into the system. A fuzzy-matching parser identifies customers, products, and quantities - even handling informal names like "Mac n cheese" or "2x chicken." When demand exceeds supply, it allocates fairly.
Approved product name aliases are stored and reused. Customer preferences are tracked - the system learns that Mrs. Johnson always wants the espetadas on Fridays. It gets smarter with every use.
The impact: Instead of writing off near-expiry stock at a total loss, the store now recovers 40%+ of the value through discounted waste sales. For a store handling hundreds of perishable items daily, this adds up to significant savings every month - turning a cost centre into a revenue recovery channel.
The system isn't a one-person tool. It was designed for the full store team with role-based access control:
Full access to ordering, analytics, imports, user management, and system configuration. Typically the store manager or owner.
Access to stock imports and waste sales operations. Designed for shift supervisors who handle deliveries and daily waste management.
Read-only access to dashboards and reports. Perfect for franchise owners who want visibility without the ability to modify orders.
What started as "we need help with our daily orders" turned into a comprehensive operational intelligence platform. The system now manages the entire lifecycle of perishable stock - from AI-assisted ordering, through delivery verification, to waste recovery.
Key results after deployment:
The system is now being used daily in a live franchise environment, processing hundreds of product decisions across 60+ departments. The AI continues to learn and improve with every order, every delivery, and every piece of feedback from the team.
This kind of intelligent, integrated business system is exactly what we build at Verto Media. Not generic software forced into your workflow - but custom solutions engineered around the way your business actually operates.
Whether it's inventory management, AI-powered forecasting, or a completely custom business system - we'll build it around your operations, not the other way around.
No obligation | Free consultation | Custom strategy