The Challenge: $78 Million Lost to Invisible Inefficiencies
This was a money-losing enterprise, and no one knew just where or why “FreshCo” (name changed) was losing money. This was a multinational food & beverage company that had operations in 23 countries, consisting of 200+ SKUs ranging from dairy products to other frozen foods and beverages.
They did not have obvious waste as a problem-it was a death of a thousand cuts:
♦ Products lost 12 percent before they were sold out
♦ Promotions produce stock-outs that cost millions of lost sales
♦ Over orders resulted in storage nightmares
♦ Demand was devastated by weather changes
♦ Changes in seasons appeared totally unpredictable
Past forecasting did terribly. They made the wrong prediction 43 percent of the time using the ERP system. Regional managers depended on what they call gut feeling, which held until it failed. A single off forecast in the holiday weekend brought them a $ 3.2 million loss in outdated goods.
The directive given by the CEO was very clear: “Do something about this, or we will quit half the markets”.
The Approach: Building a Living, Learning Supply Chain Brain
Using our own 40 years of market database, we put it in a neural network that covers their whole supply chain, and that is becoming smarter each day.
Phase 1 Data Archaeology and Integration FreshCo had 17 separate systems, with most of them not communicating with each other. We began with digital archaeology:
♦ Sales records of 5 years (4.7 billion transactions)
♦ The weather patterns in all regions of distribution
♦ Local calendar of events (festivals, holidays, and sporting events)
♦ The sentiments of social media regarding product categories
♦ Promotion actions of competitors
♦ Regional economic indicators
♦ Movements of traffic outside stores
♦ Data of product interaction (what sells with what)
This was not merely data that was organized, but this AI detected relationships. It has found out that the selling of ice creams in Singapore went beyond temperature to the degree of humidity coinciding with school concepts. It established that the sales on cheese in Mexico surged in Mexico, 3 days before specific regional festivals which were not included in any corporate calendar.
Phase 2: Predictive Model Development We built 47 different prediction models, each specialized for different scenarios:
♦ Regular demand forecasting
♦ Promotional period predictions
♦ Weather event responses
♦ Competitive response models
♦ New product launch predictions
♦ Cannibalization effect calculators
♦ Shelf-life optimization algorithms
Phase 3: Dynamic Response System Predictions are worthless without action. We created an automated response system:
♦ Automatic reorder triggers based on AI predictions
♦ Dynamic routing to minimize transport time
♦ Real-time shelf-life management
♦ Automated promotional adjustments
♦ Smart clearance pricing for near-expiry products
The Solution: 94% Accuracy and Self-Healing Supply Chains
The AI system we deployed prevented problems before they happened.
Revolution in Demand Prediction. In one-third of a year, the US leaped forecast accuracy up to 94 percent. Precision was only the start of it. The system told managers how and why demand would reach a peak or decline in value; managers were provided with actionable intelligence rather than figures.
To give an example, once there was a specific Korean drama being shown on TV that particular drama had food advertising segments, and within 72 hours, the sales of such products shot up in the market in Asia. Its schedule of popular shows was automatically followed, and inventory was changed.
Micro-Predictions and Waste Elimination The AI was used to generate micro-forecasts on each of the stores as opposed to treating all stores the same. It had the knowledge that the shop close to the university would require additional energy drinks to use during the examination period, and the shop in the suburbs would require extra family packs on weekends.
The system also estimated which products would most likely run out and accordingly programmed promotion 10 days before the actual expiration, which is early enough to sell in anticipation and not too early that it would teach the customers to wait to buy at discounts.
The Results: From Bleeding Money to Industry-Leading Margins
The transformation was dramatic and immediate:
♦ Waste reduction: 31% ($23 million annual savings)
♦ Stock-out incidents: decreased 78% ($19 million revenue recovery)
♦ Inventory holding costs: down 24% ($8 million saved)
♦ Profit margin improvement: $12 million annually
♦ Product freshness score: increased 41% (customer satisfaction up 28%)
♦ Supply chain visibility: 100% real-time tracking across all markets
The secret win was staff happiness. The maniacal ordering that was done by store managers never had to be done again through guesswork. The AI took care of the complexity, allowing human beings to concentrate in serving the customers and dealing with the local people.
According to one regional manager: It is as though we have a supply chain guru that never goes to sleep and who never forgets and who gets smarter with every transaction.
Key Lessons Learned
✔️ Integration is everything. AI is only as good as the data it can access. Breaking down silos multiplied effectiveness exponentially.
✔️ Micro-predictions beat macro-forecasts. One-size-fits-all forecasting is dead. Success comes from understanding local nuance at scale.
✔️ Explainable AI drives adoption. When the system showed WHY it made predictions, managers trusted and acted on recommendations.
Note: While this story is based on real strategies we’ve employed, specific client details have been tweaked to respect confidentiality.