The Challenge
Our retail conglomerate partner wanted to optimize their supply chains by reducing inefficiencies and enjoy greater profits.

The Solution
Using the customer’s historical data, our analytics engine provided the client with clear insights into their distribution channel, customers’ purchasing behaviors and supply chain blockers. With these insights, we helped our client reduce their overheads by 24% and optimize their distribution frequency to 48% of their network.

The Challenge
Our retail partner was experiencing challenges in managing their inventory and to clear their existing stock of fashion apparels. They wanted to identify opportunities to clear stocks while managing their cashflow.

The Solution
Our analytics engine processed their in-store transactions and identified key product-pairings to drive sales volume as well as recommended an optimal inventory for each retail store. In some locations, the partner was able to reduce inventory by up to 50% and redistribute stock to other high-volume locations—freeing up cash locked up in inventory.

The Challenge
Our aerospace partner wanted to predict which jet-engine faults were likely to occur so that they could pre-emptively service them and reduce operational downtime for their clients.

The Solution
Using millions of engine data, we developed a deep-learning model which was able to predict the engine faults over a time period with an accuracy of over 80% for each aircraft.

The Challenge
Our events partner had limited ‘live’ cameras at each golfing tournament and had a challenge to identify the best golf action amongst 140+ competitors at each tournament. Capturing the best action meant a more entertaining experience for their TV audience.

The Solution
Using historical player data and design thinking, we developed forecasting capabilities and an analytics dashboard to aid event organizers to predict how players are likely to perform at each pro competition around the world. The platform achieved 83% accuracy in its predictions in real-time.