Problem

• Technician dispatching not as efficient as possible
• Currently used algorithms were too slow and did not allow for quick reaction, recalculation of the routes/dispatching of the technicians

Solution

• Train a reinforcement learning model to achieve same or better accuracy more quickly at a lower cost

Impact

• Improved Technician efficiency, reaching more customers and resolving more issues per technician per day

Developing a state-of-the-art forecasting approach by combining multiple forecasting models to achieve a 97-98% accuracy, leading to monetary and time savings

Problem

• The customer was looking to automate the sales forecasting process which to date was performed in a manual way, making it cumbersome and error prone

Solution

• New approach to improve forecasting quality and the effort needed to maintain it using new data sources like Forward looking indicators, Financial metrics: (GDP, Inflation, FX rates) and Big events (e.g. new years in china, olympic games etc.)

Impact

• Automation -> less manual work, less errors, less time
• Higher accuracy (97-98%)
• Better planning/cost savings

Building a tool to provide a 360° view over the whole supply chain, coupled with an alerting system when a certain material is reaching low stock levels

Challenge

• Recurrent struggle with materials being out of stock resulting in significant loss in sales

Solution

• Built application enabling users to see the whole supply chain, either globally or per material
• Implemented an alerting system that reports when a certain stock level is low and will soon need replenishment

Impact

• Reduction of average out-of stock levels at a given point in time by about 50%
• Lowered loss of turnover due to materials being out of stock

Problem

• The need to improve the current forecasting process (raw-materials, finished goods and sales)

Solution

• New approach to improve forecasting quality and the effort needed to maintain it using new data sources like Forward looking indicators, Financial metrics: (GDP, Inflation, FX rates) and Big events (e.g. new years in china, olympic games etc.)

Impact

• Automation -> less manual work, less errors, less time
• Higher accuracy (97-98%)
• Better planning/cost savings

Onboarding airlines across 3 continents on a central data analytics platform with activities such as training and enablement of new users

Challenge

• Need for airlines’ data scientists and executives to explore data independently and be able to implement new use cases on their own

Solution

• Prepared educational material, such as presentations and project examples
• Gave lectures and conducted workshops to train new users to use the Foundry platform

Impact

• Enabled new users to acquire new skills allowing them to implement future use cases and to analyse data independently

Implementing a data platform to centralise maintenance data from hundreds of airlines and enable predictive maintenance models

Challenge

• Integrate data from 100s of airlines on a single platform
• Reduce or eliminate operational interruptions and minimise the number of aircrafts immobilised due to unforeseen maintenance needs

Solution

• Integrated sensor data and telemetry into central data lake
• Developed predictive maintenance model outputting the probability of maintenance need in the next 5 flights for each aircraft

Impact

• Notable improvement in speed of problem detection, triaging and resolution
• Enabled feedback loop to aircraft design teams to prevent similar issues from reoccurring