As a widely-recognized leader in aviation propulsion, Rolls-Royce sought to fully leverage its extensive data assets to create maximum value across the business. As a foundational step in this process, Rolls-Royce partnered with Deloitte to solve three critical challenges from the data science team:
- Work through a growing backlog of data science requests
- Enable citizen data science development
- Enhance existing data analysis and data science capabilities
Factors such as fragmented data pipelines and manual processes made it difficult for Rolls-Royce teams to extract actionable insights which, in turn, impacted their ability to optimize their engineering and maintenance operations. They uncovered two key areas to work through for data-driven transformation:
- Printed Circuit Board (PCB) production line optimization: The existing process was cumbersome and reliant on manually curated data and disconnected calculations, which led to bottlenecks that hampered efficiency and risked errors.
- S-curve cumulative analysis: Extracting and visualizing key engine operational indicators involved a convoluted journey across multiple platforms and spreadsheets, making it challenging to gain timely, actionable insights.
Bringing the PCB Line Optimizer to Life
Rolls-Royce built an innovation application — known as PCB Line Optimizer — with Dataiku to revolutionize the trolley optimization process that supports a robotic PCB production line. With an intuitive, low-/no-code interface, the application not only executes operations with significantly less effort (as it replaced manual data curation with automated workflows), but also empowers users of all technical levels to use these kinds of tools.
Accelerating Time to Insight With S-Curve Analysis
By consolidating their entire legacy workflow — configuration, data extraction with SQL, computation, and visualization — in Dataiku, they were able to streamline hours of preparatory analysis and eliminate the need for users to switch between different tools — making their cumulative analyses simpler, reducing errors, and accelerating time to insight.
The Dataiku Impact
Through both of these projects, Rolls-Royce has been able to make more informed, data-driven decisions while also improving their operational agility. By consolidating data workflows in a single platform in Dataiku, Rolls-Royce has reaped the benefits of their streamlined processes and reduced reliance on manual interventions. Dataiku’s impact resonates throughout the organization via:
- Empowered Data Professionals: Dataiku’s user-friendly interface has given the Rolls-Royce teams (with members of different skill levels) the ability to configure parameters, build sophisticated models, and generate insights — freeing up time to focus on bigger initiatives to drive value in the organization.
- Accelerated Insights: They also note that by replacing static spreadsheets with interactive dashboards in Dataiku, they now have a clear, real-time view of key performance indicators (KPIs). With this new clarity, they’ve been able to make faster, more informed decisions as a company.
- Enhanced Collaboration: The Rolls-Royce data science team, manufacturing teams, and Deloitte were able to seamlessly share projects, insights, and best practices with Dataiku. Their teamwork effectively multiplied their results, making everyone on every team faster and more efficient.
By empowering its data science team with Dataiku’s intuitive platform, Rolls-Royce has achieved significant improvements by using more of what was available.
A Future of Innovation in Aviation and Beyond
In close partnership with Deloitte and Dataiku, Rolls-Royce has advanced its data operations, turning complex production and analysis challenges into opportunities for innovation and efficiency. By optimizing and overhauling existing workflows and adding automation, they’ve not only built a foundational data science practice but also opened doors for all non-technical team members to be citizen data scientists. These advancements underscore Rolls-Royce’s commitment to leading in technological advancement with data-driven decision-making.
