Laying the ground for the digital transformation of European space rocket manufacture and launch
When most people think of space rockets, they think big – enormous intricately-engineered structures, high-tech futurism and massive brains spring to mind. What they don’t necessarily notice is the attention to detail and extraordinarily high production and operational standards involved. These exacting requirements do their job, but are time-consuming, labor intensive, and expensive. Set against this, cost and time savings are becoming critical, with launch customers wanting deployment ASAP and at minimum cost.
Most modern industries have driven efficiency by adopting digital ways of working, underpinned by agile test-and-learn methodologies and data automation. The launch industry has tended to take the view that while digital transformation is great, it was just not feasible in this low-volume, high-standards environment.
Enter SESAME, an initiative which has provided proof, within the most quality-exacting of conditions, that data driven solutions can indeed reduce launch costs and timescales – which means better access to space for Europe and businesses.
SESAME (‘Smart European Space Access through Modern Exploitation of data science’) was a three-year project to bring the best digital technologies into the heart of European launcher manufacturing and operation. It was part of the European Union’s Horizon 2020 research and innovation framework program. Overseen by coordinator ArianeGroup, eight partners worked together to propel the space age into the digital era.
In previous articles we have told you about some of the details of the SESAME project as it was on-going, but now that it has reached its conclusion we would like to give an overview of the findings.
The European Commission has just released its evaluation of the concluded project, with an impressive assessment: “the project has delivered exceptional results with significant immediate or potential impact”, the report notes.
SESAME tested capabilities in realistic operations scenarios, with a data management framework to proactively deal with risks in new automated production and operations environments, and employing AI, machine learning, and IoT.
