The ideas we receive, touch various strategic and operational topics critical to Metso’s customers and business. They usually are about data, information and knowledge.
In summary we have worked on following types of ideas: Capex investment planning tools for our customers, AI-based image recognition applications for process optimisation in mining and aggregates, wear detection and prediction, asset management with RFID tags, automation of sales proposal/quotation creation process, AI to determine anomalies in data, digital parts identification portal and digital customer experience in field services.
7 lessons learned
Through our experiments and trials, we have learnt tremendously. We’ve also had one failed experiment which have made us wiser about a new technology & has eventually led to patent request filing. Here’s a short summary of what we learnt:
- Our iterative development approach has helped us build trust and test drive “promise-ware” technology early on. This also helps us making wiser investment decisions on these technologies.
- We cannot undermine the importance of small improvements and the big potential they can unlock in business. This is particularly true about tools and processes that have a huge impact on employee experience e.g. copying and pasting content from one system to customer documents etc.
- We know that people learn best by doing and have learnt that VR provides this immersive learning opportunity quite well.
- We’ve also learnt about the challenges of implementing new technologies like VR. For example, the lack of hardware and technology standards can limit interoperability, and there are efforts needed to script the VR trainings. There are also threats related to IP of 3D models, to name a few challenges.
- We have learnt that Artificial Intelligence, correlation models, recognition models, etc., are eventually inevitable in our industry to enable speed, efficiency and productivity for our customers. But we also know that machines will not learn accurately without enormous training data, which means we will need to dedicate time, efforts, resources to collect and validate training data & AI models.
- Another key learning is that data needs to be kept continuously up-to-date in the source systems else it beats the point to have data dashboards. This means that efforts are needed at all levels in the organization to drive data discipline.
- The discipline to follow things all the way from idea to pilot and then to scaling up is key to the success of new innovations. This requires equal commitment from top leadership and experts.
We are proud that we have strong commitment to constantly drive new ideas forward. It’s not only about ideas, it’s about making them happen fast.