We live in an information age where large volumes of data are continuously generated by human activity, scientific processes, and adoption of new technologies. Distilling the knowledge contained in such data has the potential to transform science, technology, and business. It will revolutionize how the manufacturing processes are organized and how they function. To fulfill the potential, data science builds on techniques and theories from many fields, including mathematics, statistical learning, machine learning, data engineering, decision analytics, signal processing, visualization, and high performance computing.
As the differentiation among players narrows, the search is on for new sources of competitive advantage. Emerging technologies provide the impulse for a new wave – the digital wave. The current digital disruption is a result of the power and impact of converging technologies like big data, high performance computing (HPC), cloud, mobility, and social media. It’s enabling manufacturers to explore new business models and differentiation opportunities, both on the production front as well as with customer engagement.
There are tens and, in some cases, even hundreds of measurement points along our production lines. They generate large volumes of data, but very seldom is that data used for production optimization or individual component diagnostics. Our target is to bring intelligence to our lines, where components communicate with each other and self-optimize their performances based on mutual data exchange. This will also remarkably improve the service quality and timing accuracy by publishing the current status of each individual component. Some initial digital maintenance concepts are already available today, such as remote assistance.
Process modeling is well established at Maillefer. Now, through digitalization, the actual process measurements arrive in combination. We can help manufacturers optimize their production efficiencies and balance operating priorities for sustainable competitive gains. In order to realize working models, the process knowledge must be on an extremely high level. Models can be made for individual processes or complete factory projects. Very precise and continuous monitoring integrated with a thorough understanding of the manufacturing processes, helps our customers reduce product change cycle times and manufacturing costs. The manufacturing process improvements are also easier to achieve when critical components or process steps are defined and monitored accurately.
Digital technology combined with the analytical skills of our experts helps quantify the real customer value for each investment. In the future, it will also have huge effects on Maillefer’s R&D projects, where the possibilities offered by digitalization are rolled into our latest innovations.
Dr. Mikko Lahti
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