Predictive Analytics to Disrupt Industrial Maintenance
Cities rely heavily in human beings to supervise our infrastructure. With the rise of IoT, every asset is capable of generating valuable data. Maintenance represents the biggest single operational cost for infrastructure operators, constituting roughly 50 per cent of total expenses. The traditional way of overcoming this is to perform costly preventative maintenance.
Chicago’s unicorn Uptake uses predictive maintenance and analytics to create products that ingest and analyze industrial data, transforming it into actionable insights and outcomes. They have field engineers, technologists and data scientists who deliver software, mainly for machinery maintenance. I believe their main challenge are accessing industrial data, gathering experts, and finding scalable applications.
Better data collection, modern instrumentation, and machine-learning can allow cities to keep their assets running for longer while spending far less in maintenance. However, still many industries use old-fashioned business models. In fact, Uptake claims that as little as 1% of industrial data is being used today. Unless these data are collected and stored for further analysis, valuable information is being lost. The digital transformation of infrastructure needs to be encouraged to maximize asset availability, reliability, and efficiency.
References:
DiChristopher, T. (2018, April 16). Tech start-up Uptake makes ‘big’ acquisition in race to digitize the industrial industry. Retrieved from CNBC | Tech: https://www.cnbc.com/2018/04/16/machine-learning-firm-uptake-is-acquiring-a-huge-industrial-database.html
