There is a seismic shift underway in the engineering industries. The decreased cost of sensors, the increased amount of instrumentation on assets and need for new revenue streams are forcing engineering firms to reimagine business models. The fusion of “atoms with bytes” promises to unlock new value previously unrecognized which generate additional revenue streams predicated on intelligence generated from the data. As machines increasingly become nodes in a vast array of industrial network, value is shifting towards the intelligence which controls machines. Intelligent Platformization of machines has begun
Keeping in mind this fundamental shift in value from atoms to intelligence, Flutura has defined 5 levels of maturity to assess the machine intelligence quotient of an engineering organisation. The highest level of maturity is "Facebook of machines" with ubiquitous sensor connectivity and the lowest is an asset which is "unplugged" where the device is offline. As organisations embark on a journey to intensify the intelligence layer in their IOT offering it makes sense to map where they are in their current state of maturity.
The 5 levels of machine intelligence with specific illustrative examples are outlined below
This is the lowest level in the maturity in the maturity map. At this level of maturity, the device or sensor is 'unplugged' from the network. There are no “eyes” to see the state of the machines at any point in time. The machine is offline to the engineering organisation. A vast majority of engineering firms manufacture assets which fall into this category. For example a vast variety of industrial pumps still are completely mechanical devices with no sensors to instrument them
This is the next level of machine intelligence which exists in the maturity curve. At this level of intelligence the device is connected to the network. There is also rudimentary intelligence exists on the device to take corrective healing action. Examples of assets having edge intelligence include cars which can alert the drivers to basic conditions which need intervention. Other examples include a boiler which has edge intelligence to switch on/switch off valves based on steam pressure