"Software will eat the world"
Chris Anderson
It took some time for the industry to internalize this succinct prediction but the change which started small is dramatically picking up momentum across industries. One strong manifestation of this revolution in Engineering industries is thru Industrial IOT where Industrial grade rugged sensors + advanced machine learning algorithms + virtual reality + edge computing platforms are redefining the way operational and business outcomes are impacted. One crucial capability which an Engineering firm needs to have is the ability to surface the most important signals buried in the data. Powering this game changing transition are 3 Platforms . – Predix from GE, Cerebra from Flutura and Neuron from PTC. These 3 platforms are not generic signal extraction platforms but uniquely engineered for machine data.
One powerful story
What better way to look at the future than looking at the disruptions in the automotive industry ? It is a perfect harbinger of things to come. Two powerful examples highlight this
Example-1: Google made the first breakthrough with Autonomous car where the steering wheel is absent and is substituted by sensors/machine learning algorithms directing the car instead of a human.
Example-2: Tesla made another game-changing move with over the air software updates to the car where new features were injected into the car without the car having to be touched by a technician.
Every other industry – oil n gas, asset engineering, shipping, and the utility will experience the upheaval experienced in the car industry but the pace will be different keeping in mind the risk intensive environments they operate under.
What is common to Predix, Cerebra & Neuron ?
While the paths are chosen to accelerate signal detection could be different, there are also commonalities. In this section, we would cover 5 commonalities
Common Feature-1: Focus on Industrial outcomes
All the 3 platforms are tuned to the nuances of Industrial world which is focussed on 2 families of use cases. New business model use cases which generate new revenue streams like Asset as a service, Benchmarking services, and Operational efficiency use cases like signal triggered maintenance as opposed to time triggered maintenance and yield optimization
Common Feature-2: Engineer friendly guided signal detection processes
All the 3 platforms focus on the supply-demand gap problem - Data supply is high, Data scientist supply is low. They minimize this risk by codifying the analytical pathways into signal detection workflows. All the machine signal detection platforms have guided pathways to quickly surface a signal from high-velocity sensor data. All have them have also had abstractions to encapsulate the complexity of algorithms for an engineer to configure.
Common Feature-3: Partner ecosystems
Neuron has already a partnership program in place. Flutura will have a partner program for Cerebra in early 2016. Predix has started making moves towards a select set of partners for orchestrating apps built on Predix APis.
Common Feature-4: Collaborative signal detection between field & HO
Signal detection from machine data is not a "lone ranger" process, but one helluva collaborative process. Field service engineers, head office designers, process owners, command center monitoring folks all have an asset related hypothesis based on their experience which needs to be absorbed into the analytical model being developed. Cross-functional Real-time collaboration is a must for building high fidelity model which is reflective of the real world.
PTC acquired Vuforia for 3D virtualization. GEs pretax virtual reality environment is to be fully explored and Flutura is buildings immersive environments to get real-time situational awareness of vital remote assets using “Digital twins”.
How do we at Flutura see the road ahead for Engineering firms trying to get competitive advantage in the marketplace by “going Digital”
Virtual reality will become more mainstream in the engineering asset intensive world and combining this with advanced signal detection will definitely be a force multiplier
Trend-2: Edge + Central intelligence
Our experience in building predictive modeling for hydro turbines in remote locations taught us the edge is a very crucial part of the overall architecture when latency ( thru satellite or fiber optic) could introduce risks.
Trend-3: Engineering “Haves” and “Have-nots”
The engineering world in 2016 would be segmented into two buckets. Those like GE, Siemens, Schneider who have a Digital vision and more importantly execute on the vision using Signal intelligence platforms and those who are figuring out how to access signal intelligence platforms ( partnerships, build internally etc )
Closing thoughts
We at Flutura have always believed that machine-generated data had more transformative potential than human generated data. We are happy to see the hypothesis being confirmed with the IOT revolution sweeping the world
As always happy to hear how you see the world.
