Why Predix (GE), Cerebra (Flutura) & Neuron (PTC) will propel the IOT revolution ?

"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.

 Common Feature-5: Immersive 3 D experience of assets 

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”.

 What’s the road ahead? 3 imminent trends 

How do we at Flutura see the road ahead for Engineering firms trying to get competitive advantage in the marketplace by “going Digital”

Trend-1:  Virtual reality + Signal detection

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.

Industrial IOT + Machine Learning + Mech Eng + Elec Eng + Instrumentation Specialization + Maintenance Programs + Business Analysis = IIOT Success!

In working with clients across the Oil & Gas, Utility, Engineering, Manufacturing, Maritime and Chemical Processing spaces there is one thing in common. They’re all trying to find ways to better service their clients, reduce downtime, reduce maintenance cost, drive new revenue streams and increase profits.

Neither the customers or their current partners are gracefully marrying up Industrial IOT Data Sciences + Machine Learning + Mechanical Engineering + Electrical Engineering + Instrumentation Specialization + Maintenance Programs + Business Analysis.

There are few different areas where this is relevant for the market today. As example, predictive maintenance is vital in the Industrial IOT space. Making sure assets are running at optimal levels can greatly reduce operational expenditures which in turn can save companies tens of millions of dollars each year. There are a number of metrics that goes into using an Industrial IOT platform to enable better predictive maintenance programs. They might include industrial gateways, mini PC’s, sensors, cameras and other equipment that would plug into the systems being monitored. The end game is to understand when a piece of equipment might fail before it actually does. Some companies have a time based maintenance program but the problem with this is that the machine might be being abused and not being ran at optimal levels.

This could cause the machine to break down long before the maintenance intervals are being met. On the other hand the machines might be able to continue to operate at their optimal levels long after the recommended maintenance periods which could greatly reduce cost to maintain the machines. For example, oil and gas companies work on scheduled maintenance programs for their Top Drives, rotating equipment, turbines, compressors, etc. but if an Industrial IOT platform can provide deep insights on the actual condition of the machine then based on spare parts refurbishment via algorithms predicting when a failure will occur vs. timed events then the companies can most certainly reduce their maintenance cost. 

If a company is streaming data from sensors and industrial gateways they can with better accuracy access current machine conditions and even be able to get alerts to warnings via algorithmic signals which could tie into maintenance programs like SAP, Maximo or Oracle. By providing maintenance based programs with an Industrial IOT platform these companies can then trigger an appropriate maintenance event or process which would then make the entire process dynamic with a much better way to automate the maintenance process. 

Of course this would provide cost savings over a time based maintenance program. Other benefits of this type of program would allow for increased equipment life, increased productivity, reduce exposure to mechanical failure risks, reduced exposure to safety risks and of course reduced downtime. This methodology is pretty much the same across multiple industries including oil and gas, utilities, engineering, heavy equipment and heavy construction, chemical process plants and other industries. 

What is apparent is that a lot of companies and service providers have not figured this out yet. They are still working with aged condition based monitoring systems or have rolled out systems that only specialize in certain areas of their business. 

Business leaders would like to see an Industrial IOT system that can incorporate all these multiple data streams and give them a 360 degree view of their operations.

3 reasons why IOT digital intelligence matters for Microgrid

Context                 

A real transformation is happening in the grid. With the digitization  and innovative energy technology catching up its now possible to economically generate power from Microgrid. Microgrids are beginning to emerge  in residential communities, office park, university, military base etc. This has been accelerated by a number of local and global factors converging together – government regulations/incentives, energy consumers are becoming more concerned about their local power quality and efficiency of system. Industries and businesses have been economically impacted by local brownouts which are a  nuisance  In this context IOT and digital intelligence plays a major role for solving some of these problems. In this whitepaper we cover 3 reasons why IOT digital intelligence matters for Microgrid.

Reason-1: Prosumer Intelligence

The foundational building block of a Microgrid is the prosumer. A prosumer consumers energy and contributes to the energy grid. If the prosumers behaviour is modelled at a fine level of granularity a lot of Microgrid risks are mitigated. For example in Cerebra while algorithmically scoring prosumers behaviour it takes into account 4 distinct factors

•           Quantity of contribution
•          Quality of contribution
•          Predictable regularity of the contribution habits
•          Quantity of consumption

All of this is modelled using instantaneous power, current and voltage parameters typically at 15 min intervals or lower collected by the MDM Systems. This algorithmic prosumer can drive surgical actions to stabilize Microgrid outcomes and impact the overall economics

Reason-2: Microgrid Assets

A Microgrid consists of a variety of assets which are configured and orchestrated to deliver localized power. This could Local low-voltage (LV) and even medium-voltage (MV) distribution systems Distributed energy resources (DERs, e.g. micro turbines, fuel cells, photovoltaic), Storage devices (flywheels, energy capacitors and batteries) in order to satisfy the demands of energy consumers. Each of these assets can be diagnosed from a health perspective by deeply examining the digital IOT sensor data which are collected in SCADA/Historian systems and traditionally the signals are unexamined. Cerebra’s signal detection algorithms can forensically surface asset signals which reduce down time and enhance asset life.

Reason-3: Economics

At Flutura we have a simple formula

Prosumer behaviour intelligence + DER Asset intelligence = Microgrid economics

By carefully examining grid contribution signals in the instantaneous parameters coming from MDM systems and the asset stability signals from SCADA/PLCE sensor streams, the operator can dramatically impact the economic viability of executing a Microgrid.

Closing thoughts

Microgrids is one of the models for an alternate energy future. The economic viability of Microgrid operator is directly correlated to the Digital intelligence maturity. This calls for a comprehensive IOT digital implementation strategy for microgrids which ingests sensor, meter and ambient weather data to dramatically impact and guarantee asset stability/contribution behaviour.  Flutura with its next generation IOT intelligence platform tuned to Microgrid use cases combined with its strategic relationship with Electric Power Research Institute (EPRI – Palo Alto & Orlando) is ideally positioned to intercept the future.

Industrial IOT Factors in Reliability Engineering

 

Context 

Reliability engineering is engineering that emphasizes dependability in the life cycle management of a product. In Energy, Oil and Gas, Maritime and Aviation Reliability Engineering is crucial when it comes to machines used in these environments.  These would include complex systems and sub-systems that all depend on the reliability of components that fail regularly.

The Industrial Internet of Things (IIOT) has enabled dependability and reliability for systems and components to function under stated conditions for much longer periods of time.  While the ability of a system or component in the industrial arena can be spec’d out to give operators the ability to run a piece of equipment, system or subsystem at specific parameters this machinery rarely ever gets operated at the specs it was designed to operate at.

On Another Note

Reliability is the degree to which an assessment tool produces stable and consistent results. Test-retest reliability is a measure of reliability obtained by administering the same test twice over a period of time to a group of individuals.  Industrial IOT solutions have enabled these individuals to better support their respective teams globally through the use of IIOT.

Where Engineering and IIOT Intersect

RAM analysis is a well-known method of estimating the production availability of a system by assessing failure modes, frequencies and consequences, all the while paying attention to the effect on production.  Even though reliability is a major focus of industrial companies there is a big gap that is occurring in the market today and Industrial IOT solutions is enabling better assessments and even automated assessments coupled with automated calibrations using edge analytics. This can also include intersecting multiple systems feeding data into an Industrial IOT platform that is designed to handle high volumes of data streaming in and analytics processing power that is using the latest technologies. 

 

Conclusion

While there is a lot of knowledge when it comes to Engineering there is a lot of experience that is being lost with an aging workforce that is retiring daily. Not to mention all of the layoff’s that have been happening over the past year in oil and gas and manufacturing, etc. 

Recently I had a conversation with someone that was responsible for over twenty-four hundred operators, maintenance and other employees that all leaned on him to find ways to decrease downtime, reduce failures and enable better visibility into what level their machines were performing at.  

“It is overwhelming to say the least”, he said. 

This is a common problem across all industries.  With Industrial IOT solutions, organizations globally will be able to connect the dots between the operational and information technologies that make up the Industrial Internet of Things.  

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What can Industrial IOT data products designers learn from Harper Lees "To kill a Mockingbird" ?

"You never really know a man until you understand things from his point of view, until you climb into his skin and walked around it" - Lee Harper To Kill a Mockingbird

One of the most impressionable book for me was Lee Harper's   'To kill a Mocking bird'. Scout had a tough day at school and Atticus Finch is helping her deal with it (The actual quote is outlined above) .  His simple but effective point was powerful - by putting ourselves  in another's persons shoe and feeling their pain, we will better be able to deal with a difficult situation.

We at Flutura found this to be extremely true as we embarked on our journey to design Intelligent IOT data products for the industrial world. Let’s understand how ...

The Industrial world consists of a variety of persons all of whom experience various pain points on a variety of dimensions

1. Reliability engineers
2. Field maintenance folks
3. Command center monitors
4. Electro-mechanical product engineers
5. New Digital business model group
6. OEM customer service engineers

All of them have a specific mental model and have formed a pretty intimate relationship with the electro-mechanical assets they interact with which are increasing getting digitized with rugged sensors. So how did 'extreme empathy' help?

Let’s take one specific experience we encountered In Houston we were talking to a number of Oil and Gas OEMs and they mentioned that ratio of maintenance engineers to reliability engineer is 1200:1. This puts an enormous pressure and keeps the reliability engineers awake at night. How can Cerebra (our product) help? Srikanth & Rick our "eyes" in Houston, dug deeper and spent days in the life of reliability engineers...

1. What do Reliability engineers need to KNOW?
2.  How do Reliability engineers FEEL?
3. What specifically does a Reliability engineer DO?
4. What are their BELIEF systems?
5. What STORIES do Reliability engineers tell each other? 
6. Where specifically is PAIN encountered while executing a reliability analysis task?
7. What sub optimal SOLVERS do Reliability engineers use to solve for their current pain?
8. What MICRO INTERACTIONS occur in the current workflow between a field maintenance people interacting with the reliability engineer?

They empathized by mapping the Reliability engineers’ journey as he/she went about doing tasks to get specific jobs done and the specific friction they encounter along that journey. In 8 weeks Srikanth & Rick by "soaking" themselves in the reliability engineers’ context, mapped out a phenomenal catalog of reliability micro insights based on direct immersion in the marketplace. These unique, powerful industry factoids (spanning technical and mental models) were then decomposed into specific Cerebra features and methodically baked into the product creating "ahas" at a rate we have never seen before. (Rick is a happy camper as his pipeline looks juicy :)

                                                           

The point is - Once we feel their pain thru direct experience we were able to generate deep micro insights which are "Non Google able” (not in public domain). These "Non Google able insights" can then power the product and dramatically increase the resonance with the target personas giving a tremendous advantage to leap frog competition in a hyper competitive marketplace. The last 3 months have been "orbit shifting" for all of us at Flutura. Our learning can be summarized in one line -  feeling the pain is a powerful source of competitive advantage. Thank you Harper Lee :)

SO WHO'S PAIN ARE YOU GOING TO FEEL TODAY ?

Making Conventional Industries Unconventional using Industrial IOT Solutions

  As IOT begins to re-instrument the world with smart sensors and other types of internet enabled devices we are going to start seeing a dramatic change in the way that conventional companies start driving new revenue streams in unconventional ways. 

When Zip Car began their car sharing program it changed the way people rent cars in urban areas.  No rental car agent.  No lines to stand in.  No problem!  Now GM has announced that they are going to challenge Zipcar's car-sharing services in Boston this summer. 

GM is a perfect example of a conventional automobile manufacture using IOT to drive new revenues for the companies in a very unconventional way as compared to years past.  With this new service GM will rent their cars for $6 to $9 per hour with no annual commitment from their subscribers. 

This program has already had thousands of residents of Boston sign up for the short term rental services.  This is a starkly unconventional way for a very old and very conventional company to use IOT to drive a new and different type of revenue stream. With this IOT solution of course GM will be able to keep track of where the vehicle is, how it is performing and understand when the vehicle needs an oil change, tune up or is going to have a mechanical failure using predictive analytics. (Source BBJ).

Our team recently met with a global steel rope manufacturing company that makes ropes for elevators, bridges, ships,etc.

This company is looking to drive new revenues for their company by offering monitoring services for elevator companies.  This new system would monitor the motor, the car, the brakes, etc.  This very conventional company is now looking at using an Industrial IOT solution to drive new revenues in a very unconventional way leveraging their existing experience and relationships in the industry. 

Thyssen Krupp an elevator manufacturer began offering monitoring services using an Industrial IOT solution a couple of years back.  TEAMService is made up of 5,700 certified technicians in more than 200 locations across the country, all wired into an Industrial IOT system. They have access to the elevators and escalators leveraging the expertise of the engineering team.  They are tied into the spare parts inventory and other valuable resources so that ThyssenKrupp Elevator can deliver better service to their customers.  This Industrial IOT solution has proven to drive a new revenue stream for the company.

Another company our team met with recently is a global industrial construction company.  They traditionally build the structures and then they are onto the next job.  This company is now looking at providing remote monitoring for the structures they have built to predict and prevent failure. 

This could include monitoring the integrity of the structure itself (which is crucial in earthquake zones) as well as other vital services such as monitoring the HVAC systems and other types of automated applications.  This company is looking to leverage their long standing relationships to drive what would be a natural fit to a new division of the company to drive new revenue streams in a very unconventional way for them.

I have several other examples of how conventional companies our team has met with over the past months that are leveraging Industrial IOT solutions to drive new revenue streams or change the way they are doing business in very unconventional ways using Industrial IOT solutions.

Over the coming years the market will continue to see conventional companies to start implementing Industrial IOT solutions to change the way they do business in Unconventional Ways.  Can't wait to see what we come up with next!

Boston Business Journal linkhttp://www.bizjournals.com/boston/blog/techflash/2016/05/gm-to-challenge-zipcars-car-sharing-services-in.html

The Human Nature of the Internet of Things

Rick Harlow

EVP & Head of Americas l OEM l Engineering l Energy l Aviation l Recognized Industrial IOT Visionary

IIOT how to get started - a link for an article on how to get started on the IIOT journey.

“All of us want to be better. Human nature is always seeking advancement.” Quote by Elizabeth Kapu’uwailani Lindsey.

It occurred to me today when I was talking with my six and eight year old boys about my profession that some of us never lose our sense of excitement when learning something new. This is especially true of children as we all know. When the boys were younger and they asked me what I did for “my job” I told them I worked with truck drivers, ship captains and airplane pilots and taxi cab drivers! They thought my “job” was so cool! 

I would tell them I help the trucks, ships, planes and taxi cabs to get where they need to go. They would ask “how?” I would say well dad works with truck drivers to help monitor their trucks to make sure they are running right and to help make sure things get delivered on time around the world. I work with ship captains and airplane pilots to monitor their ships and planes to make sure they are sailing and flying at the right speed and in the right direction of the wind to make sure people and important things are delivered on time around the world. I work with taxi cab drivers to make sure people get picked up on time and get to their destinations safe and on time.

They would ask “do you work with any other people?” I would say yes I work with all kinds of people. I work with people that make gas for cars, I work with people that build tractors, I work with people that make cities work, like with buses that carry people around and work with people that make energy for people to have lighting and electricity. 

As the past couple of years have went by and we have continued to discuss what my “job” is our conversations have become much deeper. Now the boys know about different types of sensors that are used in the oilfield and in energy energy applications, they know about different kinds of software, they know what data networks are, what engine diagnostics are and even what predictive analytic's are.  In fact they even know there are different types of analytic's like Hadoop and Azure. They think both of those names are cool and funny sounding! They know what IOT is and even the difference between IOT and IIOT. 

What is so fun for me is that I have similar conversations with many people on a daily basis and like my boys they are always eager to learn about new technologies as well.  They too are very interested in learning about different types of sensors, different types of software platforms, different types of analytic's platforms and how they can use these technologies in various ways.

Some want to understand how these technologies can help them reach their business objectives like reducing expenses, increasing efficiencies and driving more profits. Others want to understand how these technologies can help them improve the image of their company, open up new revenue streams and even monetize their data they have been collecting over the past years.

It is true that human nature is always seeking advancement. For me this has always been the case and even what I have thrived on in my career, especially when it comes to the IOT and IIOT space.  This is what I also see in most all of the people I work with.  Everyone knows their business and it excites me when they start tying all the IOT and IIOT dots together and see how it will help them meet their business objectives.  I hope they also get to tell their kids stories about cool things they get to do at work with technologies as well!  

How to get started on an Industrial IoT journey ?

Industrial Companies have heard about the Industrial IoT buzz and in many industries, they have also seen their peers taste the greatness of early success.  As Industrial IoT-enabled innovation diffuses across the industrial sector, it is very useful to find out where it is in the adoption curve.  Is Industrial IOT in the Early adopter stage (“Visionary”) or is it in Early Majority (“Pragmatists”) stage? We believe that Industrial IOT has crossed the chasm and is in the Early Majority stage where pragmatists have the technology.  They certainly have an industrial problem they are trying to solve and even a business case aligned with senior leadership teams objectives.

So the question is “How do companies in the “Pragmastist” stage get started on the Industrial IoT journey?” Flutura has experienced and validated a pattern where there are Three Distinct Milestones in the journey which I have outlined below.

Step-1 : Perform a two day Executive workshop on Industrial IoT

1. Day-1 : Awareness building by focussing on case studies from similar industries and companies
2. Day-2 : White boarding and deep diving of specifics

Example:  A leading shipping company asked Flutura to look at Industrial IOT data streamed from Digitized Ships into hull management systems and identify monetization opportunities in the data.  We conducted a two-day workshop with our engineers and with the clients cross functional executive team.  When this exercise was conducted the clouds parted for the executive team and then they were able to understand the new world of Industrial IoT and how new business models could be created out of it.  

This was a Light bulb moment! 

On day two both teams went narrow and deep on one fuel efficiency use case and the business case directly related to this use case. This turned out to be a block buster hit in terms of solving a long pending problem they had.  Today this use case is fully scaled.

Step-2: 90 day pilot

A leading OEM for the Shale gas industry wanted to expand its revenue pools by providing monitoring and spare parts augmentation for their customers. They had a lot of hypothesis which they segmented into Three broad buckets

• Product feature hypothesis
• Pricing hypothesis
• Business model hypothesis.

They also identified Three forward looking customers and invited them to give feedback on their overall approach.  Once the hypothesis was iterated and refined across both the product and business model they were ready to scale out across Three industry segments and 20 countries.

Step-3: Industrialize and scale

This is stage at which the Industrial IoT solution gets baked into the operational process and is in union with the daily operational habits.  For example, a leading global manufacturer of Industrial Adhesives used in airplanes and other mission critical equipment did a 90-day pilot where sensor data was used to identify micro defect signals that had a massive impact on plant yield and associated efficiency dollars.

Having tasted Industrial IOT success this Flutura client is rolling the solution out to 30 countries where the impact on yield outcome will deliver 18 million dollars in savings per year.  That’s a significant squeeze out of inefficiencies only identified because of the Industrial IoT prognostics process they went through hand in hand with the Flutura team.

Conclusions

To summarize, Industrial IoT adoption is going to be a competitor weapon and it will discriminate the winners from the losers in the Industrial marketplace. It is clear that if manufactures, process industries and other OEM companies don’t start on their Industrial IOT journey, they risk being left behind.  Therefore, a Two-day heavily-focused Industrial IoT workshop & a 90-day pilot are crucial building blocks to power this success. 

 Industrial IOT Solutions by Flutura

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