January 20, 2017

World's first"Per Asset"​ IIoT Digital Business Model by Flutura

Flutura becomes the world's first IIoT company to create a path-breaking per asset business model predicated on per asset pricing now. This has now been executed on the ground with 3 new customers in Houston & Europe signing up in the last 90 days. We have an array of 120+ industrial customers expressing a keen interest to spawn new digital revenue streams in 2017

Since our birth in 2012, We at Flutura have always believed in the power of new business models, not just technology to leapfrog and get the competitive advantage in the marketplace. While many IOT companies focussed on IOT technology, Flutura was obsessed with the business model to monetize IOT technology.

This business model is orbit shifting in 3 specific ways for Industrial sector

1.It offers them a completely NEW digital revenue pool for asset manufacturers, previously nonexistent

2. This revenue stream is not one time, it is CONTINOUS and PREDICTABLE

3. The revenue earned is directly proportional to no of FIELD ASSETS

The Cerebra platform is the foundational bedrock on which new digital business models are being created.The platform allows a variety of business model configurations as outlined below

  1. Task-based pricing model - Diagnostic tasks, Prognostic tasks, Advanced state assessment
  2. Asset complexity - Nodal vs "Non-Nodal" asset
  3. Breadth & depth of electro-mechanical & hydraulic signals in scope
  4. No of subsystems

The final outcome is the Unit pricing model - Per asset per year, Per asset per app, Per asset per month, Per asset per app per month

While we are excited about the headroom we are also grounded in our expectations as the shift in mindset is massive and is predicated on 3 core organizational dependencies foundational to success

1. Institutionalization of Chief Digital Officer by Industrial companies

2. Skin in the game by allocating Dollars for digitization

3. Shift in industrial mindsets at the CXO level ( where progress on Digital IIoT becomes a board review item )

We are confident that this digital trend is irreversible and momentum will unfold in 2017

Flutura's stellar customer impact team has got 2 more orbit shifting business models up its sleeve. We would make announcements about these in the 3rd quarter of 2017. Stay tuned for 2017 is the year Industrial companies shift from technology to business models to power their future 

Congrats Rick, Okubo San, Sugato & Ganesh for your stellar efforts in making this model the new reality in Houston, Tokyo, and Europe. 

As they say, The best is yet to come :)

January 5, 2017

Technology Trendsetters Setting the Pace in 2016 and into 2017!

This was a year of some very notable acquisitions, mergers and divestitures not to mention some game changing technology launches. Let’s take for example the Dell acquisition of EMC. This was the largest technology merger in history with over 140k employees it will be the words largest privately controlled technology company in the world. It is a good time to be a technology partner with Dell as Flutura became earlier this year!
Another Mammoth Deal that was very notable was the acquisition of LinkedIn by Microsoft. We knew an acquisition of LinkedIn was coming but by who was the question. Kudo’s to Reid Hoffman and Jeff Weiner! Another company that made bold moves in 2016 was Accenture who had spent over $1B by mid-year on acquisitions including a buyout of CRM Waypoint from the Netherlands a cloud advisory and technology company and then a month later Accenture bought Formicary an ISV and technology consulting company. The company went on to buy a majority stake in IMJ Corp and then bought OPS Rules an analytics and data sciences company and then acquired Maglan and Israeli based cyber security company. Talk about making bold moves. Accenture has made over forty acquisitions since 2013 and does not seem to be slowing down.
The list goes on and on… IBM acquisition of Bluewolf, The leading Salesforce.com channel partner. The split of Xerox into two different companies. The continued Cisco shake up by Chuck Robbins restructuring the company to focus on a shift from network hardware to more of a software and services company. Ingram Micro was acquired by Tianjin Tianhai a Chinese investment firm.
This also has been an incredible year for Industrial IOT (Internet of Things). Whether you are talking smarter sensors being brought to market for oil and gas applications, industrial automation applications for control, informatics or mobile computingCompanies that have been talking about an Industrial IOT strategy for a couple of years have starting doing pilots and have been able to prove out an Industrial IOT solution that makes sense for their organizations
There are some very notable predictions also being made in the market by the likes of Bain, McKinsey, GE and IHS.  Bain has made a prediction that by 2020 hardware and software vendors revenues could exceed $470B. McKinsey predicts that Industrial IOT will grow to over $3.7B by 2020 andGE predicts that Industrial IOT will top $60T (That is Trillion with a T!) in the next 15 years. This could explain why GE has made bold moves in buying Predix and recently Meridium.  Finally IHS predicts that the number of installed Industrial IOT devices will grow from approximately 15.4B devices to over 30.7B devices by 2020 and to over a whopping 75.4B devices by 2025!
The focus of IIOT in the coming year will be to solve complex issues within the various industries like logistics, manufacturing, services, etc. 
There was an announcement earlier this year about how Lufthansa is using Industrial IOT to create an entirely new business around mining data from their maintenance, repair and overhaul operations and providing these new services to their customers. This would include real time aircraft, airport and weather data to improve on-time performance and optimize operations. 
Flutura has formed strategic partnerships in this arena as well as aggregating all available data for the client’s equipment and operations together to consistently deliver reliable operations to their customers. 
This has been an incredible year for Flutura being featured in Forbes, Tech Asia and other publications.  Flutura has been working with some of the biggest names of industry this year in Oil and Gas, Utilities, Industrial, Maritime and Manufacturing and has uniquely designed the architecture of the Cerebra software in a very different way than you would see from other companies to meet the needs of these industry leaders.
A Big Thank You to The Flutura team that has taken the market feedback and built a second to none Industrial IOT platform that rivals ANY other in the industry but most of all A Big Thank You to our customers and partners who have helped shaped the success of Flutura in 2016!
I’m looking forward to 2017 being one of the most prosperous years in Flutura’s history!

December 6, 2016

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.

October 17, 2016

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.

October 3, 2016

3 reasons why IOT digital intelligence matters for Microgrid


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.

August 2, 2016

Industrial IOT Factors in Reliability Engineering


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. 


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.  

July 20, 2016

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 :)