December 27, 2017

Igniting Network Effects in Industrial AI : 2 Real World Examples

70 % of successful consumer startups applied network effects to succeed. For example Apple, Google, Microsoft, Facebook, and Amazon —all flawlessly executed network effects as a strategic lever very effectively to succeed in the hyper-competitive marketplace. 

This is an incredible insight and we at Flutura have been fascinated with cross-pollinating and executing this insight to the Industrial AI sector. So the question we asked ourselves was

“How do we use network effects to achieve massively transformative non-linear outcomes in the Industrial IoT space?”

After 3 years of experimentation and learning, we finally managed to nail the playbook for “Industrial network effect”. We will share 2 industrial examples from the trenches where network effects ignited non-linear outcomes and in the process became a global pioneer in applying network effects for Industrial AI Category. So here they come …

Example-1: A leading manufacturer of Industrial glue

The problem this customer was trying to solve was reduced millions of dollars lost from defective glue quality. In order to do this, they had to process a torrent of high-velocity sensor streams across a battery of plants spread globally in 70+ countries. Flutura developed Industrial AI Nano Apps which surgically predict quality outcomes by processing sensor streams in real time.

How did we achieve industrial network effects here? There we 2 levers which we creatively combined to achieve network effects

Per-App Dimension: Multiple surgical apps to solve ultra-specific factory quality problems were created
Per Factory Line: These apps were deployed in multiple factory lines spread globally across 70+ countries which triggered massive network effects.

Example-2: A leading Oil and Gas OEM Manufacturer in Houston

The problem the customer was trying to solve reduce the operational cost of visiting the site by enabling remote digital diagnostics/prognostics using Cerebra. How did we achieve network effects here? We again activated 2 dimensions to trigger network effects

Per Asset dimension: In keeping with the customers roll out program we created a flexible per asset pricing model where cash outflow for customer is tied to their equipment rollout plans

Per-App dimension: Again we created a catalog of Industrial AI apps which surgically diagnose/predict imminent high-value failure modes for nodal assets in the upstream extraction process.
Both of the above examples illustrate the successful application of network effects to achieve non-linear outcomes in the industrial sector.

Closing Thoughts

Platform-based digital business models + Network effects are 2 powerful constructs which innovators used to disrupt traditional models in the consumer-facing industries of Retail, Banking, Telecom etc. 

As Marc Andreessen famously said

“There will be certain points of time when everything collides together and reaches critical mass around a new concept or a new thing that ends up being hugely relevant to a high percentage of people or businesses. ” 
Flutura is blessed to be at the forefront of executing platform-based business models and network effects for the non-traditional world of Oil and Gas, Manufacturing and Engineering/Energy sectors. This is a massive inflection point and we are just getting started.


October 30, 2017

Transforming 6 Operations across 15 countries for a $10 Billion Process Chemicals Giant [Use Cases]

A lot has been talked about the hype around #Industrial IoT, viable business case, a juicy ROI for IoT platforms. I wanted to share this very proud moment where #Flutura's Cerebra deployment which started as a small app back in 2015 is now the central nervous system driving plant operations worldwide for a Fortune 500 process chemical major.
The customer's global operations and quality teams called for a checkpoint review last week to assess the impact of Cerebra on its operations and looking at new avenues to leverage it for its operations.
We never imagined the number of ways Cerebra could impact day to day operations as it is today. Here are a few areas where Cerebra has triggered concrete actions to transform operations.
1.  Cerebra drives product quality benchmarking - Before Cerebra, every product manufactured had its own quality measurements and was difficult to assess, benchmark and compare the quality across manufacturing batches, across production lines, and across factories to take corrective actions. Cerebra introduced a scientific way to measure product quality in a homogenous manner enabling measurement of performance at all levels. At a single instance, the product quality can be assessed in a simple and intuitive way. The entire plant management team now reviews operations through Cerebra. Old, paper-based review processes have been discontinued.
2. Cerebra learns complex interactions - Before Cerebra it was impossible to attribute causal factors for suboptimal batches due to the multitude and complex nature of data points available. Cerebra is now constantly learning the ever-changing complex interactions and inter-relationships between human, machine, environment, processes, and outcomes throughout the manufacturing process and helping operations and quality teams to intervene at the right time.
3. Cerebra is “THE” root cause analysis system - Before Cerebra, root cause analysis was manual, highly time-consuming and many a time impossible to conduct due to the sheer complexity of the process. Cerebra is now able to isolate the needle in the haystack with ease; one of the manufacturing plants was able to isolate an issue related to the design of the blades used in the industrial mixer to be the cause for variance in viscosity of the manufactured product. The production schedule was changed in order to circumvent this constraint
4. Cerebra pinpoints critical manufacturing step changes - Before Cerebra, macro deviations or step change in operations were difficult to detect. Cerebra is now helping detect macro shifts in processes; a manufacturing plant was able to detect a change in product quality due to a change they made to a supplier of a critical raw material several months before. Cerebra now helps review Supplier performance.
5. Cerebra detects unforeseen patterns - Before Cerebra, repetitive & seasonal patterns were difficult to detect. Cerebra is now helping unearth unforeseen changes to environment leading to undesirable impact; Cerebra detected a peculiar pattern related to variance in quality from a particular shift, on the investigation, the training process was relooked into and operators are now being trained on the new process.
6. Cerebra optimizes Standard Operating Procedures - Before Cerebra, process effectiveness and continuous improvement to the standard operating procedure were subjective and reactive. Cerebra is now helping monitor compliance to standard operating procedures, the effectiveness of standard operating procedures and helping question status quo; Cerebra helped narrow down more-than-a-decade-old quality threshold established for a specific product's BOM and in turn, helped change the product development formulation.
Cerebra IIoT intelligence platform was initially deployed to gain basic visibility into product quality deviations on the manufacturing line; has now
1. Transformed Production Planning
2. Transformed Machine Resource Planning
3. Transformed Supplier Performance Assessment
4. Transformed Product Development
5. Transformed Manufacturing Process Optimization, and 
6. Transformed Operator Training.
Many plans and blueprinting are underway to leverage Cerebra in multiple areas; next being Supply Chain Optimization. 
Cerebra is serving as THE operating system on which Man-Machine interactions are being orchestrated. It has become the Central Nervous System for plant operations.
Can't wait to see a day when Cerebra supports autonomous operations.
It was an amazing moment to see the team rejoice at the impact they had made to our customer's business. Great job team. Look forward to many more transformations

October 5, 2017

Cerebra Feature Series #2 - Discovering Unknown Unknowns [Use cases]

"I have lots of data and I don't know what to do with it"
"We collect a lot of data but not sure whether it is accurate or has any value which can be unlocked"
"We have conducted a lot of experiments and proof of concepts but very few of them have resulted in big operational or financial impacts"
These are the most common experiences we hear in our conversations with customers. These are definitely not trivial questions. We call it the Data-to-$-Chasm.The questions themselves not only relate to unidentified problems to solve but also includes unidentified answers to complex questions which needs macro and micro synthesis of large volumes of data.
Now, does this exercise of finding problems to solve, or finding answers to questions which needs large breadth of data worth the effort ? What if these experiments don't yield a BIG return on effort or investment. The short answer is "YES this is worth the effort"
We have been working on solving this problem for sometime now and has resulted in a core product suite within Cerebra called Pre-Diagnostics to aid engineers' maieutic enquiry process.
In the engineering world, first principles based interrogation techniques are still widely adopted, however when Physics + Chemistry + Ambient condition + Operating Procedures + Human behavior is involved, currently first principles based modeling is complex and sometimes unattainable. You never know there might be a day when the "real world" can be expressed with a universal equation !
For the time being let us stick to Pre-Diagnostics, These are some of the "Never before seen insights" our customers discovered using Cerebra Pre-Diagnostics, which enabled operational process changes and modifications to their industrial equipment ,
  1. A leading industrial glove manufacturer discovered unusual changes to sensor behavior affecting product quality with Cerebra's Swarm Algorithm. Pollen pollution in the shop floor was ascertained as the root cause for this issue and extensive changes were done to set right the ambient operating conditions for the plant. Continuous monitoring of ambient conditions has now been institutionalized
  2. A process chemicals manufacturer discovered deviations to the composition of the end product while it was manufactured with a particular industrial mixer. This was a decade old problem which had gone unnoticed. On further investigation it was ascertained that the type of blade used in the industrial mixer was leading to deviations in viscosity of the product. The machine resource allocation process has now been changed to add constraints related to product - machine mix
  3. A gas processing plant had frequent failures on its valves resulting in periodic unplanned downtimes. The discovery process lead to surfacing insights related to non synchronized feedback loop from the control systems to the valves leading to friction. The plant is replacing its control systems architecture to prevent issues
  4. A leading mining company discovered failure patterns on their milling machines when certain load thresholds were breached consistently over a period of time. New alarms were designed and put in place to detect progressive failure paths
Not long ago people could imagine only white swans, because white swans were all they had ever seen. And so people predicted that every next swan they would see would be white. The discovery of black swans shattered this prediction. The black swan is a metaphor for the uselessness of predictions that are based on earlier experiences, in the presence of unknown unknowns -Taleb,Nassim
Cerebra Pre-Diagnostics helps in identifying problem statements worth solving and use cases worth operationalizing in a short time
Watch out for the next feature in Cerebra Feature Series.

October 3, 2017

Cerebra Feature Series #1 - Digital Twin in 48 Hrs [ Case Study ]

Flutura's team achieved the improbable when they enabled a massive oil field equipment used in early production stages of crude oil extraction to be connected in 48hrs and turned on Cerebra's diagnostics engine to look at operational insights and anomalies of this critical equipment from anywhere in the world.Prior to this, a lot of manual monitoring of the flowrates and other vital signals used to happen.
The machine by itself is a complex integration of Desander, Separation Units, Outlets, Valves and other machine units. The machine is subjected to high pressure of up-to 9000 psi which is the norm in any Oil field equipment. Data from close to 500 sensor tags across the machine units are streamed every second. Cerebra ingests this data with very high reliability and broadcasts critical signals to key stakeholders in real time.
Cerebra's universal machine model helps model any machinery and equipment with ease.
Watch out for the next feature in Cerebra Feature Series.

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 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!