Digital Transformations : From Analysis Paralysis to Execution Mode

Digital Trans

I have never been more excited about the future of Predikto. We started 4 yrs ago with the Vision of “Moving Unplanned to Planned”. We wanted to help large industrials “To harness the power of predictive analytics to optimize operational performance”. We are enabling this with:

  1. Our software platform including Predikto MAX which automates machine learning algorithm generation at a massive scale
  2. Our unique approach to data preparation optimized for Machine Learning

So why am I so excited? We have seen a big shift in the past 12 months of organizations going from “analysis paralysis” to “let’s start to execute”. We love it when prospects get what we have built. This was not the case 3 and 4 yrs ago. It takes a sophisticated organization to be ready to capitalize on our technology. We look for:

Clear Strategy

A clear strategy with the Executive support that incorporates AI, Predictive Analytics, Analytics/Digital Transformations, and investments that will enable them to increase revenues or cut costs by leveraging their own data. A recent report by IDC found that 80% of senior executives said investing in digital transformation is critical to future success. Investments in digital transformation initiatives will reach USD2.2 trillion by 2019 which is 60% more than this year.

Organizational Readiness

Organizational readiness is another key aspect of prospects and customers who are ready for our technology. Most customers have hired a Chief Digital Officer who came from the outside to change the way they have tackled innovation and digital transformations. They have dedicated teams with the power and budgets to run multiple pilots with companies big and small to learn how new technology can bring tangible value to their organization. They are learning to move fast and fail fast. The best ones are learning from startups and aligning their key initiatives with true disruptors. If you are looking for Ginni to sell you IBM Watson to solve all your problems, you are going to have a rude awakening in 18 months. We actually look for prospects who have already hired IBM and failed. IBM, please send me your list of Pilot customers from the past 3 years for Predictive Maintenance projects.

Technical Transformation

Technical transformation means a lot of different things depending on the industry vertical. Some customers did not have access to their own equipment sensor data since the OEM would keep it. They had to invest in new hardware to tap into the sensor data inside trains. Others had data stored in siloed on-prem historians and it was a challenge to get their IT Security organization to push that data to the Cloud. Others are trying to figure out which cloud provider to go with? If you are still wondering, there are only two you should consider, AWS and Microsoft Azure.

We are finding that prospects that are moving and executing have figured out all three components of their Digital Transformation. IDC also states that by 2019, 40% of All Digital Transformation initiatives, and 100% of all effective IoT efforts, will be supported by Cognitive/AI capabilities. I am excited about our future and the AI / Machine Learning software we have built to bring value to large industrial transportation companies looking to move from Unplanned to Planned using a data approach to complement their engineering based condition monitoring approach.

What about unplanned?

Everybody’s looking at process inefficiencies to improve maintenance but there’s lower hanging – and bigger – fruit to focus on first: unplanned events!

Maintenance has pretty simple goals; guarantee and increase equipment uptime and do so at the lowest possible cost. Let’s take a quick look at how unplanned events influence these three conditions.

Guarantee uptime

When production went through the evolutions of JIT (Just In Time), Lean,… and other optimisation schemes, schedules got ever tighter and deviations from the plan ever more problematic. WIP (Work In Progress) has to be limited as much as possible for understandable reasons. However, this has a side-effect of also limiting buffers, which means that when any cog in the mechanism locks up, the whole thing stops. Therefore, maintenance receives increasing pressure to guarantee uptime, at least during planned production time. Operational risk is something investors increasingly look at when evaluating big ticket investments or during M&A due diligence and for good reason; it’s like investing in a top athlete – don’t just pick the fastest runner, pick the one who can do so consistently!

Failures are bound to happen so the name of the game is to pre-emptively foresee these events in order to remediate them beforehand; planned, and preferably outside of production time.

Increase Uptime

The more you are able to increase (guaranteed) uptime, the more output you can generate from your investment. Unplanned events are true output killers; not just because they stop the failing machine but also because they may cause a waterfall of other equipment – depending on the failing machine’s output – to come to a halt. Unplanned events should therefore a) be avoided and b) dealt with in the fastest possible manner. The latter means having technicians and parts at hand, which can be a very expensive manner (like insurance policies; they’re always too expensive until you need them). In order to avoid unplanned failures, we have therefore introduced preventive maintenance (for either cheaper or cyclical events) and condition based or preventive maintenance. Capturing machine health and deciding when to pre-emptively intervene in order to avoid unplanned failures is a pretty young science but one that shows the highest potential for operational and financial gains in the field of maintenance.

Lower maintenance cost

By now most people know that unplanned maintenance costs a multiple of planned maintenance; by a factor three to nine (depending on the industry) is generally accepted as a ballpark figure. It therefore keeps surprising me that most of the investments have traditionally been made in optimising planned maintenance. Agreed, how to increase efficiencies for planned maintenance is easier to grasp but we have by now come to a level where returns on extra investments in this field are diminishing. Enter unplanned maintenance; can either be avoided (increase equipment reliability) or foreseen (in which case it can be prevented). Increasing equipment reliability has not always been the goal of OEMs. In the traditional business model, they made a good buck from selling spare parts and they therefore had to carefully balance how to stay ahead of the competition without pricing themselves out of the market (reliability comes at a cost). Mind you, this was more an economic balancing act than a deliberate “let’s make equipment fail” decision. Now however, with uptime-based contracts, OEM’s are incentivised to improve equipment reliability. Unfortunately, unplanned failures still occur; and due to tighter planning and higher equipment utilisation requirements, these failures’ costs have increased! Therefore, in order to lower maintenance costs, we have to lower the number of unplanned events. The only practical way is to become better at foreseeing these events in order to be able to plan interventions before they occur. The simple plan is: gather data, turn it into information, make predictions and take action to avoid these events. And voilà, 3-9 times more money saved than if we focused on planned events!

Life can be simple.

ARC Guest Blog: Counting toilet flushes help improve bullet train reliability

Greg Adams was a recent guest blogger on the ARC Advisory Group’s IIoT newsletter. We see a lot of data and it is interesting how mundane and often overlooked data can contain meaning. Read how counting toilet flushes is helping to increase the uptime and reliability of bullet trains.  http://industrial-iot.com/2015/10/how-wc-flushes-relate-to-locomotive-reliability/

Metro Atlanta CEO writes a piece about Predictive Analytics

metroatlantaceo

Metro Atlanta CEO has an article in their October newsletter covering Predictive Analytics and some of the interesting use cases Predikto has in Transportation.

Predikto, a leader in Predictive Analytics solutions Transportation, has begun to deploy their machine learning / artificial intelligence software to help improve equipment reliability at global companies.

Click on the article to read about actual use cases and gain an understanding of this disruptive technology.

Predikto, Inc. Partners With New York Air Brake to Incorporate Predictive Analytics In NYAB’s LEADER Advanced Train Control Technology Solutions

ATLANTAJuly 14, 2015 /PRNewswire/ — Predikto, Inc. today announced a new collaborative effort where New York Air Brake will incorporate Predikto’s auto-dynamic predictive analytics platform, MAX, into the company’s LEADER advanced train control technology solutions via its internet of things (IoT) initiative.

New York Air Brake, a subsidiary of the Knorr-Bremse Group (Munich, Germany), an innovation leader and supplier in the rail industry since 1890, will integrate a new predictive analytics component to its Advanced Train Control Technology solution, LEADER (Locomotive Engineer Assist/Display & Event Recorder). The mutually developed solution will now leverage a suite of predictive analytics software applications engineered by Predikto, Inc. Predikto’s patent pending solution, called MAX, is an auto-dynamic machine learning engine that draws upon LEADER train data in addition to capturing data external to the train itself, such as weather and line of road conditions. MAX is a self-learning artificial-intelligence solution that adapts itself to rapid changes in context in near real-time in order to provide the most accurate forecasts possible across an array of use-cases.

“Integrating predictive analytics with the rich train information from LEADER will allow the railroads to utilize their data to proactively identify opportunities to improve operating efficiency and rail safety,” said Mario Montag, CEO of Predikto. “Partnering with a premier technology company in the rail industry, such as New York Air Brake, will allow Predikto’s award-winning platform to make a defining impact on the rail industry.”

The predictions provided by MAX will enable new and existing users to incorporate advanced data analytics to enhance the capabilities currently available through LEADER. Predikto’s MAX platform has already proven success within the rail industry through forecasting failures and health in rail equipment ranging from bullet trains in Europe to wayside detection equipment in North America. This partnership will allow for the deployment of dynamic predictive capabilities that include a locomotive energy efficiency forecaster, a braking efficiency forecaster and track health. The LEADER/MAX solution is poised to revolutionize the rail industry by providing advanced insight to improve velocity and operating efficiency.

“You can have data without information, but you cannot have information without data.  Predikto’s MAX allows us to extract every bit of information and turn it into actionable insights that will improve visibility into operations, provide innovative solutions to improve safety, and provide clarity into the critical maintenance and performance indicators that impact the bottom line most,” states Greg Hrebek, Director of Engineering for New York Air Brake.  “The capability offered between us through this collaboration is unprecedented in the rail industry and will rapidly accelerate the value of the investment the railroads have made into locomotive onboard intelligence.”

About New York Air Brake

New York Air Brake, Inc., headquartered in Watertown, NY, has a long-standing history of innovation and technology in the rail industry ranging from providing advanced braking technology for trains to train control systems. New York Air Brake’s mission is to provide superior railroad brake and train control systems, products, and services with high quality and high value. For more information visit the New York Air Brake website at www.NYAB.com.

About Predikto, Inc.

Predikto, Inc., headquartered in Atlanta, GA, provides actionable solutions for the rail industry as well as industrial equipment and fleets using predictive analytics. Its proprietary data analysis and prediction engine is built on an auto-dynamic machine learning protocol that adapts to changing environments in near real time.  Predikto specializes in operationalizing predictions of key industrial events like asset failures and poor asset health to enhance a company’s overall performance.

The company is comprised of engineers, developers, academics, and industry professionals. Predikto’s technology solution enables companies to achieve seamless operational functionality, efficiency and exponential return on their asset investment.

For more information, visit www.Predikto.com.

 

SOURCE Predikto, Inc.

RELATED LINKS
http://www.predikto.com

Predikto: Making Waves in IoT!

The Internet of Things is generally defined as “Smart” + “Connected” + “Edge Devices” (Planes, Trains, Automobiles, Industrial & Farming Equipment, Medical Equipment, and Consumer Electronics)

Predikto focuses on putting the “smart” into managing smart connected devices, equipment and complex capital assets in order to forecast asset behavior/performance.

Industrial asset OEMs, operators and maintenance organizations are challenged by equipment performance degradation and failure as they impact uptime and efficiency. While reliability and condition-based solutions have been around for many years, predictive analytics (machine learning) is providing significant new capabilities to improve performance and profitability.

Approximately 2,000 hardware, software and business leaders attended the second annual O’Reilly Solid 2.0 IoT conference in San Francisco. Attendees were given the opportunity to vote on the startup they believed was making the most innovative impact in the field of industrial or consumer IoT. Of the 30 or so startups at the conference, Predikto was voted best startup by attendees for its telematics / IoT based predictive analytics, predictive maintenance and asset health management solutions.

https://www.youtube.com/watch?v=C0-cYgsT8yI&list=PL055Epbe6d5ZVlSYx7-1k72bm075HkVhq&index=21

This was great exposure for us at Predikto, and now we are up for 2 awards at the upcoming Solutions 2.0 Conference in early August.  We are going head to head against some big players in the industry in the categories of Asset Condition Management and Asset Management.  Mario Montag, Predikto CEO, will be presenting on the topic of Predictive Analytics in Asset Management.  This is another indication of the high demand for IoT products and solutions, the acceleration of Predikto within the Industrial Internet market and the large innovative technology community in Atlanta.

Mario Montag was quoted after the Solid Conference: “It is great to see validation from the market and conferences with regards to our Solution based predictive analytics technology and approach.  We are not a tool to enable customers to do more. We deliver results and bring to light full transparency on the ROI and impact we are having to solve real problems with asset reliability.”

We have also been getting some great traction with customers and partners.  We recently announced a partnership with New York Air Brake, subsidiary of the Knorr-Bremse Group in Germany, to incorporate Predikto’s auto-dynamic predictive analytics platform, MAX, into the company’s LEADER advanced train control technology solutions via its internet of things (IoT) initiative. See the full story here.

Needless to say we are all very are all very excited about the awards and recognition Predikto is receiving and it is legitimizing the need for a real solution in predictive analytics for the IIoT.

A Software Industry Veteran’s Take on Predictive Analytics

I’m about 4 months into the job here at Predikto as VP, Sales.  The predictive analytics market is an exciting new market with predictably (pun intended) its share of hype.  Nevertheless, this is key niche of the Industrial Internet of Things sector. I’d like to share some observations on what I’ve learned thus far.

We focus on asset-intensive industries, helping organizations leverage the terabytes of data they have accumulated to anticipate the likelihood of an adverse event, whether that is a battery on a transit bus about to fail, or indications that a fuel injector on a locomotive diesel engine, while still operating, is doing so at a less than desired level of performance.   We predict these events in a time horizon that allows the customer to take action to rectify the issue before it creates a problem, in a way that minimizes disruptions to operations.  Our technology is cutting edge Open Source, leveraging Spark, Python and Elastic Search hosted by AWS.

The use cases we’re being asked to solve are fascinating and diverse.   Some companies are contacting us as part of an initiative to transform their business model from selling capital assets to selling a service, an approach popularized by Rolls Royce with their jet engines, the “power by the hour” approach and similar to the software industry’s transition from selling perpetual licenses with maintenance contracts, to selling Software as a Service (SaaS).  In order to sell capital assets like construction equipment and industrial printing equipment this way, our customers will offer service level agreements, with Predikto in place to allow them to proactively deal with issues likely to degrade their service commitment.  So while our tactical focus has been on helping clients maximize product “uptime”, the strategic driver is helping them transition to a new way of generating revenue while getting closer to the customers.  It’s been gratifying to realize the impactful role our offering is playing in facilitating these transitions.

Other organizations are complex, asset-intensive businesses, where an equipment failure can have a cascading effect on revenues and customer service.  For example in the work we are doing with railroads we’ve learned there are a multitude of areas where sub-optimal performance of equipment or outright failure, can have significant impact.  The North American railroad network in 2014 set new records for revenue-ton-miles, a key efficiency metric; this was accomplished over a rail network which is highly congested.   In this environment, a delay has huge ripple effects.  Any number of factors can lead to a delay, ranging from a rockslide blocking a section of track to a locomotive breaking down, to a wheel failure on a rail car, which can cause a derailment.   On top of this, in order to operate safely and comply with government regulations, railroads have invested heavily in signaling and equipment monitoring assets, as well as machinery to maintain the track and roadbeds, which must work reliably.  Our abilities to implement in weeks and generate actionable predictions regarding locomotive and rail car health, as well as monitoring other equipment and even the condition of the rails, are making a major difference in helping to facilitate efficient, safe rail operations.

 

Having a blast…more to come.

Kevin Baesler, VP of Sales