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

Deploying Predictive Analytics (PdA) as an Operational Improvement Solution: A few things to consider

“…in data science…many decisions must be made and there’s a lot of room to be wrong…”

There are a good number of software companies out there who claim to have developed tools that can potentially deploy a PdA solution to enhance operational performance. Some of these packages appear to be okay, some claim that they are really good, and others seem really ambiguous other than being a tool that a data scientist might use to slice and dice data. What’s missing from most that claim they are more than an over glorified calculator are actual use cases that can demonstrate value. Without calling out any names, the one thing that these offerings share in common is the fact that they require services (i.e., consulting) on top of the software itself, which is a hidden cost, before they are operational. There is nothing inherently unique about any of these packages; all of the features they tout can be carried out via open-source software and some programming prowess, but here lies the challenge. Some so-called solutions bank on training potential users (i.e., servicing) for the long-term. These packages differ in their look-and-feel and their operation/programming language and most seem to either require consulting, servicing, or a data science team. In each of these cases, a data scientist must choose a platform/s, learn its language and/or interface, and then become an expert in the data at hand in order to be successful. In the real world, the problem lies in the fact that data tends to differ for each use case (oftentimes dramatically) and even after data sources have been ingested and modified so they are amenable predictive analytics, many decisions must be made and there’s a lot of room to be wrong and even more room to overlook.

“…a tall order for a human.”

Unfortunately, data scientists, by nature, are subjective (at least in the short term) and slow when good data science must be objectively contextual and quick to deploy since there are so many different ways to develop a solution. A good solution must be dynamic when there may be thousands of options. A good product will be objective, context driven, and be able to capitalize on new information stemming from a rapidly changing environment. This is a tall order for a human. In fairness, data science is manual and tough (there’s a tremendous amount grunt work involved) and in a world of many “not wrong” paths, the optimal solution may not be quickly obtained, if at all. That said, a data science team might not be an ideal end-to-end solution when the goal is for a long-term auto-dynamic solution that is adaptive and can to be deployed in an live environment rapidly and that can scale quickly across different use cases.typical solution

“…a good solution must be dynamic…”

End-to-end PdA platforms are available (Data Ingestion -> Data Cleansing/Modification -> Prediction -> Customer Interfacing). Predikto is one such platform where the difference is auto-dynamic scaleability that relieves much of the burden from a data science team. Predikto doesn’t require a manual data science team to ingest and modify data for a potential predictive analytics solution. This platform takes care of most of the grunt work in a very sophisticated way while capitalizing on detail from domain experts, ultimately providing customers with what they want very rapidly (accurate predictions) at a fraction of the cost of a data science team, particularly when the goal is to deploy predictive analytics solutions across a range of problem areas. This context-based solution also automatically adapts to feedback from operations regarding the response to the predictions themselves.

Predikto Solution Utilizing Predictive Analytics

 

Skeptical? Let us show you what Auto-Dynamic Predictive Analytics is all about and how it can reduce downtime in your organization. And by the way, it works… [patents pending]

Predikto Enterprise Platform

Industrial IoT is more about the plunge than the scope

InternetOfThings

Things at Predikto are going well. We have been growing our team, primarily sales, after closing our Series A round 3 months ago. I was asked to run a webinar with a partner company, eMaint, in March. eMaint has a large following to their monthly webinars primarily consisting of Operations and Maintenance folks from asset intensive industries. The topic was the Industrial IoT.

In putting together the content for the webinar, I realized that many of our customers do not fully understand the exact expected results and success criteria prior embarking on a Predictive Analytics initiative. In a traditional enterprise software purchase, customers are fully aware of what they need and they shop around to compare the features and capabilities that would best fit into their checklist. When customers are purchasing Predictive Analytics solutions, there is no punch list, they just take the plunge.

Predictive Analytics technologies are not new. But operations and plants have not been early adopters until now. The use of machine learning and statistical algorithms to predict an event that will likely take place in the future by utilizing historical data is very new in asset intensive industries. Predictions could be focused to improve yield, reduce asset downtimes, and increase overall plant reliability and operations.

So, next time you are trying to evaluate how to operationalize Predictive Analytics in your plant or with your assets, do not worry about having all the answers before you start. Engage with a low risk pilot that would allow your organization to test different areas of your plant or different pieces of equipment. Early wins will result in a lot of light bulbs going off across the organization and enabling you to layout in more detail the next areas of your company where Predictive Analytics can have a big impact.

$57 Trillion in infrastructure will be needed by 2030

McKinsey Global Institute released a study this year stating that the world will need $57 Trillion (with a T) infrastructure projects by 2030.  That spending is just meant to repair our existing infrastructure, which is in a fairly abysmal state in the U.S.

The most recent “report card” from the American Society of Civil Engineers gives the U.S.’s infrastructure a “D” overall and estimates that we need to spend $2.2 trillion over the next five years to get it up to snuff.

The article also discusses an interesting approach by Chile to prioritize infrastructure projects based on predicting the highest need.  A bulk of the infrastructure needs where in areas like roads, power grid, water, and telecommunication.  The US resembles countries like South Africa and China instead of other developed countries like Germany and Japan.

Advanced manufacturing and predictive techniques can help reduce the annual spend from $2.7 Trillion to $1.7 Trillion, but we like the overall theme of the report in that we need to get smarter at tackling these big infrastructure problems.  Similar to reactive vs. predictive asset maintenance, our infrastructure improvement plans should be more predictive and reactive.