Predikto raises $4M from TechOperators, ATA to predict machine failure

By Urvaksh Karkaria
Staff Writer-Atlanta Business Chronicle

Predikto Raises $4m to predict machine failures

Atlanta software firm Predikto has raised nearly $4 million to help manufacturers predict product failures earlier.

Predikto’s software engine — dubbed Max — allows manufacturers, railroad companies and other asset-intensive industries to predict equipment failure and warranty claims.

By detecting failures before they happen, companies can increase productivity, reduce downtime and tweak the manufacturing process to increase production volume, CEO Mario Montag said.

Max — an artificial intelligence and machine learning software robot built to design custom algorithms — uses real-time sensor data, historical maintenance records and past failure data to predict equipment breakdowns.

“Clients give us their data and (Max) spits out algorithms that can predict when a piece of equipment is going to fail,” Montag said

While predictive analytics software is widely used in business, manufacturing has been late to leverage Big Data to squeeze efficiencies.

Predikto is riding the wave of the industrial Internet of Things. Pumps, motors and other parts are being manufactured with on-board sensors that deliver tons of data on the health and performance of the devices.

“The industrial Internet of Things is creating a drive to do more with data,” Montag said. “The big data revolution of being able to do more and chew more information is sweeping through industrial manufacturing.”

Predikto raised the $3.6 million in a Series A round led by TechOperators, an Atlanta early stage venture firm managed by a quartet of serial entrepreneurs with billion-dollar exits under their belts.
Super angel groups Atlanta Technology Angels (ATA) and Angel Investor Management Group (AIM) also participated in the Predikto raise.

Predikto’s technology targeted to specific equipment and industry sectors, and its managed-service approach of delivering insight rather than tools, differentiates the startup, said Said Mohammadioun, partner at TechOperators.

“The market expects solutions rather than tools,” Mohammadioun said. “They don’t want to be in the business of figuring out how to use tools.”

The capital will be used for product development and sales and marketing — critical challenges for the startup.

Predikto must continue to innovate to stay ahead of the competition, while taking as much marketshare as possible, said Stephen Walden, a board member at Atlanta Technology Angels.

“That’s why this raise was so important for them to get into the market quickly,” Walden said. “Right now they’ve got, I won’t say a lock on the market, but a proprietary algorithm that nobody else can yet match.”

Launched in late 2012, Predikto is targeting a large addressable market. The industrial predictive analytics market in North America is estimated at more than $10 billion in annual sales, Montag said.

“Our sweet spots are continuous manufacturing facilities, such as steel plants, food & beverage plants and transportation companies with distributed assets — airplanes, trains and truck fleets,” Montag said. Siemens, for example, uses Predikto software to detect failures in train doors and diesel engines.

While the application of predictive analytics has so far been limited to the financial services and retail sector, the next market is industrial systems. Until recently, engineers typically followed a standardized maintenance schedule for industrial equipment, similar to annual maintenance schedules for automobiles.

Predikto relies on real-time data from sensors in the machinery, such as vibrations, electrical usage, and ambient temperature, to give engineers a more efficient predictive maintenance process. Rising temperatures, for instance, when combined with other things, can be a sign of inadequate lubrication, or an incorrectly fitting part that’s causing friction.

“The secret is in being able to run all these variables at once through a sophisticated algorithm to tell what is really going on,” Walden said.

Keeping maintenance downtime for critical equipment to the minimum required can save operators millions of dollars.

“If an engine that powers a steel mill costs $1 million for every hour it is shut down for maintenance, you’d rather not do (maintenance) every thousand hours, if you can do it every five thousand hours,” Walden said.

In the future, Predikto plans to target the booming oil and gas industry.

“The pain of asset downtime in that industry is significant,” Montag noted, adding it costs $500,000 for every day an oil rig is shutdown.

For Predikto, future success will depend on getting customers to fix things proactively, which requires a change in way of doing business, Mohammadioun said

“Companies know how to fix things that break — now, we are able to tell them when things are going to break,” he said. “That requires a change in culture.”

Via:: http://www.bizjournals.com/atlanta/blog/atlantech/2015/01/atlantas-predikto-raises-4m-from-techoperators-ata.html

 

3 Steps to improve your Decision-Making process

3 Steps to improve your Decision-Making process

The ability to make high quality decision is central to any organization. The expansion of the volume, variety, and velocity of information make this ability increasingly difficult. With the state of current data explosion, decision-makers have to respond more quickly and with greater precision than ever.

To improve your decision-making process you should be considering the following:

  1. Do not focus exclusively on historical experience Traditionally, decisions have been made on the basis of anecdotal experience of domain experts. These decisions are subjective and often inconsistent, thereby limiting their value for the organizations.
  2. Business rule tools become obsolete quickly Having the need to standardize key decisions and make them more consistent and reliable, many organizations have moved toward automated decision-making by using business rules. Although this automation provides a degree of efficiency and objective consistency, and improves the collective quality of decisions, static rules quickly quickly become obsolete in ever-changing situations and conditions, and the limits of this approach become apparent.
  3. Predict for specific conditions rather than for generalizations: Predictive decision-making, based on analysis of historical patterns and current conditions, is the basis for the highest quality means of making decisions. The reasons are because the models consider all available data, and also continuously adapt to new information, becoming smarter over time. With predictive analytics, the right decision for the given conditions can be made at the point of impact at the time when the decision needs to be made. Decisions are now customized for each unique case, rather than using generalizations for the aggregate.

Predictive Analytics tools can generate extremely useful insights and actionable predictions that will improve the decision-making process by doing it less anecdotical, less obsolete, and more relevant to the problem to solve.

How to increase the profitability of your organization by 20%

GraphProfitability

A recent report published by Gartner state that organizations that use predictive business performance metrics will increase their profitability by 20% by 2017. The report also shows that organizations should alert workers that a business moment is about to occur, and guide them on the next action to take in the context of a particular customer’s expectation.

“Using historical measures to gauge business and process performance is a thing of the past”, says Samantha Searle, analyst at Gartner. “To prevail in challenging market conditions, business need predictive metrics – also known as ‘leading indicators’ – rather than just historical metrics (aka ‘lagging indicators’).” The “Predictive risk metrics are particularly important for mitigating and even preventing the impact of disruptive events on profitability” added.

Samantha also stated that “business process directors who don’t apply predictive metrics to cross-boundary business processes will leave their organizations vulnerable to the risk of failing to execute their business strategies.”

To successfully implement a predictive analytics strategy that will help to achieve the business performance expected, Gartner recommend the following:

  • Identify the business processes that are critical to driving strategic business outcomes and strategy execution.
  • Determine how bet to measure business outcomes in a way that triggers human or automated actions before an undesired outcome occurs.
  • Explore how they can leverage existing operational data, analytics and other sources of information in more predictive algorithms.
  • Employ predictive risk metrics to avoid process failure or business disruption

Another Gartner survey showed that 71% of a universe of 498 business and IT leaders understood which KPI’s are critical to supporting the business strategy, but only 48% of them can access those metrics, and not more than 31% agreed to have a dashboard to provide visibility to those metrics. “Visible metrics won’t hep drive strategic business outcomes, such as increasing profitability, if business and IT leaders don’t have the right metrics in place”, said Ms. Searle.

Predikto is unique positioned to help organizations deliver significant operational process improvement value by leveraging the power of predictive analytics.

You can read the Gartner press release here http://www.gartner.com/newsroom/id/2650815

Transit Asset Management System 49 U.S.C. 5326 as part of the MAP21

Transit provides more than 10 billion passenger trips each year, which represents more trips each month than all of the Nation’s airlines combined will make in a year. When transit assets are not in a state of good repair (SGR), the consequences often include increased safety risks, decreased reliability, higher maintenance costs, and an overall lower quality of service to customers.

The Moving Ahead for Progress in the 21st Century Act, MAP-21, states in its section 5326 that the Federal Transit Administration (FTA) will implement a national transit asset management system, including “a strategic and systematic process of operation, maintaining, and improving public transportation capital assets effectively throughout the life cycle of such assets.”

The Transit Asset Management System will include at minimum:

  1. The definition of the ‘state of good repair’, that should include standards for measuring the condition of equipment, infrastructure, rolling stock, and facilities of the capital assets of the public transportation systems that receive FTA funding.
  2. Federal financial assistance to develop a transit asset management plan.
  3. Reports of the conditions of the systems, and any changes to it.
  4. An analytical process or decision support tool that allows for the estimation of capital investment needs, and the prioritization of the public transportation systems.
  5. Technical assistance to the FTA funding recipients.

Predictive Analytics tools for Asset Management can help to meet the requirements of the Transit Asset Management systems, 49 U.S.C. 5326, especially those points related to the implementation of a national transit asset management system, that include an analytical process or decision support tool for use by public transportation systems.

Is your organization part of the top 4%?

predictive analytics

It is not a secret that the amount of data generated in the last decade is big enough to equate multiple times the knowledge that humanity build out since its conception. A big chunk of it, is generated by sensors and pieces of equipment, and only a small proportion of that information is used by companies and corporations to learn about their past to understand the present, and finally predict the future.

All the ingredients are out there, and leveraging the power of Predictive Analytics and the necessary data, it is possible to predict with amazing accuracy when a machine will fail.

The good news is that this new reality is starting to be noticed by the decision makers and executives of companies around the globe.

Recently, Bain & Co, surveyed executives at more than 400 companies around the world (most with revenues over a billion dollars). Of those companies, only 4% are really good at analytics, improving their processes and products using actionable information extracted of their own data. The difference is noticeable:

  • Twice as likely to be in the top quartile of financial performance within their industries
  • Three times more likely to execute decisions as intended
  • Five times more likely to make decisions faster

So, if the benefits are that good, why are only 4% of the companies investing in good analytics?

We think the organizations are just starting to realize those benefits and starting to figure out how to get started.

There are companies that generate data and don’t have the knowledge to transform that into actionable predictions or companies that think their data is not complete enough or is “too messy” to extract good information from it. It is important for those organizations to understand that with relative small amounts of data, but with the correct statistical and visualizations tolls and techniques, companies can go from that 96% of companies that storage data and become part of that exclusive 4% of companies that make their data work for them.

Uptime Magazine – Can Your Machine Tell You When It Will Fail In The Future?

Predikto will be featured in Uptime Magazine  Feb 2014 issue with an article about Predictive Analytics called “Can Your Machine Tell You When It Will Fail In The Future?”.

The mission of Uptime Magazine is to make maintenance reliability professionals and asset managers safer and more successful by providing case studies, tutorials, practical tips, news, book reviews, and interactive content.

To view the article, click on the image below.


UptimeMagazine

Predicting Production Delays and Expected Yields in Future Batches

Predikto will be releasing a new Advanced Analytics solution that will enable operators at continuous manufacturing facilities to predict production delays, asset failures, and expected production yield for future batches without the need to send us sensor data on a daily basis.

Operators will enter future batch settings like Steel Quality, Billets, Speed, Temperature, and a handful of other information.  Predikto will show in a simple graphical web form the risks embedded in the batch and the recommended settings to maximize yield and reduce the probability of having delays or failures with their equipment.  This could be a game changer. Stay tuned…

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