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

Maintenance Triage – Uptime Magazine

Predikto has been featured in the article “Maintenance Triage: Identifying Sick and Injured Assets to Improve Population Health” in the Uptime Magazine February 2015 issue. The author of the article, Will McGinnis, is a Mechanical Engineer from Auburn University working at Predikto as a Senior Software Architect. At Predikto, he uses advanced machine learning techniques to leverage pre-existing data in asset intensive industries to predict failures, identify bad actors, and impact bottom lines.

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

Predictive Analytics in Uptime Magazine

Using Predictive Analytics in Maintenance

predictive analytics for maintenence
Predictive Maintenance is the best type of maintenance a company can undertake, but not all assets classes and applications justify Predictive Maintenance. That is why the best type of maintenance is the type that works for each client. The latest technology in Predictive Maintenance is the use of Predictive Analytics. In some cases Predictive Analytics is reaching accuracies above 95% to predict an asset failure. These results are much higher when compared to traditional predictive maintenance techniques like Lubrication Analysis, Infrared, and Vibration. These are all excellent techniques and companies should continue using them if they are seeing success reducing downtimes, extending the lifetime of equipment, and subsequently saving money.

In the MAPCITE blog, Eric Spiegel, CEO of Siemens U.S.A., consider that “while analytics were implemented widely in industries such as banking and communications initially, we view capital-goods organizations as a huge untapped opportunity, driven primarily by the “Internet of things” and the significant potential to optimize product development, supply chain and asset related services. One example is predictive maintenance – if we were able to better predict when critical and expensive equipment is most likely to fail, we could reduce downtimes, extend the lifetime of the equipment, and realize significant savings”. Read the entire story HERE.

Here comes the flood-analytics, Manufacturing Industries!

Manufacturing Industries

Yes – we have heard about the magic of predictive analytics and how it has helped various companies and industries in predicting the rise and fall of companies’ finances, social media and marketing and such but could the same predictive analytics methods aid manufacturing industries today?

According to Bala Deshpande’s article, manufacturing industries are not entirely oblivious to the idea of collecting data. As a matter of fact, you could call them ‘The Forefathers of Data Collecting’. Manufacturing industries have been collecting data for years on the company’s current operations and quality of their products. However, the time has come for manufacturing industries to start digging these data sets a bit deeper to improve their operations so much so that, companies would be able to improve notably their production yield.

The benefits of manufacturing industries engaging in predictive analytics can be seen when the production process becomes even more efficient and cuts unnecessary costs (i.e. unexpected machine failure). Deshpande highlighted a small company in the manufacturing industry that has already started to engage in predictive analytics by installing overhead GPS sensors that notes down the number of workers working on a particular project and if that project requires assembly so to calculate how extensive the machine is being used and predict any machine failures and such.

Whether manufacturing companies like it or not, engaging in analytics is inevitable. Especially if competitive manufacturing companies are using the same predictive analytics to measure how likely they are going to be performing much better than the other companies!

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.

Moving Ahead for Progress in the 21st Century Act – MAP21

MAP21 - Putting Performance into Action
Image courtesy of

The Moving Ahead for Progress in the 21st Century Act, also known as MAP-21, is a law that includes provisions intended to improve security reducing crashes, injuries and fatalities involving large trucks and buses.

Many of the provisions in MAP-21 track the Agency’s strategic framework to improve commercial motor vehicle safety by supporting its three core principles:

  1. Raise the bar to enter the industry and operate on the roads;
  2. Hold motor carrier and drivers to the highest safety standards to continue operations; and
  3. Remove the highest risk drivers, vehicles, and carriers from the roads and prevent them from operating.

Countries are implementing legislature and safety mandates that are raising the bar for transportation companies to improve their safety records and asset management processes. Predikto is working with some of the largest transportation companies in North America to facilitate the deployment of Predictive Analytics solutions to predict asset failures and prevent safety hazards. Predictive Analytics solutions can take into account historical maintenance records, prior asset failures, and current operating conditions of the equipment to identify “High Risk” situations and remove the highest risk drivers, vehicles, assets, and carriers from roads and railroad tracks.

In future posts we will be talking about specific sections of interest for predictive analytics, such as Transit Asset Management (Sec. 5326), Public Transportation Safety Program (Sec. 5329), and State of Good Repairs (sec. 5337).

Railroad Risk Reduction Program – Making Railroads Safer

Predikto is embarking in a new initiative to help Class I railroad companies to achieve compliance with the Risk Reduction Program (RRP) mandated by the Rail Safety Improvement Act.

The primary objective of the Risk Reduction Program is ensuring the safety of the nation’s railroads by evaluating safety risks and managing those risks in order to reduce the numbers and rates of accidents, incidents, injuries and fatalities. The requirement is not a one time exercise but is ongoing to promote safety improvement.  It also requires a proactive, analytical  approach to identify potential risks and failures and it requires railroads to implement actions to mitigate the risks before they can occur.  A Risk Reduction Program by statute must include a Fatigue Management Plan.

The Federal Railroad Administration (FRA) will reconvene in April of 2014 with RSAC with final draft of CFR 271 Notice of Proposed Rule Making.