Caterpillar is Saving Big Money using Big Data and the IoT

In the article IoT And Big Data At Caterpillar: How Predictive Maintenance Saves Millions Of Dollars, the author examines an interesting case of the company Caterpillar saving significant amounts of money using big data and the IoT.  The best part of this case study is that Caterpillar is seeing a very quick ROI on their big data investment, which is not something that can be said for most companies.  As a Caterpillar manager put it, you don’t have to look for a “grand slam” with big data; sometimes you just need multiple smaller applications of big data to experience significant savings.  In this instance, gathering as much data as possible seems to be the best approach, and utilizing experts in the processes and in the data to analyze and understand the insights gleaned helps realize real value.

Caterpillar utilized big data on in its Marine Division, mainly to analyze fuel consumption for its customers as it most affects the bottom line.  Sensors on the ship monitored everything from generators, engines, GPS, refrigeration, and fuel meters, and Caterpillar utilized Pentaho’s data and analytics platform.  Insights gained have been a correlation between fuel meters and amount of power used by the refrigeration containers, and also that running more generators at lower power instead of maxing out a few was more efficient.  The cost savings here added up to $650,000+/year.  Another insight was to the optimization of a ship’s hull cleaning schedule.  Through the collection of data of cleaned and uncleaned ships, the data showed that cleanings should be performed once every 6 months instead of once every two years.  The savings associated with this optimization was $400,000/ship.

In the grand scheme of big data, predictive maintenance analytics seems to be the most powerful tool consistently being used.  With data being generated just about anywhere you could imagine via the IIoT, understanding trends becomes easier and easier.  Interestingly and contrary to previous articles, Caterpillar believes that you can’t collect too much data.  They point out how data storage is very cheap.  In the words of a Caterpillar manager, you can’t see “relationships about relationships” in the data if you don’t collect it.  Although a more is better approach is definitely not what some companies have ascribed to, it seems to be working well for Caterpillar’s marine division as they continue to pull value of out of big data and analytics.

Quick returns on big data investments seems to be rare, so do you think that companies just aren’t utilizing the big data correctly?

Do you believe in the more is better approach with regard to collecting data?

Do you believe Caterpillar is more likely to invest in big data projects in other parts of their company due to the success in the marine division?

IoT- Predictions for 2017

In this post we will try to foresee what is in store for IoT in 2017.

IoT Will Impact the Omnichannel– The convergence of digital and physical worlds across multiple channels has dramatically changed how businesses reach and manage customer relationships. This results in a transformation of marketing.

“Things” Grow Up and Get Smarter– The average amount of computing power is growing and things are getting smarter and more connected.

Data Collection Migrates to the Cloud– Next year, data collection will move to the cloud. One of the big purposes will be to use AI algorithms to recognize not only someone’s speech but also how to optimize the operations of a machine.

Companies Will Develop More Sensical IoT Products– In 2017, we will see a growing number of consumer-facing connected products that use connectivity to solve real problems. Winning IoT products will have a service component.

Standards Will Remain Messy– There is nothing close to a shared language, and there are a plethora of competing standards.

Tesla’s Elon Musk recently made waves recently by promising that, by the end of 2017, he’ll have a car ready that can drive from Los Angeles to New York without the need for a human driver.


Connecting for the Future

In a recent article on the website, Readwrite, the initiative to turn Singapore into a smart nation by 2025, is discussed. With the merging of two government agencies, Singapore has created IMDA to oversee the evolution of the country into a smart, connected nation. IMDA hopes to use IoT, drone technology, virtual reality, connected grids and other new technologies to bring Singapore to the forefront of the globally connected world. Singapore hopes to leverage its flexible economy, non-bureaucratic business environment, and experience with innovation and technology to accomplish these ambitious goals.

Industrial IoT vs Consumer IoT

In this article we will talk about IIoT and clear up certain misconceptions that you may have.

What is IIoT?- The Industrial Internet of Things (IIoT) is simply, the use of Internet of Things (IoT) technologies in manufacturing. It incorporates machine learning and big data technology, harnessing the sensor data, machine-to-machine (M2M) communication and automation technologies.

Misconception: The IIoT is the same as the consumer Internet of Things (IoT)

The IIoT includes IoTdevices located in industrial settings. This maybe a factory floor, a high-speed train system, a hotel, a municipal lighting system, or within the energy grid itself. The requirements for IIoT are far more stringent than the consumer IoT. There can be no compromises in control, security, reliability in tough environments and it needs to be autonomous with little or no human intervention. These devices are built to withstand the test of time.

Peer to Peer rather than Push-Pull

While consumer IoT is linked to human-perceived comfort, security, and efficiency. The industrial networks have basic operating roles that do not require human intervention. Operations that must happen too quickly, too reliably, from too harsh or remote an environment to make it practical to push-pull data from any kind of centralized Internet server or the cloud. A major goal for the IIoT is to help autonomous communities of devices to operate more effectively, peer to peer, without relying on exchanging data beyond their communities.

The IIoT to IoT link

Individually, industrial devices generate the “small data” that, in the aggregate, combines to become the “big data” used for IoT analytics and intelligent control. IIoT devices that are IP-enabled could retain their ability to operate without human intervention, yet still receive input or provide small-data output via the IoT.

What is the real IIoT opportunity?

The real opportunity of the IIoT is not to pretend that it’s the same as the IoT, but rather to provide industrial device networks with an affordable and easy migration path to IP. This approach will build bridges to the IIoT, so that any given community of devices can achieve its full potential. An example of this is the IzoT platform of devices developed by Echelon.


Understanding Smart Manufacturing

In the article What is Smart Manufacturing? the author delves into what exactly smart manufacturing is, as defined by the new technology that has allowed it to come into being.  Smart manufacturing has enabled us to use continuously collected and flowing data to continuously improve processes thus taking some the human error element out of manufacturing.  It’s not to say that the smartest piece in the manufacturing puzzle isn’t still a human because it most certainly is.  It is more a realization that machines can now collect and interpret data themselves (as we have programmed them) so that they almost “think” on their own.

The author points out that some believe we are now in the 4th industrial revolution.  This industrial revolution involves big data, predictive analytics, and the artificial intelligence created using these concepts.  In essence, the data tells us what to do.  Instead of a maintenance cycle that works based on past failures, think of a maintenance cycle that continuously collects data thus giving us indicators before a failure.  Furthermore, the data can let us know what failed as well which increases efficiency.  This connection is known as the IIoT – the Industrial Internet of Things.  The emergence of cheap connected devices, coupled with the availability and affordability of mass computing power, has been the biggest driver of Smart Manufacturing.

Visibility is a big driver in understanding ROI in smart manufacturing.  The processes can alert users via messages on their phones; displays on a monitor, or a number of other ways.  Communication between machine and humans becomes simple.  When the data tells you what to do, decisions normally become easier thus reducing human error, increasing efficiencies in manufacturing processes, and finally saving money.


Will smart manufacturing eventually become so good that actual human jobs are lost?

What safeguards are in place so we know that sensors on manufacturing processes are not relaying operators the wrong data?

As I mentioned in a previous blog, do you believe there is a downside to collecting too much data?


The IoT will Give us the Big Data of The Future

In the article Big Data Analytics: The Force Behind the Next Internet of Things Wave, the author delves into how the cutting edge of the IoT is giving us the Big Data that will shape our future.  With more devises in more place, more data is being collected and thus more data must be analyzed.  The true value in this Big Data is our ability to make sense of it consequently create value.  The author examines a number of case where cutting edge sensors are allowing the IoTs to collect data that drives increased performance, predictive ability, and cost savings.

One of the first places the IoTs really took off was in our own homes.  Smart devices collecting data from meters, data on demographics, and data on energy consumption have helped customers save energy and money.  One smart meter company actually saved its customers over $500 million in energy spending.  With data analytics, forward-thinking energy management companies are able to run analyses on consumer thermostat data to better understand energy usage patterns.

Another case presented is that of an energy company using sensors in its oil and gas wells. The company collected data about the average production of oil, gas, and water from each of its wells.  It combined it with historical well performance and geospatial data to look at efficiencies and deficiencies based on location and equipment.  Based on the combination of this data and the sharing of this data with its operations, the company experienced 126 million per year in incremental revenue.


What are everyones thoughts on when we will begin to be fully connected, that is, when almost every device and thing we use is connected to each other?

Do you think we are collecting too much data?

As a customer, at what point do you believe collecting your data is an infringement on your privacy?  Is there any real way to track such invasions of privacy?

A Structured Usage of Big Data Adds Big Value

In the article Overcoming Challenges To Make Big Data Profitable, the author goes over the immensity of the data collected every day and begins to break down how to analyze it and how to monetize it.  Big data analytics, though exciting, will become most useful when it adds monetary value to a business or individual.

Big data comes from a Machine 2 Machine (M2M), Internet of Things (IoT), Mobile, Cloud, Data storage and networks.  Because the amount of Big Data is so vast, the author has broken down the analysis of such into 5 V’s: value, velocity & veracity, volume, and variety.  Variety encompasses structured and unstructured data, volume is the actual memory amount of data, velocity is the speed of processing while veracity is the uncertainty vs the reliability of the data, and finally value is how the data becomes profitable.  Once can see that big data analytics, and its uses to create ecosystems, is becoming more complex every moment of everyday.

The key is to make the data manageable and able to be monetized.  The three silos that need to be connected to be able to monetize big data and thus create the ecosystems of the future are business, technology and regulatory.  On the business side, the big data analytics need to constantly be utilized to make sure it’s working and who it’s working for.  This utilization leads to accurate forecasting.  The technology side involves a competent and cutting edge workforce that is able to work in a multi-system, yet simple, infrastructure.  The technology at work creates the ecosystem for the future.  Lastly, regulatory requirements are constantly changing and the use, storage, and collection of certain types of data needs to be monitored.  As a business, working within the means of the law is a must.

In conclusion, big data and the analytics of it present big challenges.  Overcoming these challenges means analyzing the data correctly and efficiently while bridging the gaps between the three silos of business, technology, and regulatory.  With a plan in place, big data can lead to big money.



Have we already started collecting too much Big Data?

What new laws could come about as more and more data is collected?  Is the collection of some customer’s personal data with regard to habits legal?  Ethical?

Will there come a point where businesses rely too much on Big Data, thus negating the human elements of business?