Looking for the Signal in the Noise

In the article A Signal in the Noise: How to Best Manage Big Data, the author advocates some ways to improve the use of data for manufacturers as well as his idea for the best way to approach the use of big data analytics and data management.  The author equivocates big data as sometimes “a needle in the haystack” when it comes to finding and using it in the manufacturing environment.  With so much data produced by even small firms, the management, organization, and analyzation of the data can become overwhelming if not impossible.  Industrial Internet of Things-based systems are estimated to create $4 trillion to $11 trillion in new economic value for manufacturers by 2025, according to McKinsey Global Institute.  With such large value to be had, ignoring or underutilizing data could be a catastrophic mistake for manufacturers.

In a study of manufacturers conducted by the MPI Group, 76% of respondents reported plans to increase their use of smart devices in the next two years while 66% plan to increase their investment in IIoT-enabled products over the same time period.  With many firms taking the necessary steps to collect and use data, a strategic approach is necessary.  The author suggests a number of steps to jump-start the transformation to using data analytics.  Recognizing human limits and the burden of isolation is the first suggested step.  Here the author is advocating that firms understand that human teams are just not capable getting the insights that technology is capable of.  The next step is forgetting the traditional supply chain cycle, and embrace the complexity of modern supply chains.  With the IIoT, data can be captured all along the supply chain which can offer useful insights not previously possible.

Finally, the author advocates understanding four different measures before taking on new data management and analytics initiatives.  Find the actual problem, the “signal in the noise” is the first and foremost issue at hand.  Unless there is direction to the project, the project will almost certainly fail.  Next is understanding the business case.  One has to understand the strategic advantage behind any data management implementation.  Lastly, finding where the optimization is most desperately needed and identifying the experts that are best suited to handle the data being generated is key.  Overall, the biggest pitfall in bringing on new technology is the belief that good things will just happen.  An understanding of the problem, the right people, and a tailored process will allow the technology to do the work it is supposed to.

Do you believe that understanding big data is really about finding the proverbial needle in a haystack?

Do you believe the increase in smart device usage automatically translates to more useful big data, or just more data in general?

As with most strategic IT plays, projects start off to gain a strategic advantage but quickly become part of the IT infrastructure.  Do you think we are in the middle stage between big data being a strategic advantage and a necessary IT infrastructure requirement for manufacturers?



Penn State Beginning $1.5 Million Smart Manufacturing Project


In the article Researchers to Begin $1.5 Million Project for Manufacturing Industry, the author explains how a teams from Penn State, Case Western Reserve University, GE Global Research Center, and Microsoft are collaborating on a smart manufacturing research project.  The project aims to help machines internally recognize signs and wear and fault conditions through the use of cloud-based wireless sensing and prognostic system for monitoring machinery health conditions.  The overall goal is for the machines themselves to make intelligent decisions about fault detection and proactive maintenance scheduling, hence smart machines.  The use of the IoTs, cloud computing big data analytics, and machine learning allows this smart ecosystem to exist.

The project brings experts from across academia and industry together.  On the academic side there are experts from the fields of industrial and manufacturing engineering and mechanical and aerospace engineering.  On the industry side there are experts from GE’s industrial internet of things global research center and senior managers from Microsoft.  Penn State will be hosting the project, but the project is 50% funded by Digital Manufacturing and Design Innovation Institute (DMDII).  DMDII, which was founded in 2014, is funded by the US department of Defense along with number of other entities with the purpose of digitally transforming American manufacturing.

The plan is to begin in February of 2017 and run the project for 18 months.  The actual workings of the project will be done in FAME (Factory for Advanced Manufacturing Education) which is a 10,000-square- foot integrated high-bay laboratory for teaching and research.  Senior Research Associate Wu stated the overall purpose of the project as “The overall goal of this research is to establish a generic framework for real-time process monitoring, diagnosis and prognosis for smart manufacturing using cloud computing and big data analytics.”  It’s easy to see that with lofty goals as described above, this smart manufacturing project could help transform the industry.  Only time will tell how successful the project becomes, and possibly even more important, how the results are viewed and accepted by the greater manufacturing industry.


Do you believe the team will be successful at creating a generic framework that can be used by a majority of the manufacturing industry?

Do you think the project will stay within budget and finish on time?

What are your thoughts on the fact the Department of Defense is funding part of this project?




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.

Source- http://www.ioti.com/iot-trends-and-analysis/11-iot-predictions-2017

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.

Source- http://radar.oreilly.com/2014/02/the-industrial-iot-isnt-the-same-as-the-consumer-iot.html