How to Survive the Overwhelming Tide of Data

With the increase in accessibility to production and quality data from the use of automation, the Internet of Things, and handheld devices manufacturers are finally able to gather and analyze data to improve their processes at a level hereto unseen before. However, with this seemingly limitless access data comes a new problem: having too much data. More and more companies are falling into the trap of collecting data for the sake of collecting data just because they can and this can actually be harmful to a business. As Douglas Fair states in his article “Drowning in Quality Data: How to Rise Above”, “the insight gleaned from data that is what actually benefits the business”. This means that along with optimizing their processes and machines on the manufacturing floor, manufacturers now also have to think about optimizing how they collect their data so that they are getting the most benefit from it.

When optimization the data collection process, it is important to ask these five simple questions when assessing whether or not they need to be collecting certain pieces of data.

  1. Why do we need to gather this data? What is the improvement we are trying to make with this data we are collecting?
  2. How will we use the data after collection? What are we going to do with it after we have collated it?
  3. Who will evaluate the data? Will it be automated or will we be dedicating personnel to it? Do we have the labor available right now to handle it?
  4. What is a reasonable amount of data to collect? Can we defend why we need as much as we do or could we do the same thing with less?
  5. How frequently do we need to collect the data? How often are we analyzing and using the data to make decisions? Do these coincide with each other well?

At the end of the day, the only sure fire way to make sure you don’t fall into “data gluttony” is to check yourself and ensure that you are collecting data for specific purposes, using all the data you collect, and acting on the insights gained from the data to improve your bottom-line.

 

Source: https://www.manufacturing.net/article/2019/01/drowning-quality-data-how-rise-above

 

Questions:

  1. With data becoming so centric to operations now-a-days, are we going to start seeing roles dedicated to data analysis on site at plants? How will this affect the way plants are run?
  2. What are the costs associated with “data gluttony”? Is it really as big a problem as Fair makes it out to be?
  3. How long does the process of optimizing data collection take? How often should companies review their data collection process to ensure they aren’t collecting useless data?

Internet of Things: Transforming the Industry

Its not just limited to smart phones anymore. Smart things have reached the masses. Products with wireless connectivity (from lightbulbs to thermostats to smart speakers) are more present in people’s homes today than not. A report suggests that 79% of U.S. consumers have at least one connected device at home.

But this technology actually has its roots in a world that predates the rise of remote control gadgets: industrial manufacturing.The (Industrial) Internet of Things takes networked sensors and intelligent devices and puts those technologies to use directly on the manufacturing floor, collecting data to drive artificial intelligence and predictive analytics. The IOT is driving an industry that has struggled in recent years due to talent shortages, and this offers hope for the industry’s future. It can transform traditional, linear manufacturing supply chains into dynamic, interconnected systems—a digital supply network (DSN)—that can more readily incorporate ecosystem partners. It is helping to change the way that products are made and delivered, making factories more efficient, ensuring better safety for human operators, and more often than not saving millions of dollars.

One of the greatest benefits of the IoT is how it can exponentially improve operating efficiencies. If a machine goes down, for instance, connected sensors can automatically pinpoint where the issue is occurring and trigger a service request. It can also help a manufacturer predict when a machine will likely breakdown or enter a dangerous operating condition before it ever happens. It is largely proactive in its functioning. It enables predictive maintenance, which limits the equipment downtime and improves safety. The sensors work by analyzing a given machine to tell if it’s working within its normal condition. This process—known as condition monitoring—is time intensive when we humans do it manually. But by using sensors to collect and quickly analyze data points in the cloud, prediction becomes easier.

Beyond saving money and time, the IoT can keep workers safe. If an oil well is about to reach a dangerous pressure condition, for example, operators will be warned well before it explodes. Sensors can even be used to manage and monitor workers’ locations in case of an emergency or evacuation.

Q1) How is IOT changing the status quo in industries?

Q2) How does IOT help in predictive maintenance ?

Q3) How is IOT improving efficiencies in manufacturing ?

source:https://wordpress.com/post/dcmme.wordpress.com/1921

 

Looking Forward at Smart Manufacturing and Manufacturing Industry Changes

Manufacturing has navigated a roller coaster of a landscape over the past 10 years, and the future appears to be ever-changing.  Whether it be the election of a new president in the US who promises to increase manufacturing jobs or the increasingly important technologies such as the IIoTs or big data analytics that make up the Fourth Industrial Revolution, manufacturing is transforming.  This transformation is from legacy, antiquated manufacturing techniques to what is called smart manufacturing.  Smart manufacturing is making data more and more readily available and thus the entire manufacturing process is benefiting.  And as many other articles have stated, any manufacturers that do not invest in this upcoming technological revolution risk being overtaken by competitors that do.

Though the switch to the digital data transformation isn’t quite do or die just yet, it certainly will be soon.  A report from the Conference Board of Canada from earlier this year found that some manufacturers are working with 20-year-old software.  It would appear that some in the industry do not feel that making that adapting to the newest technology is necessary which could be disastrous.  There is a golden opportunity for small to medium size manufacturers to experiment with smart manufacturing solutions as firms of these size have nimbleness and ability to make digital changes quickly.  Furthermore, applying digital technology can be as simple as smart tags for better real-time tracking of inventory or cloud technology to aggregate and transfer information across the supply chain.

Some other simpler but advantageous smart technologies that small to medium sized firms can implement are mobile technologies such as smart phones, smart tablets, and smart watches as well as IIoTs.  Mobile technologies are relatively easy to implement and now affordable, and they allow managers to record and share real-time data easily.  The IIoTs is also catching on quickly with 40% of firms surveyed saying they already have some form of the IoTs in place.  The IIoTs can be used to optimize processes as well the key benefit of preventative maintenance.  Despite these advantages, nearly one-third of declining manufacturers expect to decrease their IT expenditures in the next year, which means firms in this category will be hitting serious roadblocks in the very near future.

 

Do you think the lack of investment in digital technologies could be due to management’s fear that these technologies might actually lead to a decrease in the need for actual human workers?

Does an decrease in IT spending absolutely mean that a company will not be investing in any smart technologies?

Do you think that another holdup with regard to implementing even simple digital technologies is the lack of ability to quantify the cost savings?

http://www.manufacturingglobal.com/technology/1043/Smart-manufacturing-will-push-the-industry-forward

Big Data Analytics Creates a Smart Supply Chain

In the article Benefits of a Smart Supply Chain, the author introduces the concept of big data being used advantageously in a manufacturer’s supply chain.  Big data analytics is widely accepted as a superior way for manufacturers to predict demand and understand customers, but big data analytics can also be used on the warehouse floor to save money.  The smart factory concept is one in which the entire manufacturing area is connected by sensors via the IoTs.    In the supply chain, big data is allowing manufacturers to predict bottlenecks, avoid machine failure, and reduce replacement part inventory via predictive analytics.  This Industry 4.0 holds the key to manufacturers staying competitive in a global marketplace.

The concept of Industry 4.0, run by smart factories, was actually introduced in Europe as recently as a few years ago.  To stay competitive in the global marketplace, manufacturers will have to adapt at least in some way to this new Industry 4.0.  Interestingly, a recent study indicated that 92% of manufacturers in the UK do not understand Industry 4.0 processes, but 59% of manufactures recognize the impact these new processes will have on the sector.  Using the UK as a representative sample, it certainly appears that the manufacturing industry as a whole needs to technologically transform and educated itself.  Those who stay ahead of the curve will reap the benefits of more efficient, smarter processes, while those who do not risk losing money.

The specifics of Industry 4.0 includes the big data analytics to design a smart supply chain.  A smart supply chain can avoid many of the traditional supply chain problems such as supply bottlenecks and machine downtime.  Bottlenecks can be avoided due to the fact that a connected factory shares data with other parts of the supply chain so production can be eased or intensified based on data from the factor combined with data from down the supply chain.  Furthermore, a smart supply chain can use predictive analytics to shutdown equipment and processes before the fail.  In this case, there is less downtime.  The sensors on these processes can be programmed to monitor equipment and order parts prior to equipment failure so that excess replacement inventory is not need thus saving money.  With all of these advantages, the smart supply chain managers will invest in the smart supply chain to keep their manufacturing processes ahead of the curve an competitive in a global environment.

What will need to happen to educate those in power at manufacturing companies so that the transition to smart processes happens?

Will these smart processes create or destroy jobs?

Will they transformation to a smart factory decrease or reverse the decay in the manufacturing industry as a whole?

http://www.reliableplant.com/Read/30664/smart-supply-chain

 

 

 

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

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?

 

http://www.industryweek.com/systems-integration/what-smart-manufacturing?page=2