TechHub: Digitizing data forensics, Coke using AI and Big Data & Digital Twins

Digitizing Data Forensics

A control system that’s responsible for servicing over 150,000 people has multiple applications, each with its own data source, and requires a tedious, time-consuming process of manually sifting through paper logbooks and binder after binder of printed reports when gaps exist in data sets.

This wastes time, money and causes a major headache for plant employees.

The City of Barrie was determined to fix this, saving 60 to 70-percent of the time previously used as a result of digital transformation.

The implementation of a new data management tool allowed:

  • Data commenting
  • Workflow for report approvals
  • Tag merge
  • Lab data integration and electronic logbooks

Download the white paper to learn more about how the City of Barrie saved time, money and frustrations from their digital transformation:

Download White Paper

Join GrayMatter at WEFTEC 2017 in Chicago from Sept. 30 – Oct. 4, booth #6549

WEFTEC is recognized as the world’s largest annual water quality technical conference and exhibition, providing extensive educational opportunities and unparalleled access to the field’s most cutting-edge technologies and services.

The Amazing Ways Coca Cola Uses Artificial Intelligence and Big Data To Drive Success

Coca Cola was one of the first globally-recognized brands outside of the IT world to speak about big data, according to Forbes.

“AI is the foundation for everything we do. We create intelligent experiences. AI is the kernel that powers that experience,” said Greg Chambers, global director of digital innovation.

Coke announced earlier this year they were launching Cherry Sprite as a new flavor based on data collected from their self-service soft drink fountains, which allows customers to mix their own drinks.

Coca Cola’s Freestyle drink machine that collects data from customers’ popular drink choices.

This allows Coke to pick the most popular combinations and launch them as a ready-made, canned drink.

What’s next for Coke? They’re looking to develop a virtual assistant AI bot, like Alexa and Siri, to reside in vending machines for greater personalization.

According to Forbes, users will be able to order their favorite blend from any vending machine, with the AI adapting the machines’ behavior depending on its location. This could result in exciting, lively vending machines in malls and entertainment complexes and more functional ones in a hospital.

Digital Twins: Distilling Science from Fiction

Digital twins sounds like a concept evoked from science fiction; people recreating themselves as avatars in computer games like The Sims and living out an alternative reality in the digital realm.

That might be the world of fiction, but digital twins aren’t that far off what the creators of The Matrix envisaged, according to The Manufacturer.

Digital twins are digital renderings of physical equipment, displayed on a headset, computer, tablet or other device, showing real-time data about a range of measures such as condition and historic performance, to its current position and any external factors acting on it.

One whiskey company is using digital twinning on a scaled down level to monitor their production line.

Digital twinning has primarily been used to monitor high-value assets such as wind turbines, aircraft engines and other highly-technical and expensive engineering systems, to track all the data they produce and deliver it to technicians in remote locations.

A specific new trend for digital twinning has recently begun to emerge from manufacturing, according to The Manufacturer, of scaling down to lower levels. This allows it to be used in assets that aren’t necessarily high value in themselves, but create high value products. Read More.

 

TechHub: Automation Creates More & Better Jobs, Seegrid’s New Self-Driving Pallet Truck and More

JOHN TLUMACKI/GLOBE STAFF

Automation Creates More, Better-paying Jobs

Robots, artificial intelligence and other forms of automation are often feared due to their job-destroying potential when in fact they’re creating more, better-paying jobs.

The brick-and-mortar retail swoon has been accompanied by a less headline-grabbing e-commerce boom that has created more jobs in the U.S. than traditional stores have cut. Those jobs, in turn, pay better, because its workers are so much more productive, according to the Wall Street Journal.

Throughout history, automation commonly creates more, and better-paying, jobs than it destroys. The reason: Companies don’t use automation simply to produce the same thing more cheaply. Instead, they find ways to offer entirely new, improved products. As customers flock to these new offerings, companies have to hire more people.

In the Amazon facility’s packing area, computers tell workers precisely which size box to use. PHOTO: ADAM GLANZMAN FOR THE WALL STREET JOURNAL

James Bessen, an economist at Boston University School of Law, has found in numerous episodes when technology was supposed to annihilate jobs, the opposite occurred.

After the first automated tellers were installed in the 1970s, an executive at Wells, Fargo & Co. predicted ATMs would lead to fewer branches with even fewer staff. And indeed, the average branch used one-third fewer workers in 2004 than in 1988. But, Mr. Bessen found, ATMs made it much cheaper to operate a branch so banks opened more: Total branches rose 43% over that time.

There are still plenty of logistics that only humans can handle. When the new 1.2-million square foot Amazon warehouse opened in Fall River, Massachusetts, Amazon workers had trouble stowing long, narrow things like shovels and rolled-up rugs, which don’t stack very well. Their solution? Large cardboard tubes, typically used to form concrete pillars, were fashioned into rows and rows of improvised barrels, according to the Boston Globe.

“One thing we learned is to find the cheapest and easiest solution possible,” said Andrew Sweatman, the Fall River general manager.

City leaders rolled out the red carpet for Amazon with generous tax incentives and a prime location on Innovation Way. Its arrival was the single biggest job creation event anyone could remember.

“We had people with a skill set that was nontransferable,” says Jasiel F. Correia II, Fall River’s 25-year-old mayor and a first-generation child of immigrants from the former Portuguese territory of Cape Verde. “Where does a person who sewed textiles for 20 years go if they’re laid off? Places such as Amazon fill that gap,” he says. “They got a chance to work for a Fortune 500 company. This community doesn’t get those chances very often.”

Seegrid Rolls Out New Self-Driving Pallet Truck

Seegrid has rolled out a self-driving pallet truck the Pittsburgh-based robotics company said doesn’t need human intervention.

As the leader in connected self-driving vehicles for materials handling, they’ve recently expanded the company’s suite of automated solutions with the announcement of the GP8 Series 6 self-driving pallet truck.

SOURCE: Seegrid

Further enhancing the Seegrid Smart Platform, which combines flexible and reliable infrastructure-free vision guided vehicles with fleet management and enterprise intelligence data, the self-driving truck has fully automated material movement to execute hands-free load exchange from pick-up to drop-off, according to Seegrid.

In the automotive industry, self-driving vehicles are used for consistent delivery of parts to line. The self-driving pallet truck picks up and drops off palletized car parts without human interaction, increasing productivity amidst labor shortages for automakers. In e-commerce, it enables fully autonomous delivery of goods to keep up with fulfillment industry growth and demand.

Operating without wires, lasers, magnets, or tape, it allows manufacturers and distributors to change routes in-house, operate in manual mode, and effortless scale their fleet as they grow.

As part of the Seegrid Smart Platform, the Series 6 is aligned with Industry 4.0 and lean initiatives, helping companies transform into smart factories of the future.

Developing a Work Culture that Embraces its CMMS and Values Data Accuracy

First, establish new behaviors by creating a set of CMMS guiding principles.

Creating a culture that embraces CMMS and values data integrity begins with leaders changing their behavior. If they expect their organization to change, O&M leaders, including materials, procurement, and engineering functions, should jointly develop a set of CMMS guiding principles.

The development process creates ownership and alignment within the cross-functional group around new leadership behaviors. After completion and approval, leaders should post the CMMS principles, which will allow them to hold each other, as well as the organization, accountable. It will also enable the organization to begin adjusting to the new behaviors they observe. When leaders consistently behave differently, the organization will adapt and follow, according to Industry Week.

Guiding Principles

  • No work order, no work
  • 400-percent rule
    • 100% internal labor, 100% of materials, 100% of contractor cost documented on work order, 100% of the time
  • Completed field work documented
  • All equipment failures receive a Root Cause Analysis (RCA)
  • All spare parts have a stores item number
  • All lowest maintainable equipment is identified by unique number, title, hierarchy and criticality rank
  • Measure the process as well as end results
  • Weekly work order audits
  • Periodic communication
  • Periodically audit the CMMS

Developing a culture that embraces utilization of a CMMS and values data integrity starts with leadership vision and behavior.

Read More.

Pulling Needles out of Your Data Haystack

3 steps to act on data you’re already collecting. 

The Impossible is Now Possible

Industry 4.0 is moving fast and I’d like to let you in on a few very valuable developments about the data you’ve been collecting. It can now help you make better decisions. You can talk to your industrial data and it’s talking back to you, letting you know what’s working inside your operation and what’s not.

It’s exciting for me to see GrayMatter and our partners innovating by taking the data you’ve been collecting through sensors on industrial equipment and applying artificial intelligence and machine learning in the cloud so you can find insights on performance. Then you know exactly where to make improvements.

You need a system to sort through the haystack of data and pull needles out to focus your subject matter experts. That’s what we can do now.

It all starts by framing up the action strategy in three parts.

Step One: Set Your Goals

Start at the end and work backwards.

What return on investment do you want to see? You don’t need all the data you think you need. What information will help you solve the problems you want to solve? What’s the path to getting there? Having this roadmap first is critical, because otherwise a lot of time and money can be wasted.

Step Two: Start Creating Digital Twins

You hear the term digital twin, but what does it really mean?

Simply put, creating a digital twin is the process of merging physical and digital worlds.

The process takes a physical machine and uses technology to get all the information about past states, present states and predictions. That information creates a digital model that’s alive – taking in a stream of data – using that to adjust so the model is personalized to be a precise representation of the asset.

The software version is used for what used to be a physical inspection – requiring people to be right next to the machine. The virtual version can be done from anywhere and at any time, expanding the value of those inspections and allowing them to have more of a real-time impact. It creates a constant inspection that allows the operators to predict failures sooner.

Digital Twin Value

The digital model of a machine, built and run in a virtual environment used to be available only to the biggest companies with the largest budgets. But the Industrial Internet and an explosion in sensor technology have lowered the cost and broadened the access beyond the elite.

pulling needles out of data haystack

People are not only connected to people, they’re connected to every kind of device at home and now work. Manufacturers stand to win big from this. Factory floors are outfitted with tremendous amounts of sensors to collect data, but because that data has been locked up it hasn’t provided value.

The digital twin allows us to unlock that data and not just for one asset at a time. We can now model machines in groups – for example, a machine builder with thousands of machines installed across hundreds of customers – will now be able to operate best in class using digital twins.

There’s potential to unleash productivity and efficiencies like we’ve never seen before.

Step Three: Get Behavioral Information from Digital Twins

In order to move to more advanced use cases, such as adaptive diagnostics, condition-based maintenance or predictive failure, Industrial IoT systems need to know more than simply the current device state.

They need to know why. Knowing current device state only helps from a monitoring standpoint.  While important, it’s really just the beginning of what we can expect out of IIoT systems. If we know why an asset exhibits a certain state, we can determine what conditions lead to that state and take proactive steps to prevent future occurrences. 

This new layer of getting insight through behavioral information allows you to ask for more. You can search your data and get answers back right away. It’s like an instant messenger for operational technology.

Step Four – Get Digital Twins to Interact

Achieving this may mean digital twins built using multiple discrete machine learning algorithms potentially spread across multiple IoT platforms, not simply relying on one. Eventually, we should expect that digital twins will interact with one another in virtual space.

Leap Ahead

If you’re short on time, staffing or budget – GrayMatter can get you up and running to achieve value quickly. You know you need an Iot strategy in the near future, but may not know how to go about it. Rather than trying to design, source and build it yourself we can put the strategy in place in days or weeks.

You also don’t have to do everything at once, you can start with a limited selection of assets and scale up or down as you learn performance and asset behaviors.

Our strategy is a Salesforce version of a remote monitoring and diagnostics center that you can buy and implement incrementally.

GrayMatter’s Digital Twin Strategy

We use data, predictive capabilities and machine learning to identify your best and worst performers in each asset group. Your operators are automatically alerted to the worst performers, then they use an intuitive web interface, to turn the worst into the best.

Continuous improvement becomes expected, simplified, and routine. As your team builds new improvements or optimized settings, they can be scaled out, automatically, to every instance of a specific machine or piece of equipment.

You Don’t Need a Data Scientist

The complex algorithms that can leverage your data are pre-built so anyone can start creating the models and analytics to generate insights. One person no longer holds the keys to data, with this unique platform everyone gets a better understanding of your businesses processes, so you’re not focusing on the math to bring the insight, you’re focusing on creating better outcomes for your customers.

Think Big, Start Fast

You need to think big to truly transform your organization, but you also have to start acting on your data today.

We’re anxious to spread the word about how easy this is and to un-complicate it for you. Let me know if you’d like to discuss further.

– Jim Gillespie, GrayMatter CEO.

Click to download the case study to start acting on your data:

Download Now

TechHub: GE Looks to Become IIoT Leader, Industrial Job Growth in US & Industrial Cyber Security

GE Relocates To Boston, Looks to Become an IIOT Leader Amid Transformation

General Electric, the largest US industrial company, is going through a transformation. The company has a new CEO at the helm, relocated headquarters to Boston and most importantly is trying to position itself as a manufacturing leader in the digital era, according to Industry Week.

“If we go back in time, say in 2011 to 2012, when as a business, we were facing challenges, Jeff (Immelt, former CEO) realized that unless we leverage software and analytics, true productivity would not be gained,” said Mark Bernardo, GE’s VP of Professional Services, at the company’s temporary new headquarters in Boston on August 30.

Since then, GE has made several strategic business moves to leverage its core competencies: spun off its financial services unit, GE Capital; acquired French company Alstom’s power and grid business; launched GE Digital to bring various software groups such as engineering, product development, and IT under one umbrella, and opened its cloud-based software platform for the Industrial Internet, Predix, to outside developers.

Despite GE’s goal to cut $2B by the end of 2018, “there is no change in long-term strategy as it relates to digital,” said Jeff Erhardt, GE’s VP of Intelligent Systems.

Erhardt said that machine learning, artificial intelligence along with domain expertise will help boost GE’s digital future. And Predix, its own IOT platform, will play a central role in managing complex data. The platform currently has 100 apps – from MRI machines to turbines to jet engines – created by developers, including many from outside, who mine complex industrial data to make the machines perform more safely and efficiently.

GE’s former CEO Jeff Immelt at Minds + Machines 2016 in San Francisco.

Join GrayMatter in October at Minds + Machines, GE’s premier Industrial Internet event dedicated to software, innovation, and the most powerful digital industrial outcomes.

Minds + Machines will bring together the best and the brightest of the technology world, including GE customers, developers, partners, industrial thought leaders and technology innovators.

Learn More

What’s Driving Job Growth in Industrial America?

Manufacturing and mining jobs are up this year after having fallen in 2016, in which 100,000 jobs were lost, according to the New York Times.

Rising commodity prices has resulted in a jump in hiring within the mining sector. After plunging in late 2014 and throughout 2015, energy prices have somewhat recovered. That has helped stabilize employment in the oil industry. Meanwhile, surging prices for metals like gold and copper are spurring activity in the mining industry.

The US dollar dropping throughout 2017 has also played a major role in manufacturing success. As major exporters who are dependent on overseas customers for a big part of their sales, manufacturers often find themselves at the mercy of the dollar.

When the dollar surged in 2016, American-made equipment was effectively more expensive for foreign buyers. This year’s drop, on the other hand, is a boon for manufacturers as well as for big American companies who draw a big portion of their sales from overseas, like Caterpillar and McDonald’s.

Software-defined Industrial Networks Deliver Cyber Security Breakthroughs

Finding a cost-effective cyber security plan for industrial control systems remains a pain point.

Cyber security is often cited as the leading barrier to growth of the Industrial Internet of Things (IIoT).

ARC Advisory Group published a report suggesting two solutions of promising software-defined networking (SDN) technologies, which can improve cyber security in both new and existing industrial control systems.

One: Network Management Through SDN Protocol OpenFlow

OpenFlow is a single protocol that replaces existing routing and access protocols embedded in Ethernet switches, allowing the entire network of switches to be managed from a central SDN controller.

This has typically been used in large enterprise networks, such as Google, and in large data centers to improve performance.

Ukraine power grid

Kiev, Ukraine, was one of the victims hurt the most in multiple malware attacks on the Ukrainian power grid at the end of 2016 and entering into 2017.

This is also applicable to a much smaller industrial network, or part of an industrial network.

Engineers report that the largest advantage of their SDN network is the ability to lock down the substation network and immediately become aware of any unexpected packets entering. This improves the overall security of networks within electric power substations, which have increasingly become high value targets for cyber warfare internationally.

Two: Host Identity Protocol

Host Identity Protocol (HIP) creates and manages a secure identity-based overlay network that serves the automation components while cloaking them from general visibility.

The fundamental idea is to decouple the IP address from packet forwarding rules, instead authorizing and delivering network services based on provable cryptographic identities.

It also delivers a new Host Identity Namespace that is compatible with existing IP and DNS Namespaces, enabling global IP mobility. It allows organizations to overcome IP addressing issues and move an IP resource within and between physical, virtual or cloud networks without having to change the IP or overlay network policies.

Using HIP in place of IP addresses as identity, it allows a secure identity to be established among sets of devices and the IP address continue to serve its purpose as a locator only.

ARC Recommendations

ARC ends the report with recommendations that manufacturers and utilities should develop use cases leading to broader plans for how SDN could improve cyber security, mobility support and remote access services for existing plants, familiarize themselves with the roadmaps of their network infrastructure suppliers to apply to their own use cases, as well as carefully evaluate the current and evolving technologies and their potential impact on cyber security and performance.