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Why the Age of IIoT Demands a New Security Paradigm ERP Enables Direct-to-Consumer Model for Avery How COVID-19 Pushed ExxonMobil’s Automation Limits Discussing the Data Layer in Regulated Environments Mobile Robot Safety Standard Published Reinventing Remote Management

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Why the Age of IIoT Demands a New Security Paradigm

As manufacturers modernize for data-driven business, industrial automation has become a target for cyber attacks. Here’s how companies should assess and plan for a new cybersecurity landscape.

How Intelligence is Powering Supply Chain’s Next Wave Artificial intelligence and machine learning technologies are behind a host of emerging applications, especially for manufacturing demand planning and warehouse applications.

ERP Upgrade Enables Direct-to-Consumer Model for Avery QAD’s latest cloud-based enterprise resource planning system has helped Avery better integrate its operations to provide the flexibility needed to depart from its traditional big box pallet model.

3/26/21 2:15 PM




Exclusive content from videos, podcasts, webinars, and more


An Investment Firm’s View of Industrial Automation Spending Trends


Reinventing Remote Management


IoT Platform Partnerships Continue to Gain Traction


Motor Selection Insights Advantech and the Artificial Intelligence of Things How COVID-19 Pushed ExxonMobil’s Automation Technology Limits

David Greenfield Director of Content/Editor-in-Chief / 678 662 3322 Stephanie Neil Senior Editor / 781 378 1652 David Miller Senior Technical Writer / 312 205 7910 Emma Satchell Managing Editor / 312 205 7898 Aaron Hand Editor at Large / 312 222 1010 x1180 Jim Chrzan VP/Content and Brand Strategy / 312 222 1010 x1470 Kim Overstreet Content Strategist James R. Koelsch, Lauren Paul, Jeanne Schweder and Beth Stackpole Contributing Writers


Filippo Riello Art Director / 312 222 1010 x1200 George Shurtleff Ad Services & Production Manager / 312 222 1010 x1170


Work on Artificial Intelligence and Machine Learning Standards Begins Mobile Robot Safety Standard Published PMMI News Predictive Maintenance 101


Kurt Belisle Publisher / 815 549 1034 West Coast Jim Powers Regional Manager / 312 925 7793 Midwest, Southwest, and East Coast


Protective Camera Enclosure Continuous Servo Drives High-Precision Micro-Machining Laser Safety Relay Module And more...


David Newcorn Senior Vice President, Digital & Data Elizabeth Kachoris Senior Director, Digital & Data Kelly Greeby Senior Director, Client Success & Media Operations Jen Krepelka Director, Websites & Digital Design Strategy



The Electrification of Transportation and Mobility By Dick Slansky

Kurt Belisle Publisher / 815 549 1034 Jake Brock Client Success Manager / 312 222 1010 x1320 Sharon Taylor Director of Marketing / 312 222 1010 x1710 Amber Miller Marketing Manager / 312 222 1010 x1130 Sarah Loeffler Director, Media Innovation / 312 205 7925 Janet Fabiano Financial Services Manager / 312 222 1010 x1330


Discussing the Data Layer in Regulated Environments By Eric Resiz


How Can Industrials Build for an Intelligence-Led Future? By Brian R. May


All Automation World editorial is copyrighted by PMMI Media Group, Inc. including printed or electronic reproduction. Magazine and Web site editorial may not be reproduced in any form without the written permission of the publisher.

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PODCAST SERIES How to Implement Edge Computing—From Concept to Launch In this episode, we discuss how to successfully plan for and implement edge computing technologies at both OEM and manufacturing/processing facilities with Stratus Technologies’ Aric Prost and John Vicente.

AUTOMATION WORLD TV Watch Automation World’s David Greenfield and PMMI’s Glen Long, Tom Egan, and Bryan Griffen discuss current trends in robotics, and how this field has been advancing in the food and beverage industry.

THE AUTOMATION PLAYBOOK The Automation Playbook is a useful source of information as you look for guidance in how to approach the Industrial Internet of Things, communication protocols, controls implementation, safety, asset management, predictive maintenance, the mobile workforce, and much more.

AUTOMATION WORLD E-BOOK Automation World’s Take on COVID-19

Read our summary on the destruction and innovation caused by the 2020 pandemic in this hard-hitting round up COVID-19 articles from the 2020 pandemic. Despite facing major set backs, the hardships of the past year led to numerous industry innovations. Find out more in our handbook.

ON-DEMAND WEBINARS Automation 201 – Clarifying Your Requirements for Project Success

Automation 201 provides both new and experienced automation professionals with insights around core automation requirements and their performance trade-offs. This webinar will leave you with business-ready insights based on Epson’s nearly four decades of experience and leadership in industrial robotics.

Design Guide to Eliminate Binding in Parallel Linear Guides

In this webinar, you will learn about linear components designed to compensate for installation misalignment in parallel rail installations, design recommendations to properly assemble linear guides across wide installation spans, and how to reduce assembly and linear rail alignment time.

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3/26/21 2:15 PM



An Investment Firm’s View of Industrial Automation Spending Trends Editor-In-Chief/ Director of Content, Automation World


hile there’s certainly no shortage of trend outlooks for manufacturing technology spending based on near-term plans, looking at where industrial companies have been investing offers interesting insights into where things could be headed. According to Andrew Obin, managing director of Bank of America Merrill Lynch Equity Research, one of the biggest trends he’s seen is how spending on software has overtaken hardware spending by U.S. manufacturers. In his presentation at the virtual ARC Forum, Obin noted that, although software spending in 2020 was flat, the fact that spending remained in line with 2019—despite the pandemic—supports the investment firm’s position about the continued strength of software versus hardware spending in industry. Essentially, what researchers like Obin are seeing play out across industry underscores the trend seen in virtually every business sector—that software has become the predominant technology. Automation World discussed this trend with Michael Risse of data analytics software supplier Seeq in late 2019 for the article “Is Software Eating Industry Yet?” ( Obin clarified that much of the manufacturing industries’ spending on software in 2020 focused on enterprise level software, such as enterprise resources planning (ERP), customer relationship management (CRM), and supply chain management (SCM) than on specific manufacturing floor software. However,

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The energy sector lags behind manufacturing

Diverging from the manufacturing industries’ overall spending on technology is the lack of spending on such technologies by the energy sector. Obin’s data shows that, in 2019, manufacturing as a whole spent $228 billion on plants and equipment, while oil and gas spent $174 billion. More striking was the disparity in software

spending between manufacturing and the oil & gas sector. In 2019 the oil & gas sector spent $8 billion on software while the manufacturing industry spent $134 billion. The oil and gas industry has sort of done the opposite of the manufacturing industry, said Obin, noting how oil and gas spending on technology declined even as oil prices recovered. “It’s remarkable just how little oil and gas industry is spending today,” Obin said. “[But] if the oil and gas industry focused more on efficiency and less on growth in the new post-pandemic world—where we have more focus on green [initiatives]—it could present a revolutionary opportunity for the industry.” Read more about expected automation spending trends in Automation World’s feature article “Automation Expectations: Discrete Manufacturing”

US manufacturing annual investment spend: Software vs. Property, Plant & Equipment (1990-2019) US manufacturing investment spending, gross ($bn)

By David Greenfield

he noted that industry’s spending on software supports “one of the most powerful stories in U.S. manufacturing over the past 20 years— the significant margin expansion [of the industry] despite big offshoring trends in the U.S. [during that period]. Even though we didn’t have growth in the U.S. [manufacturing industries], the [industry’s] software investment has enabled very significant improvements in the profitability of U.S. manufacturing operations over the past 20 years as focus shifted away from growth and more toward efficiency.”

Source: Bank of America Merrill Lynch Equity Research.

3/30/21 11:11 AM



Reinventing Remote Management By Stephanie Neil Senior Editor, Automation World


anufacturers are well aware of the benefits of remote management technology in light of social distancing mandates and travel bans related to COVID-19. Armed with a camera, an internet connection, and some special software, maintenance technicians located offsite can help onsite operators manage or fix equipment on the plant floor. This setup, however, can be limited to the line of sight of the operator holding the tablet or smartphone used to stream the machine data to the offsite expert. What’s really needed to make remote management an integral part of the factory of the future is a way to get a holistic view of the machine, the line, and the entire plant floor. Enter Adtance, an after-sales service technology provider, which earlier this year introduced new capabilities within the Adtance Support module of its cloud-based Smart Services Platform 4.0. The service platform supports everything from ticketing, document management, workflow, parts, visualization, and predictive maintenance. Now, with the addition of the Adtance Fieldstreaming offering, the suite includes a module for live remote support capable of connecting every type of camera to the system, including cameras within smartphones, tablets, computers, security cameras, drones, and even underwater remote operated vehicles (ROVs). This enables live stream views of machines from various angles simultaneously. “We’ve seen customer demand for live remote support, training, and collaboration skyrocket, in part because of the current COVID-19 challenges, but also due to increas-

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ingly complex machines and a shortage of highly skilled technicians,” said Nils Arnold, co-founder and CEO of Adtance. “Our new Adtance Support and Fieldstreaming solution is uniquely suited to address the growing set of remote service use cases. Companies want service technicians to be able to view machines from a variety of angles in real-time, conduct live remote user training from multiple perspectives, and collaborate remotely using multiple cameras. Our new Support Module and Fieldstreaming Solution makes all these use cases possible and simple.” The Fieldstreaming system comes with a case of equipment comprised of any number and types of cameras as well as a power supply unit, an internet cable, and connection cables for each camera. Using Adtance Support, the stationary cameras can be controlled remotely by a service technician as can moving cameras, which allows for tilt and zoom for viewing a machine from different angles or to get a big picture view of the plant. In addition to remote support for maintenance, the Support Fieldstreaming set up can be used for training, factory acceptance tests (FATs), or to improve collaboration and communication. Users can start a session by sending a message, document, or video recording in addition to initiating a session through a video call. No app is needed, as it runs on all browsers and mobile devices, the company said, with invitations to sessions shared by simply sending a link. At any time during a session, users can send messages, documents, recordings, open a live video chat, or collaborate using a digital whiteboard. In addition, snapshots can be taken during live video streams and users can mark up the video with circles, arrows or pertinent notes. The module also allows closed sessions to be reactivated and viewed again at any time. The documentation remains in place and participants can be invited back into the session. This saves time and significantly improves customer service, especially when there is a complex or recurring problem with the same customer. While there are many after sales offerings

available to manufactures, what sets Adtance apart is its comprehensive offering, Arnold said. “What makes us unique is that we are the only company currently which can provide our customers with a single point of connection if they want to digitize their customer service,” he said referring to the centralized Smart Service Platform which includes the following modules: • Adtance Support—Enables live remote support, inspection, and maintenance for customers, utilizing devices such as smartglasses and multi-camera fieldstreaming. • Adtance Workflow—Digitizes, analyzes, and optimizes all workflows, including everyday processes, such as maintenance instructions. • Adtance Process Visualization and Monitoring (PVM)—Monitors the performance of individual machines as well as entire industrial plants. • Adtance Predictive Maintenance (PM)— Analyzes and evaluates operational data in real time through the continuous monitoring of machines or the entire production plant using built-in sensors. Manufacturers or machine builders can start with a single software module and build up to the full offering or integrate individual modules into an existing system, including competing systems. What also makes Adtance different, Arnold said, is the ability to connect to any kind of camera—be it a security camera or a drone— and the platform’s ability to do language translation. For companies that operate globally, communication barriers are overcome as Adtance Support now includes translation for 147 different languages. Users simply select a language, speak, then click a button and the system translates their speech into the selected language enabling companies to work with technical experts around the world. The Adtance Smart Services Platform 4.0 with the integrated Support module and Fieldstreaming is immediately available in a software-as-a-service subscription set up.

3/26/21 2:14 PM

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IoT Platform Partnerships Continue to Gain Traction By David Miller Senior Technical Writer, Automation World

“Cloud-based IoT platforms are also seeing growing adoption as many companies look to invest in low-cost, scalable options that can ease the strain of building their own IoT framework from the ground up.”

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artnerships and collaborations of all types are becoming increasingly common among technology suppliers as companies embarking on digital transformation initiatives seek to transcend their domain-specific expertise, grapple with unprecedented quantities of data, and ultimately bridge the divide between operational technology (OT) and information technology (IT). Recently, IT equipment and service provider Fujitsu expanded its relationship with software company PTC to become a systems integrator for PTC’s ThingWorx Internet of Things (IoT) platform, which aids end-users in sourcing, contextualizing, and synthesizing data from IoTconnected assets to more easily produce insights on how to optimize processes. Fujitsu is also working with PTC’s Vuforia augmented reality (AR) offering that facilitates remote collaboration between frontline OT workers and distant subject matter experts via video and audio feeds. “Our alliance with PTC will be pivotal in helping manufacturers thrive in an increasingly challenging business environment. Our enhanced Smart Factory framework, bolstered by the ThingWorx and Vuforia platforms, will enable our customers to create real-time operational insight and intelligence across manufacturing domains, helping to improve tactical and strategic operations,” said Paul Bresnahan, head of manufacturing at Fujitsu America Inc. “Together, Fujitsu and PTC are enabling our customers to bring in new business that wouldn’t have been possible otherwise.” AR in particular has seen an uptick since the onset of COVID-19, with many using the technology to guide less-experienced workers through complex technical procedures without needing to physically enter

a facility. Moreover, cloud-based IoT platforms are also seeing growing adoption as many companies look to invest in low-cost, scalable options than can ease the strain of building their own IoT framework from the ground up. Through the new partnership, Fujitsu and PTC hope to support shop floor efficiencies, enable customers to reduce costs, increase productivity, and redeploy previously idled assets. In addition, when paired with the Vuforia AR platform, ThingWorx can allow front-line workers to obtain the information they need to do their jobs more safely, efficiently, and accurately, the companies say. “We are pleased to enter into this new phase of our relationship with Fujitsu. Fujitsu and PTC share a common vision to help manufacturers accelerate innovation in the factory to deliver meaningful business impact with speed and at scale,” said Catherine Kniker, divisional vice president and global head of corporate development at PTC. “We both understand the challenges that come with migrating to a digital factory, and believe our robust experience positions us well to help our customers quickly achieve value with their digital transformation initiatives.”

3/30/21 11:10 AM


Motor Selection Insights By David Greenfield

Editor-In-Chief/Director of Content, Automation World


hile software and other advanced technologies often capture the automation technology spotlight, the fact remains that not much happens in the manufacturing or processing industries without motors. From simple applications like powering conveyors, fans, and pumps to more complex systems, motors are central to countless production operations. And though all motors deliver similar functions, there are many different types available. The increasing use of drives for higher level motor control and energy efficiency purposes provides another facet of consideration when selecting motors for a specific task. With so many options to consider for such a core technology, it’s no surprise that end users have a lot of questions about motor selection. One question submitted recently to Automation World was: What’s the difference between standard and inverter duty motors? To help answer this question, we connected with Brent Kooiman of system integrator Interstates for a recent episode of the “Automation World Gets Your Questions Answered” podcast series. Kooiman began by explaining the different motor types commonly used in manufacturing applications. “Typically, in industry, we're focused on three-phase motors,” he said. “But there's also many single-phase motors out there. Single-phase motors use a one-phase wire and a neutral wire—this is most common for smaller motors. Three-phase motors use three different phases in the electrical systems, which are all 120 degrees out of phase with each other. This allows the motor to run much smoother and much more efficiently than a single-phase motor.” The key difference between general purpose and inverter duty motors is that inverter duty motors are specifically built to be operated by a variable frequency drive (VFD). “A VFD allows that motor to be run at different speeds,” said Kooiman. “A typical motor starter on a general-purpose motor is only going to operate that motor at full speed.” An important consideration when selecting between motor types is that, since the motor

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General purpose inverter duty rated motors from Siemens. isn't always operating at full speed, the cooling of the motor is critical. “Most motors are cooled by a fan connected directly to the motor shaft,” said Kooiman. “So, if the fan is spinning at a slower speed than the motor, less air is moving across the motor and the motor won't cool as well. This is especially important in classified areas (e.g., Class 1, Div. 2) where the temperature of the motor needs to be kept below the ignition point of the classified area. Inverter duty rated motors often will have more cooling capacity to make sure that that motor doesn't overheat.” Kooiman noted that VFDs have high-frequency spikes in the drive’s electrical system— caused by the switching of the transistors in the drive to adjust the motor’s speed. This is why inverter duty rated motors have thicker insulation on the motor’s windings so they can hold up to these spikes longer over time. That’s why inverter duty rated motors tend to have a longer lifetime when running on a VFD. He added that you can run a standard motor on a VFD, but you should be aware that it

could overheat the motor (since standard-duty motors won’t feature the extra insulation). Over time, the lifetime of a standard duty motor will be shortened by operating it on a VFD.

RPMs, duty cycles, and cost

Revolutions per minute (RPM) is, of course, a significant motor characteristic that comes into play for specific applications. Kooiman explained that RPM is “really the standard on how fast you want the motor to spin and is determined by the design of the motor itself. Both inverter duty and standard duty motors are available in many different RPM configurations. When you have a VFD, the difference is that you can operate that motor from zero to 100% of that RPM. That's really the main difference between standard and inverter duty motors when it comes to RPMs—being able to control the rate of RPMs using a VFD or running at 100% of RPMs with a standard duty motor.” Kooiman explained that the duty cycle of a motor is about how often those motors are

3/30/21 11:12 AM

Application considerations

It’s common when selecting a motor to focus primarily on the motor’s capability as well as the drive (if using an inverter duty rated motor). But Kooiman cautions to not overlook the cabling. “Using a good qual-

ity VFD cable to connect the VFD to the motor is essential to making sure that the system will perform as intended,” he said. “It really helps minimize harmonics that can leak into the rest of the electrical system and affect instrumentation and other sensitive electronics.” Grounding is another element to factor in. “Most issues I run into with VFDs and motors are related to improper grounding of that motor, the VFD, and the cable. If the system is installed as designed, then the harmonics are mitigated through the VFD and we won't see as many issues with overheating in the motor or causing interference with the electronics and instrumentation.” Kooiman also noted that VFDs allow for a motor to be run in reverse without having to add additional hardware. And the ability to run the motor at slower speeds also helps with energy savings.


starting and stopping and how much of the time the motors are running. “It's often defined as the number of times per hour that the motor starts and stops,” he said. “With most applications, you're going to want a continuous duty rated motor, as that motor is often going to be running for multiple hours at a time. Both standard and inverted duty rated motors will have continuous duty ratings, whereas a duty cycle motor would be one that's only running for short periods of time.” Given the inverter duty motor’s higher insulation and flexible design, Kooiman said these motors tend to be about 15% more expensive than a standard duty motor of similar horsepower.





Single-phase standard duty motor from Grizzly.

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3/26/21 2:18 PM


Advantech and the Artificial telli e e T�i s By David Miller

Senior Technical Writer, Automation World

Getty Images


n the opening day of Industrial Internet of Things (IIoT) platform provider Advantech’s online conference, company representatives and other industry experts gathered to discuss new developments on the horizon for IIoT, artificial intelligence (AI), and industrial networking. In particular, many sessions focused on the hurdles that still remain if IIoT and associated Industry 4.0 technologies are to see ubiquitous adoption in the future. Perhaps the greatest take-away from the first day of the event was that, while the real bedrock of value provided by IIoT is to be found in the data it generates, nothing can be attained from it unless that data is effectively gathered, communicated, and analyzed. As such, several speakers spotlighted burgeoning technologies such as 5G wireless connectivity, intelligent sensors, and AI as the most consequential industry trends going forward. Through the improvements

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these technologies enable in data gathering, transmission, and analytics, Advantech envisions industry moving beyond IIoT and toward an Artificial Intelligence of Things (AIoT) that allows cloud-delivered applications to make real-time, autonomous decisions at the device level. Within this framework, cloud-based AI trained on large amounts of data can provide industry operators a means of more easily extracting value from their IIoT infrastructure in exchange for furnishing AIoT platforms with the datasets necessary to continue expanding their capabilities. Allan Yang, chief technology officer at Advantech, stressed the need for a platform approach if AIoT is to be realized in a timely and cost-effective manner. “AIoT is cross-disciplinary. It requires edge computing, cloud platforms, data know-how, and domain expertise in many specific areas. No one company can do this alone successfully. However, we have seen many companies that

are still trying to build their essential technology modules in-house, rather than adopting a platform approach,” he said. “This takes a lot of time and involves a lot of trial and error. We strongly encourage all companies, regardless of their size, to evaluate the possibility of collaborating or engaging in a partnership to speed up adoption.”

The future of IIoT

The Advantech event also explored why IIoT adoption rates have not yet met projected expectations, with Dirk Finstel, deputy managing director at Advantech Europe, noting that although 50 billion IIoT devices were expected to be in operation by 2020, only 8.5 billion have been deployed in reality. According to Finstel, much of this can be attributed to shortcomings in the associated infrastructure needed to make large-scale IIoT a reality. He believes that the high speed and bandwidth capacity of 5G networking will improve the

3/26/21 2:13 PM


feasibility of many IIoT technologies that rely on cloud computing in the near future. Advances in edge computing are also expected to play a larger role in IIoT deployments by easing the burden of sending large quantities of data in and of out of plants via cloud computing applications, said Jerry O’Gorman, associate vice president at Advantech North America. Not only does O’Gorman see edge computing reducing costs and accelerating adoption, but by extending cloud-native software to the edge, latency can be reduced and less bandwidth will be required for data transmission. In fact, he estimated that by extending cloud-native software to the edge, up to 75% of data generated may never need to be sent to the cloud. He also noted how software-as-a-service (SaaS) models are likely to grow in prominence as 5G allows complex applications to be rapidly delivered to the edge. O’Gorman perceives that this could greatly reduce costs for end-users, making increasingly sophisticated AIoT applications easily accessible even to small-and-medium sized enterprises.

Business considerations

Though AI promises to offer impressive new functionalities, end-users shouldn’t expect it to solve all issues surrounding IIoT deployment and integration, said William Webb, author of “The Internet of Things Myth,” during his presentation at the Advantech event. “There’s a number of promising new developments in this field, but they need to be treated with caution and used in the right way. AI only works when you’ve got the data in the first place, and that means it can only enhance an IIoT system that’s already there and working well,” Webb said. “Until you’ve got an IIoT system in place delivering all of the data, you can’t really use AI to make sense of that data.” According to Webb, approaching IIoT projects with an eye toward harmoniously adjusting overall business processes may be the best way to ensure success. In numerous early IIoT technology deployments, it was not uncommon for operators to put new systems in place without fully realizing the degree to which they would need to alter their overall operations to efficiently act on insights de-

rived from their data, Webb noted. For example, even when equipment had been outfitted with IIoT technology to allow failures to be predicted in advance, this information could only be used to yield productivity gains once new processes were designed to efficiently allocate labor to maintenance on machines that needed it and redirect it to other valuable activities when they didn’t. So, while predictive maintenance is more efficient in theory, without proper systems support, fixed and regular maintenance schedules are more simplistic and easier to keep to in practice. Of course, operators are shaking out these kinks, and predictive maintenance is now one of the most common applications for IIoT technology. Still, Webb stressed that it is challenges like these that highlight the importance of viewing IIoT projects not only as technological installations, but initiatives that also require cultural, workforce, and business-oriented changes within an organization.

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How COVID-19 Pushed ExxonMobil’s Automation Technology Limits By David Greenfield

Editor-In-Chief/Director of Content, Automation World


OVID-19 created the conditions that “pushed us to test our limits and we learned that we could do a lot more work remotely than we previously thought,” said Dominic Clausi, vice president of engineering, ExxonMobil Research and Engineering, during a presentation at the 2021 virtual ARC Forum. “The time efficiency [gained] doing remote work or remote support allowed us to actually better leverage our technical resources to improve the coverage of those events we were supporting remotely.” Prior to the onset of the pandemic, Clausi said it was generally perceived at ExxonMobil that the perceived risk associated with remote access and support would render it less effective than the in-person support the company had traditionally relied upon. Speaking of industrial companies in general, Clausi said, “We often have the tendency to overestimate the risk and underestimate the value of digital technologies.” One example of a remote access and support advance made at ExxonMobil in 2020 involved the company’s Baton Rouge, La., isopropyl alcohol facility. Isopropyl alcohol is a key ingredient in hand sanitizers and disinfectant sprays, so demand for this facility’s product increased at a rapid pace in early 2020. Clausi said ExxonMobil was able to “leverage our remote connectivity tools with our advanced dynamic matrix control capability to rapidly and remotely update control applications. And we did that in sync with physical modifications we were making to the unit at the time. [Remote connectivity] allowed us to very rapidly expand the production capability of that unit and that helped us meet what was really a step change in demand growth and meet a key societal need.”

Beyond pandemic-related benefits

Clausi also mentioned the value ExxonMobil discovered in remote access technology when Hurricane Laura impacted its Beaumont,

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Texas, facility in 2020. “We had just set up a machinery monitoring group in one of our technical centers in Malaysia when our Beaumont facility was working through an orderly shutdown in preparation for the hurricane,” he said. Because the staff in Beaumont had to perform the shutdown with a reduced number of engineers on site to manage the COVID-19 risk, remote monitoring capabilities allowed engineers in Malaysia to help guide the staff in Beaumont through the shutdown. Engineers in Malaysia were able to observe “critical machines in detail as they were being brought down,” Clausi said. “The remote connectivity and visibility of the data allowed us to do engineering support during an abnormal operation from across the globe. In addition, the team in Malaysia was able to uncover vulnerabilities that ultimately could have compromised an orderly restart and address those vulnerabilities before the units came back up.” Accelerated work with remote access and support technologies were not the only technology related advances experienced as a result of the pandemic, according to Clausi. ExxonMobil also pushed its use of robotic inspections further. “During the pandemic there was a premium on minimizing the number of people at our sites, as well as limiting the personal interactions in the field and this helped drive the deployment of various robotic inspection techniques that we had currently under development,” he noted.

ExxonMobil’s tech outlook

Beyond robotics and remote access, another area in which ExxonMobil sees significant opportunity is in autonomous operations and process control. Clausi noted that, in ExxonMobil’s manufacturing facilities, operators are tasked with running complex processes safely and efficiently 24 hours a day, seven days a week and “they need to synthesize large quantities of data and manipulate potentially hundreds of variables to optimize an outcome. And that makes this a rich area to

leverage digital technology.” In response, Clausi said ExxonMobil is pursuing the development of what they call an intelligent self-optimizing planner. “The goal here is to take advantage of cognitive learning and adaptive capabilities based on artificial intelligence and machine learning to reduce human error and make better decisions,” he said. Components of this system include a digital assistant for operators called SmartLane, which uses artificial intelligence to guide a console operator through an optimized transient event. Clausi explained that an example application of SmartLane could be “executing a grade switch on a reactor so that real time benchmarking and self-correcting guidance allows the operator to reduce off-spec material and avoid costly shutdowns and trips.” Clausi noted that this SmartLane technology is already deployed on ExxonMobil’s polymer plants and the company is in the process of adapting it for other platforms across its facilities. ExxonMobil is also piloting the use of video images for process control for a program that uses mathematical models to interpret video images and then translate them into signals that can be used to “monitor and ultimately control an operation,” said Clausi. “This becomes particularly useful in areas where traditional instrumentation falls short, like monitoring the control of a flare.” Another technology in the early stages of development at ExxonMobil is a virtual assistant for a console operator called Sofia. “Sofia means wisdom in Greek,” explained Clausi, “and the way to think of Sofia is that it’s like Amazon Alexa but designed for console operators. It will contextualize and mine data from various sources and then provide real-time answers to the challenging questions that an operator has in the day-to-day operation of the unit.”

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3/29/21 11:04 AM



Work on Artificial Intelligence and Machine Learning Standards Begins By David Miller

Senior Technical Writer, Automation World


hile wide use of artificial intelligence (AI) and machine learning in manufacturing may still be several years off, both technologies are beginning to make their way onto the plant floor and beyond. Potential applications for these technologies run the gamut as unprecedented amounts of data delivered via connected sensors and devices enable properly trained AI algorithms to help optimize production processes. Still, being a relatively new technology, standards for AI are currently lacking, which could hinder further application in industry. In particular, the lack of standards could result in difficulty in implementation for operators, a lack of interoperability with other systems, poor knowledge about best practices, and even potential cybersecurity vulnerabilities. That’s why the development of standards often signals a new technology beginning to mature. Standards don’t only help suppliers by speeding innovation—they also signal to end-users that a technology has been determined to be effective based on criteria agreed upon by numerous participants in the standard’s development process. Simply put, standards cut costs, communicate vital information, and increase reliability. The ETSI Securing Artificial Intelligence Industry Specification Group (SAI ISG), which is currently the first organization dedicated to securing AI, recently released a report describing the primary obstacles, with a focus on machine learning and the barriers related to confidentiality, integrity, and availability at each stage of the technology’s lifecycle. In addition, the report examines broader challenges facing AI, such as bias, ethics, and the potential for misuse. “There are a lot of discussions around AI ethics but none on standards around securing AI. Yet, they are becoming critical to ensure security of AI-based automated networks,” said Alex Leadbeater, chair of ETSI SAI ISG. “This first ETSI report is meant to

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come up with a comprehensive definition of the challenges faced when securing AI. In parallel, we are working on a threat ontology, on how to secure an AI data supply chain, and how to test it.” Within the report, the machine learning lifecycle is broken down into eight stages, each of which comes with its own unique risks: data acquisition, data curation, model design, software build, training, testing, deployment, and updates. In the data acquisition and curation stages, the predominant issue is integrity. In other words, when integrating data from multiple sources or in multiple formats, incongruities in measurement parameters or data structure could create maladaptive machine learning algorithms resulting in poor or even dangerous decision-making. Moreover, the report also considers the possibility that a malicious actor might intentionally poison data to sow chaos within an operation. While the model design and software build stages are identified as being relatively safe,

the report finds that similar—or even more severe—security issues could present themselves in the training stage of a machine learning algorithm. For instance, the confidentiality of a training dataset could be compromised if an attacker were to augment it with synthetic input data designed to trick the algorithm into outputting labels containing information about the original training data. This type of data leak could entail businessrelated information such as intellectual property or sensitive personnel information. Currently, no definite standards to ameliorate these issues have been put forward, though by clarifying the primary concerns surrounding AI and machine intelligence, SAI IG hopes the process can begin. View the full ETSI SAI IG report at awgo. to/1159.

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S P Le S

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Predictive Maintenance 101


redictive maintenance is one smaller part of the broad concept of digitalization, or ‘Industry 4.0.’ It refers to the ability to monitor a machine, or machine component, and avoid unplanned downtime by foreseeing machine failure and allowing the opportunity to take preventative action. Since plant downtime can incur losses in the millions of dollars of lost output, an investment in predictive maintenance technology is a logical step. According to Packaging and Predictive Maintenance from PMMI’s Business Intelligence, “developments in predictive maintenance functionality for plant-level software have been taking place the longest. All the major digitalization platforms will offer the ability to perform advanced analytics on data being collected by existing automation infrastructure; and there are numerous start-ups developing new platforms too.” Applying predictive maintenance technologies on the factory floor may create potential

By Kim Overstreet, Content Strategist, Alignment, PMMI

issues with assets that may or may not be connected to an existing infrastructure. Likewise, measurements being collected from those assets that are connected may not provide the right data to be most effective at performing predictive maintenance. To solve this issue, smart sensors have become a hot technology in the last three to four years. Small MEMS-based devices with integrated wireless communications, and microprocessors that can be placed on existing infrastructure to create new points of asset measurement have become more affordable thanks to a broad uptake in consumer applications. This development is enabling legacy assets to be retrofitted and monitored in a way not previously possible. The report also found that 29% of the packaging and processing industry professionals interviewed are evaluating predictive maintenance technology; 22% are piloting it; and up to 24% have already implemented the technology. Batch manuufacturers, such

as food and beverage production operations, will widely adopt this technology, since equipment failures in this industry can result in the loss of an entire batch. Other industry candidates are fast-moving consumer goods companies, which are known for their low margins and high volumes—an area where downtime could be financially catastrophic. Primary packaging machines tend to be complex and to have the most demanding requirements made of them by end users; therefore, application of predictive maintenance here can be complicated. Primary packaging is also the first layer of protection—responsible for protecting the product—and in most cases can’t be manually applied if the line goes down, making downtime a much bigger liability than with secondary or tertiary machines. Download the free Packaging and Predictive Maintenance report at

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Mobile Robot Safety Standard Published By David Miller

Senior Technical Writer, Automation World


utonomous mobile robots have been gaining traction in industry for several years now. Often seen as a replacement for the automated guided vehicles (AGVs) once used to automate material handling tasks, autonomous mobile robots offer the advantage of being able to navigate complex environments without the need for permanent wire strips or magnetic tracks along the floor to guide them. Since the onset of COVID-19, the adoption of mobile robots has swelled due to the robots’ ability to help meet social distancing requirements in industry verticals ranging from healthcare to manufacturing. Warehousing applications have seen an uptick in mobile robotics adoption as well, as homebound consumers ordering goods online has spiked demand for rapid order fulfillment at a time when fewer workers are able to occupy facilities. Yet one major concern that still remains is safety, particularly as mobile robots see their payloads increasing. Until recently, no official safety standard had been devised.

The new standard

Enter R15.08 Part One, the first standard for mobile robot safety from the Robotics Industry Association (RIA). R15.08 has been in the works for more than four years and was published in late 2020. The standard provides technical requirements for the design and integration of mobile robot bases equipped with manipulators such as robotic arms and other accessories. According to Melonee Wise, CEO at Fetch Robotics, and a member of the R15.08 standard committee, R15.08 is a type-C standard, meaning it is specific to a particular technology, as opposed to type-A standards, which cover basic concepts and general design principles across all machinery, and type-B standards, which entail safety principles that can be used across a range of machinery within a certain category. The type-C designation is noteworthy to end users for several reasons. Firstly, the development of type-C standards often signals a new stage in the maturity of an innovative product, and tends to be followed by more ubiquitous adoption. Fixed robotic arms and AGVs both followed this pattern, Wise says. Therefore, those with their eye on the space should expect

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deployment of mobile robots to accelerate underneath the new standard. In addition, while legal compliance with type-C standards is not usually enforced by the Occupational Safety and Health Administration (OSHA), voluntary conformity may still be expected by the organization. “One of the things that’s happened in a lot of cases in the U.S. with type-C standards is that, because it’s so hard to keep up with the technology, OSHA doesn’t enforce specific compliance. So, technically there is no robot standard that OSHA is [currently] enforcing, but they are saying it’s important to conform to these standards for worker safety,” Wise says. “Although these legal entities may not absolutely require these things, if you don’t conform, they can still say ‘look, these are the best practices, and you should be following them.’ In that case, you may still be cited or forced to pay a fine for gross negligence if you didn’t follow through with the prevailing standard.”

Updates and improvements

Previously, the American National Standards Institute (ANSI) had devised standard B56.6 for AGVs and R15.06 for fixed robotic arms. However, B56.6 only addresses vehicles that follow fixed guide paths and R15.06 does not consider robotic arms that have been integrated with mobile bases. By contrast, R15.08 is specifically tailored not just for mobile robots—but for both mobile robot bases designed to carry materials on their own as well as those equipped with robotic arms, manipulators, and other peripheral accessories. Accordingly, R15.08 is broken down into three sections: The AMR base, fixed robotic arms mounted on the base, and other

mounted accessories such as tooling. Wise notes that this is a unique structure for a standard, but is necessary because mounting other parts on an AMR base can have a significant effect on performance. “Because an arm or other accessory can impact things like sensing, driving, steerability, and stability, the person who makes that [robot], whether it’s an integrator or someone else, is essentially becoming a manufacturer,” Wise says. “So, this standard might present a difficult transition for some integrators who, in the past, have not had that level of responsibility for certifying or proving that they are in conformity with standards of this nature.” Wise stresses that integrators looking to avoid being squeezed out of the market need to adapt to this increased expectation, as mobile robot customers will inevitably begin demanding conformity with the standard as it becomes more prevalent. However, on the end-user side, there are only benefits to be had. According to Wise, R15.08 will make it faster and easier to deploy mobile robots without needing to conduct an independent safety assessment. This will result not only in cost reductions, but lower risk and liability for end-users. Going forward, the R15.08 committee will be developing R15.08 Part Two, which will include expanded safety requirements for mobile robot systems and systems integration as well as safety requirements for mobile robot fleets. R15.08 Part Three will further expand safety requirements for end-users of mobile robots. Part One of R15.08 is available at awgo. to/1160.

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Why the Age of IIoT Demands a New Security Paradigm By Beth Stackpole Contributing Writer, Automation World

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As manufacturers modernize for data-driven business, industrial automation has become a target for cyber attacks. Here’s how companies should assess and plan for a new cybersecurity landscape.

a tale of two

water supplies portends the coming security nightmare as industrial assets are connected to the Internet of Things (IoT). Hackers, potentially linked to Iran, were able to breach an unprotected humanmachine interface (HMI) system at an Israeli water reservoir, allowing them to tamper with water pressure and temperature changes. More recently, a plant operator working in a water treatment facility serving Oldsmar, Fla., discovered an unknown hacker had gained entry and successfully altered chemical levels in the county’s water supply—the timing of this incursion is notable as it took place during Super Bowl weekend, which was hosted in nearby Tampa. While both incidents caused no immediate public harm, they raise alarming concerns about security vulnerabilities as factory equipment, remote industrial assets, and critical public infrastructure are synced to the cloud and enterprise systems in support of new initiatives designed to garner efficiencies, improve operational performance, and deliver proactive maintenance. While IT has actively embraced cybersecurity practices, including patching and configuration management, OT (operations technology) has historically eschewed such measures, primarily due to concerns about how unplanned, illtimed, or inadvertent changes could bring systems down, negatively impacting worker safety and plant resiliency. “This is a story of cultures colliding—in the IT world where change is a good thing… to the world of industry where change is bad and introduces risk,” says Grant Geyer, chief product officer for Claroty, a supplier of industrial cybersecurity technology. “But to gain access to advanced analytics, just-intime ordering systems, and unlocking new insights, it’s inherent that we connect the world of aversion to change with the world of attraction to change—that is really the core of the problem.”

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A shifting security landscape The growing complexity and connected nature of the modern industrial landscape introduces risks that simply didn’t exist before. The spread of IIoT devices, more widespread deployment of edge analytics, the continuous transmission of time-series data, and the adoption of digital twins open up new attack vectors in industrial environments, which were never designed with cybersecurity in mind. Not only is the line of attack expanded—threat actors are becoming more attuned to the opportunity to disrupt business through industrial operations. “Frankly, industrial systems are easier to compromise or get into than business systems, but they are harder to exploit,” says Francis Cianfrocca, CEO and founder of Insight Cyber Group, which delivers a managed IoT security service. Cianfrocca explained further that a certain level of skills is required to do real damage to industrial equipment. “You need real knowledge to mess with a centrifuge or robot, whereas anyone can mess with a Windows computer because everyone has one,” he says. A Trend Micro report on IIoT security identified several emerging attack scenarios, such as: compromise of an engineering workstation through a malicious industrial add-in for stealing trade secrets, trojanizing a custom IIoT device to become a bad actor, and exploitation of a vulnerable mobile HMI to tap sensitive information or to take over the device. Infiltrating MES systems to create defects in the final product or to promote denial of service attacks that block production is another growing concern as is the ability to inject malicious automation logic into a complex machine, paving the way for information theft or untended machine movement. Perhaps the most dangerous and potentially prolific security threats are employees, experts contend. “We fear Russia in terms of cybersecurity breaches, but the goodhearted employee is the most dangerous,” says Greg Baker, vice president and general manager for the Cyber Digital Transformation

organization at Optiv, a security systems integrator. “The employee that tries to stretch their responsibilities by updating a Windows XP workstation to Windows 10 and shuts the factory down—they’re the most dangerous threat actor.” Historically, security of OT environments has been addressed by preventing connectivity to outside sources or walling off as much as possible from the internet using a strategy many refer to as an “air gap.” With the latter approach, firewalls are the focal point of the security architecture, locking down an automation environment, perhaps in a specific building, to prevent external access as opposed to a strategy predicated on securing individual endpoints on the industrial network such as HMIs or PLCs. “We used to live in a world that was protected—you didn’t need to put a lock on your jewelry drawer because you had a huge fence around the property and no one was getting in,” explains John Livingston, CEO of Verve Industrial, which markets an industrial control system endpoint security platform. “Now that the fence has come down, you need to protect the assets inside rather than relying solely on network protection.” While manufacturers have been gathering data for years through data historians, the data remained siloed or, at best, was shared within the internal network. In today’s environment, the flow of data has been altered— not only is plant data pushed out through the cloud to enterprise systems or automation experts for analysis, there is also inbound traffic to initiate changes, whether that's calibrating machinery to optimize performance or correcting a glitch that is causing quality issues. “With IIoT, people don’t just want to analyze—they want to act,” says Verve’s Livingston. “What was a one-way street is now a two-way street and there are risks associated with that. If you’re making a temperature change to a boiler, for example, you’re also changing its pressure. Now, you’re potentially not just making a bad decision, but taking a bad action.”

3/26/21 1:59 PM


The Claroty Platform deploys five detection engines to automatically profile all assets, communications, and processes in an OT environment. Image courtesy of Claroty.

Tripwire’s Risks and Vulnerabilities view shows an individual asset or zone presented in a spider web chart to highlight risks. Image courtesy of Tripwire.

The need for visibility The first step for any manufacturer trying to elevate industrial security is to have visibility into what’s actually in their environment—a picture that is lacking at most companies. Prior to deployment, organizations need to gain a deep understanding of their current operations technology asset and network environment so they can establish where the risks are and evaluate how new IIoT initiatives might impact future exposure. Many shops are unaware of IIoT devices that have come in under the radar, such as a random router added to create a Wi-Fi hotspot in a dead zone or a device connected to the backplane of a controller that’s part of the distributed control system governing plant processes. “Wireless connectivity bridges the theoretical air gap, which is one of the key security components companies rely on,” Livingston says. “As a result, unpatched systems can now be exposed to the internet through the backplane of a controller.” Once taken, asset inventory needs to be mapped to a risk profile predicated on things like business revenue or regulatory compliance. If both the business and network infiltration risks are determined to be high, that asset should be red flagged for immediate

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action, whereas other high-risk assets that map to areas of lower vulnerability can wait for security remediation, according to Insight Cyber Group’s Cianfrocca. Insight Cyber Group’s NetRadar managed IoT security service reportedly collects data from cyberphysical environments in a “non-invasive” way to get an accurate inventory picture without disruption to industrial processes and production, he says. Their approach also favors intelligent monitoring and incident response services as opposed to conventional IT firewalls. “Intelligent monitoring is the way forward—it’s non-invasive and proactive, and the way security changes, you need to move past traditional technology to something based on monitoring, visibility, and artificial intelligence (AI),” he explains. Differing approaches to OT cybersecurity Cisco is parlaying its enterprise security muscle to the industrial space, but is committed to adapting its offerings to meet the needs of the OT world where it exists rather than coerce them into IT-driven solutions, notes Wes Sylvester, Cisco’s global industry director, manufacturing & energy. Visibility into assets, but specifically visibility into the

next-level details related to those assets is important; for example, knowing the kind of data, where it’s coming from, and if it’s secured, Sylvester explains. Through device recognition and data tagging, Cisco’s CyberVision platform builds a view of asset inventory, communications patterns, and network topologies while also extending IT cybersecurity capabilities to the OT domain, including protocol analysis, intrusion detection, behavioral analysis, and OT threat intelligence. The platform creates a converged IT/OT security operations center, bringing detailed information on OT assets and threats to enterprise security infrastructure like firewalls. “In the best case, OT has a different security posture; in the worst case, it has no posture,” Sylvester says. “You can’t flip the switch and have them be on the IT security side.” While IIoT cuts a path to real-time analysis and the ability to boost operational performance by calibrating automation systems, the very ability to modify equipment creates risk by establishing new access paths into the industrial control network. As a result, organizations need to move away from conventional perimeter-based security measures to a software-driven approach and a focus on hardening endpoints such as HMIs, worksta-

3/30/21 11:09 AM


tions, controllers, and PLCs against potential attacks, security experts say. Verve’s Endpoint Protection Platform is said to tackle the problem with agent and agentless technology in that the platform uses agent-based asset management capabilities to provide a view into each subnet and asset on a real-time basis without scanning or scripts and consuming minimal bandwidth. The agentless device interface gathers data on firmware, configurations, and network device rules. The platform also combines asset inventory, vulnerability management, configuration management, and patch management into a single platform while supporting open APIs (application programming interfaces) so telemetry from both the IT and OT worlds can be integrated for end-to-end enterprise visibility. Tripwire Industrial Visibility also makes OT network assets visible to enterprise security teams. The platform extends IT security controls—automatic discovery of assets, AI-driven network zoning and segmentation, and known and zero-day threat and anomaly

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detection—to the OT landscape by supporting a diverse range of industrial protocols and by incorporating passive, active, and AppDB scanning capabilities for visibility. The Claroty Platform is evolving the OT security model with new capabilities to address remote work—now the norm even for industrial companies due to the global pandemic. With its Continuous Threat Detection 4.2 and Secure Remote Access 3.1 capabilities, the Claroty Platform features remote incident management features, including alerts on remote user activity and providing help to prioritize remediation, as well as insights into similar events across the Claroty user base to contextualize whether alerts are true threats or false positives. “In IT, active scans that touch every device and every query over the network are the norm, but in industrial environments, those practices can bring a plant down,” says Tripwire’s Tim Erlin, vice president of product management and strategy. “We’ve changed the technology to support passive assessment…and found different ways to approach visibility.”

Platforms that straddle the needs of both enterprise and industrial security are an important path to fostering IT/OT alignment, which is essential to a successful cybersecurity strategy. While IT has a deep bench of talent devoted to cybersecurity practices like managing patches and doing vulnerability testing and configuration management, that level of domain expertise is lacking in OT. Because of that delta and the need for end-to-end visibility, fostering alignment between IT and OT through education and joint collaboration is crucial for success. “Education is the biggest hurdle on the OT side of the house,” says Richard Wood, product marketing division manager at Moxa Industrial Automation. “The average worker doesn’t understand that plugging their cell phone into a USB port on an industrial computer potentially risks infecting the entire network. People have to understand that security is not something you buy—it’s a continuous process like quality.”

3/26/21 1:59 PM


How Intelligence is Powering Supply Chain’s Next Wave Artificial intelligence and machine learning technologies are behind a host of emerging applications, especially for manufacturing demand planning and warehouse applications. By Lauren Gibbons Paul

Contributing Writer, Automation World


ike most everything in 2020, global supply chains were thrown into disarray during the pandemic. Demand plans and equipment maintenance schedules went out the window with U.S. e-commerce volume growing 44% as consumers stayed home to buy everything they needed to sustain life. In response, many supply chain managers increased safety stocks to hedge against increased volatility. Now, as we begin to emerge from lockdown, everyone is looking for ways to boost supply chain velocity and efficiency. Artificial intelligence (AI) and machine learning (ML) technologies seem a natural fit for helping wring out greater efficiency and better decision-making in this area. Steve Banker, vice president of supply chain services at ARC Advisory Group, wrote recently about a host of AI-driven supply chain use cases, ranging from those that are still hype-stage to those with established return on investment. Banker cites, in order from most hypothetical to most mature: blockchain, autonomous trucking, ML for warehouse management, robotic shuttle optimization, ML for transportation, ML for demand planning, real-time location services, and IoT for transportation. Vendors are rushing to add AI/ML capabilities to their software. “It’s the arms race of enterprise software,” says Shaun Phillips, director of product management for QAD DynaSys. But there is real value to be had—now—for use cases like demand planning and warehouse management.

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Demand planning, the low-hanging fruit

For many companies, demand planning is a good place to begin with AI/ML. According to Bill Panak, vice president of data sciences at Logility, there’s overwhelming proof that ML algorithms outperform the classic models that were used for building a forecast. ML enables auto-tuning, or automatic adjustment, says Panak, that is especially useful in so-called “black swan” events like the pandemic. The ML capabilities inherent in Logility’s demand-planning application can spot patterns and trends in all types of data (structured and unstructured, internal, partner, published sources, public) long before a human planner would. ML is extremely useful for helping optimize pricing and promotions, too, he says. QAD also tackled demand planning as its most immediately useful AI/ML use case, working with clients starting in 2018 to understand what its clients really needed. “We sat down with customers at our user event,” says Phillips. “Some of our customers said it’s all hype,” recalls Phillips. But four emerged with interesting potential use cases, and they were willing to share their strategy with QAD. The first use case was cluster analysis for demand planning. Since companies can’t do a demand plan for every SKU, they would choose an attribute and aggregate it up to all the SKUs with the attribute for planning purposes. But making these decisions at the aggregate level was not giving the right decision on a more granular level. “We analyzed millions of SKUs—when did they peak, when did they trough? What was the customer service level? Was it an esoteric item? What were the different types of raw materials used?” says Phillips. The QAD DynaSys team built clusters based on shared common sales behavior. “They were getting smarter forecasts for less work. When they introduce a new product, they would align by the brand and product size and we put it into a cluster.” The result

was a much more accurate plan. This application was based exclusively on internal data, including sales history, service level, unit cost, price point, and bills of material. The next use case was using ML to analyze complex sales behavior for three customers, including a retailer and an alcoholic beverage maker. For the retailer, the application leveraged external data sources to analyze the sales of complex products that contributed greatly to total profit but were only sold three months per year—things like sunscreen or Christmas trees. “We focused on trying to predict the sales each month to handle demand outside the key sales periods (summer and the holidays, respectively, for the products just mentioned). For the beverage company, the application analyzed public customs data to see when a certain type of glass bottle entered the country. If these bottles came in at higherthan-expected quantities, that was a signal that their competitor was gearing up to meet a peak demand. The three companies are still benefitting from these applications. QAD DynaSys also developed an app for daily forecasting, which was useful for companies that make products that expire quickly, such as fresh meats, produce, and newspapers. “You can’t sell Monday’s demand on Tuesday because it has expired,” says Phillips. “We give them weekly data broken down into days.” QAD is now into the second phase of its AI/ ML journey, planning to offer adaptive supply chain planning in its flagship ERP application. (The company has not yet announced when it expects to release these capabilities.) Phillips calls this application a “supply chain digital twin.” “We will digitally simulate the supply chain, automatically refreshing it with data like bills of material, the run rates, the yields, procurement and supplier lead times to be able to make real-time adjustments to plans and inventory levels. “Say I buy from this supplier every week and they ship on a 14-day lead time, so I always keep enough stock for 14 days,” he says. But if you can take your lead times from 14 days down to seven,

you can make your supply chain much more agile, reducing the amount of inventory and thereby holding onto more cash.

Optimizing warehouse management

Manhattan Associates’ focus is applying ML algorithms to warehouse management applications via its Manhattan Active Warehouse Management product. Order streaming involves coordinating and optimizing all the aspects of the distribution center—the people and the automation equipment, as well as the shipment windows and requirements for fulfilling orders. “I have all these moving parts in the distribution center. Order streaming optimizes across all of those, adapting over time,” says Adam Kline, senior director of product management. The core of order streaming is the work release engine, which gets inputs from all over the distribution center (DC). “It understands the layout of the DC. You may have 50 aisles full of storage locations, one-way aisles, bi-directional aisles. It understands how to build the most efficient pick path,” says Kline. The ML capabilities predict how long the work will take, which helps determine the sequence of work and who it should be delivered to. It’s still early days for adoption, says Kline, with about 12 customers currently using Manhattan Active Warehouse. An apparel company and a home décor brand are currently using order streaming to drive lower order cycle times, better picking and packing efficiency, and an elimination of the number of orders that need to be sent with upgraded shipping, which takes a bite out of profit. Beckhoff uses AI to enable more accurate predictive maintenance of equipment used in order and parcel fulfillment, another application that became more important with the e-commerce boom last year. “Distribution centers typically have their peak season between November and the end of the year,” says Doug Schuchart, material handling and intralogistics man-

For the beverage company, the application analyzed public customs data to see when a certain type of glass bottle entered the country. If these bottles came in at higher-than-expected quantities, that was a signal that their competitor was gearing up to meet a peak demand.

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ager at Beckhoff. “But there has been a peak since early spring of last year and it hasn't slowed down since.” Throughout the pandemic, says Schuchart, AI has been instrumental in turbo-charging predictive maintenance applications. Companies need to know when the components are going to fail so they can replace them in advance of downtime. With no end to the peak season in sight, it’s challenging for maintenance crews to schedule operational downtime to do scheduled maintenance. Instead they can optionally apply AI theories to maintenance, replacing equipment prior to failure. Equipment sensor data (such as vibration and temperature) along with other data including hours of operation and scheduled maintenance) is used to build the AI model via MatLab, Python, or other open data science platforms. Once the model is trained, it is put directly on the machine controller so it is processed locally in real time, according to Schuchart. “We don’t have to send so much data to

the cloud or to the enterprise systems. We can do some initial analytics locally,” which minimizes the bandwidth of data sent and reduces cloud storage costs,” he says. Beckhoff customers are also using the AI capabilities to optimize energy usage as part of their sustainability initiatives. This is important, as the ongoing peak demand in the fulfillment centers has radically increased energy usage. Beckhoff uses AI/ML to fine-tune performance of equipment within automated storage and retrieval systems, for example. Shuttles numbering in the dozens and sometimes hundreds run back and forth to their target pick locations, potentially 24x7. “If we apply AI, we can look at the equipment profiles and analyze the time it takes to get the shuttle to its intended location. We can optimize the jerk and acceleration of the equipment, so it runs using less energy and minimizes the wear on the shuttle,” says Schuchart, two benefits for the price of one. “We can optimize the accel-

eration on any drive and motor in this way.” Coming out of the pandemic, no one really knows how e-commerce demand will be affected. But it’s safe to say that online ordering will never go back to its preCOVID-19 levels. E-commerce demand will continue to reshape supply chains, making efficiency and optimization more important than ever. Toward that end, AI/ML will play an increasing role. Of course, what really drives supply chain value is the data. As companies fix quality issues and build up data as the foundation for AI/ML projects, they tend to realize just how limitless the prospects are with these technologies. “You can improve things like demand planning, but what really drives value is when you realize what data you have,” says Panak of Logility. “Investing in that data is just as important as what you want to do with it.”

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ERP Upgrade Enables Direct-to-Consumer Model for Avery QAD’s latest cloud-based enterprise resource planning system has helped Avery better integrate its operations to provide the flexibility needed to depart from its traditional big box pallet model. By Aaron Hand, Editor at Large, Automation World


he Amazon Effect—in which consumers expect to be able to get exactly what they want, how they want it, when and where they want it—is impacting all reaches of business and manufacturing. Avery’s operations in the UK, France, and Benelux, where the company makes pressure-sensitive adhesive labels, has seen its business shift from selling primarily to wholesalers to shipping and selling more direct to consumers.

This has meant big changes from Avery’s traditional pallet-based shipments. No longer does the labeling company sell only large multi-packs to wholesalers, who in turn sell to dealers, who then sell to consumers—more and more, Avery is shipping directly to the customer. It’s a change that was a fundamental strain on its existing enterprise resource planning (ERP) system. Although the company was generally happy with the old system

it had in place, last year they decided it was time to talk with QAD about an upgrade. “It was extraordinarily resilient. But it had been installed in 2001, had heavy customization, not many updates, and the customizations we had didn’t allow us to transition from that big box pallet model to third-party wholesalers and contract stationers,” says Sean Flanagan, operations director for Avery’s UK and France operations. “The more we were want-

Day one of go live with the upgraded ERP system at Avery’s manufacturing site in Northampton, UK.

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Avery dispatches its first shipment after completing the picking process with the new QAD capabilities.

ing to deliver direct to dealer or consumer, the more the system was tripping us up.” One of the biggest issues that the heavy customization of the previous ERP system caused was that the system had been designed to pick single quantities—the outer packages that typically go to wholesalers. “In our foundation business—that’s still important to us, by the way—we might make five or 10 boxes of labels, and then we would package them in an outer box. That outer box would be sold to a wholesaler or contract stationer, and then that wholesaler would break down the box and sell it to dealers in smaller packages,” Flanagan explains. Avery’s new business model, however, needed to be much more dynamic in the way it operates shipping and logistics. “We wanted to be able to ship in single packs and multiple packs, and it was difficult to get our old system to do that.”

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Linking disparate systems

Avery’s previous ERP linked to a number of other systems within the business. The finance package was completely separate, which created problems, Flanagan says. A separate planning model also created issues with continuity across the enterprise. “All planning in operations was done via an Excel planning model that had no interaction with stock and materials availability and finished goods availability,” he explains. QAD Adaptive ERP, based in the cloud, was able to bring all these disparate systems together to help Avery get better visibility across its operations. Designed specifically for manufacturing, it supports industry-specific business processes and flexible deployment options. It moved Avery away from spreadsheets that were not well understood by employees to process maps with training embedded into the system, says Peter

Jones, business consultant for QAD who’s been working with Avery in the UK, Australia, and continental Europe for about eight years. “Any small tweaks can be done fairly easily and are built into those process maps,” he adds. “Training records are built into it. Statements of work in procedures are built into it. This helps with implementation and the training of people.” The really big win from Flanagan’s perspective was the ability to use planning systems through QAD. “That changed our world because our planner has now got total visibility of raw materials and a link to raw materials and also to finished goods,” he says. “We had a planner who spent almost eight hours a day planning. Now our planner spends just two hours a day planning and then is doing more strategic work for the rest of the day. That’s really been a huge, huge change for us.” With the freed up hours, Avery’s planner

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has been able to spend more time optimizing the production capacity, adding different pieces of equipment into the plant that would’ve been difficult to do with the old Excel model. “The world for us has become much more dynamic,” Flanagan says. “We’re adding new equipment every couple of months now. That was incredibly difficult to include in our old planning system. And now we’re able to do that.” The previous ERP model was also very dependent on a third party. “That hurt us quite a lot. If we wanted to add customers, we were completely dependent on something outside; a link with other systems,” Flanagan says. “The beauty of what we’ve got now is we’ve eliminated a number of alternative systems—paper or Excel-based systems—whether that be labeling, planning, shipping. More or less, all that has come under the umbrella of QAD, which includes finance…Now we’re all on QAD and that links throughout the whole process, where the warehouse automated solutions product talks directly to finance rather than going through interfaces that we have to manually set up.”

to the operational team.” Putting ERP into the cloud makes it easier to deploy, scale, and secure. “We showed them that the cloud meant it took away the need for them to have people employed full time,” Jones says. For a company like Avery with a limited IT department, it can be difficult to manage the growing complexity and functionality of today’s ERP system. Now, with Avery’s ERP in the cloud, “QAD is offering back-office support, monitoring performance, doing patches, upgrades—it’s all looked after by QAD.” This saves the investment that Avery requires for IT personnel, but it also saves the capital needed to invest in other assets, notes Acyr Borges, QAD’s vice president of packaging, commenting on the scalability of ERP in the cloud as well as the agility to make changes.

Benefits (and trepidation) around the cloud

Moving forward

Flanagan does not consider himself a pioneer in any way, and he was very reluctant to take Avery’s ERP system into the cloud. “I was 100% against it,” he says. “I really had serious doubts—about connectivity, about ownership. Our old system, despite its restrictions, was robust and it served us well.” Nonetheless, Avery has been taking its ERP move to the cloud slowly, Flanagan says, avoiding any intrusive customizations, but he’s happy with what he’s seen so far. Reliability has been a big benefit, he says, but he expects bigger benefits to come as they expand further into the cloud’s capabilities. “I think the longer-term benefits of the cloud is still to come for us,” he says. “As we talk about upgrades and changes, they should be a lot simpler than they would’ve been if it had been on premises.” One of the reasons Avery decided to move to the new ERP system was because of an issue that might sound familiar to manufacturers in all industries—a retiring workforce and difficulty finding skilled workers. “We had one person based in Paris who wished to retire, and only goodwill kept him in the company,” Flanagan describes. “He used to do all our customizations, all our configurations. We were very dependent on one person.” Not only has that situation gone away, but the region has reduced its system support from the team of five it had four years ago to just a team of one dedicated to the system. “What has been a great help to us is the process owners have become the systems experts,” Flanagan says, explaining that the various managers of production, e-commerce, etc., have been able to become highly skilled in using the QAD system. “We’ve devolved what was an external system expertise largely

says, noting the manufacturer’s plans to grow and evolve its ERP system to provide even more capabilities. “You sort of don’t know what you don’t know until it’s in front of you. But what I think we’ve got now is a solution that we’re growing in confidence with that will allow us to be able to control our supplies, control our raw material storage, control our planning, control our finished goods storage and shipments— and allow us to be flexible in the new consumer world.” QAD has been in continued talks with Avery about its next stages of upgrades. The new system and its basis in the cloud will make those changes easier. “It’s now small, incremental changes rather than a huge change,” Jones says. “We now look after those changes and can do them in an incremental fashion.”

The cloud-based system also gives Avery the “agility and fleetness of foot” it needs to move forward, Flanagan

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Protective Camera Enclosure

autoVimation, The Chameleon XS enclosure series for the Intel RealSense Depth Camera family has recently been expanded with the addition of a new enclosure for the D455 camera. The 500g lightweight aluminum enclosure provides IP66/67 ingress protection all-around, allowing the depth cameras to be employed in challenging industrial environments. The outer dimensions are 150 mm by 64 mm by 34 mm. Like the existing Chameleon XS enclosures for the RealSense sensors D415, D435, and T265, the D455 model ensures shade-free images thanks to the special arrangement of the windows. A cable gland with an extralarge sealing inlay enables tool-free entry of standard cables with USB-C plugs.

Continuous Servo Drives

Advanced Motion Controls, The FE060-45C-CM and the FE060-45C-EM have recently been added to the Advanced Motion Controls FlexPro digital servo drive family. The two micro-sized drives are each capable of outputting 45 amps continuous current and support CANopen and EtherCAT communication, respectively. This is nearly double the continuous current rating of previous FlexPro models of the same size. Designed with compact form and power density in mind, the microsized FlexPro drives can outperform larger-sized digital servo drives and still be integrated into tight spaces. In addition, both servo drives are available in developer board versions, the FD060-45C-CM and FD060-45C-EM, with large connectors for research, development, and prototyping purposes.

High-Precision Micro-Machining Laser

Aerotech, The AGV3D is a new 3D beam deflection system for laser micro-machining. The three-axis laser scanner can be used for high-precision manufacturing of complex components for the medical, microelectronics, and automotive sectors, as well as additive manufacturing. With integrated water and air cooling, the new scanner also achieves increased thermal stability. Temperature gradients within the AGV3D are avoided, reducing errors caused by thermal drift. This results in consistent performance over the entire processing cycle with high laser processing accuracy. In addition, due to the AGV3D’s large field of view, smaller, more efficient movement mechanisms can be used for positioning the workpiece. Depending on the process requirements, the threeaxis scanner is available with different apertures for different beam diameters.

Safety Relay Module

IDEC, The new HR5S safety relay module is the first on the market designed specifically to meet ISO 13849 Category 2 safety requirements. Safety relay modules, like the HR5S, are a key component for interlocking equipment and driving it to the safest possible state in case an emergency stop pushbutton or other safety input signal is activated. The HR5S safety relay module can be used to warn users of impending issues instead of automatically stopping equipment, providing productivity gains through increased uptime. If designers are using a system with Safety Category 3 or 4, then redundant safety inputs, controllers, and outputs must be provided. However, when Safety Category 2 is used, only single input connections, less expensive safety relays, and single outputs are required.

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Photoelectric and Proximity Sensors

AutomationDirect, Several small sensors have been added to the AutomationDirect proximity sensor lineup. New rectangular 8 mm by 16 mm inductive proximity sensors are available in flush and non-flush mounting options, NO or NC output, and NPN or PNP logic. In addition, round 8 mm capacitive proximity sensors are now available. These sensors have a potentiometer to adjust sensitivity and are available with an M8 quick-disconnect or 2m pigtail cable. Finally, two new series of photoelectric sensors have been added. This includes an 8 mm round option and an 8 mm by 8 mm by 4 mm rectangular model.

Two-Channel Oscilloscopes

Saelig, The PicoScope two-channel 9402 Sampler-Extended Real-Time Oscilloscope (SXRTO) series features two models—the 9402-05, which has a 5 GHz bandwidth, and the 9402-16, which has a 16 GHz bandwidth. Designed to analyze repetitive or clock-derived signals, the oscilloscopes feature two high-resolution 12-bit channels driven by the equivalent-time sampling (ETS) of up to 1.0TSa/s (1.0ps) for the 5GHz model and 2.5TSa/s (0.4ps) for the 16GHz version. The wide-band inputs can accurately measure transitions as fast as 22ps, pulses down to 45ps wide, and permit clock performance and eye diagram analysis of up to 11Gb/s signals (to third harmonic).

Passive Fiber Optic Splitters and Combiners

L-com, A new line of passive fiber optic splitters and combiners designed to work in various passive optical network (PON) and gigabit passive optical network (GPON) applications has recently been released by L-com. The new line consists of 18 new models that are comprised of LGX-style cassettes as well as 900um and 250um fiber cable form factors. The LGX cassettes are available with 1x02, 1x04, 1x08, 1x16, and 1x32 even splits and feature SC fiber connectors with an APC polish. The 900um field units are available with 1x04, 1x08, 1x16, and 1x32 even split options as well as non-connectorized versions or connectorized versions with SC/APC polish connectors. The 250um field units are available in 1x02, 1x04, 1x08, 1x16, and 1x32 even splits using bare fiber (no connectors).

Linear Actuator with Encoder and Temperature Sensor

Moticont, The SDLM-051-070-01-01 direct drive linear actuator with integrated position and temperature sensors is the latest addition to the patented series of zero backlash, zero cogging, high acceleration, high speed, high resolution actuators from Moticont. The actuator, also referred to as an electric cylinder, is 50.9 mm in diameter and the compact housing length is 69.9 mm. The total length, including the shaft, is 101.6 mm. The linear optical quadrature encoder is protected inside the actuator housing and directly connected to the shaft for improved accuracy.

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T�e le tri ati Tra s rtati a d By Dick Slansky Senior Analyst, ARC Advisory Group


ransportation is being electrified, with an increasing number of all types of vehicles that have traditionally been powered by fossil fuel making the transition to electrification, and this trend will continue to surge into this decade. While more electric vehicles (EVs) are hitting the personal vehicle market, the growth is surging in trucks, buses, and other forms of mobility. Today, we are also witnessing EV technology moving to areas of transportation such as electrified aircraft, enabled by a new generation of lightweight and powerful electric motors. For car makers to develop the next generation of EVs, they must have the design/ build technology that allows their engineers to design all elements of an electric powered vehicle. This would include all aspects of EV engineering—from electric powertrain to electrical/electronic system architecture to vehicle engineering, battery, and thermal engineering, along with simulation tools for every stage of EV development. PLM market leaders like Dassault Systèmes and Siemens Digital Industries Software are currently providing electrification design tools and solutions across all areas of EV design, development, and engineering. As auto OEMs prepare to ramp up production, some are re-prioritizing EV production lines to meet the expected strong demand and to fulfill regional regulatory requirements, such as the EU’s strict target for CO2 emissions. While the overall number of EV sales has declined in China and Europe during this crisis period, the actual market share for EVs has risen, and post-pandemic demand is expected to take off. EU leaders have maintained a strict fleet-

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wide CO2-emission target of 95 grams of CO2 per kilometer by 2021. Many major EU-based OEMs have publicly committed to reaching that target and have rolled out an unprecedented number of battery-powered and plug-in hybrid EV models. Additionally, EU governments have introduced new purchase subsidies, tax credits, or a combination of incentives to encourage EV adoption. These incentives, combined with the increase in EV models, has led to an upsurge in consumer demand—despite the continued COVID-19 pandemic.

Next generation tools will drive electrification

Designing and building electric vehicles that will ultimately replace internal combustion engine (ICE) vehicles presents a formidable set of technological challenges. It not only requires a new set of technologies to address the electrification of the vehicle, but it requires developing and building a completely new infrastructure to keep EVs on the road and running efficiently. Car makers must completely re-think vehicle engineering and the vehicle experience for the driver while trying to keep the existing DNA of their car brands as they convert iconic brands to electric drives. The design of an electric vehicle involves the engineering and integration of all the elements required to produce an EV. This would include the electric drive train, inverter, battery cells and battery pack, onboard charger, chassis design, package and body design, HVAC systems, and DC-DC converter for onboard systems. Each one of these areas poses specific engineering and technological challenges and requires design tools that address the systems engineering requirements for integrating functional, logical, and physical systems to meet full vehicle integration. For example, the electric powertrain design process involves integration of complex engineering designs for motors, gears, torque converters, and other components with powertrain control system design, and electronics and performance design. Further, the electric drive design must consider factors such as thermal requirements and constraints, electric ripples, packaging, scal-

ility ability, a common-part strategy, and manufacturability. Electric motor torque requirements in the overall drivetrain of the vehicle present engineering challenges. There can be no loss of power compared to ICE drivetrains. Passenger space cannot be compromised by drivetrain and battery pack requirements, and the overall cost to produce the EV must be managed within the context of design requirements. All of this requires robust design/build tools that can address the challenges of integrated electrification design. Battery cell and battery pack engineering presents some unique challenges to EV design. Battery packs must be engineered to maintain temperature gradients of less than two degrees, as temperature variants can have significant effect on battery performance. Additionally, battery packs must be designed to meet ridged safety specifications in terms of thermal states and durability. The manufacture of batteries for the EV industry is a unique process which begins with the active material manufacturers finding the right source of natural materials to power battery cells. Battery cell manufacturers provide the basic battery power that serves multiple industries, in this case, EVs. Battery module/pack assemblers provide the battery pack configurations that meet the design requirements specified by EV designers for specific vehicles, and the overall transportation and mobility industry.


While the electrification of mobility is focused primarily on the EV market and the automotive industry that produces these vehicles today, manufacturers across all areas of transportation should be aware that the long-term outlook is for the electrification of all modes of mobility. ICE powered vehicles dependent upon fossil fuels will eventually be phased out, even if that is most likely several decades away. Meanwhile, the technology of electrification will continue to progress, enabling a host of opportunities for new business and manufacturing advancement.

3/30/21 11:09 AM


Discussing the Data Layer in Regulated Environments By Eric Resiz Lead Engineer, Panacea Technologies


s regulatory agencies catch up with the information age, thoughtful data historization is becoming more vital to the normal operation of any good-practice facility. Pharmaceutical companies now hoard truckloads of data, devoting significant resources and capital to ensure data integrity and 21CFR Part11 compliance. Many appear to be content limiting archived data to a historian system, creating a sprawling data lake where end users are left to make sense of it. But how is all that data beneficial outside of compliance and where is the return on investment? Why stop at compliance if a little effort will put that data back to work for you? That’s where the data layer comes in! So, what is the “data layer” and why is it important? The data layer is comprised of the automated, intelligent functionality that collects and assesses data produced by a system, as well as contextualizes and links associated data which then can be used for higher level processing, such as process analytical technology or machine learning. It also includes shuttling readable data to the end user via reports generated automatically or manually. While it may seem complicated, simply put: The key principles of developing a data layer are collection, contextualization, association, and reporting. Data collection consists of two primary types of data—time series and relational data. Most process data falls under time series data such as temperature, pH, points collected by a historian system with timestamps, and other metadata. Equally important is the relational data that is typically stored in an ELN, LIMS, SQL, and/or MES system. Examples of relational data include raw material lot information, batch execution schedules, and sample data.

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Contextualization is the step of giving context to archived data in a historian system. Data storage is cheap, so cost isn’t a barrier in collecting data as it may be useful in the future. Most historized data isn’t relevant to end users. A critical step is defining what data is important and when it is important. This entails clearly defining your process and any vital sub steps, then listing the critical process parameters (CPPs) for each step. This delineates data context and characterizes relationships to process steps. Association ties related parts of the process together via automated functionality. This is accomplished by establishing a hierarchical structure from the overall process and sub steps, essentially a logical hierarchy of data bins, and configuring automation within the data layer to recognize and link data bins together. For instance, the largest bins would encompass the whole process. Enclosed therein would be individual unit operations which, in turn, contain the logical divides of a batch like media prep, inoculation, growth, and harvest. Additionally, any batch event like alarms, log events, sample data, or lot info would be placed in the batch container. CPP data is contained in the bottom layer of the hierarchy making it available to all the higher levels of the process. Selecting software with the functional ability to access and link historian and relational databases is vital. Despite popular belief, there are cost-effective, reliable, and 21CFR Part11 compliant software options available that fit the bill. Establishing logical ties enables the data layer to reference data in the final step of the process—reporting. Reporting is where everything comes together! Contextualized and associated data enables reporting tools to easily acquire related data needed to build trends, tables, and other information they require. Auto generated reports can be developed and executed via event triggers, like the completion of a batch, utilizing the association hierarchy to acquire all relevant data and events. Typically, these are exception reports which include log events, sample data, any alarms that occurred, and other information related to batch release. Reports can also be automatically generated based on machine

learning triggers to identify when a process trends outside normal operating parameters or identifying equipment requiring preventative maintenance. This gets important information not just to operators on the plant floor, but the individuals responsible for making process decisions and planning maintenance—much faster than processing manually acquired data. End users can use the association hierarchy with manual report templates designed to allow users to select an enumerated event from the hierarchy enabling the system to acquire all information and data applicable to the hierarchical level selected. This greatly reduces potential hours spent collecting data for reports. Implementing software and hardware within the data layer to accomplish data collection, contextualization, association, and reporting can streamline post process analysis and expedite batch release. Planning and developing the data layer enables end users to focus less on acquiring and processing raw data, making them more effective and moving the cost of operation away from legwork.

T�e data layer is comprised of the automated, intelligent functionality that collects and assesses data produced by a system, as well as contextualizes and links associated data which then can be used for higher level processing such as process analytical technology or machine learning.

3/30/21 11:08 AM


How Can Industrials Build for an Intelligence-Led Future? By Brian R. May Managing Director, Industrial North America, Accenture


eading industrial companies are moving toward greater use of automation. Some are harnessing data at scale to power intelligent operations. But while real progress is being made, there’s an outstanding opportunity for companies across the sector to go further. When it comes to being future ready, new research from Accenture shows that as many as 94% of industrial companies could accelerate their journeys to intelligent operations. How? By maximizing the strategic value of data, process transformation, future-ready talent, and new digital technologies.

Cost cutting doesn’t cut it

Up to now, industrial companies have often been reactive in their approach to business transformation. They’ve favored tried and tested methods—such as brute-force cost reduction—to ensure profitability when a cyclical downturn was imminent. In an increasingly intelligence-led, digital world, a new approach is needed. Everything moves much faster than it used to. Companies in every industry now need to respond quickly to new customer requirements, endconsumer expectations, and technological possibilities. They need highly agile operations that can sense change and then pivot to new opportunities. Reactive cost-cutting exercises won’t deliver those kinds of agile capabilities. To become truly future-ready, many industrial companies need a strategic rethink of the whole operating model and technology plan, building in the smart orchestration of both human talent and machine intelligence. Using insights from data and machine learning, they’ll enable predictive, preventa-

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tive, and autonomous operations across the whole business. Of course, companies won’t create these kinds of intelligent operations overnight. There needs to be a carefully considered journey of increasing technology, data analytics, and skills maturity. At the same time, given the rapid pace of change, there is a real sense of urgency. Action is needed now, at scale.

Three priorities for the near term

So how can industrials improve their futurereadiness? There are three areas to focus on: data, talent, and technology.

1. Get more value from all that data

Industrial companies can now access vast amounts of data. That includes not only the obvious sources—like operational data from ERP systems or customer data from point-of-sale systems—but also the broad external data sets that many organizations don’t even consider. For example, how many OEMs are analyzing indicators from the consumption end of their value chains? Leaders are doing this to uncover invaluable information to predict demand patterns and understand what’s coming down the road. Others need to do the same. Combined with advanced data analytics— including machine-learning tools—this kind of creative thinking about data can transform industrial companies’ demand forecasting abilities. This, in turn, allows them to fire up their supply chains proactively, avoid last minute surprises, and meet rapidly evolving customer expectations effectively. It’s all part of a wider shift from selling a product to delivering customer outcomes. And it needs the whole business to be able to leverage data at scale—the back office just as much as front-line operations.

that means having the right people with the right skills. A refreshed talent strategy to complement the data strategy is another priority for industrials. Companies should be looking to retool existing talent and/or bring in resources with experience in analytics and data science. Some are working with partner organizations who can provide these insights as a service. But no matter how they go about it, it’s vital to have people who can discern the signal from the noise and derive actionable insights from the data.

3. Think about the platform

The third priority is to consider the digital platform the business is using to bring all this operational intelligence together. Rationalizing the ERP system may be part of that story, but there’s a great opportunity for the industry to go broader. Companies today should now be looking to leverage the power, speed, scalability, and innovation of the cloud. Flexible cloud platforms can coexist with legacy ERP environments, while providing fast and cost-effective access to cuttingedge data analytics and other innovative services. These cloud solutions are now arguably essential for running a modern industrial business.

Focus on the future

The last 12 months have illustrated just how important it is for industrial companies to be able to respond quickly to new circumstances on the ground. Shifting to intelligent operations is the way to create that agility—with a new datadriven culture that thinks big, leverages insight at scale, invests proactively in technology, and meets rapidly escalating customer expectations.

2. Cross the talent gap

It’s not just about having the right data. A business needs to know what to do with it and how to understand what it’s saying. And

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Of the 746 manufacturing companies surveyed, 69% are looking to bring production back to North America, 38% are actively hiring, and 55% said they are likely to invest in automation, specifically as it pertains to production performance, process control, and product testing and quality. Stephanie Neil on the role of automation in reshoring efforts.

Variable frequency drives have high-frequency spikes in the drive’s electrical system caused by the switching of the transistors in the drive to adjust the motor’s speed. This is why inverter duty rated motors have thicker insulation on the motor’s windings so they can hold up to these spikes longer over time. David Greenfield on motor selection insights.

Advantech envisions industry moving beyond IIoT and toward an Artificial Intelligence of Things (AIoT) that allows cloud-delivered applications to make real-time, autonomous decisions at the device level. Within this framework, cloud-based AI trained on large amounts of data can provide industry operators a means of more easily extracting value from their IIoT infrastructure. David Miller on Advantech’s concept of widely connecting artificial intelligence with Industrial Internet of Things technologies.

A functional specification or configuration specification contains lots of technical jargon, requiring an understanding of both the technical content as well as the structure of the document. Was the document written for GAMP5 processes? Was it structured for S88 batch context? Knowing these will help in your review by knowing the expectations of the document and, ultimately, the project. Mark Noseworthy of Superior Controls on reviewing engineering documents.

A differentiator for Flowserve is its dedicated remote monitoring facility, which is staffed by a team of technical specialists who not only identify problems but also help companies understand how to fix the issues. Aaron Hand on Flowserve’s introduction of the RedRaven service platform for monitoring of flow control equipment.

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Automation World April 2021  

Renown for editorial excellence, Automation World covers the entire spectrum of industrial automation technologies, software and devices. AW...

Automation World April 2021  

Renown for editorial excellence, Automation World covers the entire spectrum of industrial automation technologies, software and devices. AW...

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