Analytics in the Age of the Digital Twin · of the Digital Twin. A Smart Industry Technology Report on Data Analytics & Modeling. ... at the Smart Industry 2016 conference in Chicago

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Analytics in the Age of the Digital TwinA Smart Industry Technology Report on Data Analytics & Modeling

Machines have long produced reams of data about their performance. But only in the recent years of our digital transforma-

tion have we had the capacity to digest and fully understand this information and, via advanced analytics, make informed

decisions to optimize operations across industry. Likewise, new modeling technologies provide manufacturers the ability to

employ digital twins of their products and processes. New designs can be tested in the virtual world, saving time, money

and resources, and the performance of existing assets can be modeled against their digital twins in pursuit of performance

improvement and predictive diagnostics.

TECHNOLOGY REPORT


TECHNOLOGY REPORT

CONTENTS

Transforming the plant floor with digital data

Digital twin to enable asset optimization

Digitalization changing how products come to life

UOP pioneering ‘optimization-as-a-service’

Of digital twins and threads: New metaphors weave new meaning

Closing the loop on product usage data


TECHNOLOGY REPORT: ANALYTICS IN THE AGE OF THE DIGITAL TWIN

Transforming the plant floor with digital dataOptimizing extruders with analytics and IT-based networking.

“Old digital Neanderthals like me talk about digital transformation on the manufacturing floor, but young digital natives talk about digital transformation of the plant floor,” said Dennis Hodges, CIO of Inteva Products, a large, global automotive supplier, mostly serving vehicle manufacturers. “To us, the Industrial Internet of Things (IIoT) is connected stuff—ma-chines talking to machines, machines talking to people, and vice versa. IIoT includes data manage-ment, applications, people, global networks and intel-ligent devices. The main question is what big project and purpose do we want it to do?”

Hodges and Jon Sobel, CEO and co-founder, Sight Machine, presented “Impactful digital transforma-tion on the manufacturing floor: what works and why” at the Smart Industry 2016 conference in Chicago. Purpose-built for discrete and process manufacturing, Sight Machine’s analytics platform uses artificial intel-ligence, machine learning and advanced analytics to address quality and productivity challenges.

Hodges reported that, while global networks and security are important to many users, simple data management is not a topic that gets talked about a lot. “ERP gives a backflash view of the shop floor with help from XML and MES, but a lot of good data gets lost in various buckets.

“XML can be the worst data prison,” Hodges added. “MES can gather data on parts production and operat-ing parameters, but devices have more information on operating and environmental parameters, such as how long it took to get to the right torque or operating temperature.”

BIGGER DATA

Hodges explained that big data technologies can give a more comprehensive view of shop-floor applications by getting out and getting closer to sensors and data acquisition systems. “This is where Sight Machine really helps us analyze data, get a physical and logical view of our plant, and build an analytics workbench,” he said. “This lets the three most important people—the plant, corporate and machine operations manag-ers—look at data from the perspective that each of them needs.”

To make big data and its analytics work, how-ever, Hodges added it’s also vital to remove the walls between operations technology (OT) and information technology (IT), so companies can work as one. “We do this, too. When Inteva was spun off from Delphi and GM, our OT and IT were joined, and then we brought in quality and engineering people, too,” he added. “This can change everything.

“For example, our digital transformation project includes our ERP system and extruder machines and equipment, which make rolled, synthetic-leather products for dashboards and doors. These devices make leather very quickly, have a high scrap rate, and stream out data that Sight Machine takes and sends to our ERP system. Previously, we had to do analysis after production, but now we’re closer to real-time. These analytics are improving our productivity and profitability.” Inteva is presently using Sight Ma-chine software on four extrusion machines that make Inteather at its plant in Metamores, Mexico, near Brownsvile, Texas.



“Previously, all of these production systems had manual data entry, and only captured partial data and production points,” added Sobel. “Now we’re capturing backend machine temperatures, oil pressures, extruder flows and pressures, and other parameters.” This infor-mation is sent from the extruder via its RS232 port to a local Sight Machine server on Inteva’s plant floor for calculation and analysis, where the data is processed with Kepware software for bridging hardware and software applications and with a REST JSON API, which can take data in any form, run it though Sight Machine’s software engine, and send it via TCP/IP up to the cloud.

The triple acronym includes representational state transfer (REST) web services for interoperability

between computer systems on the internet; JavaScript Object Notation (JSON) open-standard format that uses human-readable text to transmit data objects; and application program interface (API), which is a set of software routines, protocols and tools for building software applications.

“We can do calculations and analysis locally on the plant floor, or we can do it on the cloud with a service like Amazon Web Services of others,” added Sobel.

“Previously, we had to do analysis after production, but now we’re closer to real-time.” Inteva Products’ Dennis Hodges discussed how the company’s imple-mentation of plant-floor analytics is improving produc-tivity and profitability by delivering timely, role-based information to operational stakeholders.

Simple data management is not a topic that gets talked about a lot.



Digital twin to enable asset optimizationMachine-specific data combined with engineering models promise new era of individualized asset performance.

By Mike Bacidore

Amazon, Apple and Google have successfully lever-aged internet technologies and an in-depth knowledge of their customers to create tens of billions of dollars of value in a very short time. And if Colin Parris has his way, GE will do the same in the industrial space. But the gold to be mined in the industrial space isn’t data about individual people, it’s data about individual industrial assets, Parris, vice president of research for GE Software, said.

“We look at the life of the asset to create a psy-chographic model, a model of one,” Parris explained. This model, or digital twin, consists of engineering models combined with machine-specific data and allows the development of per-asset analytics and ultimately an individualized profit-and-loss (P&L) relationship unique to each asset, Parris said. Clearly, GE intends to build the infrastructure that will fa-cilitate the new products and new revenue created to serve these assets, just as Apple, Amazon and Google facilitate fulfillment of our needs as individuals.

“Apple, for example, went from $8 billion in revenue in 2004 to $183 billion in 2014,” Parris explained. “They went from music to entertainment, from enter-tainment to playlists, from playlists to apps, from apps to home, from home to health.” They started with a demographic profile, then added data to create a psy-chographic profile. Next, they use applications to drive business outcome, Parris said. “They use segmentation to start, then go from a model of one to a P&L of one. And when you put it on a platform, it’s cheap and easy to add other products.”

CREATING THE CLOUD-BASED ECONOMY

In an industrial setting, “the digital twin is a set of continuously updated models that deliver asset insight relative to a specific business objective,” continued Parris. This translates to operational optimization of individual assets. For example, GE has used the digital-twin concept to reduce maintenance costs on its GE90 aircraft engine. “Data gathered during each flight allows us to predict the cumulative damage,” Parris said, “so I don’t need to take the engine out of service for inspection.”

“If I have the data on the exact environment that the asset is operating in,” Parris enticed, “I can de-risk the performance I give.”

GE also partnered with Infosys to create an asset-ef-ficiency testbed, which fostered a landing-gear digital twin to reduce unscheduled downtime, improve main-tenance scheduling and enhance customer satisfaction. “More than 20 sensors are used for early identification of eight failure modes,” explained Parris.

“The digital twin is creating the cloud-based data economy,” said Parris. “Let’s say we’ve built the twin capability; then you have that data that says here’s how I operate to maximize my output and minimize my cost. Dynamic teams of cooperative machines with similar operational or financial objectives can generate and share content on their current and optimal states.”

SOCIAL MEDIA FOR MACHINES

Think of the way content posted on Twitter or Linke-dIn or Facebook invite or entice others to post com-



ments or additional remarks on that original post. Can machines also generate content that attracts content from other machines?

For example, a piece of equipment might need to know the optimal setting for new operational modes. “Let’s say a new wind turbine needs to know about prevailing wind direction, generally 183°, plus or minus 5°,” explained Parris. Why should it not ask its neighbors? “Or it could be a failure mode inquiry. Maybe the turbine is seeing an anomaly and can ask if it’s a precursor to a failure.” Another turbine may have seen the same anomaly, then experienced a blade rupture two days later. “In the world of machines, you can do the same thing as you do in business.”

“But industrial analytics are different,” cautioned Par-ris. “There’s model generation and automation, and there’s knowledge extraction. You have to know the semantics and create a taxonomy. It’s all different. When are these parts used? Why are they used? How are they used?”

The digital twin also includes data gathered through an asset’s lifecycle, from design to manufacture through service. “In the data economy, I know exactly what to tell my engineers to design,” Parris said. “They’ve got to figure out how to manufacture it on time, with a reduced cycle, in the right quality and differentiation.”

Parris also described the development of an industri-al “app store,” where core and third-party applications can be combined to create manufacturing processes, and ecosystem participants collaborate through a Digi-tal Manufacturing Commons (see related article).

“This is a very different time for us all—the tools are building better tools,” Parris said. “As costs continue to come down, it will increase what I can do in terms of simulation and innovation. We have to figure out how to use this industrial strength internet to deliver billions in value and drive a whole new economy.”

Can machines also generate content that attracts content from other machines?



Digitalization changing how products come to lifeYou need to think digitally across the entire value chain.

“Digital Darwinism is a significant threat,” said Helmuth Ludwig, chief information officer at Siemens’ Munich headquarters. “Twenty-six of the Fortune 500 dropped out this past year because they missed the train.”

The train comprises the digitalization of processes, as well as reality, and it continues to gain momentum and increase speed with each passing month. From mobile-device applications, such as Pokémon Go, to manufacturing and assembly, the potential of digitized activities is limitless.

“Have you seen the Bulbasaur?” Ludwig asked the crowd of keynote attendees at the Smart Industry 2016 conference in Chicago. He was referring to the Pokémon monster, standing in the middle of the room but visible only to individuals with the app that could “see” it on their mobile devices. “Augmented reality is coming,” Ludwig said, “but what does it mean for customers and suppliers?”

“In the automotive industry, what’s happening is fascinating,” said Ludwig. “It’s not just that they’ve significantly improved their processes. When you walk in an automotive factory, it’s completely different than it was 10 years ago. They now define themselves as mo-bility companies. They believe there’s an opportunity for autonomous mobility.”

Augmented reality is just a small car on the train, and digitalization is just a section. Three elements—digitalization; smart products and production; and insight from data—are barreling down the tracks, and companies will need to decide quickly whether to get onboard, be left behind or run over.

“What does it mean for a company?” asked Lud-wig. “We all have simulations, but how are they put together? Digitalization is changing the way products come to life. With generative design, we’re able to look at hundreds of options, instead of just a few. It’s changing the way products are realized through ma-chine learning, additive manufacturing and advanced robotics. And it’s changing the way products evolve through cloud technology, knowledge automation and big-data analytics.”

FROM IDEATION TO UTILIZATION

“You need to think digitally across the entire value chain,” suggested Ludwig. “We create a lot of intel-ligence that can be used in product design for ideation, in production planning and production engineering for realization and in production execution and service for utilization. Information in real time is of enor-mous value to companies. But how do you integrate all of this—big data and production engineering and design?”

For example, Italian automobile maker Maserati has proven high quality and maximum efficiency can be combined in its luxury sports sedan, the Maserati Ghibli. “From design to execution planning, every-thing is done digitally,” explained Ludwig. “It used to take Maserati 30 months from start to end, but the Ghibli took only 16 months because of digitalization.” It yielded a threefold manufacturing productivity increase, tripling the volume of the car.

“We have all of these islands of test data and simu-lation data and customer-usage data and historical



data,” explained Ludwig. “If you are able to put all of the information in one place, you are able to dial the clock forward. Right now, we are looking at how to integrate all of these. For example, one customer of ours was able to integrate additive manufacturing into the design process and produce a tractor component with less materials and more efficiently.”

And, at Dell, it used to take them weeks to ana-lyze data. Now, with the right tools, it only takes hours, said Ludwig. “The key is identifying the digitalization tipping point for your business. Today,

we need to integrate across ideation, realization and utilization.”

Another other example Ludwig cited is Local Mo-tors, a Phoenix-based automotive company that uses open-source designs and multiple manufacturing facilities. “Local Motors has 50,000 contributors send-ing in their ideas,” he explained. “The CEO then finds places to 3D-print the car near the customer. Partner-ing with Local Motors means combining elements of the digital machine shop and smart manufacturing into micro-factories of the future.”

“You need to think digitally across the entire value chain.”



Refineries and petrochemical facilities are among the most complex process optimization challenges in the industrial world. Raw materials of varying composition, a multiplicity of final products as well as multiple, interacting continuous processes have long challenged model-builders to predict, control and optimize the real-time behavior of these billion-dollar assets.

There exist real-time optimizers that can rise to the challenge, but too often optimization is treated as a project, a discrete event, notes Zak Alzein, vice presi-dent and general manager, Connected Performance Services, for Honeywell UOP. Exchangers foul, feed compositions change, and catalysts lose potency. “Models degrade over time, and a lot of expertise is needed to maintain performance and results,” he says. “Sometimes a project team returns to an optimized site after a year or two only to find the optimizer turned off.”

The advent of secure cloud environments together with the Industrial Internet of Things (IIoT) is representative of the enabling technologies that now allow UOP to marry its deep process know-how with software to solve difficult problems for its customers on a continuous basis. Honeywell is uniquely positioned to deliver on this promise in part because UOP process technology is so pervasive across the global refining and petrochemicals landscape. This allows Honeywell to up the ante on other providers’ basic equipment monitoring solutions, providing reliability, optimiza-tion, utility management and predictive maintenance

services on entire processes—not just pieces of equip-ment, Alzein says. “It’s more complex, but we can deliver more value over time on a platform that allows for continuous innovation.”

The service continuously monitors streaming plant data and applies UOP process models and best practices, big data analytics, and machine learning to find latent and emerging performance problems, alert plant personnel and make specific operational recom-mendations. These recommendations are reported simultaneously to a dedicated Honeywell UOP process advisor, who also monitors performance and provides additional direction and resources.

Connected Performances Services (CPS) was launched in September, 2016, as joint offering between Honeywell’s UOP and Process Solutions business units, and already has signed on PetroVietnam sub-sidiary Binh Son Refining and Petrochemical Co., Ltd., (BSR) as well as AL WAHA Petrochemicals Company of Jubail, Saudi Arabia.

BSR has subscribed to Connected Performance Services to improve refinery and plant performance at its naphtha complex in Quang Ngai City, Vietnam. “The CPS system is an important tool to help our refinery produce more gasoline and consume less en-ergy,” says Tran Ngoc Nguyen, president and CEO, BSR. “We believe it will improve our staff ’s capabili-ties so they can keep our operation running at peak performance.”

AL WAHA will use a tool within CPS called Process Reliability Advisor at its plant in Jubail,

UOP pioneering ‘optimization-as-a-service’Vietnamese, Saudi processors among first subscribers to Honeywell continuous optimization offering.

By Keith Larson



Saudi Arabia, to continuously analyze the operation of its UOP Oleflex olefins unit, to quickly detect and resolve problems to ensure the plant operates at peak capability. The tool uses Honeywell UOP process and fault models, fed by current plant data, to provide key performance information and process recommendations.

“Process Reliability Advisor provides detailed analy-sis of the unit operation and makes corrective recom-mendations to prevent production interruptions and improve plant performance,” explains UOP’s Alzein. “AL WAHA will be using it to maximize onstream

reliability of its Oleflex unit, while also optimizing propane consumption.”

CPS is a one part of Honeywell’s broader Con-nected Plant initiative that helps manufacturers leverage the IIoT to improve the safety, efficiency and reliability of operations across a single plant or several plants across an enterprise. “CPS integrates Honeywell UOP’s process and service expertise into the Connected Plant cloud platform,” Alzein adds. “With this platform we can bring our collective expe-rience across hundreds of thousands of units to bear on behalf of our customers.”

“Models degrade over time, and a lot of expertise is needed to maintain

performance and results.”



Of digital twins and threads: New metaphors weave new meaningBy Keith Larson

The growing import of two related concepts con-tinues to stick with me: those of the digital twin and the digital thread. The digital twin refers to a digital model of a particular asset that includes not only its design specifications and the engineering models that describe its behavior, but also the operational data and as-built data unique to that particular machine and its life experience. Similarly, the term digital thread refers to an integrated view of asset or product data woven throughout its lifecycle and across historically siloed functional perspectives. (For more information on these concepts, read the stories based on presentations given at the Smart Industry conference: GE’s Colin Parris on the digital twin, and Bill King of the Digital Manufacturing & Design Innovation Institute on the digital thread.)

The fact that industry has found it useful to latch onto these new metaphors indicates a new level of subtlety not encompassed by our older vernacular. It also imbues a deeper meaning to the phrase that’s often over-used to describe the value proposition of the Industrial Internet of Things (IIoT): “delivering the right information to the right place at the right time.”

Building a smarter industry isn’t just about enabling real-time decision-making based on the latest infor-mation, nor is it about integration solely along the vertical dimension of the Purdue model pyramid. An enormous opportunity and challenge for industrial companies today is smartly integrating data from the software-based tools—and the people that use them—along an entirely different axis. Namely, through time.

Take the design process for a typical capital project—a new line or perhaps a new plant. In the continuous process industries there exist well-evolved simulation tools to aid in process design, but how well do they really integrate with the mechanical design tools they feed? Do process engineers’ systems allow them to “design for constructability,” based on feed-back from a plant’s mechanical design systems?

Similarly, sophisticated tools for three-dimensional plant design provide for instrumentation and con-trol wiring and schematics—and in some cases even accommodate control system configuration—but instrumentation, control and optimization strategies still tend to bring up the design caboose. How many process designs could have been dramatically improved by the control strategy innovations of automation spe-cialists had they been involved from the beginning?

In the end, the smart industry opportunity and chal-lenge is not just a technical integration problem but a cultural one. Can people responsible for upstream lifecycle functions understand and accommodate the priorities of those downstream when a better lifecycle outcome is at stake? And are feedback mechanisms in place to affect those changes?

Often, the least risky path forward is the way we did things last time. Those companies that succeed in tomorrow’s smarter industry landscape are those that embrace these integration challenges, effectively pulling together data and information across time to provide solutions optimized not just for right now, but for a lifetime as well.



Much of industry today operates on silos of infor-mation—on products, on customers, on manufactur-ing processes. But what if all these silos could work together with field data from products themselves as they’re being used? It’s not too hard to imagine the im-provements in logistics, service, billing, sales, manu-facturing, quality, inventory management and product development that could be achieved.

Such is the revolution in decision-making enabled by Internet of Things (IoT) technology and by the integration of IoT data with other business systems by JP Provencher, senior director of IoT solution strategy at PTC/ThingWorx, together with Rachel Trombetta, software director for DevOps & Service Reliability at GE Intelligent Platforms Software.

“IoT is real, IoT is now,” said Provencher, “and machine data is not enough. Companies are already driving their IoT initiatives today” to bring detailed product usage data back into the hands of designers and marketing in order to achieve outcomes such as optimizing the next set of product features.

CRM UPSIDE DOWN

“Think of CRM upside down,” Provencher said. “Cur-rently you pick your 10 best customers and call them to understand how they are using your products. With IoT data, you’re calling the machines and asking, ‘How are the customers really using the products? What capabili-ties are they using? Are they using products in ways that are compatible with how they were designed?’—all to better understand and serve the customer.”

Provencher sees great opportunity in the potential for the IoT to increase operational performance and reduce service time in manufacturing. “Customers are achieving a 20% reduction in service costs” by reduc-ing the number of service calls through remote data analysis and troubleshooting, Provencher said. “And, when service technicians do get sent, the technician will already have had the opportunity to understand the problem, diagnose the problem, and show up with the right service parts for a better chance for a first-time fix.” On an operational level, organizations that deploy smart connected operations can generate 5-10% process improvements in the form of higher yield, higher throughput, lower inventory, reduced lead time, and improved operator productivity, Provencher added.

FOUR DIGITAL PILLARS

GE’s Trombetta visualizes the integration of machine and enterprise data sets as a “digital thread” that runs throughout the facility, connecting embedded machine sensors to execution and planning systems as well as to sales data. The end result is improved visibility into how equipment is performing and how to optimize production. The thread weaves together the four pillars of the digital factory:

1. Sensor enablement: The new generation of work-ers “can’t go knock on the machines anymore and hear what’s going on.” They need the data itself and a visualization layer to drive productivity on the shop floor.

Closing the loop on product usage dataIoT technologies help forge digital thread across product lifecycle.

By Thomas Wilk



2. Factory optimization: How do you move data across all enterprise systems to make decisions faster? “Nobody should be doing whiteboards anymore, we should all have the data and the intelligence at our fingertips.”

3. Supply chain optimization: How do you get “quality information from your suppliers and vendors before it ever leaves their manufactur-ing plant, and bring it into yours?” Deliverables include lower total cost of ownership (TCO) and reduced quality risk.

4. Virtual manufacturing: Using simulation to an-ticipate and solve process-related issues before the processes are even built.

She added that GE Transportation CIO Jamie Miller characterizes the “digital thread” approach as

“Lean on steroids with lots of data,” incorporating both design feedback and production feedback loops to drive efficiencies.

When asked at the end of the panel what level of payback that GE was achieving at their IoT-enabled manufacturing facilities, Trombetta cited an example from GE’s Transportation group, where the opera-tional insights at one plant resulted in learning that an operator was doing things backwards, resulting in $200,000 savings in the first month.

Provencher added that ThingWorx had been applied by one global manufacturing organization across ap-proximately 30-35 plants to understand reasons behind unplanned downtime, and that shared benchmarking resulted in a productivity improvement of 10% in pro-ductivity and are able to sustain that improvement.

“IoT is real. IoT is now. And machine data is not enough.”

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Analytics in the Age of the Digital Twin · of the Digital Twin. A Smart Industry Technology Report on Data Analytics & Modeling. ... at the Smart Industry 2016 conference in Chicago