Digital Transformation in Manufacturing: Key
Insights & Future Trends
An Industrial Internet Consortium Tech Brief
2020-09-21

Digital Transformation in Manufacturing: Key Insights & Future Trends

Table of Contents
About this Tech Brief ............................................................................................................. 3
How the Pandemic Accelerated Digital Transformation .......................................................... 4
Analyst Insights: Dr. Phil Marshall, Chief Research Officer, Tolaga Research ........................... 5
The Promise of 5G and Industry 4.0 ............................................................................................... 5

MILC Thought Leader: Seth De Santis, Director, Brilliant Manufacturing P&E, GE Aviation ...... 8
Lessons Learned over the First Five Years of Digital Transformation ............................................... 8

MILC Thought Leader: Farid Bichareh, CTO, AASA, Inc. ......................................................... 10
In Response to the Pandemic, Consider Re-Assessing Your Digital Portfolio .................................. 10

How the IIC Is Addressing Digital Transformation ................................................................. 13
IIC Testbeds ......................................................................................................................... 14
Smart Factory Machine Learning for Predictive Maintenance ....................................................... 14
Smart Factory Web...................................................................................................................... 15
Time Sensitive Networks (TSN) for Flexible Manufacturing ........................................................... 17
Other Testbeds of Interest to be discussed in future reports......................................................... 18

IIC Test Drives ...................................................................................................................... 19Intelligent Video ......................................................................................................................... 19
Other Test Drives of Interest to be discussed in future reports ..................................................... 19

Copyright ............................................................................................................................ 20

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Digital Transformation in Manufacturing: Key Insights & Future Trends

ABOUT THIS TECH BRIEF
This Industrial Internet Consortium (IIC) Tech Brief was conceived, written and compiled by the
IIC’s Manufacturing Industry Leadership Council (MILC). It is the first in a series of Tech E-Briefs
that the MILC will publish in the coming months and years. The MILC, which features some of thebrightest minds in the manufacturing world, is an active advocate for the manufacturing industry.
Our goal with these publications is to provide guidance. We help manufacturing leaders keep
pace with the rapid emergence of new technology while shining a spotlight on the remarkableadvancements driven by the MILC, the IIC working groups, and the IIC members. In this initialentry, we tackle digital transformation, which promises to disrupt manufacturing processes andimprove business outcomes in the next decade. As you’ll see, digital transformation is alreadychanging how manufacturing IT organizations operate, service and maintain equipment.
From thought-leading papers, to real-life implementations, IIC members are the pioneers in thisworldwide adaptation. This effort currently includes: The Smart Factory Task Group;
Foundational frameworks related to manufacturing; IIC Testbeds, Test Drives and Challenges.
Learn more about our ongoing initiatives at https://iiconsortium.org.

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Digital Transformation in Manufacturing: Key Insights & Future Trends

HOW THE PANDEMIC ACCELERATED DIGITAL TRANSFORMATION
Digital Transformation in manufacturing has been an inevitability for quite some time. In an
industry where scalability is everything, digital transformation paves the way for the advanced
solutions which make rapid scalability realizable. Organizations can either adopt change willingly
and proactively, or forcibly by market demand. Any choice in the matter faced an unlikely dictator
this year, in the form of a worldwide pandemic. One in which some organizations had no choice
but to scale in ways that they had only previously envisaged, within the context of a long-term
strategy.
On the flip side, some manufacturers faced the tough decision of how to move forward and toprioritize investment strategies, when the markets they serve had grounded to a resounding halt.
In this scenario, organizations could have remained idle, and attempt to shoehorn existing
practices into a drastically changed world. The forward thinkers reinvested in technologies thatwill allow for future growth, reduced risk and reduced overhead. Results which will become
increasingly important as the market becomes more and more attuned to the reality that no one
is insulated from natural disaster.
For example, automotive manufacturers currently confront tremendous disruption fromcompanies like Tesla, at a time when their balance sheets have been weakened by reduced
vehicle sales during the pandemic. As automotive manufacturers retool their facilities to support
market demands for key technologies for autonomous vehicles and vehicle electrification, they
depend on digital technologies and must be bold, while at the same time avoid series missteps
in their transformation efforts.

4Digital Transformation in Manufacturing: Key Insights & Future Trends

ANALYST INSIGHTS: DR. PHIL MARSHALL, CHIEF RESEARCH OFFICER, TOLAGA RESEARCH
THE PROMISE OF 5G AND INDUSTRY 4.0
Industry 4.0 is a hot topic for manufacturers as they play their partin the Fourth Industrial Revolution. With Industry 4.0,
manufacturing equipment is augmented with wireless connectivity,
sensor technology and intelligence to enable cyber physicalcapabilities and digitize manufacturing processes. Notable Industry
4.0 use cases include agile factory automation, preventative maintenance, workforce management and augmentation, machine optimization and remotemonitoring and control.
Wireless connectivity is needed to achieve the agility demanded by Industry 4.0, particularly inlarge scale manufacturing facilities. Currently wireless is only used sparingly, typically with
unlicensed spectrum technologies. Several competing wireless technologies are emerging as
candidates for Industry 4.0, including WiFi-6 for unlicensed spectrum, and 4G-LTE and 5G inlicensed spectrum bands. Recent 5G technology releases (Releases 15, 16 and 17) haveincorporated key capabilities for Industry 4.0, such as ultra-reliable low latency connectivity(uRLCC), Ethernet-TSN (Time Sensitive Networking) integration, and a variety of features formassive machine type communications (mMTC).
The first 5G networks went commercial in April 2019 and targeted enhanced mobile broadband(eMBB) services for consumers. For communication network operators (CNO), eMBB is a naturalextension of their successful 4G-LTE services. However, 5G promises much more as CNOs look tovertical industry applications, albeit with complex ecosystem demands and systems integration
challenges.
For 5G to gain a foothold with Industry 4.0, it must have the support of a broad range of
ecosystem stakeholders, including CNOs, communication and industrial technology providers,systems integrators, and Tier 1 manufacturers.
At Tolaga Research, we recently conducted a study using natural language processing (NLP) of
online content to investigate the 5G sentiment of key Industry 4.0 ecosystem stakeholders. Oversix hundred companies were identified from online searches of news and press releases relating
to smart manufacturing and Industry 4.0.
Targeted searches for each company were conducted to identify online content publishedbetween 2016 and 2019 that related specifically to 5G and manufacturing. In each case where
relevant content was identified, it was tagged with a sentiment ranking. The sentiment rankingof the content was then associated with an ecosystem category, so that aggregate category ranks
could be calculated, see Exhibit 1. The key ecosystem categories shown in Exhibit 1 include:
5Digital Transformation in Manufacturing: Key Insights & Future Trends•

Communication and connectivity technology providers, such as Cisco, Ericsson, Huawei,
Nokia and ZTE, who have strong incentives to drive demand for 5G technology and are
pioneering use cases for industry 4.0. The rank estimates in Exhibit 1 have communicationand connectivity providers being the first to become publicly engaged with 5G for
manufacturing in the 2017 time-frame.

•

Communication Network Operators (CNO), such as Deutsche Telekom, NTT, Singtel,
Telefonica and Verizon, who have been trialing Industry 4.0 initiatives and are eager toexpand 5G into vertical markets. The rank for CNOs shown in Exhibit 1 has a similar profile
to communication and connectivity providers, but with a lag of approximately 12 months.

•

IT hardware, software and services companies, such as Amazon AWS, Dell, IBM, HPE,
Mahindra and Microsoft Azure. These companies are already providing solutions to manyindustries, including manufacturing. The companies are not dependent on the success of
5G but are eager to capitalize on the broader Industry 4.0 opportunities that might
depend on 5G connectivity in the future.

•

Semiconductors and embedded system providers such as Analog Devices, Arduino, Intel,
nVidia, NXP and Qualcomm, who have varying degrees of interest in 5G. Companies suchas Qualcomm, who are heavily invested in 5G are pioneering use cases, including thoserelating to manufacturing. Other players recognize that they must be prepared in advanceto respond to 5G market demands should they arise.

•

Industrial technology, robotics and automation solution providers such as ABB, Applied
Robotics, Hitachi, Honeywell, Rockwell, and Siemens, who provide a range of general
purpose and specialized solutions to manufacturers. Some companies have trialed 5G,
but full engagement from companies in this category will only come once there is clear
evidence of 5G demand from manufacturers.

•

Manufacturers including those for the automotive, heavy industries, life sciences andaerospace and defense, will ultimately determine the fate of 5G with Industry 4.0. Somemanufacturers are aggressive in their pursuit of 5G. For example, Volkswagen in Germany
has acquired industrial radio spectrum resources and plans to deploy private 5G. Whileother manufacturers have trialed 5G and investigated other technologies like WiFi-6,
many manufacturers have yet to investigate their wireless strategies.

Wireless and industry 4.0 adoption are inextricably linked. Industry 4.0 is challenging to
implement and has greater prospects with manufacturers that are being disrupted. For example,
the automotive industry is being disrupted by companies like Tesla and every industry will be
disrupted in varying degrees in the aftermath of the Covid-19 pandemic.

6Digital Transformation in Manufacturing: Key Insights & Future Trends

Exhibit 1: Online Content Sentiment for 5G and Manufacturing.
(Source: Tolaga Research, 2020)
Although 5G momentum is increasing, 5G is still nascent and must mature with clear justification
for all ecosystem stakeholders. Radio spectrum must also be availed. While some companies (e.g.
Volkswagen) are capitalizing on dedicated industrial spectrum allocations for Private 5G, radio
spectrum will mostly come through partnerships between manufacturers and local CNOs. The 5G
standards must continue to evolve to incorporate industrial technologies and to account for thesalient characteristics for Industry 4.0 operating environments.
Manufacturers and industrial technology suppliers must be engaged as these standards aredeveloped and 5G solutions must be created specifically for industrial environments. In addition,
5G has competition from emerging unlicensed spectrum technologies like WiFi-6. Industry 4.0
needs wireless, but there is a lot to be done for 5G to prove that it is the right solution.

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Digital Transformation in Manufacturing: Key Insights & Future Trends

MILC THOUGHT LEADER: SETH DE SANTIS, DIRECTOR,
BRILLIANT MANUFACTURING P&E, GE AVIATIONLESSONS LEARNED OVER THE FIRST FIVE YEARS OF DIGITAL
TRANSFORMATIONGE Aviation started its IIoT journey during the second quarter of
2015. That journey started when a group of engineers were looking
to code custom PLC logic to capture a handful of machine variables,with the goal of better understanding the process better and thenmaking it more efficient.
While they were performing this work, they realized there were many more variables that could
be captured in near real time. These variables could enable not only learning out a process, butalso understanding the condition of the machine to better predict the likelihood of it breaking.
And the likelihood of it producing a non-confirming part.
The initial scope of this work in 2015 was spread out across ~15 manufacturing assets [ex:Furnaces] at two different sites. The approach involved writing custom drivers for these assets,flowing the data from the PLC into Kepware middleware, and then ultimately into a local SQL
Server database. Many stored procedures were used to attempt to action the data and there wasno vehicle for easily viewing the data.
That first approach led to some initial lessons learned, including:••

Driver Standardization: Creating custom drivers for manufacturing assets was not anefficient approach. This resulted in a mandate on new asset purchases that they comestandardized with MTConnect. This provided a semantic layer for common data
understanding
Data Visualization: An end user not being able to easily access and review the dataprecludes identifying potential collection issues as well as stalls the cultural change tomake manufacturing decisions by using machine data. This resulted in an ongoing datavisualization journey.

With some Proof of Concept experience under the team’s belt, 2016 brought in a more
centralized approach to sensor enabling manufacturing assets. Part of this approach was
ensuring that the assets in scope for sensor enablement were appropriately defined to ensurethat a positive ROI would be possible for any data insights gained and actioned.
This was made possible by only sensor enabling constrain assets, assets that cause a bottleneck
and are a limiting factor to an organization’s performance, the obstacle to the organizationachieving its goal across our supply chain.

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Digital Transformation in Manufacturing: Key Insights & Future Trends
With scope defined, the teams focused on their tech stack and approach. Having already madethe decision to standardize on MTConnect at the driver layer, the team also standardized on theirstorage vehicle. Here, the team would deploy local GE Digital Historian time series databases
locally at each site and then also centrally to aggregate the changes in the data. One central
Historian instance is deployed for each different type of data classification (Export Controlled,
non-Export Controlled, etc.).
The team also explored different visualization tools, ultimately landing on Cimplicity, a GE Digitalproduct already in use at Aviation for Human Machine Interface but not for machine datavisualization. These Cimplicity instances were built per site and were customized based on eachsite’s needs. Data that one would see would range from the operating status of enabled assets
to KPI’s on uptime and availability.
During 2016 and 2017, the number of connected assets grew from ~15 across two sites to ~350
across 7 sites. Benefits that were starting to appear in pockets included:• The reduction of scrap by stopping a process before it completes a non-conforming part.
• Cost avoidance through increased utilization of constraint machines; from visually seeing
when a constrain machine becomes available to being able to analyze asset usage
patterns, the teams were able to avoid purchasing new assets by getting more cycles outof existing assets.
• Reduction of maintenance hours in the capability to programmatically monitor an asset’s
variables, as opposed to a human checking those variables (via, say, a gauge reading),
allowed more assets to be maintained by a smaller number of workers.
In 2018 and beyond, the sensor-enabled footprint continued to grow. The number of connectedassets grew from ~350 across 7 sites in 2017 to ~1700 across 27 sites by the end of 2019. In
addition to the footprint growing, so did the team’s experience.

9Digital Transformation in Manufacturing: Key Insights & Future Trends

MILC THOUGHT LEADER: FARID BICHAREH, CTO, AASA, INC.
IN RESPONSE TO THE PANDEMIC, CONSIDER RE-ASSESSING YOURDIGITAL PORTFOLIO
The global pandemic is shaping a different world. In this new world,
despite the short-term decrease in demand, in the medium-to-longrun, the COVID-19 digital transformation impact, seems to be
extremely positive. The need for organizations to transform andrespond to an unprecedented change in customer behavior andmarket risks – with extensive digitization being one of the key enablers of such positive impact.
In response to the pandemic, longer term strategies and digital portfolios need to be re-assessed
and re-prioritized and manufacturers need to accelerate their digital transformations to remain
relevant and capture opportunities or risk their very survival.
In the time of pre-COVID-19, approximately 92% of manufacturers were thinking that theirbusiness models would need to change given digitization. Studies from the same period indicatethat the top 10% of manufacturer, grow revenue at two times the rate of the bottom 25%.
The difference in their respective approaches come into play among the top 10% manufacturers.
Manufacturing technologies such as AI and automation, are adopted five times faster andstrategies are put in place that give high confidence in the reliability of the data.
Let’s make an example, a global manufacturer that had implemented Digital Twin capabilities
and AI tools to optimize its supply chain before the COVID-19 outbreak. They were able to accessand analyze critical information quickly, enabling it to deglobalize and move supplies as close to
production sites as possible; avoiding shutdown of a single production line for lack of materials
or any other possible disruption.
This proves the manufacturers with the right digital foundation, adapt to crises quicker.
On the other hand, the latest analysis after the 2020 pandemic shows that Internet of Things
projects will shift the focus to Manufacturing and IIoT and in the comeback of industries. Thus,
Connected Supply Chain, Connected Product and Connected Transportation will be leading theconnectivity world.
Due to Covid-19, manufacturers think of not only resuming operations in a safe and responsive
manner, but also in setting the groundwork for long-term resilience through the successful
integration of IT and OT systems. Now the question is how the combination of technologicalcapability and business impact can be successful and help the manufacturer. Many
manufacturers will look for business impact in three levels:
1. Immediate: Efficiency and cost saving,
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Digital Transformation in Manufacturing: Key Insights & Future Trends
2. Midterm: New revenue streams and customer experience and
3. Long term: Business transformation
While the technology provider’s response is in three levels:
1. Immediate: Monitoring and reporting tools,
2. Midterm: Control capabilities and tools and3. Long term: Autonomy
That being said, manufacturers will be looking for solutions that:•••

Directly solve the pain points,
Can be implemented quickly (days and weeks, not months), andHave the potential for long-term, post-COVID-19 impact.

IIoT and Connectivity
IIoT, as a critical part to digital and Industry 4.0 transformation, features the use of sensors toconnect manufacturing equipment to IT systems, driving valuable insights about manufacturing
operations and performance. With the proper sensors and analytics tools, manufacturers cancapture and analyze data from every point in the manufacturing process, driving businessbenefits. Digital Transformation and IIoT are enabling manufactures to discover new
information and make informed, predictive decisions about their operations and supply chains.
Cloud, AI
By passing manufacturing data to cloud-based analytics platforms for deep analysis andmodeling of machine learning, they can benchmark operational performance to identify
improvement areas and make predictions and proactive responses to future operational
outcomes or industry-wide events. As a result, the adoption of cloud data which unlocks other
innovative technologies and AI will be among top priorities for manufacturers.
MES, Monitoring and Dynamic SchedulingWith manufacturing applications such as MES, performance monitoring and dynamicscheduling, manufacturers can increase transparency into production and product quality, scale
productivity and adapt to shifting regulatory requirements without overtaxing existing
resources. For example, many estimates show improved performance management thoughsuch tools and applications can boost labor productivity by 20% to 40%.
5G, Li-Fi
The use of wireless sensors, enabled through 5G and Li-Fi, in manufacturing environments
makes it easier for manufacturers to collect and analyze real-time performance metrics about
their equipment and labor. 5G and Li-Fi with low latency and greater bandwidth, accelerate therate of data download, enabling the use of real-time data in industrial operations. Images anddata can be downloaded much faster with Li-Fi and 5G integrated IIoT devices. As a result, data
can be easily shared remotely. Li-Fi and 5G location system can also provide real-time track and11Digital Transformation in Manufacturing: Key Insights & Future Trends
trace capability, as well as accurate indoor autonomy such as autonomous indoor vehicles andnavigation for true Smart Factory implementation.
Automation
While pre-COVID-19 manufacturers were a bit hesitant to take on large capital-intensive projects
like automation, considering social distancing regulation, production line flexibility and increasing
capacity right now, the payoffs of a workforce that doesn’t take breaks, doesn’t get sick and do
not need training and insurance will be appealing to those with strong capital positions – enabling
acceleration in efficiency during this time that will cut costs and boost bottom lines.
Smart Cybersecurity, AI, ML, Automated & Adaptive Network
With five times the increase of cybersecurity attacks, the manufacturing industry has become
the reported the second highest rate of such attacks. In general, AI technologies can be used tohelp protect against increasingly sophisticated and malicious malware, ransomware, and social
engineering attacks. Machine Learning (ML) can certainly provide an important component forcybersecurity in threat intelligence. Both can be applied to monitor and detect anomalies in the
network and identify new threats without known signatures. Automation also allows for themonitoring of networks and automatic updating of defense framework layers, in addition todiagnostic and forensics analysis for cybersecurity.
The conclusion is that manufacturers must embrace a business environment where the focus will
no longer be only on efficiencies and cost-effectiveness. Digitalization and IIoT solutions that
boost responsiveness and resiliency have emerged as an even greater business consideration.
That being said, manufacturers must engage in renovation of their operations, including digitaltransformation and IIoT solutions. It is now more essential than ever to start this journey towards
a more agile, secure and productive manufacturing future.

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Digital Transformation in Manufacturing: Key Insights & Future Trends

HOW THE IIC IS ADDRESSING DIGITAL TRANSFORMATIONThe Industrial Internet Consortium (IIC) was established in 2014 to look ahead to these inevitablechanges in business practices, and to form a blueprint for making them realizable. Representing
industrial organizations across over thirty nations, our role is to bridge the gap between
technology vendors and the industries they serve.
Equipped with a foundational library of horizontal digital transformation expertise, the IIC has
enlisted industry to assist in creating solutions which will optimize efficiency, improve businesspractices and deliver return on investment. In Q2 2020, the IIC held its Manufacturing Industry
Leadership Council (MILC) to draw in the expertise of technology users, who are driving the needfor scalable solutions in manufacturing across multiple vertical markets.
A rich tapestry of technologies is being developed to support digital transformation for
manufacturing. These include:•••

Edge computing and artificial intelligence (AI) to support capabilities such as condition
monitoring and predictive maintenance;
Connectivity technology robotics and autonomous systems to enable agile factory
automation, and;The convergence of operational technologies (OT) with information technologies (IT) to
enhance workflows and drive factory efficiencies.

Since the rich tapestry of technologies that underpin digital transformation are complex, the IIC
has collaborated with its members to develop a range of test beds for industrial companies toestablish digital best practices. These collaborative efforts are a rising tide that lifts all ships, inthe wake of challenges and opportunities that face us all.

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Digital Transformation in Manufacturing: Key Insights & Future Trends

IIC TESTBEDSIIC Testbeds are where innovative opportunities can be initiated, thought through, and rigorously
tested to ascertain their usefulness and viability before going to market. New technologies,
services, processes and applications can be trialed in a live environment by one (or more)member companies, with the guidance of the IIC Testbed Working Group. Below are someexamples of IIC Testbeds which have been making waves in manufacturing.
Three testbed smart factory case studies are presented in this Tech Brief:
1. Smart Factory machine learning for predictive maintenance
2. Smart Factory web
3. Time Sensitive Networks for flexible manufacturing.

SMART FACTORY MACHINE LEARNING FOR PREDICTIVE MAINTENANCE
Participants: Aingura IIoT, aicas GmBH, Bosch, Microsoft, Real-Time Innovations.
Companies are continuously searching for innovative ways to remain competitive by evolving
their analytics approach to gain more meaning from the data they are acquiring. With proper
analysis, the information can provide a wealth of insight into company’s system operations,overall operating and maintenance costs.
This knowledge leads to actionable insight, enabling companies to move away from traditional
preventative maintenance, with regularly scheduled maintenance times, to that of Predictive
Maintenance, for optimized system operation and asset utilization.
This testbed focuses on exploring the application of Machine Learning techniques andalgorithmic approaches using new innovative technology. This includes Aingura IIoT’s Oberon
intelligent systems performing data acquisition, sensor fusion, analytics and Machine Learning
powered by Xilinx ZU9 Programmable SoCs for time-critical Predictive Maintenance andincreasing energy efficiency, availability, and lifespan of manufacturing production systems.
Case Study: Value of Predictive Maintenance in a High-Volume Manufacturing System.
Shortly after experiencing initial problems, an unknown degradation in system operation led toan extremely costly system failure during operation. This description is an actual failure that
happened to a production line with an average cost of $50K per hour lasting a total of 10 days,
with critical impact in turnover. The repair time was directly related to the availability of spareparts.
A machine learning-based monitoring system would have detected the first failure peakindicating the system degradation and providing adequate time to stop the line in a controlledmanner. A production and workforce could then have been reassigned to reduce the failure
impact over production line productivity.
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Digital Transformation in Manufacturing: Key Insights & Future Trends
In this case, application of a machine learning algorithm would have detected the anomaly withina suite of variables, effectively identifying and alerting operators to the problem when firstencountered. Advanced failure prediction through the application of Predictive Maintenance canhelp to reduce unplanned downtime, in this example case study savings would have reduced the
downtime from ten days to only eight hours.

SMART FACTORY WEB
Participants: Fraunhofer IOSB, Korea Electronics Technology Institute, Microsoft, SAP SE.
The Smart Factory Web aims to form a network of smart factories with flexible adaptation of
production capabilities and sharing of resources and assets to improve order fulfilment. Keyquestions are: How can we connect factories to the Smart Factory Web and exchange datareliably? How can we provide the information securely at the right granularity to authorized
partners? How can production capabilities be adapted quickly and efficiently in response toorders?
As shown in Exhibit 2, the Smart Factory Web applies a dual plane three-tier IIC implementationarchitecture with IIoT technologies on both planes.

Exhibit 2: IIC three-tiered architecture for Smart Factory Web.
Source: IIC, “The Industrial Internet of Things, Volume G1: Reference Architecture”
The upper plane comprises the Smart Factory Web portal to handle cross-factory interactions
through gateways to the individual smart factories. The lower plane is a Smart Factory with a15

Digital Transformation in Manufacturing: Key Insights & Future Trends
Factory Digital Image for secure, performant communication with the Smart Factory Web.
Secure Plug & Work techniques based on the standards AutomationML and OPC UA are applied
to adapt factories on-the-fly by inserting new manufacturing assets into the factory production
with a minimum of engineering effort. These standards are applied for information modellingand communication in both planes.
The testbed creates and validates new business models with flexible assignment of production
resources across factory locations. This will create new opportunities for SMEs, allowing them torespond flexibly to manufacturing orders.
The Korean-German collaboration between KETI and Fraunhofer IOSB will advance theinternational usage of key standards such as AutomationML for the plant description and OPC
UA as a communication mechanism as well as architectures for Industrial Internet of Things
systems. This will reduce IT system integration and installation costs with faster engineering andramp-up of components, machines, plants, and IT systems. The mission of Fraunhofer IOSB and
KETI is to transfer research results to real applications in industry.
According to Byunhun Song, head of the KETI Smart Factory ICT Center, “the Smart Factory Web
is the first testbed in Korea approved by the IIC. This will have great influence on the distributionof the smart factory technology in Korea, especially, as it will enable interoperability between IIC
and Industrie 4.0”. This activity is established and operated by the Korea Smart FactoryFoundation (KOSF) and financed by a grant of the Ministry of Trade, Industry and Energy (MOTIE).
“The Smart Factory Web is open to other companies. Providers of factory components, insoftware or hardware, and operators of smart factories around the world are invited tocontribute to the Smart Factory Web if they comply with the idea of a collaborative factory
network based upon open standards,” emphasizes Thomas Usländer, spokesperson of the
Fraunhofer IOSB businTess unit Automation and head of the department ILT at Fraunhofer IOSB.
In addition to the technological innovations the Smart Factory Web will enable new businessmodels when implementing the Industrie 4.0 use case “Order driven adaptive production” in the
manufacturing domain. This will also strengthen and promote the ITS OWL initiative, explains
Jürgen Jasperneite, head of the Fraunhofer IOSB Application Center Industrial Automation (INA)
in Lemgo, Germany. The INA is a founding member of the leading-edge technology cluster,Intelligent Technical Systems OstWestfalenLippe (known as OWL).

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Digital Transformation in Manufacturing: Key Insights & Future Trends

TIME SENSITIVE NETWORKS (TSN) FOR FLEXIBLE MANUFACTURING
Participants: University of Stuttgart, Bosch, Fraunhofer IOSB, Kalycito Infotech Private Limited,
Moxa, SICK AG, TRUMPF.
Manufacturing operations require tight coordination of sensing and actuation to safely andefficiently perform closed loop control. Typically, these systems have been deployed using nonstandard network infrastructure or air-gapped (unconnected) standard networks. This approachleaves devices and data much harder to access and creates a technical barrier to IIoT which ispredicated on the ability to consume data anywhere throughout the infrastructure.
To address these needs of IIoT all the way to the control system, the IEEE organization has beenworking to update the standards for Ethernet and wireless (IEEE 802) to support TSN. The
technology will be used to support real-time control and synchronization of high-performancemachines over a single, standard Ethernet network, supporting multi-vendor interoperability andintegration.
When appropriate, the TSN Testbed will integrate industrial automation protocols which are
adopting TSN, such as OPC UA. The TSN Testbed includes such things as developing simple
applications and data definitions to be communicated utilizing TSN over OPC UA Pub-Sub, to be
implemented by TSN Testbed participants.
TSN will open up critical control applications such as robot control, drive control and visionsystems to the industrial internet. This connectivity then enables customers, suppliers andvendors to more readily access data from these systems and to apply preventative maintenanceand optimization routines to these systems.
Since the testbed’s inception, two physical instances have been established. One is hosted in
North America at the headquarters of National Instruments and the second is hosted in Germany
at the University of Stuttgart’s Institute for Control Engineering of Machine Tools andManufacturing Units (ISW).
These testbeds are used for plug-fest activities where member companies collaborate to test
implementations and interoperability. They also serve as standing units where individualcompanies can work on updates and integration, and they are transported to various industry
shows and events.
The testbed displays the following:•••

Combined critical and best-effort traffic flows on a single network based on IEEE 802.1
Time Sensitive Networking.
Integrate relevant Industrial Automation protocols that adopt TSN, such as the OPC’s
United Architecture (UA) Pub-Sub communication.
Vendor interoperability using standard, converged Ethernet.
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Digital Transformation in Manufacturing: Key Insights & Future Trends•••

Security architectures in conjunction with critical control traffic and feedback on the
secure-ability of initial TSN functions.
IIoT incorporation of high performance and latency sensitive applications.
Integration mechanisms and architectures for smart edge-cloud control systems directly
connected into flexible manufacturing.

OTHER TESTBEDS OF INTEREST TO BE DISCUSSED IN FUTURE REPORTS••••

Negotiation Automation Platform
INFINTE - SERENA Predictive maintenance use case
Smart Printing Factory
Factory Automation Platform as a Service

18Digital Transformation in Manufacturing: Key Insights & Future Trends

IIC TEST DRIVES
Test Drives are short-term, rapid-engagement pilots for technology end users to employ andadopt Industrial Internet of Things (IIoT) technologies. They stimulate IIoT adoption acrossindustry through accelerated implementation. The IIC’s neutral collaboration platform fosters
partnering to address leading-edge IIoT use cases in three to six-month projects based upon
technology end users' real problems.

INTELLIGENT VIDEO
Participants: NetApp, Corlina.
Video will become a mainstream IoT sensing platform, with use cases such as computer vision,as advanced analytics prove to deliver business value. Currently there lacks the ability to deploy
at scale or fully protect devices, or data in transit, from cyber threats.
This test drive focuses on capturing and processing images for content analysis in a secure, open
architecture for cross industry use cases including quality assurance, situational monitoring,
behavioral analysis and compliance verification. The Intelligent Video Test Drive has previously
been deployed in marine waterway management.
Too often the importance of security is underestimated, particularly as manufacturing andindustrial environments become increasingly digitized. The use cases addressed by the Test Drive
include quality assurance, security surveillance, health and safety of factory workers, customer
sentiment analysis, patient condition and monitoring.
The benefits seen thus far have been in quality assurance, security surveillance, health and safetyof factory workers, customer sentiment analysis and patient condition monitoring. Industries
addressed include manufacturing, energy and utilities, mining and metals, public safety andretail.

OTHER TEST DRIVES OF INTEREST TO BE DISCUSSED IN FUTURE REPORTS•

IOT Sensor Implementation

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Digital Transformation in Manufacturing: Key Insights & Future Trends

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