Smart Factory Applications in Discrete Manufacturing

Smart Factory Applications
in Discrete Manufacturing
An Industrial Internet Consortium White Paper

Authored by the Smart Factory Task Group

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TABLE OF CONTENTS
1
2

Executive Summary ........................................................................................................ 3
Industrial Applications – Where is the Business Value? ................................................... 5
2.1
2.2
2.3
2.4
2.5
2.6
2.7
2.8
2.9

3

Core IoT Technologies – What Enables Change?............................................................ 21
3.1
3.2
3.3
3.4

4

System-wide Visibility – Data Flow from the Shop Floor to the Top Floor ............................6
Production Automation – Fewer Tradeoffs Between Cost, Quality, and Speed ....................9
Predictive Maintenance – Machine Downtime Visibility and Reduction ............................ 11
Industrial Digital Thread – Linking Manufacturing and Service Data .................................. 13
Global Supply Chain Integration – Supplier to Factory to Customer and Back .................... 14
Resource Efficiency – Management of Input Costs and Environmental Impact................... 15
Human-machine Collaboration – Improved Safety and Productivity .................................. 17
Mass Customization – Dynamic Manufacturing Processes................................................. 18
Business Model Evolution – Monetize Data and Shift to Services ...................................... 19
Alignment Has Begun – Cost and Capability Drive Feasibility............................................. 22
Designing the Smart Factory – Aligning Technical, Market, and Business Value ................. 23
The Standards Landscape – Meeting Requirements and Filling Gaps ................................. 25
Standard Advisory ........................................................................................................... 28

IoT Adoption Barriers in Manufacturers – What Prevents Change? ............................... 29
4.1
4.2
4.3

Legacy Assets .................................................................................................................. 29
Data Overload – Excess Data, Insufficient Insight .............................................................. 30
Securing Industrial Internet Solutions .............................................................................. 30

5Conclusion ................................................................................................................... 33

6

About the Industrial Internet Consortium ..................................................................... 33

7

Contributor Acknowledgements ................................................................................... 34

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1 EXECUTIVE SUMMARYAs the world of traditional manufacturing fuses with information technology, organizations aretapping into a level of technical orchestration never attainable before. Symphonies of systems
facilitate real-time interactions of people, machines, assets, systems, and things. This is the Smart
Factory; the factory ecosystem of the future. It is an application of the Industrial Internet ofThings (IIoT) built with sets of hardware and software that collectively enable processes to govern
themselves through machine learning and cognitive computing.
The result is a level of lean operations that was previously impossible to achieve. Downtime ispredicted and prevented, waste and defects are eliminated, surplus production is minimized,
machine behavior is optimized as conditions change, and systems can make context-based ‘next
best’ actions. Connected devices in the factory report their status, giving operations personnel
and decision-makers access to real time, actionable information. Wearable technology tracks
employee location and status in case of emergency. A global ecosystem of partners ensures thatspecific parts are replenished based on automated, real time needs analysis. The list of
applications goes on. But significant challenges remain in assessing, implementing, and operating
IIoT systems.
All incremental value of a Smart Factory system is based on your ability to accomplish threeobjectives:
1. Aggregate a broad array of data from your equipment, as well as relevant internal andexternal systems.
2. Analyze the data to generate information that can be used to inform operational andbusiness decisions.
3. Act on your new insight by automating processes, optimizing systems, and informing
business strategy.
The development of powerful yet affordable edge and cloud computing technologies provides
companies with the storage and processing capabilities to aggregate, analyze, and act on data.
Scalable clouds aggregate the flow of big data. Meanwhile, analytics is increasingly performed on
the edge in gateways, inside controllers or in miniature data centers located in facilities. Analytics
software cross-analyzes diverse data streams to generate actionable information andunanticipated insight. And the results are shared from the cloud and edge processors tooperators, equipment, and enterprise software to drive action.
Industrial Internet Consortium (IIC) member companies such as Bosch, National Instruments,
Cisco, and TechMahindra are deploying Smart Factory solutions to help companies realize thesebenefits:

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










Increased Shop Floor Visibility: Greater insight into the production floor enables
issues to be identified and addressed before they occur to improve quality control,
equipment uptime, and maintenance scheduling.
Intelligent Supply Chain Management: Sensors, such as radio frequency
identification (RFID) tags, allow inventory monitoring and process automation.
Decreased Total Cost of Ownership: Smart grid technology predicts and optimizes
power consumption, IoT-enabled heating, ventilation and air conditioning (HVAC),
and load balancing.
Streamlined Human Resources: Continuous data means continuous analysis, risk
assessment, and process coordination resulting in fewer field service calls, remotemonitoring and diagnostics, and proactive equipment maintenance. Reducing routine
work allows your teams to refocus on value adding activities.
Reduced Environmental Impact: Sensor networks and machine learning enablefactories to improve efficiencies of energy and material inputs, while better
monitoring outputs to ensure regulatory compliance.
Increased Profitability: Together Smart Factory optimizations are leading to better
operational results, new business opportunities, cost savings, and increased revenues
for early adopters.

The Smart Factory is an elaborate, calculated composition that has been years in the making andthat will continue to evolve over the coming decades. Members of the Industrial Internet
Consortium are delivering manufacturing-centric testbeds to prove out collaborative innovations
developed across industries and functions. This whitepaper explores some of the key challenges
and best practices that you should consider as you evolve your Smart Factories.
This white paper was written with mid-sized brownfield operations in mind. Greenfield planning
is (relatively) simple. But what if you have a traditional factory with old machinery? In previous
decades, only industrial giants were able to invest in connecting sensors, controllers, and data
analytics. Today, the cost of sensors, connectivity, and analytics software has plummeted, making
it possible for every operation to upgrade, retrofit, and prepare for digital automation. The
process to becoming a Smart Factory is a journey. Companies that delay may find themselves
out-competed, out-classed, and out of business.
The Internet of Things (IoT) has become everyday jargon, but how does it relate to Smart
Factories? It’s simple – the first three industrial revolutions were powered by steam, electricenergy, and electronics. Today’s Industrial Revolution is powered by data. And that data comesfrom the “things” that fill your factories and supply chains. Sensors, controllers, edge-computing
nodes, and devices all talk to one another and to the cloud in order to turn data into insight andinsight into action. These “things” possess varying levels of processing functionality, ranging from
simple sensing and actuating, to control, optimization, and full autonomous operation.

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The Smart Factory is one aspect of a Smart Enterprise managed by connected assets and humans
operating in conjunction through “systems of systems.” Importantly, the systems that impact
your Smart Factory will not all be internal. External systems from weather networks, suppliers,
logistics partners, and technology providers share data with internal systems to drive insight andto coordinate action. System linkages rely on standardized Internet and network protocols thatenable secure access to information over cloud and fog platforms. Systems that do notcommunicate in a standardized language cannot share data to create business value.

2 INDUSTRIAL APPLICATIONS – WHERE IS THE BUSINESS VALUE?
IoT technologies do not impact factories in isolation. They also impact manufacturers’ customers,
suppliers, value chain partners, competitors, and employees. Customer expectations regarding
product, warranty, payment, and service are changing rapidly. Suppliers and partners create,deliver, and capture value in new ways. New competitors from seemingly unrelated fields may
enter your markets with innovative value propositions. And the skill sets of your employees must
evolve to match your business transformation.
These and other changes have created challenges as well as opportunities for manufacturers.
Several challenges are now, more than ever, on the minds of manufacturing organization
executives:
1.
2.
3.
4.
5.
6.
7.

Customer expectations for increasingly complex and customized products and services
Business model disruptions like “Manufacturing-as-a-Service” changing the value chain
Rapid change from accelerating innovation cycles and time-to-market expectations
Productivity plateaus in developed economies due to the lack of young, skilled labor
Regulatory pressure driving energy efficiency and pollution-reduction targets
Rising input costs as population growth drives up labor, land, and raw material costs
Globalization resulting in factories, vendors, and customers distributed across borders

IIoT technologies are not a miracle solution for all challenges facing manufacturers. In fact, manyof the core technologies have been in use for decades. However, falling hardware andconnectivity costs, coupled with rapidly improving data analytics, is enabling a broad array of use
cases that were previously impossible or unaffordable. Consider, for example, dynamic industries
like consumer electronics where a delayed product release can significantly reduce sales andmargins. The “sashimi theory,” coined by Yun Jong Yong, former CEO of Samsung Electronics,
notes that fresh raw fish can be sold at a premium in an expensive restaurant; the next day, thefish can be sold for half the price at a second-tier restaurant; on the third day, the fish sells for
one-quarter of the original price; and after that it is cat food.1

1 Pete Engardio and Moon Ihlwan, Business Week, Samsung’s Sashimi Theory of Success

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Consumers are unwilling to pay premium prices for a product that lost its fresh appeal. To avoid
commodity pricing, industries from fashion to frozen food are innovating and launching products
faster. There are several ways in which the IIoT can shorten time-to-market and help companies
maintain competitiveness, for example:
1. Cross-disciplinary collaboration: Improving data flow between plant systems andbusiness applications enables cross-disciplinary collaboration. Product development
teams can access production line data to design in manufacturability. And manufacturing
engineering can digitally pre-test production line processes to reduce error and accelerate
time to market.
2. Iterative improvement: Adding connectivity to products enables manufacturers torelease a product with a basic feature set and iteratively add advanced features throughfirmware updates. For example, every new Tesla is capable of driving itself. 2 Full roll outof autonomous driving features may remain years off. But as soon as both regulations andthe software are ready, Tesla can make the capability available immediately through overthe-air updates.
We have identified nine areas where the IIoT is delivering bottom line business value today. Someapplications, such as System-Wide Visibility, can provide value in a broad range of situations.
Others, such as Mass Customization, describe a specific but very significant use case. Eachapplication is exemplified by an IIC testbed. Testbeds are real projects, implemented
collaboratively by member companies to increase our collective understanding of the technicaland business aspects of the IIoT.

2.1

SYSTEM-WIDE VISIBILITY – DATA FLOW FROM THE SHOP FLOOR TO THE TOP FLOOR

Manufacturing is typically characterized by a hierarchical structure. In this “automation pyramid”
sensor and actuator data are inputs to local control loops governed by a Programmable Logic
Controller (PLC), Micro-Controller (MC), or Industrial PC (IPC). Supervisory Control and Data
Acquisition (SCADA) networks, Manufacturing Execution System (MES), and Enterprise Resource
Planning (ERP) systems in the different layers of the automation pyramid govern the local control
loops. The sensors and actuators in the manufacturing system are capable of generating massive
amounts of data, of which only a fraction is used by the PLC/MC/IPC to manage the control loop’s
automation task. Transferring those comprehensive data from the operational technology (OT)systems in the shop floor to the information technology (IT) systems at the top floor would enablea variety of Smart Factory use cases supported by analytics of big datasets aggregated frommultiple sources. Figure 1 illustrates the integration of IT and OT systems through IIoT
enablement.

2 Jack Stewart, Wired, Elon Musk Says Every New Tesla Can Drive Itself

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Figure 1: Current IT/OT state and transformational potential3

There are three approaches to establishing the “shop floor to top floor” connectivity required tomake all sensor and actuator data available to IT systems in near real-time:
1. Direct Communication: Each sensor and actuator is equipped with active communication
modules enabling a direct connection to the IT system.
2. OT Upgrade: The PLC/IPC is (re-)programmed to deliver all data to the IT system in
addition to performing its traditional automation task.
3. Gateway Aggregator: An aggregation point inside the control loop (e.g., an IO module) isconnected with a gateway. This integrated device extracts the data from the real-timesystem and provides them to the IT system without the need to reprogram the PLC/IPC.
A facility operator will select one of these three options depending on the benefits and costs ofproviding the equipment with IoT capabilities. Generally, the OT Upgrade approach is suited forgreenfield installations where an appropriate PLC/IPC can be chosen. In brown-field situations,
PLCs/IPCs are seldom capable of performing the additional data transport task. Reprogramminga PLC entails significant costs and downtime, so the Gateway Aggregator solution is usually
preferred. The Direct Communication approach is an alternative for targeted, high-value
brownfield upgrades. However, this approach does not scale well and can be very costly if many
sensors are equipped with communication capabilities.

3

Industrial Internet Consortium, Volume B01: Business Strategy and Innovation Framework

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System-wide visibility can be expanded to cover the entire value chain, from Research andDevelopment to Production to Customer Service. It can also be distributed globally to facilitate
coordination and benchmarking. Figure 2 illustrates IT-OT connectivity across the organization to
enable system-wise visibility. The IT-OT boundary will likely be blurred as the IIoT develops andconnectivity is widely integrated into operations.

Figure 2: The Connected Enterprise4

End-to-end system visibility is not possible without the seamless transfer of data between
business systems and operations systems. To imagine the range of opportunities simply ask
yourself: “Where would additional data allow better decision making?” For example, ProductDevelopment departments typically use surveys and market studies to guide new productdevelopment. They must rely on end consumers to answer questions honestly and accurately.
And they rely on dealers or customer service representatives to relay feedback. There issignificant room for human error to result in bad data.
IIoT solutions use sensors fitted into finished goods to track usage patterns and allow engineers
to view exactly how end consumers use the product. Engineers assess real usage data – whichfeatures are used, how frequently, when, by whom, for how long, in what order, when do parts
break – and derive insight to improve product design by identifying, feature gaps, non-valueadding features, use-case segments, and engineering weaknesses. Improved design contributes
to increased customer satisfaction and brand recognition, while reducing non-value-adding
features and product defects.
Manufacturers operating multiple factory sites to manufacture similar products catering to global
customers can also set up a “Central Operation Center” and compare quality parameters acrosssites in near real-time. Centralized visibility provides management with a macro view and enables
standardization of global quality metrics. This is accomplished by retrofitting factory equipmentwith sensors to track operating parameters. This data is analyzed to identify the root cause ofquality deviations.

4Peter Klement, XMProIIC:WHT:IS2:V1.0:PB:20170222-8-

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IIC Factory Operations Visibility and Intelligence Testbed
MARKET SEGMENTS: Manufacturing of complex, composite
products (e.g., IT equipment, consumer electronics, network
appliances)

CHALLENGE: Most data generated by factories is not currently used to generate actionableinsight due to three challenges. Contextual and background data is often stored in various,
incompatible formats. Storage and distribution of large amounts of raw data requiresconsiderable computational resources, which are rarely locally available. The cost to install andmaintain equipment to collect and distribute data is significant.
SOLUTION: Establish new factory operation visualization scenarios with data upload andsimulation capabilities to augment physical factory equipment. Use a cloud-based platform thatcombines factory sensor and operational data to enhance the visibility and analysis of key
manufacturing processes to improve operational efficiency. Capabilities include: support for
Manufacturing and Repair process control; extensibility with enhanced visual rendering tools to
track process flow, product status, scheduling, and machinery status; plug-in capabilities foranalytics and diagnostics software.
COMMERCIAL BENEFITS: In the factory, system-wide visibility enables more timely productmanufacturing and shipment, reduced rate of product rejection, faster product repair
turnaround, and enhanced production throughput. Other benefits are applicable across theenterprise.

2.2

PRODUCTION AUTOMATION – FEWER TRADEOFFS BETWEEN COST, QUALITY, AND SPEED

Manufacturing productivity growth has slowed worldwide according to data from theInternational Labor Comparisons (ILC) program of The Conference Board.5 More than half of theeconomies covered in their dataset saw less than 1 percent growth in 2015, and 6 of the countries
posted negative productivity growth rates. US Government studies indicate that productivity
growth over the period of 2007-15 fell about 50% from 2000-07 levels.6
However, despite this global trend of weakening manufacturing growth, the German
manufacturing industry expects to see a €90 billion to €150 billion boost in productivity over the
next five to ten years. Their estimated productivity improvements will be between 5-8%.7 The

5The Conference Board, Press Release: Manufacturing Productivity Growth Slows to Crawl Worldwide

6 United States Bureau of Labor Statistics, Labor Productivity and Costs
7

Boston Consulting Group, Industry 4.0: The Future of Productivity and Growth in Manufacturing Industries

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positive forecasts for the German manufacturing sector reflect the expected ROI of pragmatic
adoption of IIoT technologies in brownfield scenarios by mid-sized manufacturers.
Time Sensitive Networks (TSN) are one solution being used to enhance production automation.
The goals of a TSN is to achieve real-time control and synchronization of high performancemachines over standard Ethernet. TSN enables high performance in latency sensitive applications
and provides for the integration of edge-cloud control systems into IIoT infrastructure andapplications.
IIoT systems such as TSN are required for advanced automation of process and machine controls
where latency is critical to meeting closed loop control requirements. Flexible processing lines
with multiple integrated control systems and peer-to-peer communication are needed to reduce
the tradeoff between cost, quality, and speed. They also enable low-volume-high-mix
manufacturing with the use of reconfigurable robotics.
IIC Time Sensitive Networking Testbed
MARKET SEGMENTS: Manufacturing, Utilities, Oil and Gas

CHALLENGE: Manufacturing operations require tight coordination of sensing and actuation tosafely and efficiently perform closed loop control. Typically, these systems have been deployedusing non-standard network infrastructure or air-gapped (unconnected) standard networks. Thisapproach leaves devices and data much harder to access and creates a technical barrier to IIoT,
which is predicated on the ability to consume data anywhere throughout the infrastructure.
SOLUTION: To address these needs of IIoT all the way to the control system, the IEEE organization
has been working to update the standards for Ethernet and wireless (IEEE 802) to support time
sensitive networking. The technology will be used to support real-time control andsynchronization of high performance machines over a single, standard Ethernet network,
supporting multi-vendor interoperability and integration.
COMMERCIAL BENEFITS: TSN will open up critical control applications such as robot control, drive
control and vision systems to the Industrial Internet. This connectivity then enables customers,
suppliers and vendors to more readily access data from these systems and to apply preventative
maintenance and optimization routines to these systems.

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2.3

PREDICTIVE MAINTENANCE – MACHINE DOWNTIME VISIBILITY AND REDUCTION

Predictive Maintenance is the application of predictive analytical algorithms against real-timeobserved data to proactively identify potential concerns before they arise and to make guided
recommendations to address the issue. A recent study on Asset Efficiency maturity from Infosys
and the Institute for Industrial Management (FIR) at Aachen University revealed that 85 percent
of manufacturing companies globally are aware of asset efficiency, but only 15 percent have
implemented it at a systematic level.8
Using machine learning to develop strong predictive algorithms requires broad historical datasetsthat are analyzed in centralized datacenters due to the large computational requirements.
However, algorithms can be applied on site with embedded hardware. Currently, measurements
are often manually performed by an aging skilled labor force leading to inconsistent analysis andpoor coverage. Adding connected sensors to the asset enables continuous online measurementsof early wear indicators and the utilization of human expertise globally via cloud applications.
Automated analysis and maintenance scheduling also enables a new quality of predictive
maintenance for critical assets.
Maintenance of enterprise assets is evolving rapidly as new technologies allow organizations tomove from reactive to preventative models. Figure 3 illustrates the maturity continuum acrossseven EAM elements. Predictive IIoT technologies must be implemented before an organization
can graduate from Planned Maintenance (stage 2) to Predictive Maintenance (stage 3). Advanced
machine learning is then necessary to achieve Reliability (stage 4).

8

Infosys, Industry 4.0: The State Of The Nations

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Figure 3: Maintenance Maturity Continuum9

IIC Asset Efficiency Testbed
MARKET SEGMENTS: High Tech, Discrete and Process
Manufacturing, Automotive, Aerospace

CHALLENGE: Challenges include the lack of asset instrumentation, real-time data analytics, andcontextual data provided by other systems. The resulting lack of insight prevents companies fromholistically managing energy usage, asset utilization, operations, and serviceability to optimize
asset efficiency.
SOLUTION: The goal of this testbed is to collect asset information efficiently and accurately inreal-time and run analytics to make the right decisions. Use cases include failure mode prediction
and modelling of asset performance.

9

Genesis Solutions, EAM Assessment Methodology – A Graphical Viewpoint

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COMMERCIAL BENEFITS: Conditioning monitoring provides a high ROI in many scenarios byimproving asset life, reducing asset downtime, maximizing production, and enabling thepredictable delivery of services for assets.

2.4

INDUSTRIAL DIGITAL THREAD – LINKING MANUFACTURING AND SERVICE DATA

The Industrial Digital Thread is a complex and comprehensive concept that weaves design,
manufacturing, engineering, and supply chain functions together. It provides the capability fordesign, manufacturing, and service data to be captured, harnessed, and leveraged to enable theanalytics that drive advanced manufacturing.
The Industrial Digital Thread addresses three significant challenges faced by manufacturers:
1. Faulty production techniques lead to costly unplanned downtime of industrial assets withroot causes that are difficult to determine. The causes of such failures are hidden in
manufacturing processes. Careful analysis is required to return assets back to production
but field engineers and service teams often lack data and insights needed to troubleshootthe underlying issues. The Digital Thread provides the data and analytics needed tounderstand root causes.
2. Hundreds of thousands of parts go into large complex product such as aircraft structures.
These parts are provided by multiple supplier facilities spread across the globe. Issues
regarding the mechanical fit and quality issues often result in expensive returns andreworking. The Digital Thread maintains visibility across the supply chain.
3. Product defects could be the result of a fault in design, material, or supply-chain.
Expensive recalls are regular occurrences across industries. The Digital Thread helps toreduce variability and faults, and to trace the cause when they occur.
IIC Industrial Digital Thread Testbed
MARKET SEGMENTS: Industrial Manufacturing, Discrete &Process Manufacturing, Automotive, Aerospace, High Tech

CHALLENGE: Field engineers and service teams often lack data and digital insights needed toassess and troubleshoot large industrial assets when performing corrective and preventative
maintenance activities. Quality Assurance engineers need to understand why a particular
problem is recurring, or why parts from suppliers are mismatched in assemblies. The root cause
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is usually hidden in design, manufacturing processes, supply chain logistics, or production
planning.
SOLUTION: The Industrial Digital Thread Testbed uses a platform stack that is integrated acrossthe supply chain and manufacturing enterprise. Data is collected from the design of the asset, its
assembly structure, production planning, manufacturing, field servicing, and supply chain flow.
COMMERCIAL BENEFITS: The insight generated from the Industrial Digital Thread Testbed
improves overall efficiency of manufacturing setup and end product quality, while reducing asset
downtime during service.

2.5

GLOBAL SUPPLY CHAIN INTEGRATION – SUPPLIER TO FACTORY TO CUSTOMER AND BACKThe globalization of markets and supply chains has been a boon for manufacturers over the past
decades. However, these opportunities come with their own challenges. Manufacturers requirevisibility into diverse variables such as input material prices, quality specifications, and supply
chain disruptions across regions. Incomplete information can impact cost, quality, and the timely
delivery of finished goods.
Access to new markets also requires adherence to a broader range of specifications and increased
customization to meet the requirements of a diverse customer base. Many manufacturers haveattempted to solve this challenge with regional factories. However, regionalization complicates
operations such as enforcing quality standards, coordinating group sourcing, and addressing
operating inefficiencies.
IIoT solutions provide manufacturers with centralized visibility into supply chains, distribution
networks, output quality, operating parameters, and business systems. Business and engineering
teams based in headquarters can troubleshoot local emergencies without flying across the globe.
Your factories may be distributed globally, but your data and management talent do not have to
be isolated into regional silos.
Real-time visibility into logistics routes and material flow across the supply chain increases theefficiency of your production and inventory management. Benefits of IIoT-enabled integration of
supply chain and factory systems include:● Error-proofing automated processes that stretch across multiple facilities.
● Increasing visibility at each stage of an end-to-end production process.
● Increasing throughput of assembly lines by addressing pain-points related to theavailability of materials and spare parts.
● Improving inventory and production status visibility for customers and supply chain
planners.
● Optimizing production dynamically by adjusting output based on supply and demand.

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IIC Track and Trace Testbed
MARKET SEGMENTS: Industrial Manufacturing, Power Tools,
Fleet Management

CHALLENGE: The production of many industrial and consumer goods requires exacting work down to the precise force used to tighten a screw. However, misuse of tools can result in serious
accident or injury, especially in fine, repetitive actions.
SOLUTION: Tracking technology will enable factory systems to detect the location of a tool withina meter. Smart, hand-held tools used in manufacturing, maintenance, and industrial
environments will be connected to enable precise automation. Asset management and work
management will be integrated with factory manufacturing systems.
COMMERCIAL BENEFITS: This technology will enable the "Tools-as-a-Service" business model forequipment vendors. It will also provide manufacturers with enhanced productivity, production
quality, and work safety.

2.6

RESOURCE EFFICIENCY – MANAGEMENT OF INPUT COSTS AND ENVIRONMENTAL IMPACT

Between 2004 and 2014, manufacturing costs increased in most of the top 25 exporting countries
around the world.10 Population growth and environmental regulations will continue to impact
land and raw material prices. Meanwhile, the cost of labor increased between 1.9 to 2.6 percent
in year 2015, globally.11
Globally, manufacturers are also under mounting pressure from governments to improve energy
efficiency and reduce carbon emissions. In a 2016 survey of US manufacturers, three-quarters of
respondents considered regulatory burdens to be one of the main challenges facing theirsector.12 Initiatives like the European Union’s Energy Savings Opportunity Scheme (ESOS), the US
government’s Better Plants program and the ISO 50001 certification have made this an issue that
manufacturers can no longer ignore.
The US Environmental Protection Agency has stepped up monitoring after the Volkswagen AG
emissions scandal in 2015. In October 2016, a US automotive manufacturer settled a $28.5
million claim with the EPA for violating the Clean Air Act. Manufacturers cannot afford to identify
regulatory infringements after the fact. They must anticipate and prevent costly violations.
10

Boston Consulting Group, The Shifting Economics of Global Manufacturing
United States Bureau of Labor Statistics, Economic News Release: Employment Cost Index Summary
12
Carolyn Lee, Shopfloor, Tax and Regulatory Burdens Still Top of Manufacturers’ Minds
11

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The impact of IIoT technologies on regulatory compliance can be summed up by Peter Drucker’s
insight, “What gets measured, gets managed.” Low-cost sensors can monitor gas emissions,energy usage, temperatures, and water contamination to provide real-time alerts. Analyzing dataflows with machine learning algorithms enables you to predict when you are likely to exceed
regulations with sufficient lead time to prevent critical events from happening.
The cost of inputs and fines is unlikely to shift downward in the near term. But pragmatic
adoption of IIoT solutions in brownfield environments will help manufacturers stay competitiveby reducing waste.
Next generation machines are using integrated sensors to measure and optimize energy
consumption based on their usage patterns. Today energy metering capabilities can be added toany existing automation-system without impacting the system itself. Meters with embedded
Ethernet connectivity enables cloud-based systems that dramatically reduce the cost of data
storage, visualization, and analytics. Combining operating data with external data, like weather
forecasts, energy prices, and production schedules can be used to automate processes, informbusiness decisions, and optimize operational parameters to manage costs.

IIC Smart Energy Management Testbed
MARKET SEGMENTS: Local Government, Critical
Infrastructure, Organizations with Large Campuses,
Emerging Markets

CHALLENGE: Energy costs continue to increase globally, driven by the depletion of fossil fuels,
geopolitical instability and disruptions in the global supply chain. At the same time, organizations
are constantly thriving to reduce overheads and minimize the cost of the operations. Current
challenges include lack of instrumentation to get the energy consumption details and lack of tools
to visualize and analyze the energy consumption patterns. This hinders stakeholders from taking
any meaningful action to optimize energy consumption and reduce the overall energy cost.
SOLUTION: Commercial buildings and equipment will be instrumented to measure the electricity
and provide tools to monitor, visualize, analyze and optimize energy consumption within theorganization. The testbed will provide IT tools such as alarms, notifications, workflows, a ticketingsystem and augmented reality to improve the operations.
COMMERCIAL BENEFITS: The testbed will result in 5 to 10 percent year over year reduction inenergy consumption. It will also provide tools to plan the expansion of operations and to achieve
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sustainability objectives. Overall energy utilization will be normalized and equipment will run
optimally resulting in the reduction of operating expenses.

2.7

HUMAN-MACHINE COLLABORATION – IMPROVED SAFETY AND PRODUCTIVITY

Human-machine collaboration can be segmented into mobile Human-Machine Interfaces (HMI)and Human-Machine Collaborative Systems (HMCS). HMI technologies such as smartphones,
tablets and wearables, combined with Internet access to data and information (analytics andaugmented reality) will transform the way machine operators and factory floor engineers work.
Personnel no longer need to be in close proximity to the main operator panel in order to monitor
or manage performance. Portable wireless devices also expand operator capabilities by providing
ready access to product documentation and maintenance manuals using QR-codes or augmented
reality headsets to identify an asset.
Today, most operators only have access to information from automation systems. Augmented
operators can now access information from all of the needed enterprise systems. Meanwhile,
the relationship between the operator and the system (machine) through HMI is evolving fromdetailed operations to mission-control. Systems are increasingly able to detect and address issues
automatically and to alert operators only when human intervention is necessary.
HMCS technologies are intended to provide flexible robotics for small lot manufacturers with
rapidly changing assembly lines and the need to augment, not replace, legacy equipment. They
provide collaborative robot systems in which humans work directly with robots in dynamic
environments. Common capabilities of HMCS include object recognition and action sequence
analysis. Object recognition is useful for a range of quality control situations in which humanassessment is error prone. A robot can be trained to flag products with specific unwanted
characteristics, such as a solar panel with imperfections on more than 0.5% of surface area.
Action sequence analysis enables robots to dynamically learn new tasks, such as assembling a
new product. The learning sequence involves repetition, rather than programing, and can becompleted by a line worker with no IT training.
IIC Edge Intelligence Testbed
MARKET SEGMENTS: Vertical industry testbeds

CHALLENGE: Many emerging industrial IoT applications call for coordinated, real-time analytics
at the "edge," using algorithms that require a scale of computation and data volume/velocity
previously seen only in the data center. Frequently, the networks connecting these machines do

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not provide sufficient capability, bandwidth, reliability, or cost structure to enable analyticsbased control or coordination algorithms to run in a separate location from the machines.
SOLUTION: The Edge Intelligence Testbed will significantly accelerate the development of edgearchitectures and algorithms by removing the barriers that many developers face: access to awide variety of advanced hardware and software configurable to directly resemble state-of-theart edge systems at very low cost to the tester/developer.
COMMERCIAL BENEFITS: More rapid development of testbeds and IIoT innovation. Having theright work instructions directly on the mobile device will not only improve the productivity but
also the safety of the operator.

2.8

MASS CUSTOMIZATION – DYNAMIC MANUFACTURING PROCESSES

Customers expect an increasingly high level of customization in an increasingly short delivery
window. The rigid, high volume production lines of Industry 3.0 are under pressure as the “lotsize of one, delivered to the customer’s door in days” is becoming the new standard. There has
always been a tradeoff between customization and scale. The Industrial Internet of Things (IIoT)
is now enabling manufacturing environments that provide customization at mass market scale.
Small batch and batch of one customization is enabled by system of system interaction between
e-commerce, supply chain, productions, and logistics systems. The underlying trends driving thisinnovation are the falling costs of bandwidth and embedded processing which simplify the
management of complex supply chains and production processes. A static, preprogrammed
product line cannot accommodate customization due to the cost of retooling and changing
material inputs.
The IIoT enhances just-in-time manufacturing to enable single-piece workflow by single units tobe tracked through the supply chain and production line in real time. New management models
and operational architectures are required. To seize this opportunity, companies must masternew ways of operating and collaborate more closely with IT and IoT platform vendors to achievea tighter integration of OT and IT solutions across their internal and external supply chains.
The twin requirements of customization and delivery times are forcing manufacturers to rethinkinventory, production planning, machine retooling, and overall manufacturing processes.
Companies that meet these expectations will satisfy customers and grow market share.
Manufacturers that fail to improve flexibility and service levels will be under pressure ascustomers’ options increase.
Factories that enable mass customization are often greenfield. However, Harley-Davidson
reconfigured their York, Pennsylvania factory to equip all machinery with sensors and location
awareness. The factory was already tooled for modest customization. But the reconfiguration
reduced the lead time to produce customized motorcycles from 21 days to six hours. Today, each
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line.13
IIC Smart Manufacturing Connectivity
MARKET SEGMENTS: Discrete manufacturing

CHALLENGE: The discrete manufacturing domain is characterized by a strictly hierarchicalstructure of the automation systems, commonly referred to as the automation pyramid. Dataacquired by a sensor typically flows through an input/output, IO-module into a PLC which
manages the local real-time control system. However, because a PLC was selected to exactly
match the requirements of the environment within which it was intended to operate, it often
cannot support tasks such as communicating data through additional interfaces.
SOLUTION: This testbed implements an alternative solution by substituting IO-modules thatconnect the sensors with the real-time automation system by a gateway that extracts the sensor
data and transfers them to the IT system through an additional communication channel via OPC
Unified Architecture (OPC UA)(IEC 62541). This “Y-Gateway” re-uses existing physicalconnectivity and supports the easy integration of an IO-Link sensor with the IT by using a common
device model based on an open standard and thus enables the remote configuration of thesensor.
COMMERCIAL BENEFITS: The testbed enables retrofit-able hardware solutions that reduce thecosts of the physical installation. Easy access to a high volume of near real-time data enables the
improvement of current analytics and the development of innovative applications.

2.9

BUSINESS MODEL EVOLUTION – MONETIZE DATA AND SHIFT TO SERVICESThe Smart Factory is as much about disruption of business models as it is about operational
efficiency. New manufacturing business models are developing at a similar degree of magnitude
as when XEROX transformed the office equipment market in 1959 by charging for photocopies
made rather than machines. XEROX achieved a 41% compound annual growth rate for 12 years
running. But more importantly, their innovation prompted a business model revolution that is
now the subject of MBA courses in top business schools.

13

Richard Howells, SAP, How Harley-Davidson And Other Companies Deliver Individualized Products

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Manufacturing business models are being disrupted the same way today by 3D printing, Robotsas-a-Service, and the overall servitization of the manufacturing value chain. The line between
products and services is blurred by the growing adoption of value-based pricing.
The pioneering engine manufacturer, Rolls-Royce, is a prime example of how innovative businessmodels can increase revenues and profitability. In 2015, Rolls-Royce used business model
innovation to rescue the company from financial struggle. They shifted their focus to providing
Outcomes-as-a-Service. In their case, it meant selling “power by the hour” instead of selling newengines as a once-off transaction. This benefits the customer by transferring a large portion ofoperating risk to the manufacturer. And it benefits the manufacturer because it increases thecustomer lifetime value and barriers to switching vendors.
Rolls-Royce also used operating data from their engines to increase the value-added services they
offer to customers. They’ve signed contracts to help customers such as Singapore Airlines reduce
fuel consumption through data analytics. Their focus on monetizing their data and providing
more services contributed to half of the company’s £14.6 billion revenue in 2015.14
Outcome-based business models give manufacturers like Rolls-Royce the opportunity to turnservices into a significant revenue driver. By leveraging data from connected products to provide
smarter services, manufacturers can stay ahead in the increasingly competitive businesslandscape.15
IIC Smart Factory Web Testbed
MARKET SEGMENTS: Industrial automation

CHALLENGE: The Smart Factory Web testbed aims to form a network of smart factories withflexible adaptation of production capabilities and sharing of resources and assets to improveorder fulfillment. Key challenges include: exchanging data between connected factories reliably,providing information securely at the right granularity to authorized partners, and adapting
production capabilities quickly and efficiently in response to orders.
SOLUTION: The Smart Factory Web applies a dual plane 3-tier implementation architecture with
IIoT technologies on both planes. The upper plane comprises the Smart Factory Web portal tohandle cross-factory interactions through gateways to the individual smart factories. The lower

14

Margi Murphy, Computerworld, Singapore Airlines inks 5-year deal with Rolls Royce for fuel efficiency data
analytics
15
World Economic Forum, Unleashing the Potential of Connected Products and Services

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plane is a Smart Factory with a Factory Digital Image to enable secure 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 thefactory production with a minimum of engineering effort.
COMMERCIAL BENEFITS: The testbed creates and validates new business models with the flexible
assignment of production resources across factory locations. This creates new opportunities for
SMEs, allowing them to respond flexibly to manufacturing orders.

3 CORE IOT TECHNOLOGIES – WHAT ENABLES CHANGE?
The technological foundations for the IIoT are not new. Modern manufacturing is the product ofcontinuous technological innovation, from steam engines to assembly lines. In the last 20 years,
investment into robots, actuators, sensors, and next generation control systems on the shop floor
have laid the foundation for the IIoT. Digitalized “things” are not new in the automation space,but so far they have lacked the “I”, Internet connectivity to link equipment and IT systems.
Automation systems, sometimes also called OT, were traditionally proprietary systems, isolated
from the fast evolving, globally connected world of IT. OT and IT have developed in parallel formany decades. It is their convergence, based on both commercially available and emerging
standards for manufacturing processes and technologies, that will transform industries over the
next decade.
We are now taking advantage of improvements and cost reductions in Internet connectivity, data
analysis, and sensors. Together, these technologies enable use cases that were previously
technically or financially impractical across all areas of a manufacturing organization. Theseemerging use cases are based on aggregating, analyzing, and acting on data.
Data aggregation is a necessary first step in IIoT implementation, whether from existing PLC,SCADA and Distributed Control System (DCS) systems or from newly installed sensors. Cost
effective communications technologies are then necessary to move the data. And, mostimportantly, analytics platforms capable of managing large, diverse datasets are required to
extract insight that can be acted on. No technology in itself will disrupt an industry. But the
combination of existing and new technologies has created the foundation for significant
technology innovation.
However, technology innovation is necessary but not sufficient to disrupt the manufacturingsector. It is necessary to look beyond technology at the people and processes that run yourbusiness. Cisco coined the term “Internet of Everything” to draw attention to the interaction
between connected “Things” + “Data” + “People” + “Processes.” Successful disruption requires
evolving an organization’s management of people and processes to create value from connectedthings and abundant data.

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Looking outside of a specific company into the value chain technology silos remain common,
especially in manufacturing. As in the IT sector, the most impactful disruptions will involve
breaking down siloes as value chains evolve to accommodate the dynamic flow of information.
Figure 4 illustrates information availability across an industrial cooler’s value chain.

Figure 4: Traditional Industrial Cooler Value Chain16

Information about the condition and usage of the cooler are of interest to multiple players in thevalue chain, but the siloed data and systems prevent insights that can enable value chain
efficiencies, improve the cooler product, and streamline cooler-related services. It is insufficient
to connect sensors to the cooler. The data those sensors produce must be made available tomultiple teams within and across organizations.

3.1

ALIGNMENT HAS BEGUN – COST AND CAPABILITY DRIVE FEASIBILITY

The building blocks to make the manufacturing value chain more effective and efficient areavailable and are being used to generate technology and business innovations today. There are
two primary drivers that enable new IIoT use cases – cost reductions and capability
improvements.
Cost reductions are making long-existing technologies become affordable. For example, sensors
are getting cheaper due to improved manufacturing processes for sensor technologies andrapidly growing economies of scale. Costs are also driven down by relatively new technologies.
Cloud computing provides affordable, scalable storage and processing capabilities without therisk of investing in fixed assets. The cloud makes big data analytics affordable for risk-adverse
small and mid-sized manufacturers. As a result, both manufacturing data and the capacity tostore and analyze it are growing in scale and affordability.

16

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Meanwhile, the capabilities of new technologies are increasing rapidly, especially on the IT sideof the equation. For example, innovation in machine learning has rapidly increased theintelligence of Apple’s Siri and IBM’s Watson. Algorithms now have better, and much more rapid,
pattern recognition capabilities than humans in many situations.17 Integrated software-hardware
solutions are enabling innovation in the “things space.” Consider for example the use ofAugmented Reality in asset maintenance, drones to assess damage following natural disasters,
and the use of 3D printing to manufacturer stronger, lighter aerospace components.
Interoperability remains an innovation bottleneck and must be addressed in order for diverse
IIoT technologies to exchange data. Fortunately, we see standards gaining prevalence on themarket. Standardization is progressing in protocols like MQTT (MQ Telemetry Transport) and OPC
UA, as well as data formats like HyperCat.
Standardization is a significant challenge and faces many impediments due to the variety oflegacy systems and competitive dynamics. Fortunately, forward thinking companies like GE andthe aforementioned Rolls Royce have started to sell the data produced by products such as jet
engines to generate new revenue streams.18 This concept of “Product-as-a-Service” provides
companies with a means of monetizing data and thus incentivizes further interoperability.

3.2

DESIGNING THE SMART FACTORY – ALIGNING TECHNICAL, MARKET, AND BUSINESS VALUE

We have seen that the core technical building blocks for industry disruption are in place. It is now
a question of which manufacturers will leverage the windows of opportunity to gain acompetitive advantage. From a design thinking point of view, the technical feasibility of thedigital transformation of businesses and value chains is a given. The market desirability andbusiness viability of specific use cases needs to be explored further.
Two ongoing developments that appear to meet requirements of technical feasibility, market
desirability, and business viability are “Digital Twins” and “Integrated Value Networks.” A “Digital
Twin” is the digital representation of a physical asset such as a machine on the shop floor or atruck on the street. This requires the physical asset to be equipped with PLCs or sensors that
inform about its current state and, if required, allow changing the state of the physical thing via
changes to its “Digital Twin.” The Sydney Harbour Bridge is an example of a physical asset with arobust “Digital Twin.” The physical and digital versions of the bridge are aligned with 2,400
sensors that monitor its physical integrity.19

17

Roger Parloff, Fortune, Why Deep Learning Is Suddenly Changing Your LifeGE Aviation, Digital Services & Data Analytics
19
P. Runcie, S. Mustapha & T. Rakotoarivelo, National ICT Australia (NICTA), Advances in structural healthmonitoring system architecture
18

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Traditional value chains imply that the flow of goods and services is sequential between value
chain participants. In our increasingly connected world, value chain participants are starting tointeract in non-sequential ways. Value chains are becoming value networks. “Integrated Value
Networks” rely on the seamless exchange of data, enabled by interoperability technologies andstandards. In the example of the cooler manufacturer mentioned above, this transformation tovalue networks might look like Figure 5.

Figure 5. Integrated Value Network in an Industrial Cooler Value Chain 20

The ability to aggregate, analyze, and act on data requires one or more IoT data management
and analytics platforms to be integrated with enterprise systems and external solutions via an
IoT integration and orchestration platform. A key selection criterion for any IoT platform is outof the box integration with existing IT and OT systems. “Plug and play” integration will
dramatically accelerate implementation and reduce the required investment. Figure 6 illustrates
how core IIoT systems integrate with enterprise IT and OT systems from the business processperspective, very similar to a business process management (BPM) system in the IT world. The
IIC Industrial Internet Reference Architecture (IIRA) provides further detail into the components
of an IIoT System.212021Peter Klement, XMProIndustrial Internet Consortium, Industrial Internet Reference Architecture Technical Report

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Figure 6: IoT Business Solution Architecture22

3.3

THE STANDARDS LANDSCAPE – MEETING REQUIREMENTS AND FILLING GAPS

Standards play a key role in the rate of technology adoption. Standards are required to allow
smart connected products, machines, and assets to interact in a transparent fashion. This goesbeyond the simple communication protocols, and involves the creation of standard semantics
and mechanisms that will allow smart devices to discover each other and interoperate. Standards
exist in this area but they are incomplete and do not cover all aspects of manufacturing. 23
It is important to define both existing and missing standards that fulfill the specific requirements
of factory automation. These are primarily related to speed, determinism, and functionality.
Basing IIoT platforms on standards enables critical abilities such as scalability, portability, andinteroperability. Vendors also follow standards to offer backward compatibility so that theirsoftware can be ported to different generations of processors and operating systems. For
example, OPC UA remains compatible with the previous OPC ‘classic’ devices.
It is also important that standards make it possible to recover when a supplier leaves themarketplace, whether due to bankruptcy, discontinuation of a product line, or merger and

22

Jess Thompson, Gartner, The Hybrid Integration Platform: Foundational Infrastructure for a Digital BusinessJohn Conway, Schneider Electric, The Industrial Internet of Things: An Evolution to a Smart ManufacturingEnterprise
23

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Smart Factory Applications in Discrete Manufacturingacquisition activity. Standards allow that supplier to be replaced by a direct plug-in provided byan alternative vendor. OT legacy systems have typically been proprietary, and are referred to asde facto standards when they reach a broad level of adoption in the marketplace. They were
designed to lock manufactures to a specific vendor, not to provide interoperability between
vendors.
This was more acceptable when machines were not connected. However, the connectivity thatis core to IIoT systems will not be widely adopted in an environment where communications
protocols, data formats, and programming methods are not based on standards. At a minimum
proprietary legacy platforms will need to provide a prescribed level of interoperability during thetransition to IIoT. True “Smart Factory” solutions will require seamless data flow throughmultivendor environments.
A classic example is that of deterministic industrial networks. In the 1990’s, Ethernet TCP/IP wasquickly recognized as inadequate for machine control because delivery of data packets could not
be guaranteed. In response, proprietary networks were developed and still proliferate as ‘flavors’of Industrial Ethernet today. Connectivity between the enterprise (or cloud) and the facility will
become less expensive and closer to ‘real-time’ as we move beyond the need for intermediate
systems to communicate between incompatible protocols.
Data acquisition, ‘recipe’ downloads, and software updates do not require a deterministic
network; however, machine control does. Critical applications that require real-time,
deterministic networks include motion control, robotic control, and networked safety across all
manner of multi-axis servo controlled machinery, from machine tools to packaging machinery.
In some instances, over 100 servo axes may need to be synchronized at a network update rate in
the hundreds of microseconds, with jitter measured in nanoseconds. Each servo axis provides
useful feedback data. For example, increased motor current draw suggests mechanical wear thatis requiring more force to overcome. The IIoT will put additional pressure on network bandwidthby adding real-time monitoring and adjustment to situations that today are periodically sampled,
reviewed, and corrected after the fact, if at all.
Network performance will need to be maintained as node counts and IIoT data volumes increase.
Today, networks are typically separated into machine control (highly synchronized), cell or line
control and data handling via Ethernet TCP/IP. The vision of the IIoT is to flatten this topology
using an (Institute of Electric and Electronics Engineers) IEEE 802 standard protocol. The Time
Sensitive Networking Testbed is an IIC initiative studying the feasibility of this approach. This andrelated determinism use cases will be used to help define architecture specific to the Smart
Factory.
The most prominent standardized industrial communications interface is OPC UA. OPC UA is aplatform independent industrial machine-to-machine communication standard. The OPC
Foundation is a standards organization that is collaborating with both the IIC and complementary
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standards organizations, such as Object Management Group (OMG), to develop a strategy for the
OPC UA and Data Distribution Service (DDS) connectivity standards. While OPC UA is not currently
defined as deterministic, it is hoped that developments will lead to OPC UA becoming suitablefor highly deterministic machine and line control functions in multivendor environments.
Standardized programming, data handling, state models, event processing, and operating modes
also play a key role in the Smart Factory. These standards include Unified Modeling Language(UML), ISA TR88.00.02-2015 (a.k.a., PackML), IEC 61131-3, and MTConnect. The purpose of thesestandards is to accommodate interoperability and consistent operation and data definitions
across different machine types, machine builders, and controls suppliers. This is required formany reasons - more efficient machine operation, reduced total cost of ownership, more direct
machine-to-machine communication, cell and line control, production data acquisition, andoverall equipment effectiveness implementation, to name a few.
The Smart Factory will rely heavily on data handling, software and communications standards
like these to make the connection from machine to cloud as direct as possible, delivering
consistent and accurate decision making information in a timely manner.

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3.4

STANDARD ADVISORY

The IIC Smart Factory Task Group (SFTG) will make every effort to identify relevant best practices,advisory groups, standards bodies, and manufacturing-specific standards. We welcome both
input and enquiries from the readers of this white paper.
Organization
A3 - Association for the
Advancement of Automation
AMT - Association for
Manufacturing Technology
ANSI - American National
Standards Institute
DMDII - Digital
Manufacturing and Design
Innovation InstituteIEC - International
Electrotechnical Commission
IEEE - Institute of Electricaland Electronics Engineers
ISA - International Society ofAutomation
ISO - International Standards
Organization
Modelica Association
MT Connect Institute
NIST - National Institute ofStandards and Technology
OASIS - Organization for the
Advancement of Structured
Information Standards
OMAC - Organization for
Machine Automation andControl
OMG - Object ManagementGroup
OPC Foundation

OpX Leadership Network
PLCopenStandards Supported
Various
MTConnect

Notes
Umbrella organization for AIA, RIA and MCMA.
Focus on vision, robotics, and motion control.
Focus on machine tool industry.

Various

US government standards body.

MTConnect

Various government and industry initiatives.
Focus on CNC and robotics.

Various, notably IEC 61131-3

Industrial programming language standard.

Various, notably IEEE 802‘dot’
Various, notably ISA 88,
TR88.00.02 (PackML), ISA 95,
and ISA 99
Various, notably MQTT
(ISO/IEC PRF 20922)
FMI (Functional Mockup
Interface)
MTConnect

Standards body for Ethernet and TSN.

Various
MQTT

ISA TR88.00.02 (PackML),
MTConnect, and STEP NC
Various, notably CORBA,
DDS, and UML
OPC UA (Open Platform
Communications Unified
Architecture)
Various, notably OEE, FAT,
RFP, TCO
IEC 61131-3

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Professional organization and standards bodycorresponding to IEC and ISO in Europe.
Non-governmental standards body based inEurope.
Standard for model exchange and simulation.
Develops open standards to access machine
device data.
US Dept. of Commerce agency to promote US
innovation and industrial competitiveness.
MQTT is a lightweight messaging protocol used
on top of TCP/IP protocol.
Packaging and Manufacturing Workgroups
advance best practices in industrial
automation.
OMG manages the Industrial Internet
Consortium.
Interoperability standard for data exchange inindustrial automation.
Comprised largely of consumer goods
manufacturers responsible for OT.
Supports and expands on the IEC programming
language standard.

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4 IOT ADOPTION BARRIERS IN MANUFACTURERS – WHAT PREVENTS CHANGE?
Several barriers will need to be overcome before next generation IIoT systems are widelyadopted across the manufacturing sector. Three of the most pressing challenges include
retrofitting legacy assets, accommodating rapid data growth, and protecting sensitive assets andsystems from cyber-attacks.

4.1

LEGACY ASSETSPerhaps the foremost hindrance to IIoT adoption in manufacturing is the nature of capital
equipment, which is often expected to perform for a decade or longer, frequently with noupgrades to the original hardware and software. As OT and IT systems become more tightly
integrated, OT will become the limiting factor for productivity and capability improvements dueto the inability for hardware to evolve at the rate of software. Technology that would beconsidered obsolete in an IT department soldiers on in manufacturing. And reliability trumps
innovation and performance as the operational priority. Despite the importance of a modern OT
platform, one in four manufacturers report that they do not have a specific annual productivity
improvement objective.24
The chronic productivity declines reported by The Wall Street Journal and other news sources
can be laid to a large extent at the feet of legacy factories and supply chains. Compare brownfield
factories in the United States with greenfield factories in China that are equipped with the mostadvanced European manufacturing technologies. China’s relative pricing advantage is shiftingfrom labor costs to equipment efficiency in its most mature industries. Manufacturers inadvanced economies must replace or augment their legacy assets with advanced manufacturing
technologies to maintain competitiveness as their equipment ages. IIoT analytics will provide thebasis for making those investments.
There is a longstanding complaint among manufacturing professionals that manufacturing is
considered a ‘necessary evil’ in the c-suite and an overhead cost rather than a competitive
advantage. This truism may be facing a reversal due to the development of leading edge
technologies like mass customization and additive manufacturing. These technologies will place
advanced manufacturing at the center of business strategy by enabling new pricing models andproduct-service offerings. They entail step changes in capability, not incremental improvementsin efficiency.

24 Kronos Corporation and IndustryWeek, The Future of Manufacturing: 2020 and Beyond

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4.2

DATA OVERLOAD – EXCESS DATA, INSUFFICIENT INSIGHT

Data is at the heart of every IIoT system. An IDG Research Survey reported that 64% of senior
executives felt that integrating data from disparate sources and formats in order to extract
business value from that data is the single biggest challenge the IIoT presents.25 In a typicalfactory, more than 99% of data is discarded without attempting to derive insight due to highstorage and transport costs. There are relatively few organizations worldwide that are truly goodat managing data. Google, Amazon, and Facebook come to mind. Data is their business. For mostmanufacturers, data is a byproduct of production. They lack the knowledge and capabilities tomanage it efficiently.
A Smart Factory requires collecting, analyzing, transporting, and storing vast amounts of data.
The data must be filtered and processed to extract meaning and value, either at the edge or in
the cloud. New systems are required to handle the challenges posed by the volume, velocity andvariety of these data sets. Solution vendors must either improve data management capacity ordevelop algorithms that are better at extracting insight from small samples of data. Until thischallenge is addressed, the IIoT will be typified by vast data lakes that are siloed, unstructured,and temporary.
A third way to reduce the manufacturer’s data management burden is by turning the data into arevenue stream to offset the cost. Data has value to many organizations. Companies should
assess the feasibility of monetizing their data by selling it to third parties such as investors orequipment vendors. This requires an honest assessment of the risks of sharing particular data
points. In the past, manufacturers have largely been conservative regarding data sharing.
Management typically lacks IT expertise and is thus unable to accurately assess the potential
risks. In this environment of uncertainty, the default is to silo or delete data.

4.3

SECURING INDUSTRIAL INTERNET SOLUTIONSPart of the IIC’s mission is to bring together different viewpoints to share information and findcommon ground for progress. Frankly, the SFTG has not encountered a topic more polarizing
than cybersecurity. But we believe that understanding the different business and technicaldrivers behind OT and IT is essential to creating a best practices security framework that will
benefit industrial enterprises. This topic is the domain of the IIC’s Security Working Group and IT/ OT Task Group, which can be contacted to discuss the issue in greater depth.
IT security is every bit as problematic an issue in the Smart Factory as in IIoT initiatives at large.
Industrial networks, which were originally designed to be isolated, are now exposed tocontinuous attacks of ever-increasing sophistication. Additionally, with the proliferation ofconnected devices worldwide, there is a need to protect against not only malicious intent but

25

Bitstew Systems, CIOs Speak Out on The Real Barriers and Opportunities Presented by the Industrial Internet

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also errors and mischance. The IIC believes that these factors combine to create a perfect stormthat represents a major threat to safety and security in factories.
In its latest Piracy Study of 2016, the German Engineering Federation, VDMA, revealed that 70%
of manufacturing companies surveyed in Germany had been affected by product or know-how
piracy, which in turn cost the national GDP an estimated 7.3 billion euro and caused the loss of
34 thousand jobs. The targets for counterfeiters are not just components (62%), but also designs(47%), and entire machines (41%).26 Globally, WIBU Systems found reverse engineering to be theleading source of counterfeit products

Figure 7: Product Piracy27

Each element of the production line, including all connected internal or outsourced modules,
should be secured. The most vulnerable component in the chain are the endpoints such as
computers, embedded devices, PLCs, and sensors that are collecting, processing, or producing
data. By embracing ‘Security by Design’, some vendors are embedding security technology
directly in their new generations of IoT boards, gateways, modules, and systems. Hardware
secure elements, in which encryption keys are safely stored, become an intrinsic part of the26

Steffen Zimmermann, VDMA, VDMA Study Product Piracy 2016

27

Wibu-Systems, Infographics: Product Piracy

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Smart Factory Applications in Discrete Manufacturingarchitecture and are easy to adopt when retrofitting existing production systems or building new
greenfield sites.
The Industrial Internet Security Framework (IISF) evolved naturally from, and builds upon, theIIC’s previously published IIRA.28 This ensures that security is not just bolted onto thearchitecture, but rather is a fundamental part of it. With its landmark IISF, the Security Working
Group has developed a common approach to security and a rigorous methodology to assess
security in Industrial Internet systems. It describes the consequences of merging different
security fields, provides guidance on how to select and achieve security objectives, and describes
how to leverage technologies to overcome cyber-sabotage and cyber-espionage.
The IISF emphasizes five different characteristics of trustworthiness applicable to IIoT systems:
SystemCharacteristic
Security
SafetyReliability
Resilience

Privacy

Definition
Security is the condition of the system being protected from unintended or
unauthorized access, change, or destruction.
Safety is the condition of the system operating without causing unacceptablerisk of physical injury or damage to the health of people, either directly or
indirectly, as a result of damage to property or to the environment.
Reliability is the ability of a system or component to perform its required
functions under stated conditions for a specified period of time.
Resilience is the condition of the system being able to avoid, absorb, and/or
manage dynamic adversarial conditions, while completing assigned mission(s),and to reconstitute operational capabilities after casualties.
Privacy is the right of individuals to control or influence what information
related to them may be collected and stored, and by whom and to whom that
information may be disclosed.

Whether your business is considering, or already in the midst of deploying an IoT strategy,
security concerns should be top of mind. As we have learned, the rush to deploy should notovershadow security requirements. Some questions to ask your IT/OT teams:What is the best way to keep our facilities and systems secure and our people safe?
How can you manage the risk of cyber-attacks on our business, or our customer’sbusinesses?
How do clouds, private clouds and fog change the equation?

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Industrial Internet Consortium, Industrial Internet Reference Architecture (IIRA) and Industrial Internet Security
Framework (IISF)

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The IIC takes all of these concepts a step further with actual testbeds to prove out and optimize
the security of real-world IIoT systems. Members contribute to the evolution of the technology,
developing improved endpoint protection, communication security, and data protection.

5 CONCLUSIONThe Smart Factory is at the heart of the digital transformation that enterprises are currently
undergoing. The fusion of OT with IT is allowing manufacturing organizations to tap into a newlevel of technical orchestration. Systems that were previously siloed are now becoming digitally
integrated to enable real-time coordination between people, assets, and things. This emerging
Industrial Revolution is driven by our ability to access, aggregate, analyze, and act on data. Someof the foundation technologies, such as sensors, are relatively mature. Others, such as machinelearning algorithms, are new to market but advancing rapidly. Together, these IoT technologies
will power the next generation of efficiency improvements, business model innovations, anddisruptive value chain evolutions.
The factory of the future will use data to drive efficiencies and improve capabilities in three ways.
First, connecting people, assets, and things to the internet will enable use cases such as predictive
maintenance that reduce operating expenses and improve equipment uptime. Second,
integration with non-production departments, such as product development and after-sales
service, enables new business insights to drive product and process improvements. Lastly,
improved visibility between companies enables collaborative business models such as the Smart
Factory Web.
Technological innovation alone is not sufficient to produce change. Business leaders must
recognize opportunities, develop new business models, adapt organization structures, anddevelop people with the right skillsets. The authors hope that this and other documents
published by the IIC’s Smart Factory Task Group (SFTG) will help to guide business leaders on thisjourney. We welcome questions, comments and recommendations regarding this white paper or
the SFTG in general. Please direct your questions to the SFTG co-chairs at
smartfactory@workspace.iiconsortium.org.

6 ABOUT THE INDUSTRIAL INTERNET CONSORTIUM
The Industrial Internet Consortium® (IIC™) is a global, member supported, organization that
promotes the accelerated growth of the Industrial Internet of Things by coordinating ecosystem
initiatives to securely connect, control and integrate assets and systems of assets with people,
processes and data using common architectures, interoperability and open standards to delivertransformational business and societal outcomes across industries and public infrastructure.

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The goals of the IIC and its members are to:● Drive innovation through the creation of new industry use cases and testbeds for realworld applications.
● Define and develop the reference architecture and frameworks necessary forinteroperability.
● Influence the global development standards process for internet and industrial systems.
● Facilitate open forums to share and exchange real-world ideas, practices, lessons, andinsights.
● Build confidence around new and innovative approaches to security.
The Industrial Internet Consortium is managed by the Object Management Group (OMG).
Membership is open to those interested in advancing the adoption of the Industrial Internet. Visit
www.iiconsortium.org to learn more.

7 CONTRIBUTOR ACKNOWLEDGEMENTS
This document is a work product of the Industrial Internet Consortium. The Industrial Internet
Consortium wishes to thank the following contributors, and especially Erik Walenza-Slabe, fortheir leadership on this white paper.
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Ajay Jasti
Cheryl Rocheleau
Daniela Previtali
Dhaks Ramesh
Erik Walenza-SlabeJohn Kowal
Kirsten Schwarzer
Klaus Albrecht
Mark Maas
Peter Klement
Thomas Nuth

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NTT DATA
Industrial Internet Consortium
Wibu-Systems
Tata Consultancy Services
IoT ONE
B&R Industrial Automation
XMProSchneider Electric
TE Connectivity
XMPro
MOXA

[©

2017 Industrial Internet Consortium. All rights reserved. The Industrial Internet Consortium is a
registered trademark of the Object Management Group (OMG). For a listing of all OMG trademarks, visit
http://www.omg.org/legal/tm_list.htm. All other trademarks are the property of their respective owners.

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