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= Industrial Internet of Things =

Definition of IIoT
The technology Industrial Internet of Things (IIoT) is based on the Internet of Things (IoT). The IoT is a technology that allows the integration of the physical world into computer-based systems. Physical devices and items are embedded with electronics, sensors, software, and actuators, where each object represents a node in a virtual network, continuously transmitting a large volume of data of itself and its surroundings. This connectivity allows for data collection, exchange and analysis resulting in improvements in efficiency and economic benefits.

IoT is applied in different areas. Often a distinction is made between industrial IoT (further referred to as IIoT) and consumer IoT. IIoT relates to the use of IoT in various industrial settings such as manufacturing, while consumer IoT applications include smart homes and wearables.

Boyes et al. provide a complete definition of the IIoT:

“IIoT is a system comprising networked smart objects, cyber-physical systems, associated generic information technologies, and optional cloud or edge computing platforms, which enable real-time, intelligent, and autonomous access, collection, analysis, communications, and exchange of process, product and/or service information, within the industrial environment, so as to optimise overall production value. This value may include; improving product or service delivery, boosting productivity, reducing labour costs, reducing energy consumption, and reducing the build-to-order cycle”.

Enablers of IIoT
The following technologies enable IIoT: cyber security, cloud computing, mobile technologies, machine-to-machine, 3D printing, advanced robotics, big data, Internet of Things, RFID technologie, and cognitive computing. The most important ones are described below:


 * Cyber-physical Systems (CPS): the basic technology platform for IoT and IIoT and therefore the main enabler to connect physical machines that were previously disconnected. CPS integrate the dynamics of the physical process with those of software and communication, providing abstractions and modelling, design, and analysis techniques for integrated the whole.
 * Cloud computing: With cloud computing IT services can be delivered in which resources are retrieved from the Internet as opposed to direct connection to a server. Files can be kept on cloud-based storage systems rather than on local storage devices.
 * Big data analytics: Big data analytics is the process of examining large and varied data sets, or big data.
 * Artificial intelligence and machine learning: Artificial intelligence (AI) is a field within of computer science in which intelligent machines are created that work and react like humans. Machine learning is a core part of AI, allows software to become more accurate with predicting outcomes without explicitly being programmed.

Connectivity
Before the introduction of IIoT, physical devices operated as independent entities, solely programmed to fulfill their own specific task. With the IIoT technology, connectivity becomes basic as machines now have the possibility to be connected to each other, enabling machine-to-machine (M2M) communication. Thus, machines can be controlled and monitored from a remote location. In case of an emerging problem with a machine, the device can send a detailed report about it to the responsible person. This enables interoperability between the different systems.

Reprogrammable and smart
In traditional manufacturing, a complex machine or device is specifically designed and built for the purpose of a particular task. On the contrary, with IIoT, an existing machine can be reprogrammed or updated with new software/functionalities to perform different tasks. It can either be done by the supplier or by the machine itself autonomously (machine learning). For instance, the same robot can then be deployed to a different task, just by reprogramming it, making production faster, cheaper and agile.

Digital traces
Digital traces refer to the traceable activities that are saved as data after usage of a digital device. In the case of IIoT, this can be applied to data generated from the use of a connected machine or device. When digital traces from these machines or devices are analyzed, users or companies can gain insight into how exactly the devices are being used. Combining the input data with already existing knowledge and data, unintentional discoveries can occur, such as the uncovering of certain patterns, which can lead to (new) innovations and eventually competitive advantages. This is called wakes of innovation.

For example, the automotive company Tesla is capturing data from all of its connected vehicles on the road, to later analyze the information how the drivers are using their car. This information leads to insights that the producers did not think of when first building the car. By releasing software updates, Tesla is able to innovate the cars later on when analyzing the data. The latest software update released on the 5th of October 2018, has made it possible to now record and store video taken from the front-facing camera, and use it as a dash cam. Where the front-facing camera had been serving different purposes before, new functionality is being added as a result of new customer gathered data.

Next to these positive impacts of digital traces, insecurity may, however, arise with regard to all the data that is being saved in the process. More on this in security.

Modularity
With the same purpose that reprogrammability is used for software, modularity is applied for hardware. The same machine can be used for different tasks by replacing or expanding one module or part of it, depending on the tasks the machine needs to execute (e.g. a robotic arm maintains constant and just the hand needs to be replaced).

Modularity reduces the system complexity, the modification risks, and improves the system reconfigurability and maintainability. It also allows customer-centricity, flexibility and agility (rapid response to the market), and a dynamic environment: to plan for the unpredictable and capabilities for switch changes.

Interoperability
The ability of a product or system to work with other products or systems is called interoperability. As a result of the connectivity of IIoT, a huge potential for collaboration between new generation information technology (IT) systems has arisen.

However, this might not be as easy as it looks on paper. As existing businesses might still have a large portion of operation technology (OT) systems in place, this could lead to an increase in costs and complexity of the implementation of IIoT systems if they are not able to work together. “A fully functional IoT solution will require seamless data sharing between machines from different manufacturers and other complex systems within an organization”. Research therefore proposes industry-wide standards in order to unlock the potential for interoperability.

In theory, due to the characteristics of IIoT, the interoperability could drastically increase on different levels, but this potential can only be realized if all manufacturers would operate in the same open ecosystem. Otherwise, the complex components might only decrease any form of interoperability and lead to high costs of implementation.

Real-time information for operational efficiency
CPS and other IIoT sensors are continuously collecting and analyzing data generated from the manufacturing process. This real-time capability impacts the operational efficiency of the industry in general and of the companies in particular. Some of these consequences are: cost reductions, more flexibility and agility, higher product quality, focus on decisions and planning. These consequences are explained more specifically in the industry and firm level sections.

Convergent functions and new business opportunities
IIoT itself is a convergence of different technologies and the physical and digital world as explained in the definition and the enablers. Furthermore, IIoT allows the merge of products, functionalities, and industries. First, ecosystems have emerged within industries, where now suppliers, manufacturers, and logistics companies work together to better satisfy customer needs. Second, technology companies such as Siemens, IBM or General Electrics have developed industrial platforms. Finally, products also have merged functionalities, for instance, digital glasses, which are used as tools in the manufacturing process and merge  workforce, instructions, supervision, documentation, and communication within a single device.

Servitization
Companies who have traditionally offered products are now shifting into offering services. They add value to an existing product and transform it into a service, to differentiate from their competitors and to improve their customer experiences. For example, ThyssenKrupp AG uses networked sensors in their elevators for its predictive maintenance system. Thus, time and unnecessary trips by service personnel for elevator repairs are reduced. Other companies offer digital consulting services to sell their self-developed industrial platforms.

Platforms
Platforms allow the monitoring of all actors within the manufacturing process and act as an orchestrator. Software platforms connect and align all parties of an ecosystem to achieve desired outcomes by providing rules, structures, and incentives. A platform can collect and analyze data from all participants and comprise extended networks of innovators, including software developers, start-ups, customers, partners, suppliers and competitors turned “co-opetitors”. These can collectively amplify the value creation opportunities for all participants, and solve problems and apportion liability if one or more parts of the system fail.

Security
A consequence of the connectivity that is being marked as the main threat for companies implementing IIoT in their business are security issues and the governance resulting from them. As the connectivity allows for all the devices and machines creating becoming a large communication network, cybercriminals can get access to a great part of a company’s operations by hacking into the network. Once the hackers get in, all digital traces become accessible, which means large amounts of personal user and organization data will be exposed. Furthermore, in the context of IIoT, the security measures not only affect digital security but can affect physical health and safety as well. Within companies where machines integrated with IIoT and people work together, it is important to be able to forecast actions of machines to ensure the health and safety of humans. It is necessary to keep in mind that networks and machines can be hacked and reprogrammed, which cause danger for people who work with the machines.

Layered modular architecture of IIoT
Based on the layered modular architecture of a digital technology defined by Yoo et al. 2010, it is applied to IIoT.

The device layer refers to the physical components: CPS, sensors or machines. The network layer consists of physical network buses, cloud computing and communication protocols that aggregate and transport the data to the service layer, which consists of applications that manipulate and combine data into information that can be displayed on the driver dashboard. The top-most stratum of the stack is the content layer or the user interface.

IIoT growth and deployment
The Industrial Internet of Things (IIoT) is redefining the business landscape. Due to the characteristics of IIoT, there is a great deal of opportunities for IIoT over a wide spread of industries like healthcare, logistics, agriculture, manufacturing, energy (oil and gas) production, aviation and automotive. As IIoT adoption can impact these big industry sectors, representing 62% of the gross domestic product (GDP) among G20 nations, IIoT can have significant implications for the global economy. Accenture has recently estimated that the Industrial Internet of Things (IIoT) industry is expected to continue growing exponentially and could add US$14.2 trillion to the global economy by 2030. These promising future predictions influence the perspectives of industry players and therefore the industry dynamics at hand. Currently, the core focus of IIoT deployments is to improve productivity and increase operational efficiency, which will then lead to incremental value for cost optimization.

Industry 4.0
The nature of manufacturing is being disrupted in such an extraordinary innovative way that experts are already declaring the dawn of a new industrial era: Industry 4.0. This term originates from a high-tech strategy plan of the German government that promotes the computerization of manufacturing. As in each industrial revolution, new ways of conducting business and meeting customer demand are now created. The third revolution came with computers, making automated production possible, which also included speed, efficiency, minimization of process variability caused by humans. In the latest phase, often referred to as Industry 4.0, cyber-physical systems control and monitor activity through computer-based algorithms.

The main investment area of IoT is industrial IoT (IIoT); 40.2% of IoT is spent on manufacturing and business processes. The fourth industrial revolution will have a major impact on large and global industries such as the oil and gas industry and the automotive industry.

IIoT industry players
The IIoT industry involves several industry sectors and players, or actors, that are part of the IIoT landscape. These can be related to the layered modular architecture in terms of the actors needed to make this architecture functional. There are hardware manufacturers that provide the components that can be incorporated into an IIoT architecture (e.g. semiconductors, sensors and microprocessors). Network service providers provide the connectivity needed (e.g. wifi, Zigbee). This layer also includes data storage and data processing providers (e.g. data center, cloud services). Middleware vendors, whose software enables the integration and management of IIoT devices (e.g. data sharing protocols), and software vendors of data analytics and business intelligence tools. Additionally, IT service providers provide professional services to aid in the implementation and operation of IIoT systems. Consumers purchase and deploy IIoT systems. Other players who also influence the IIoT ecosystem include the government who regulate business consumer environments, and industry groups who encourage the advancement of the IIoT ecosystem; stimulating innovation through collaboration outside a specific firm (e.g. the Industrial Internet Consortium, founded by Cisco, IBM, GE, Intel and AT&T). From this broad ecosystem, some key industry sectors and players will be discussed.

Automotive industry
The automotive industry is one of the biggest industries of the world. According to Deloitte, the global automotive suppliers made in 2018 a revenue of $500M+. These suppliers are based in North America, Europe, Japan, China, India, and other parts of the world.

Disruption and its effects
Historically, disruptions in this industry have changed the world. For example, the introduction of the assembly line by Henry Ford in 1913 led to the second Industrial Revolution, based on electricity, mass production, and assembly line. This traditional car production involving hundreds of identical vehicles lined up in a row no longer exists though. Nowadays, with the rise of IIoT and Industry 4.0, the automotive industry is again drastically changing. Cars are now manufactured to order. The options available to customers are now so vast that each car becomes a unique and individual object. The disruption not only affects the streamlined processes, but also the outcomes, making cars more efficient, safer, and intelligent while increasing the overall customer satisfaction.

Benefits
Using IIoT in car manufacturing implies the digitalization of all elements of production. Software, machines, and humans are interconnected, enabling suppliers and manufacturers to rapidly respond to changing standards. IIoT enables efficient and cost-effective production unlike ever before. Data goes from the customers to the company’s systems, and then to individual sections of the production process. With IIoT, new tools and functionalities can be included in the manufacturing process. For example, 3D printers simplify the way of shaping pressing tools by printing the shape directly from steel granulate. These tools enable new possibilities for designing (with high precision). Customization of vehicles is also enabled by IIoT due to the modularity and connectivity of this technology. While in the past they worked separately, IIoT now enables humans and robots to cooperate. Robots take on the heavy and repetitive activities, so the manufacturing cycles are quicker and  the vehicle comes faster to the market. Factories can quickly identify potential maintenance issues before they lead to downtime and many of them are moving to a 24-hour production plant, due to higher security and efficiency. The majority of automotive manufacturers companies have production plants in different countries, where different components of the same vehicle are built. IIoT makes possible to connect these production plants to each other, so that the possibility to move within facilities exists. Big data can now be visually monitored which enables companies to respond faster to fluctuations in production and demand. Lastly, IIoT can be also integrated into vehicles and promote safe driving by assisting drivers in avoiding road incidents, for example.

Challenges
Manufacturers in the automotive industry face some challenges while working with IIoT. First, because of the high amount of actors involved in the supply chain(e.g. contractors, employees etc.). The second challenge has to do with data security. When dealing with (confidential) company-related data, it is important for companies to have high digital security. If this is not the case, companies become vulnerable to cybersecurity threats. The last challenge includes big data management. Many automotive manufacturers are still finding difficulties in analyzing and using all this data to their advantage. Companies will need to implement software that includes analytics and machines learning possibilities to its full potential.

Besides, many automotive facilities still need to reach the ultimate level of connectivity; where humans and machines effortlessly work together. This is the principle of Industry 4.0, which will lead automotive manufacturers and suppliers to greater profitability.

Oil and gas industry
The oil and gas industry is also known as the petroleum industry. Companies within this industry are within the biggest corporations of the world and their locations are spread over the planet. The most import producers of oil and gas are the United States, Saudi Arabia, Russia, and Iran.

Disruption and its effects
Previously, oil and gas companies used traditional methods to monitor processes within the companies. These traditional methods included the use of Programmable Logic Controller (PLC) systems, satellite communications, and physical monitoring. These systems are a form of wired internet communications, which request a lot of costly maintenance, have an inflexible architecture and adjustments are hard to make. Furthermore, the connectivity of the communications via satellite can be costly and the monitoring of processes has to be done manually by employees. As a consequence, companies in the oil and gas industry were limited in processing great amounts of data related to the condition of the drilling gear, machines, and processes over the whole field, because these amounts were so high that it was too expensive and not feasible. Approximately 90% of the data would be discarded.

With IIoT support, these large amounts of raw data can be stored and sent by the drilling gear and research stations for cloud storage and analysis. The oil and gas industry now has the capability to connect machines, devices, sensors, and people through interconnectivity. This enables all these actors to collaborate all over the world in real time. In order to integrate IIoT within oil and gas companies, they must transform their business models, processes, and IT operations, so that it becomes possible to tie back predictive information gained from data visualization into the financial plans.

Benefits
IIoT can help companies to better address fluctuations in demand and pricing, address cybersecurity, and minimize environmental impact.

Across the supply chain, IIoT can improve the maintenance process, the overall safety, and the connectivity. Drones can be used to detect possible oil and gas leaks at an early stage and at locations that are difficult to reach (e.g. offshore). They can also be used to identify weak spots in complex networks of pipelines with built-in thermal imaging systems. Increased connectivity (data integration and communication) can help companies with adjusting the production levels based on real-time data of inventory, storage, distribution pace, and forecasted demand. For example, a Deloitte report states that by implementing an IIoT solution integrating data from multiple internal and external sources (such as work management system, control center, pipeline attributes, risk scores, inline inspection findings, planned assessments, and leak history), thousands of miles of pipes can be monitored in real-time. This allows monitoring pipeline threats, improving risk management, and providing situational awareness.

Benefits also apply to specific processes of the oil and gas industry. The exploration process of oil and gas can be done more precisely with 4D models built by seismic imaging. These models map fluctuations in oil reserves and gas levels, they strive to point out the exact quantity of resources needed, and they forecast the lifespan of wells. With IIoT, the production process can be optimized and costs can be reduced. The application of smart sensors and automated drillers gives companies the opportunity to monitor and produce more efficiently. Further, the storing process can also be improved with the implementation of IIoT by collecting and analyzing real-time data to monitor inventory levels and temperature control. IIoT can enhance the transportation process of oil and gas by implementing smart sensors and thermal detectors to give real-time geolocation data and monitor the products for safety reasons. Lastly, smart sensors can monitor the refinery processes, and enhance safety. The demand of products can be forecasted more precisely and automatically be communicated to the refineries and production plants to adjust production levels.

Challenges
Besides the benefits, IIoT also brings challenges for the oil and gas industry. Cybersecurity is identified as the most important challenge considering IIoT. Catastrophic events can happen when systems of these companies are hacked, which can eventually lead to serious data breaches, and health and safety risks. Another challenge has to do with the connectivity of oil and gas companies. Due to the fact that many oil and gas companies are spread across the world, achieving flawless connectivity is quite challenging. When, for example, disturbances in connectivity occur between different locations of a company, gathered data can become less reliable. Finally, the challenge of planning the IIoT integration to actually integrating it is for some companies difficult to reach.

Firms affected by IIoT
This part describes how firms react to adapting IIoT. There are different ways companies react to disruptive innovation.

Incumbents
To compete effectively, incumbent companies will need to shift their business practices and begin thinking in terms of ecosystems. Developing the technology and related capabilities to deliver business outcomes is a challenging task. Few companies, even the world’s largest ones, are in a position to own emerging digital value chains. To be successful, companies will need to have a clear strategy on how they want to participate in emerging industry platforms and ecosystems. An example of innovative ecosystem is the “Light as a service” project Philips implemented in Schiphol airport in 2015. In the aim of Schiphol airport to become one of the most sustainable airports in the world, Philips has installed the program “light as a service”. The service works as a circular economy where all partners benefit. Schiphol only pays for the light it uses (pay-as-you-go model), so they have less fixed costs. The rest is done by the suppliers. Philips retain the ownership of the lighting and Cofely (the third main company of this ecosystem) takes care of the installation and maintenance. The fixture components can be individually replaced or upgraded (modular design), that reduce maintenance costs and raw material consumption. Furthermore, lightning fixtures last 75% longer and consume 50% less than typical LED-based products. At the end of the contract, fixtures will be re-used elsewhere (reserve logistics) after upgrading (reprogrammability), resulting in maximum resource reduction. Supported by Cofely’s round-the-clock presence at Schiphol, Philips and Cofely can provide real-time management of the lighting system to generate an optimal lighting experience and sustainability.

Another example of an incumbent organization treating the introduction of IIoT technology as an opportunity is Siemens AG. Siemens AG, supported by the German government plan to develop fully automated, Internet-based “smart” factories. Its most digitized factory is the plant in Amberg (Bavaria), which builds automated machines for the likes of BMW. It is roughly 75 percent automated, has 1,150 employees mainly operating computers and monitoring the process. Furthermore, Siemens has created an open operating IoT system by the name of Mindsphere. The platform provides data analysis for companies using connected machines from any manufacturer. The platform revolves around open standards and open interfaces with the goal of developing industrial applications. When looking at Siemens’ response from the three incumbent responses – Racing (defensive), transition (offensive), and retreat by Adner & Snow (2010). Siemens’ response can be categorized as an offensive transition response. Instead of fleeing the emerging technology, or shielding the way they are processing data, Siemens allocated resources for setting up a platform to share their knowledge with other (potential competing) businesses, manufacturers and third-parties to create a rich and evolving ecosystem. A move that shows Siemens is trying to step up as a leader within the rise of the new technology.

A third example of an organization is Rolls Royce, a leading aircraft engine manufacturing company. They provide a suite of predictive maintenance and repair services for its jet engines, including monitoring engine health and modifying engines to increase reliability and durability. They have been offering engine maintenance services for the past two decades. Their power-by-the-hour “TotalCare® Services” business model allows customers to pay based on engine flying hours. This means that engine reliability and maintenance is a service, that is the responsibility of Rolls Royce. As they embed their engines with sensors, the increasing data flow from aircrafts and aircraft equipment provides this big company new opportunities to analyze and gain new insights from this data in transformative ways, to serve customers better. Advanced data analytics can provide solutions for flight delays, “aircraft on ground” reductions, and other disruptions that have high costs for the airline industry. With these data analytics, the minimal fuel load can be determined to carry on each flight, as additional weight that is unnecessary will also lead to much higher fuel consumptions by the aircraft. With many other factors affecting fuel efficiency, the airline industry is always looking for ways to improve efficiency of maintenance activities. Determining what information matters is becoming more difficult due to the large amounts of data generated. As data analysis also becomes more complex, incumbent firms such as Rolls Royce need to form partnerships with cloud computing services such as Microsoft Azure to optimize their business operations.

These examples suggest the importance of ecosystems that allow for collaboration between different actors. Incumbents such as Philips, Siemens and Rolls Royce can improve their current business operations, and disrupt industries by adding significant value to their customers. How new entrants are using IIoT as an opportunity, is elaborated on in the following interview to the CEO of the WS-System company.

New entrants
New companies have neither rigid process nor entry barriers to IIoT. They can embrace IIoT directly from the beginning in order to gain competitive advantage and differentiate from their “older” competitors. This is the case of WS-System, a German company founded in 2011 which applies IIoT (Industry 4.0) in the manufacturing of pieces for the automotive and naval industry. WS-System has developed an application within the company that monitors all the firm’s processes. The application can be improved through modularity and reprogrammability. Every machine, robot, and workplace is connected to the firm’s network. Machines are part of the team and are treated as human colleagues, and are even named after musicians or fictional characters (f.e. Rihanna or Iron Man).