Manufacturing and Industry

The application of IoT concepts to industrial environments has attracted a lot of interest. GE has coined the term “Industrial Internet,” IBM is pushing the concept “Smart Factories,” German industry uses the term “Industry 4.0,” while Airbus talks about the “Factory of the Future.” Precise definitions are few and far between, and many of these concepts go beyond the notion of next-generation manufacturing to include logistics and supply chain management, mining and off-shore drilling, and even smart grids and building automation. In some cases, a worthwhile distinction is made between the industrial IoT and the consumer IoT. As we saw in the introduction, our definition of Enterprise IoT is less about specific application domains and more about openness and integration maturity. In this section, we will take a closer look at some of the more industrial applications of Enterprise IoT, starting with a discussion about how IoT will transform manufacturing from the perspective of both product engineering and production technology.

Integrated Production for Integrated Products

We believe that the IoT will have two main areas of impact on the current manufacturing landscape. The first concerns the organizational structure that is required to produce truly integrated IoT solutions. As discussed in the introduction, the IoT involves a clash between two worlds in which those in the machine camp and those in the Internet camp will be required to work together to create products that combine physical products with Internet-based application services. In an IoT world, many companies will discover that being just a manufacturing company or just an Internet company will no longer be sufficient; they will need to become both – or become subsumed in an ecosystem in which they play a smaller role. For manufacturing companies, this means they will have to build up capabilities in IoT service development and operation; in other words, the achievement of “Integrated Production for Integrated Products.” Many of these companies will find this challenging, because it is not in their DNA. Nor is it just a question of developing additional IT skills (beyond the embedded skills most will likely already have), value propositions will have to evolve too, which will necessitate change in almost all parts of the organization, from engineering to sales right through to aftermarket services.

The second area where the IoT will have a significant impact on manufacturers is of course in the area of manufacturing technologies. As promoted by initiatives such as the German government’s Industry 4.0 strategy, connected manufacturing equipment, connected logistic chains, cyber-physical systems, and big data-based analytics of production processes will help improve the way the physical parts of a connected IoT solution are produced. In a sense, this second area of impact can benefit from the first; what is an integrated product to one company – a machine component manufacturer, for example – is an advanced production technology to another (a manufacturer using the connected machine component in their assembly lines, for example).

Drawing on these two key assumptions, the figure below provides a detailed overview of the manufacturing value chain of tomorrow. Note in particular the integration of IoT Service Implementation with IoT Service Operation.

Before we look at how new production technologies will help improve manufacturing processes in the future, we need to briefly recap on what we know about the products of tomorrow; because, ultimately, the nature of these new products will have an impact on all other processes, from design to manufacturing right through to aftermarket services.

As discussed in the introduction, the assumption is that the products of the future will be connected (1) and become part of what we call the Internet of Things. We are also assuming that products will have embedded computing capabilities, enabling local intelligence and digital services (2). These digital services can be applications or content. For example, a car app store might provide a new navigation application, and the application itself allows the purchase of additional maps.

The combination of physical product and connected backend service will have a sizeable impact on product design. Firstly, it’s possible that the design of the physical products themselves will change. For example, a product’s embedded display and keys could be dropped in favor of a mobile app. This would constitute a significant re-design of the product’s physical components. Secondly, products will be increasingly reliant on remote services, often in the cloud. Building these kinds of related IT services is not usually part of the traditional product engineering process. It will require someone to oversee the design of both elements – the physical product and its associated backend software services or platform – and ensure that everything results in a nicely integrated product offering. See also our discussion in the introduction on the “Clash of two worlds”.

Finally, connected products will provide a rich source of product usage data (3), which will serve as input for all other stages of the value chain, from sales, marketing, and product design through to manufacturing and after-sales services.

Sales/Marketing and New Business Models

New business models made possible by the emergence of the IoT will drive the future of product design. These business models will also have a significant impact on the sale and marketing of these products. As we’ve seen in the introduction, servitization (4) involves transforming a company’s business model from one focused on selling physical products to one focused on services. For example, Rolls-Royce now earns roughly 50% of its revenue from services; by leasing jet engines to airlines on a “power-by-the-hour” basis, for example. This completely transforms the way in which products are sold and serviced.

However, it also means that sales teams will have to completely adjust their sales strategy. Incentive models based on upfront revenues will have to be revisited in favor of models that support recurring revenues, which allow for the stabilization of revenue forecasting.

Marketing teams will be able to leverage detailed product usage data (3) to drive marketing campaigns and define precise market segments. This direct link to the customer via the product can be of huge value for sales and marketing teams, making it easier for them to run targeted cross-selling and up-selling campaigns, for example.

Another key driver is product customization (5). More and more markets are demanding fully customized products. Ranging from custom-designed sneakers to cars built to customer specifications, this trend has two key implications. Firstly, products are now being sold before they have been produced, and not the other way around. In the figure above, we can see that sales comes before manufacturing, contrary to what we would normally expect. Secondly, this trend has a major impact on the manufacturing process itself; for example, “batch size 1” production is a basic requirement of custom manufacturing (7).

End-to-End Digital Engineering

Digital engineering is a reality in most large manufacturing organizations today. These organizations have invested heavily in the integration of tool chains that support the entire product lifecycle. CAD (Computer-Aided Design) tools are used for product design and simulation, CAPE (Computer-Aided Production Engineering) tools support the design and simulation of manufacturing systems while MES (Manufacturing Execution Systems) tools help ensure the integration of product data right across the product lifecycle while also supporting resource scheduling, order execution and dispatch, material tracking, and production analysis.

3D models are also playing an increasingly important role that transcends the traditional domain of product design. Modern 3D PLM systems have integrated CAD design data with Bill of Material (BOM) data and other information to better support end-to-end digital engineering. The 3D model becomes the master model for all product-related data (6). 3D data also support the simulation of entire assembly lines, helping to optimize manufacturing efficiency and minimize the risk of costly changes after the assembly line has been set up.

One of the key benefits promised by the IoT is that it will help link the virtual world with the physical world. 3D models are a very important type of virtual model. The use of sensors, lasers, and localization technologies has enabled the creation of links between the virtual 3D world and the physical world. For example, Airbus uses 3D data to emit laser projections over aircraft bodies in order to guide assembly line workers [AB1]. Similarly, at the Hannover industrial trade fair in 2014, Siemens showcased a complete (physical) assembly line with an associated virtual model in their 3D factory simulation environment. Sensors on the moving parts of the assembly line send movement data back to the IT system, which then updates the position data in the 3D system in real time. As can be seen in the image below, the virtual 3D model is fully in synch with the actual production line.

Augmented Reality is another interesting area in which we are seeing convergence between 3D models and the physical world, especially in the context of training and quality assurance. For example, Airbus`s MiRA (Mixed Reality Application) allows shopfloor workers to access a 3D model using a specialized device consisting of a tablet PC with integrated sensor pack. Leveraging location devices on the aircraft and on the tablet PC, MiRA can show a 3D model of the aircraft from the user`s perspective, “augmenting” it with additional, production-related data. Airbus’s adoption of MiRA has allowed them to reduce the time needed to inspect the 60-80,000 brackets in their A380 fuselage from 3 weeks down to 3 days [AB1].

Manufacturing

We’ve already discussed the need to become increasingly flexible and capable of supporting highly customizable products. From a manufacturing point of view, this means that concepts like “Batch Size 1” (7) and “One-Piece Flow” are becoming even more important. One of the visions of Industry 4.0 is that it will enable the de-coupling of production modules to support more flexible production. One potential way of achieving this is through the use of product memory. Products, semi-finished products, and even component parts will be equipped with an RFID chip or similar piece of technology that performs a product memory function (8). This product memory can be used to store product configuration data, work instructions, and work history. Instead of relying on a central MES system to manage all aspects of production, these intelligent products can tell the production modules themselves what needs to be done. This approach could be instrumental in paving the way for Cyber-Physical Systems (CPS), another key element of the factory of the future. This is discussed in more detail in the SmartFactory case study later.

Improved “top floor to shop floor” integration is another important benefit promised by Industry 4.0 (9). Concepts like MOM (Manufacturing Operations Management) have emerged to help integrate and analyze data from different levels, including the machine, line, plant, and enterprise level. With IoT, additional data will be provided from the machine level directly.

The extent to which the IoT movement will deliver new technologies and standards in this area also makes for an interesting discussion. For example, one already widely established standard for integrating machine data is OPC/OPC-UA [OP1]. It remains to be seen whether OPC and similar standards will be simply re-labeled as “IoT-compliant,” or whether an entirely set of new standards will emerge.

Similarly, many machine component suppliers are already providing either standards-based interfaces (OPC, for example) or proprietary interfaces (DB-based, for example) for accessing machine data. Again, the question is whether it is necessary to invent new standards and protocols, or whether in this particular case it is more important to drive integration at a higher level, based on an EAI (Enterprise Application Integration) or SOA (Service Oriented Architecture) approach, for example. One of the main issues here seems to be heterogeneity; the very issue that EAI and SOA were specifically developed to address.

Another interesting discussion relates to the integration that needs to take place one level down, i.e. at the bus level. For decades, industrial bus systems (EtherCAD, Modbus, Profibus, SERCOS, etc.) have been used for production automation, enabling communication with and control of industrial components often via PLCs (Programmable Logic Controller). Most of these bus systems are highly proprietary, because they are required to support extremely demanding real-time requirements – which is difficult to achieve using IP (Internet Protocol). This, again, poses a problem for the overall vision promised by the IoT – the IP-enabled integration of devices of all shapes and sizes. So it will be interesting to see if the efforts of the IEEE’s Time Sensitive Networking (TSN) task group [TS1]) succeed in establishing technologies for machine and robot control based on IP networking standards.

Other important examples of technologies that could become relevant for the factory of the future include:

• 3D printing: Especially in the area of prototyping and the production of non-standard, low-volume parts, 3D printing is set to become very important in the not-so-distant future.
• Next-generation robots: Robots are already being used in many high-volume production lines today. In terms of how they will evolve, one interesting area is the ability of robots to work in dynamic environments and ensure safe collaboration with humans.
• Intelligent power tools: As we will see in more detail in Part III, power tools such as those used for drilling, tightening, and measuring are becoming increasingly intelligent and connected. The tracking and tracing of these tools is an important IoT use case.
• High-precision indoor localization: The tracking and tracing of moving equipment and products in a factory environment will be primarily achieved through the use of high-precision indoor localization technology.

IoT Service Implementation

The ability to combine manufacturing with IT service implementation is not yet widely established. Apple is still seen as a leader in the field, because of their ability to produce physical products (iPod, iPhone, etc.) that are tightly integrated with IT services (iTunes, iCloud, etc.). As we’ve seen in the introduction, many manufacturers today are still struggling to establish organizational structures where both capabilities are available and integrated to a sufficient degree. Regardless, the ability to combine physical product design and manufacturing with embedded, cloud/backend-based software service development is seen as a key capability of the IoT.

This integration must take place on both an organizational and technical level. The Ignite | IoT Methodology described in Part II specifically addresses this issue from an IT service implementation perspective.

IoT Service Operations

The ability to make the transition from manufacturer to service operator is essential to the achievement of success in an IoT world. This applies not just to the technical operation of the service, but also to the operation of a business organization capable of supporting strong customer relationships. The DriveNow car sharing service discussed in the introduction, is a good example of this. Formed as a result of a joint venture between BMW and Sixt, the service successfully combines BMW’s car manufacturing expertise with Sixt´s expertise in running a considerably more service-oriented car rental operation.

Another good example is the eCall service, an IoT service that requires a call center capable of manually processing incoming distress calls from vehicles and/or vehicle drivers. For more information, see the Connected Vehicle chapter.

Apart from the business operation itself, there is the question of operating the IT services associated with the IoT solution. Some of the capabilities required here include traditional IT operations capabilities, such as operating the call center application used in the eCall service described above. However, some of the capabilities required are also very IoT-specific. Managing remote connections to hundreds of thousands of assets and devices is challenging from an operational point of view, not least in terms of scalability and security.

Remote software distribution is another area worthy of discussion. It offers a huge opportunity for many manufacturers, but also requires the provision and operation of a suitable infrastructure. A good case in point is the recent recall of 1.9 million vehicles by a large OEM due to problems with the on-board software [TY1]. This OEM could have saved itself massive amounts of money if it had been able to distribute the required software update remotely. Smartphone platforms also provide a good insight into the challenges involved in running remote software updates on a very large scale. Although they are now much better at handling software updates than they were in the past, the situation is far from perfect and occasional problems still persist. In the case of in-car software, this would be unacceptable.

Aftermarket Services

In an era of IoT-fueled “Servitization” especially, aftermarket services are becoming increasingly important.

Remote Condition Monitoring (RCM) is one of a number of basic services that can have a fundamentally positive impact on customer service quality. The ability to access product status information in real time is invaluable for support services, not least because it makes for much more efficient root cause analysis and solution development. RCM is not new; it is most likely one of the most widely adopted M2M use cases. The challenge for many large manufacturers today is one of heterogeneity. A large manufacturer with thousands of product categories can easily have hundreds of different RCM solutions. The issue here is not so much the need for new and improved RCM for next-generation products, it’s about the implementation of efficient IT management solutions that are capable of managing this heterogeneity. This could be achieved by automating virtualization and improving secure connection management, for example.

The next step in the evolution of RCM is predictive maintenance. The use of sensors for thermal imaging, vibration analysis, sonic and ultrasonic analysis, oil and liquid analysis, as well as emission analysis allows the detection of problems before they even occur. For buyers of industrial components, predictive maintenance has the potential to significantly improve OEE (Operational Equipment Efficiency). For end-consumer products, predictive maintenance is a great way of improving customer service and ensuring extra sales or commission (“You should replace your brakes within the next 5,000 kilometers. We can recommend a service station on your way to work.”).

In general, product usage data will really help with the identification of cross-selling and up-selling opportunities. When combined with the ability to sell additional digital services, the proposition becomes even more compelling. For example, the performance of many car engines today is controlled by software. We could have a scenario where a car manufacturer produces one version of an engine (the high-end version), and then uses configuration software to create a lower-performing version. The digital service in this case could be the option to temporarily upgrade engine performance for a weekend trip (“You have just programmed your navigation system for a drive to the country. Would you like to upgrade your engine performance for this trip?”).

Naturally, this newly won customer intimacy will require solid security and reasonable data access policies in order to retain customer trust in the long term.

End-of-lifecycle data can be used for remanufacturing and recycling offers, or simply to make the customer an attractive product replacement proposals.

The boundary between IoT services and aftermarket services is not always clear. From our perspective, IoT services are part of the original value proposition. Take the eCall service, for example. In this case, the service is essentially the product that is being sold. Aftermarket services generally take the form of value-added services (which can also be IoT-based).

Work Environment

Some people are concerned that these new manufacturing concepts will threaten the workplace of the future, bringing with them as they will increased automation and the wider use of robots. While there is strong evidence that automation may actually reduce the amount of tedious and repetitive labor, there is also an argument that work will become more specialized and thus more interesting and varied. In particular, the flexibility inherent in the Factory of the Future will demand an approach that is more geared towards problem-solving and self-organization. Robots that help with strenuous, manual labor are viewed by many as an improvement for the work environment. Airbus’s wearable robotic devices or exoskeletons, which are intended to help with heavy loads and work in difficult spaces, provide a good case in point. [AB1].

Adaptive Logistics and Value-Added Networks

Finally, one key element synonymous with the Industrial Internet and advanced Industry 4.0 concerns adaptive logistics and value-added networks. The idea here is that traditional supply chains will evolve into value networks. For example, these networks will need to have structures that are capable of adapting rapidly in order to address batch-of-one requests between different customers and suppliers.

The ability of the IoT to monitor containers, trucks, trains, and other elements of modern transportation systems in real time will also help optimize logistics processes. Improved integration at the business process level will also help make logistics systems more adaptive.

Other Industrial Applications

Of course, the Industrial IoT presents many opportunities beyond those related purely to manufacturing. Some of the opportunities covered in this book include:

• Mobile equipment tracking: The tracking of industrial equipment and containers was one of the first application areas of telematics and M2M and will evolve and contribute to value-added IoT solutions. The Intellion, Kärcher, and PurFresh case studies at the end of this chapter provide some great examples of this.
• Nuclear physics research: As we will see in the CERN case study, one of the areas in which sensor technologies are most widely used is in nuclear physics research, where they are deployed to reconstruct digital images of nuclear collisions.
• Energy: Since it is such a large application domain for IoT, we have dedicated an entire chapter to energy (see Smart Energy)

And of course there are many other potential applications of the Industrial IoT, from cross-energy management (see Smart Energy) to mining right through to offshore drilling.

Industry Initiatives

Given the momentum of the Industrial IoT and its related concepts, it is no surprise that the raft of industry initiatives in this area has become a little confusing. Some examples of these initiatives include the Smart Manufacturing Leadership Coalition (SMLC), the Open Connect Consortium (OIC), the European Research Cluster on the Internet of Things (IERC), M2M Alliance, IEEE Industrial Working Group, etc. In this section we will focus on two initiatives that are gathering strong momentum; Industry 4.0 and the Industrial Internet Consortium.

Industry 4.0

Industry 4.0 began as a special interest group supported by German industry heavyweights and machine manufacturers. Its goal was to promote the vision of a fourth industrial revolution, driven by the digitization of manufacturing. Today, the initiative is mostly led by the Industry 4.0 Platform, a dedicated grouping comprising industry members such as ABB, Bosch, FESTO, Infineon, PHOENIX CONTACT, Siemens, ThyssenKrupp, TRUMPF, Volkswagen and WITTENSTEIN as well as IT and telecoms companies such as Deutsche Telekom, HP, IBM Germany, and SAP. Government agencies and industry associations have also lent their support. The main focus of Industry 4.0 is on smart factories and related areas such as supply chains and value networks, as opposed to wider Industrial IoT use cases such as smart energy, smart building, etc. The initial report that defined the Industry 4.0 vision [I41] defined use cases such as resilient factory, predictive maintenance, connected production, adaptive logistics, and others.

The following interview provides some background on the adoption of Industry 4.0 at Bosch, a large, multinational manufacturing company. Olaf Klemd is Vice President of Connected Industry at Bosch where he is responsible for coordinating all Industry 4.0 initiatives across the different business units within Bosch.

Industrial Internet Consortium

Another noteworthy organization promoting the adoption of Industrial Internet related topics is the Industrial Internet Consortium. GE, which coined the term “Industrial Internet”, initiated the creation of the Industrial Internet Consortium in 2014, with AT&T, Cisco, Intel and IBM joining as founding members. While initially driven by US-headquartered companies, the Industrial Internet Consortium takes a global take on the Industrial Internet, with more than 100 130 new members from many different countries joining the Industrial Internet Consortium in its first year. The Industrial Internet Consortium takes a relatively broad perspective on the Industrial Internet: in addition to manufacturing, the Industrial Internet Consortium also looks at energy, healthcare, public sector and transportation. The Industrial Internet Consortium sees itself more as an incubator for innovation.  Its Working Groups address the architecture and security requirements for the Industrial Internet, but the Industrial Internet Consortium itself is not a standardization body. An important tool to drive the adoption of new technologies and business models in the Industrial Internet is the so-called Testbed. A Testbed is a member sponsored innovation project that supports the general goals and vision of the Industrial Internet Consortium and is compliant to the Industrial Internet Consortium reference architecture. An example for an Industrial Internet Consortium Testbed is the Track & Trace Solution, which is described in detail in part III of the book. This Industrial Internet Consortium Testbed is utilizing the Ignite | IoT methodology for solution delivery.

Case Studies: Overview

The remainder of this chapter provides a number of case studies to illustrate some of the different facets of the Industrial IoT. We are always on the lookout for additional case studies so if you feel you have something to offer in this space, please visit our website.

• SmartFactory: This case study is an industrial-grade research project that showcases key elements of the Smart Factory, including de-coupling of production modules and product memory
• Tracking of mobile equipment
  • Intelligent lot handling: This case study describes the use of high-precision indoor localization technology to optimize wafer production
  • Cleaning equipment: This case study looks at fleet management for mobile cleaning equipment and an innovative management dashboard
• Cool chain management: This case study goes beyond traditional container tracking and looks at actively managing the environment inside the container
• Nuclear particle physics: This case study looks at one of the largest pieces of industrial machinery built by mankind and its extremely advanced use of sensors

Part III of this book provides a further industrial IoT case study on the Trace & Trace solution for handheld power tools.