Terms like the Industrial Internet of Things (IIoT), Digitalization, Industry 4.0, Artificial Intelligence, and Big Data are omnipresent in the industry and of strategic relevance for many industrial organizations. The subject area is extensive and covers a wide range of technological and economical subtopics. The term Internet of Things (IoT) for example, what does it represent? Is it related to a trend, a platform technology, a smart and connected sensor, a business model innovation, a scientific paradigm, or even an industrial revolution? Moreover, how does this term differentiate from or relate to other terms like Digitalization or Industry 4.0?
Regardless of the discussion on the exact meaning of each of the terms industrial organizations acknowledge the importance of the topic and often engage by initializing and conducting digital transformation projects. But because of the fuzziness of the topic, many organizations are having a hard time to capture and define tangible projects and applications which would digitize their products, services or processes in a value-adding manner. Projects quickly become large, complex, and full of unknown variables.
In order to keep projects manageable a common approach in industrial practice is to start with something simple and straightforward like integrating an additional sensors into an existing product or collecting readily available data. Important considerations on how to access sensor data or how to evaluate and use the data are typically postponed to a later, indefinite point in time. Although this approach occupies the developers, it does not lead to a valuable project outcome because the key elements of a successful and value-adding IoT application are missing.
If you want your digital transformation projects to deliver tangible results you have to make sure you cover all key elements of a successful and value-adding IIoT application from the very beginning of development. So, what is a successful and value-adding IIoT application then?
A successful and value-adding Industrial Internet of Things application automatically triggers actions or decisions based on meaningful information on physical objects and their environment generated by data sensing, transmission and evaluation technology.
Based on this simple definition, an IIoT application or any IoT application in general can be described by a framework consisting of three key elements:
Moreover, the second key element data processing chain can be divided into the three subelements data sensing, data transmission, and data evaluation. This conceptual framework allows to capture and describe IoT applications and makes sure you address the development holistically.
Let us illustrate this framework based on the simple but well known and widespread example of smart storage containers. Such containers have probably existed years before everyone started to talk about IoT and Industry 4.0 but they are a perfect representation of an IoT application. Smart storage containers are containers with an integrated sensor to measure fill level. These containers are used to store production parts such as screws for example. Whenever the fill level reaches a predefined minimum a refill order is triggered automatically. This example can be mapped on the framework:
|physical object||production parts (e.g. screws)|
data processing chain ||
smart storage containers |
|data sensing||fill level sensor (e.g. weight sensor)|
|data transmission||refill order (e.g. automatic order email)|
|data evaluation||comparison to minimum fill level (e.g. <20% fill level)|
|added value||automatic stock management|
This framework can be used as a tool to define all key elements of any IoT application you intent to develop or you are already developing. However, to achieve this a deeper understanding of what each key element represents is important. So, let us dive into each of the elements.
The beauty of IoT applications is that meaningful information on the physical world and its many objects can be generated automatically without any human activity required. In contrast to most of the Information available on the Internet today, where humans had to register or include it manually; IoT applications are able to seamlessly and autonomously provide unique information.
With the possibility of automatically creating information on objects and their environment we can start thinking about which objects are of interest to us. In an industrial context this means which objects and which related information are relevant for me while I am doing my job? Is it the chair I am sitting on, the machines or tools in our production, the products we produce, the cars our sales agents use to travel, or the entire factory building?
Identifying an object which matters to an industrial user and defining the information relevant for doing his job is a good starting point when developing an industrial IoT application.
The data processing chain covers all information and communication technology (ICT) necessary to automatically generate and distribute the meaningful information on the physical objects involved. The necessary ICT technologies usually consist of Software and Hardware—electronic components and network infrastructure.
Data sensing possibilities are immense. From sensing technology for object identification (i.e. QR code or RFID), object localization (i.e. GPS or Ultra-wideband), up to detection of almost any physical property (e.g. Acceleration, Moisture, Force, Light, Temperature, etc.) of a physical object or its surroundings; generating data is usually not an issue. The challenge is to find the simplest and most economical sensor, which provides data that correlates with the desired information and works with the available power supply. The power consumption of a certain sensor is especially important in low-power applications.
Similar to data sensing a variety of data transmission technologies exist which can be chosen from, depending on the required bandwidth (i.e. the frequency and amount of data to be communicated), and available power supply and location (e.g. indoor vs. outdoor, stationary vs. mobile, or long vs. short distance). When a standard internet network (i.e. LAN, WLAN, LTE) is applicable HTTP and MQTT protocols are popular communication standards. In remote and low-power applications other networks with particular protocols are used (i.e. Sigfox, LoRaWAN, NB-IoT, Bluetooth). Data storage technology is also part of the data transmission architecture to provide historical data.
The data evaluation subelement is the most important one of the data processing chain and deserves special attention. The technology used for data evaluation transforms data into meaningful and valuable information. Without this element, all data collection efforts are worthless. Data evaluation can happen at the device level (i.e. on the edge), centralized (i.e. in the cloud) or anywhere in between (i.e. fog computing). In addition to electronic hardware with computing power, software is of crucial importance.
Data evaluation does not mean that every IoT application needs to run artificial intelligence (AI) algorithms and machine learning (ML) cores. In many cases a tailored user interface remotely displaying the information about a physical object relevant for a certain target user and his job is enough. However, one should avoid displaying information which is not relevant to the user. Of course, automated logical processes and state of the art AI algorithms allow the generation of richer meaningful information.
In contrast to consumer IoT applications, industrial applications must deliver an economic added value in order to be successful. From the perspective of an industrial organization added value is ensured if the development of an IoT application establishes or maintains competitive advantage. With reference to Michael E. Porter, a competitive advantage is achieved when the IoT application either enables an offer differentiation (i.e. new or better products and services) or a cost reduction (i.e. increased organizational efficiency). Therefore, target users could be internal (i.e. employees) or external (i.e. customers).
Regardless of whether the target user is internal or external the IoT application must satisfy at least one of his needs. As the main benefit of an IoT application is the automatic generation of meaningful information it should satisfy a user's need for information. Information is valuable for a user when it empowers him to do a certain job better by triggering necessary actions or correct decisions. At best, the quality and reliability of the information generated is high enough that the user's job can be automated and the released human capacity can be used for something more important.
A successful and value-adding industrial IoT application consists of the three key elements physical object, data processing chain, and added value. The three key elements as a conceptual framework allow to capture and holistically describe any industrial IoT application. While developing IoT applications, the framework ensures that object related, ICT technology related, and business related aspects are covered.
In fact, the presented framework can cover almost every value-adding technology application which is perceived in connection with the terms Internet of Things, Digitalization, Industry 4.0, Artificial Intelligence, and Big Data. It can help you to identify where the application you are developing lacks important elements and has room for improvement.
Feel free to try the framework yourself by capturing an industrial IoT application you know with the key elements presented in this article. We are eager to know how it went and if the framework helped you to improve the development of your application.