is connected with everything, but based on which “language”? This issue can, for instance, be seen in the discussion about cables and connectors in the computer/mobile phone sector. It started with a lot of diverse single solutions and finally led to the USB solution as unified standard.
One well‐known and established standard for identification in the global retail business is the GS1 EPCIS standard and the Global Trade Item Number (GTIN). By scanning the GTIN number/barcode at defined “control points” in the supply chain, a track and trace function can be established from the source to the customer. Another common standard that is mainly used in the automobile industry is the VDA/Odette barcode label. Although such common identification systems exist, we can find additionally numerous labels of each logistics service provider for their specific operations or parts of the supply chain. The further development of the IoT in the future will show which identification and communication technology will prevail. The “Industrial Internet Consortium” (Object Management Group 2020) and the “Industry 4.0 platform” (Plattform Industrie 4.0 2018), for example, are working closely together to standardize and ensure interoperability in the industrial sector (Plattform Industrie 4.0 2019).
Security
With increasing amounts of data transferred between the connected Things and increasing relevance of and dependability on data to manage supply chains, the security of data transfer, data handling, and data storage needs to be looked at. Any connected smart Thing could be a potential target for cybercriminality as it could grant access to a company’s network. In recent years, a lot of “leaks” stories in social media and the financial world have clearly shown the importance of data security. For a further extension of a connected supply chain, it will be crucial to ensure secure data exchange between the Things and the actors along the supply chain, like the customer, their supplier, logistics service provider, and customs agencies. Technologies like blockchain might provide a solution for this secure data transfer and secure identification.4
To ensure trust and security in digital systems, Bosch has launched an initiative and invited different supply chain partners to the first Digital Trust Forum in Berlin on 16 May 2019. Representatives from leading international associations and organizations, including the Institute of Electrical and Electronics Engineers (IEEE), DigitalEurope, European Telecommunications Standards Institute (ETSI), the Eclipse Foundation, Trustable Technology, Plattform Industrie 4.0, the Industrial Internet Consortium (IIC), and the Trusted IoT Alliance, came together to discuss this topic.
With more Things connected, and more data being transferred, the capability or bandwidth of the “connecting technology” to transfer large amounts of data in a fast way has become a huge business. If short reaction times are needed, but the latency of the data transfer to and from a “center of decision making” is too long, certain applications might not work. Further innovations in connecting technology, like 5G, are on the way to provide such solutions. Another approach to come up with fast decisions on the Thing level is to avoid data transfer to a central brain and to have built‐in computing power and intelligence on the Thing level, which is referred to as “edge computing.”5 To share these decentral decisions and their results across the whole system and to enable big data learning for a continuous improvement process, a transfer of data from the Thing to a central data pool, a so‐called data lake, is still needed. Fast reaction or decision can be achieved through local, decentral intelligence while the continuous improvement process is based on big data on a central level.
Get Decisions
From a logistics management point of view, one major purpose of connecting Things and transferring, handling, and storing data in the IoT is to get decisions in a better (time, cost, quality) way than before. In general, we can separate into three different types of decision making: manual, automated, or autonomous.
In the manual decision way, the collected data is analyzed or displayed by certain applications that help the decision maker to come to better decisions. The decision itself remains a task of the related person. For example, in a transport company, based on GPS tracking, all trucks are displayed on a screen showing the map of the road combined with traffic and weather information. In the case of a certain event such as a traffic jam, the “decider” must decide where and how to intervene to keep promised lead times.
In case the decision process can be described in a standardized way, automated solution finding and decision making can be possible. Based on predefined checking and decision finding processes, the “application” (App) in use executes automatically the predefined solution process or gives a proposal to a human decider. Logistics management has to think about which kind of decision situations (e.g. data given by a Thing) can occur in which process and how the decision processes can be defined in a standardized way, including standardized answers. This is also discussed in Chapter 5.
The next level of decision making is currently in development using AI. Such AI systems can autonomously find solutions and come to decisions. Using big data and a self‐learning mode, the “machine” or application itself is smart enough to decide what to do next at a much higher speed and accuracy than humans. Logistics management has to determine which type of decision making is appropriate for which process based on which data from which Thing. Regardless of which type of decision making is applied, the analytics of the data as an input for the decision making can be done in mainly four different ways: descriptive, diagnostic, predictive, or prescriptive Figure 3.3 (Porter and Heppelmann 2015).
In a descriptive way, all selected information is gathered, and the monitored situation is described (e.g. condition, environment, process). Using the diagnostic way, the root causes of deviations between the set target value and the actual monitored value are analyzed. Hence, both the descriptive and diagnostic ways of analytics look back and explain what happened in the past. In a predictive way, the analyzed data is used to detect indications that signal impending events in the future. For example, if the system shows a certain number of Things in the inbound area of a warehouse while in parallel the internal data indicates that several workers are ill, it will most likely result in a delay or congestion in the goods receiving process – if no actions are taken. Lastly, the prescriptive form of data analytics strives for identifying measures to improve results or correct errors.
Figure 3.3 Get decisions.
Different applications need different ways of conducting data analytics. What they all have in common is that they get the data out of the pooled data location. In the first part of this chapter, we have discussed how the data get into the data lake and how to get improved decisions out of it. In the section that follows, we will discuss what needs to be considered to get prepared for logistics management in an IoT world.
Get Prepared
In logistics management in an IoT world, the management of material and information flows in supply chains still has the same overall goals as before: to provide the right Thing, in the right quantity, at the right time, to the right place (Pfohl 2016, 2018). As outlined above, the IoT world provides new tools to realize these targets more effectively and efficiently. But this new IoT approach also demands new skills and a new way of thinking in logistics management. Aside from the new IoT‐related technologies, the prevailing characteristics of IoT and its management are the data.
Data Quality
Logistics management has always had to handle a lot of data to manage supply chains