we still see a lot of inefficiencies due to bad data quality. Material master data (e.g. weight, dimension, country of origin, packaging information, etc.) are missing or often incorrect that can lead to wrong decisions or interruption of operations. As disturbing as such problems of data quality are already today, data quality will be the crucial prerequisite for logistics management in an IoT world. In summary, getting more bad data in a faster way will not improve the process but instead have the opposite effect.
Organization
Most international companies still have a functional supply chain organizational structure. However, operating in an IoT world requires close cross‐functional collaboration. Research and development (R&D), information technology (IT), engineering, logistics, purchasing, marketing, and sales will need to be aligned on how the “Thing” will be used and how to use the data lake. Especially the integration of and collaboration with the IT team have shown to be particularly important. A functional setup of data storage, data analytics, and data security might not be the most effective and efficient way of dealing with an increasing data volume. For this reason, some companies are already building up some dedicated “data groups” who only deal with these increased requests of data storage, analytics, and security in a cross‐functional way.
Skills
People working in IoT logistics need to have the right skills to get Things connected and get the best out of big data (Heckel 2017). This involves know‐how of IoT technology, logistics process understanding, data analysis, and systems. Companies who want to make full use of IoT have to develop such multiskilled logistics employees.6 On a management level, this requires skills to “see” supply chains from an IoT point of view. This contains the abilities to handle the above‐shown topics: “get connected,” “get decisions,” and “get prepared.” On an operational level the following skills are needed:
Data analytics – skills to handle and analyze big data and discover new insights/patterns of the IoT logistics processes.
Process mapping and description of reaction processes – understand the actual processes and define/describe (for automated execution) potential reaction processes on detected deviations.
Ability to react faster – to get full advantage of online data, fast reactions are necessary: Is the associate prepared for this? Does he have the authorization to do so?
Check reliability of IT/AI decisions – Is the data correct? Do the automated/autonomous decision‐making processes make the right decisions and show the expected results?
Human–machine interaction – with more automation and robotics, especially in handling areas like goods receiving, warehousing, and loading/unloading, the interaction between robots and machines will only increase (Klumpp 2018). Is your staff prepared and trained how to work with a robot? In the case of small technical errors, can your staff troubleshoot the issue in terms of working with the software program of the robot, or can they do maintenance on the machine, such as exchanging batteries or conducting simple repairs?
Ecosystem
To set up and run an operation in an IoT world, new players are necessary. Besides the logistics service providers, producers of embedded systems and sensors, telecommunication companies, IoT platform providers and operators, application developers, certification companies, data analytics providers, and a lot more have to be coordinated and managed to put IoT solutions into practice. A whole IoT ecosystem (Papert and Pflaum 2017) needs to be managed (see Figure 3.4).
This stresses once more the strong need for close coordination with IT‐related players and the “classical” material flow‐related players. Today, the different players in a supply chain often use their own proprietary systems to handle their data and to manage the material flow (Vial 2019). In an IoT world, the sensors of the Thing will provide all data needed for the business. In the collaboration with internal and external partners, this brings up several questions:
1 Who owns the data?
2 Who gets access and controls permissions to the data lake for which kind of data?
3 Will these original data from the Things replace the different labels, barcodes, and other storage technology in use today?
The ownership of the data might change the role of the supply chain manager and the current logistics service provider. Some big OEM customers might be the driver of the implementation of IoT logistics solutions, some logistics service provider might see some new business opportunities, but the full success of IoT logistics will only come in a connected IoT ecosystem. One example of such an IoT logistics service provider and IoT ecosystem can be seen with Alibaba’s Cainiao that operates a logistics network in China (Chou 2019). Cainiao is managing the supply chain and associated express delivery companies such as STO Express, Yunda Express, YTO Express, or ZTO Express (Wang 2018). Based on the big data of the parent company, the Cainiao network uses its own IoT platform for standardized communication and collaboration with the associated companies, which allows using IoT devices and robots in all of its warehouses and deliveries.
Conclusion
The target of logistics management in an IoT world is still the same as before: managing material and information flows. With smart devices (Things) connecting everything with everything, the way of doing to reach this target will change. Logistics management must address new questions related to connected Things. Data must be collected in order to be able to analyze it, so that decisions can be made. New IoT ecosystems consisting of a lot of new players must be managed.
Beside the Things, the major topic of logistics in an IoT world will be the big data generated by the Things. The major challenge will be how to use and share this data in an IoT ecosystem most efficiently and effectively. Standards, trust, and connectivity will play a crucial role.
Figure 3.4 IoT ecosystem.
Source: Based on Papert and Pflaum (2017). © John Wiley & Sons.
The connected world will offer new possibilities of doing business in terms of providing sensors for track and trace services, offering sensor‐based preventive maintenance and repair services, and helping companies to reach their logistics goals in terms of cost, lead time, and quality in a better way. This also requires new skills and a mindset change. Logistical process know‐how has to be combined with data analytics and technology. Lastly, cross‐functional collaboration, especially with internal or external IT teams, will be a crucial success factor.
The use of more smart and connected Things will enable the supply chain to implement an increasing number of automated processes. Things in the supply chain triggers the automated goods receiving, putaway and transport, commissioning, and packing of boxes and pallets for the next customer order. All devices in the physical world will be represented as a “digital twin” in the digital world. In addition to the already known classical 3D models or computer simulations, the digital twin is a virtual model of the real Thing based on “sensed” real‐life data. Design, operation, and optimization of logistical processes, infrastructure, or global supply chains can be done based on the digital twin either in real‐time or as a simulation of different scenarios (Gesing and Kückelhaus 2019).
The implementation of IoT logistics and automation solutions requires substantial investments in hardware like RFID gates, barcode readers, AGVs, smart racks, and Wi‐Fi, as well as the corresponding