through new and better services, on the other hand, will be the major driving factors in this calculation.
As in every transition phase from old technologies to new ones, there is a certain time period where it is necessary to handle both worlds. This also applies for IoT. Logistics management in an IoT world must ensure that the IoT ecosystem can handle IoT and non‐IoT logistics at the same time although the realization of the future picture of IoT logistics must clearly be in focus. A vision for the IoT logistics of the future can be that Things will be transported, handled, and stored in an automated way by robots and controlled by self‐organizing, decentralized, smart, AI‐facilitated networks of Things.
Key Takeaways
Internet of Things (IoT) is spreading and transforming almost all sectors including logistics, manufacturing, agriculture, healthcare, or building management. IoT enables physical devices, embedded with electronics, software, sensors, actuators, and network connectivity, to collect and exchange data.
Logistics managers must understand IoT‐related technologies such as sensors, networking technology (e.g. 5G), big data, AI, autonomous robots and vehicles (e.g. drones, AGV), augmented reality (AR) and virtual reality (VR), and the overall IoT concept as well as its implications for managing material and information flows in supply chains.
As IoT is about “connection,” logistics management needs to assess how to get connected. It is important to define which “Thing” in the supply chain, e.g. a product component, finished product, box, or container, needs to be monitored by sensors and always be online.
Based on customer’s requirements, criticality of the “Things” to influence the supply chain performance, and requirements from the business model, it must be decided which Thing should be connected to enable faster and more efficient operations.
Connecting “Things” allows to gather big data that allows for better decision making in terms of time, cost, or quality than before. Logistics management must decide which data is needed, e.g. data about the on‐time delivery of products, quality of the product in transit, or security of facilities and vehicles, and how decisions should be made.
Decisions can be either manual, automated, or autonomous, with varying degrees of human intervention necessities and possibilities. In the highest level of automated decision making, AI systems will autonomously find solutions and come to decisions based on self‐learned behavioral patterns.
Logistics management in an IoT world demands new skills and a new way of thinking about how to provide the right Thing, in the right quantity, at the right time, to the right place. Such skills include data analytics, process mapping, ability to react faster, verification of AI decisions, and human–machine interaction.
Finally, logistics management must work on improving the currently often low level of data quality to enable more automated decisions and closely collaborate not only with the “classical” material flow‐related players but also IT‐related ones.
References
1 Bousonville, T. (2017). Logistik 4.0. Wiesbaden: Springer Fachmedien Wiesbaden (essentials) https://doi.org/10.1007/978‐3‐658‐13013‐8.
2 Chou, C. (2019). How cainiao plans to digitize China's logistics industry. https://www.alizila.com/how‐cainiao‐plans‐to‐digitize‐chinas‐logistics‐industry (accessed 17 February 2020).
3 Gesing, B. and Kückelhaus, M. (2019). Digital twins in logistics. https://www.dhl.com/content/dam/dhl/global/core/documents/pdf/glo‐core‐digital‐twins‐in‐logistics.pdf (15 March 2020).
4 Heckel, M. (2017). Skills for industry 4.0. http://www.delivered.dhl.com/en/articles/2017/02/skills‐for‐industry‐4‐0.html (accessed 8 October 2018).
5 Kern, J. and Wolff, P. (2019). The digital transformation of the automotive supply chain–an empirical analysis with evidence from Germany and China. https://www.innovationpolicyplatform.org/www.innovationpolicyplatform.org/system/files/imce/AutomotiveSupplyChain_GermanyChina_TIPDigitalCaseStudy2019_1/index.pdf (accessed 7 May 2020).
6 Klumpp, M. (2018). Automation and artificial intelligence in business logistics systems. International Journal of Logistics Research and Applications 21 (3): 224–242.
7 Object Management Group. (2020). Industrial internet consortium. http://www.iiconsortium.org (accessed 14 January 2020).
8 Oztemel, E. and Gursev, S. (2020). Literature review of industry 4.0 and related technologies. Journal of Intelligent Manufacturing 31 (1): 127–182. https://doi.org/10.1007/s10845‐018‐1433‐8.
9 Papert, M. and Pflaum, A. (2017). Development of an ecosystem model for the realization of internet of things (IoT) Services in Supply Chain Management. Electronic Markets 27 (2): 175–189. https://doi.org/10.1007/s12525‐017‐0251‐8.
10 Pfohl, H.‐C. (2016). Logistikmanagement. 3. Aufl. 2. Berlin, Heidelberg: Springer Berlin Heidelberg http://www.worldcat.org/oclc/967690663.
11 Pfohl, H.‐C. (2018). Logistiksysteme. 9. Aufl. 2. Berlin, Heidelberg: Springer Vieweg. doi: 10.1007/978‐3‐642‐04162‐4, T4 ‐ Betriebswirtschaftliche Grundlagen M4 ‐ Citavi.
12 Plattform Industrie 4.0. (2018). https://www.plattform‐i40.de/I40/Navigation/EN/ThePlatform/PlattformIndustrie40/plattform‐industrie‐40.html (accessed 14 September 2018).
13 Plattform Industrie 4.0 (2019) Shaping Industrie 4.0. Autonomous, Interoperable and Sustainable. Berlin. https://www.plattform‐i40.de/PI40/Redaktion/EN/Downloads/Publikation/2019‐progress‐report.pdf?__blob=publicationFile&v=7 (accessed 2 March 2020).
14 Porter, M.E. and Heppelmann, J.E. (2015). How smart, connected products are transforming companies. Harvard Business Review 93 (10): 96–114.
15 Ranger, S. (2020). What is the IoT? Everything you need to know about the Internet of Things right now, ZDnet. https://www.zdnet.com/article/what‐is‐the‐internet‐of‐things‐everything‐you‐need‐to‐know‐about‐the‐iot‐right‐now (accessed 7 May 2020).
16 Robert Bosch Manufacturing Solutions (2020). Nexeed
