Logistics Community

Data administration is a key success factor for logistics companies today. Since logistics is a key discipline for the implementation of industry 4.0, it needs to become even more digital and data-driven than today. This for instance involves working with the help of data science and machine learning so that logistics companies can optimize both their internal processes and the supply chains of their customers. The data infrastructure in the logistics sector is however defined by numerous local data “islands”. This inhibits the integration of data and the generation of company value on their basis.
Consequently, a great deal of potential remains unrealized in cross-company or collaborative processes along the supply chain. The IDS aim to create a virtual data space for the standardized, secure exchange and trading of data while maintaining full sovereignty over those data. A scalable, secure architecture based on modern information technologies was proposed for this purpose. Since logistics creates and connects a global value chain, its need for a standardized and secure data exchange infrastructure is extraordinarily high.

Companies are facing numerous new challenges today. In view of the latest developments, both in technology and the economy, existing company structures are undergoing an extensive transformation process. Digitization, automation, individualization and decentralization are driving new demands that must be met decisively.  The digital transformation of processes, products and business models is becoming more immediate and leading to developments such as the platform economy. The platform economy is defined by trilateral markets in which a platform operator serves as the intermediary between market players allowing them to work together directly without specialized agents. As intermediaries for economic and social activities, platforms offer a whole new business opportunity in which growth and size are more important than short-term profitability. In addition, existing business structures are loosening. Compartmentalization and the division of labor are increasing with industry 4.0 and its decentralized systems. This is leading to a tremendous increase in cooperation and coordination, a multiplication of the participants and an increasing decentralization of company infrastructures. There is great demand for scalability and the adaptation of information networks to decentralized structures, but traditional ERP systems only offer limited tools for these scenarios.

A new demand is developing for individualization, the on-demand delivery of products and services and hybrid products. These combinations of physical products with digital services are influencing business models, production chains and supply chains. Since data are becoming the focal point of business, their role is changing from a process result and product enabler to a discrete asset. Data themselves have become a separate product often not even referring to a physical object anymore. This leads to far-reaching changes in the handling, maintenance and exchange of data that require special treatment.
These new developments are accompanied by increasingly shorter business cycles, so that companies have to reduce their response times. The growing uncertainty, volatility, complexity and ambiguity of processes is becoming increasing challenging especially in logistics. Global requirements are less stable today and change more quickly since disruptions accumulate. The complexity of modern logistics systems and global supply networks makes forecasting more difficult and leads to less deterministic work flows.

While companies find themselves confronted with numerous new requirements, these developments also harbor considerable potential. A current PwC study estimates that robotic process automation (RPA) will be able to automate 45% of work activities and reach a business potential of US$2 billion. Services made possible by the availability and use of knowledge in the form of data and documents form the foundation for the use of technologies such as RPA. Machine learning and the algorithms for large data volumes required for this task need special infrastructures that enable them to generate their maximum value. The progress of industry 4.0 in production and logistics is accompanied by the exponential growth of sensor performance, computing power, storage capacity and broadband real-time networking. Rather than computing power, the main concern today is the development of tailor-made algorithms, platforms and distributed IoT devices as well as sophisticated artificial intelligence (AI) technology. A well-founded understanding of data and sovereign data sharing capabilities are prerequisites for realizing these technology advancements. On this basis, logistics has the potential to establish visibility, transparency, predictive ability and adaptability for global supply chains.

An integrated view of the entire ecosystem is necessary to connect the various participants, elements and influences that have to be integrated into a silicon economy. The entire data value chain has to be examined for this purpose – from the production and trading of data to the organization of (logistics) processes. This can be realized through a certain infrastructure with an IoT broker, blockchain broker and logistics broker.

IoT brokers represent important data sources in the Internet of things. They connect cyber-physical systems such as intelligent containers and pallets as well as intelligent machines using 5G technology, RFID or conventional networks and make corresponding data available over the Internet.

Blockchain brokers can conclude smart contracts offering payment methods for logistics services according to the latest standards such as cryptocurrency and micropayments.
Logistics brokers are software companies that organize logistics services and their processing. They in turn are connected to logistics service providers and shippers creating a dense network. Typical providers offering their services through a logistics broker are transport platforms, supply chain companies and fourth-party logistics providers (4PL). These overlapping platforms enable new business models for a future data economy by converging additional data and refining them with AI and big data algorithms. Since data serve as the basis for all of these activities, maintaining sovereignty over them is essential. This makes it possible to securely exchange and use data in conjunction with distributed ledger technologies.