1 The Digitization of Everything and Artificial Intelligence in Everything Will Change Everything for Everyone

By the end of the 1920s, AI algorithms will determine, regulate, and control nearly everything in the logistics sector—and not only there. Platforms will hoard data and generate knowledge. Swarms of autonomous robots will explore their surroundings, negotiate with each other, and organize themselves.

A new “Silicon Economy” is emerging. It will outclass the business models of Silicon Valley and turn the world upside down. And there is no alternative to the introduction of AI—human intuition and hierarchical order have failed in the attempt to master the complexity of existing networks and processes. AI algorithms and their machine learning will define the game. Logistics and supply chain management are the crucial domains where the initial stage of this new B2B competition will be decided.

The Coincidence in Time Is Crucial

The introduction and universal application of AI characterizes the era we live in. Autonomously interacting entities increasingly determine the course of development. Driven by the hardware development of digital semiconductors such as memory, low-power sensors, and processors, the automation of entire processes and supply chains on the basis of autonomous entities in software and hardware is now becoming focus of attention. The decisive factor here is the coincidence in time of a wide range of technical developments:

  • Industry 4.0 and the Internet of Things including AI in devices (Edge AI)

  • Real-time networking (5G, Wi-Fi 6)

  • AI-based platforms (AI Platform as a Service)

  • Blockchain (distributed ledger) and automated negotiation (smart contracting)

  • Swarms of autonomous robots (LoadRunner®)

  • Virtualization and simulation (simulation-based AI)

  • Immersive technology such as augmented reality (AR) and virtual reality (VR), which connect humans with AI

  • Cognitive computing

  • Quantum computing

The common element in each case is the universal application of AI—albeit in a wide variety of forms: from the simplest, rule-based systems in the trackers of our containers, the support vector machines in “intelligent” sensors, and simulation-based reinforcement learning of swarms of autonomous vehicles to deep learning algorithms in supply chain management. Obviously, it seems to be logistics where all these technologies are now breaking through simultaneously. Due to its comparatively simple processes performed millions of times and yet its enormous systemic complexity as a whole, logistics is the sector that is virtually a prime example of AI application. For example, the automatic identification and measurement of individual packages via camera and AI is already a market worth billions. However, the real market potential will be leveraged when the process chains on the AI platforms of future supply chain management close and AI algorithms fully permeate logistics networks both vertically (from the sensor to the cloud) and horizontally (along logistics processes).

There is no single development that is currently leading to a disruptive change or by which the entire era is named. It is the temporal coincidence that concentrates a multitude of exponential developments on just one point. However, the outcome is indeed singular. And then, in turn, a “connected and autonomous supply chain ecosystem” [1] emerges: the Silicon Economy.

Social Change

It’s not just about engineering and technology but also about an essential change in our society. Humans will no longer be the “decisive authority,” but will hand over the reins of action to machines and their algorithms. The change is universal and will not take the form of a machine man, as Fritz Lang once depicted in his film Metropolis. On the contrary, imitating humans in robot form would essentially be a waste of resources and the corruption of a technology that can do many things, but is by no means human. It will be essential to relocate humans and their position in relation to AI.

In the first three industrial revolutions, mechanical work was transferred to machines and robots. For such an industrial application, it was pointless to think about whether computers could develop creativity or intuition. Today, their abilities are far beyond human capabilities in certain areas. For example, even painting pictures and composing pieces of music can be learned comparatively easily by a computer via AI [2]. Even experts are no longer able to distinguish whether some works were based on human or machine creativity.

In the Silicon Economy, intellectual work is increasingly being transferred to machines. In logistics, for example, this will manifest itself in the planning, control, and scheduling of processes and in new business models. In this context, Anders Indset et al. [3] speak of an emerging “knowledge society” in which we as humans only react primarily to predefined knowledge from our search engines and databases that has been algorithmically processed by AI and which must therefore be overcome. This would make humans increasingly obsolete—at least in terms of repetitive skills or the representation of knowledge.

After the automation of assembly processes in the third industrial revolution and the associated loss of jobs, cashiers at the supermarket checkout could lose their jobs or the banker who might only reproduce what the automated check via AI revealed. Today, operations are supported by AI. A surgeon who operates on a cataract might be replaced at some point in future, or the teaching profession might be enriched by artificial avatars—at least as far as pure knowledge transfer is concerned. The consequence of this development is the demand for a change from a purely reflective knowledge society to an “understanding society” in which a return to humanistic values and the abilities for philosophical, artistic, and scientific discourse are considered essential for human beings.

It is a technical question how we leverage the potentials of neural networks in our computers; it is another question how AI changes the neural networks in our brains. Elon Musk, founder of Tesla and SpaceX, faced this question and came to the conclusion that we have to combine the human brain with AI in order to avoid ending up as its own pet. In his characteristic consistency, he founded the company Neuralink in 2016 and now intends to connect the human brain with a computer to enable paralyzed people to use computers. However, this should only be the first step in ensuring the intellectual participation of humans in future and in connection with the machine world.

However, AI will develop in relation to humans, and one thing seems indispensable: A profound debate is needed about what it means to be human today and tomorrow.

Sharing Economy

The universal challenge of the ubiquitous introduction of AI raises the question of how to ensure the participation and sharing of many people and companies.

On the one hand, the aim is to prevent AI from becoming independent, as feared by Elon Musk et al. (see above).

On the other hand, however, the dimension of this development exceeds what can be achieved by a single organization—no matter how large it may be. At the same time, “sharing” is the new generation’s leitmotif of developers who have grown up with the principle of swapping and sharing on the Internet and have internalized a different logic of giving and taking: “Using instead of owning” is their motto. The principle has spread to large areas of the economy and has become the basis for new value creation models.

This leitmotif is followed by the open-source software movement, i.e., the freely accessible provision of source code, which offers people and businesses the opportunity to use, adapt, and distribute this source code. The publication of construction plans as open hardware or the provision and use of data as open data are also expressions of the sharing mindset, as are open innovation processes with internal and external forces (open innovation). Common to all these trends is the underlying confidence that business potentials generated by intact and open ecosystems can be better leveraged together—for example, through greater innovative strength, through better stability and IT security, or through the avoidance of licensing costs, etc.

Open-source software is now an integral part of the digital economy in Germany and a constituent part of almost all innovation processes—across countries and with the participation of numerous organizations. This does not only apply to the Internet economy but also to industrial production where 50% of the code base is now built on open-source software [4]. It is impossible to imagine today’s world without it. The digital transformation and therefore also the Silicon Economy will not succeed without using open source.

2 Potential of the Silicon Economy for Logistics and Supply Chain Management

The importance of logistics has increased strongly in recent decades in parallel with the growth in world trade. Logistics forms the basis of global trade. It connects places and companies in global networks—from the physical flow of materials and goods to the exchange of data in the flow of information and the flow of finance in logistics management. In this respect, logistics is one of the most important factors influencing free world trade.

Before presenting the potential of the Silicon Economy in this domain, the terms Logistics and Supply Chain Management should be defined.

Logistics and Supply Chain/Logistics Management

Logistics describes the reasonable movement of things, in places, through time, and in relations. It is a fundamental principle that permeates everything physical and its movement. At the same time, it is an expression of man’s striving to set things in motion. Based on Delfmann et al., we will define logistics as an applied science, as an industry, as well as an operational function. Logistics analyzes and designs economic systems as flows of objects (above all, but not exclusively: goods and people) in networks, supplying recommendations for action on the design, implementation, and operation of these networks.

Across Europe, the logistics market amounts to around 1.050 billion euros. Important economic functions are the control of goods and information flows and the transport and storage of goods.

More than any other industry, it is highly standardized and thus ideal for the widespread use of digital platforms, blockchains, and AI processes. AI-equipped technology such as intelligent containers and pallets that negotiate autonomously and route and pay themselves to the recipient, or swarms of autonomous vehicles in factories, exemplify that and how value chains will function in the future.

As with the abovementioned definition of logistics, no single definition or use of the term Supply Chain Management (SCM) has been established. Overlapping with the statements made at the beginning, SCM (or: Logistics Management) should be understood as follows: SCM encompasses “both the targeted development and design of company-related and cross-company value creation systems according to logistical principles (strategic management) and the targeted control and monitoring of the flow of goods and information in the value chains under consideration (operational management).”

On the one hand, SCM understood in this way addresses the basic, goal-oriented design, which describes the initial planning as well as the structural organization of a logistic process, system, or network—in order to create it as an object and as a unit capable of action for the reasonable movement of goods and people. On the other hand, it includes the ongoing, permanent planning and design of logistical processes, systems, or networks in terms of continuous, goal-oriented further development. The execution and realization of logistical activities and their monitoring and control are also largely assigned to SCM. As a central and increasingly important component of management, SCM should be understood as an integrative, cross-functional perspective on and along the entire life cycle of logistics processes, systems, and networks. The primary elements of SCM therefore include design and organization, planning, execution/implementation, and monitoring.

The Open and Federated Approach of Silicon Economy

The potential for optimizing processes or designing new digital services and new business areas appears almost endless. Digital platforms and their AI are crucial for this. Companies like Amazon have demonstrated how a new business model can completely change and even dominate a market within a few years through the intelligent combination of logistics and IT. The consequences of this development are already evident for companies in the logistics industry (see Fig. 16.1). The market shares of a coming platform economy have not yet been allocated in the B2B sector, but the race is on. The winners will be digital platforms with AI algorithms that permeate the entire logistics sector and thus the economy. Globally, the logistics industry is coming under the scrutiny of technology developers and investors [5, 6]. Given the high degree of standardization in logistics, it can be assumed that within a few years, logistical AI will negotiate, control, and schedule the flow of goods in this world. At the same time, the consistent expansion of the Silk Road reflects China’s extraordinary commitment to the field of physical logistics in an increasingly globalized world economy.

Fig. 16.1
figure 1

Characteristics of Supply Chain Ecosystems in the Silicon Economy (adapted from [1])

The Silicon Economy is being developed in Germany: with over three million employees and more than a quarter of a trillion euros in annual turnover, logistics is the third largest industry in Germany, ahead of mechanical engineering and telecommunications. Deutsche Post DHL is considered the largest logistics company in the world. DB Schenker and Kühne + Nagel are two other companies from German-speaking countries among the global top ten. The same applies to the technology sector with SSI Schäfer (2nd place) or Beumer (largest manufacturer of sorting machines in the world at 8th place) [7].

The development is slower in logistics than in the times of the e-commerce hype and is therefore not perceived as decisive by the public. This is partly due to the much higher complexity of B2B logistics applications. However, this effect is increasingly being compensated for by high investments in technology and start-ups. The classic methods of Silicon Valley, which is focused on the B2C sector, are increasingly giving way to long-term commitments in terms of a Silicon Economy.

The challenges lie at both the operational and strategic levels. And the venture of implementing a Silicon Economy must develop both technical and management solutions to overcome existing limitations.

  • Heterogeneous and fragmented system landscapes: Historically grown and highly fragmented system landscapes result in data silos and a lack of information transparency.

  • Specialized and multimodal value chains: Different logistics areas and segments have very specific requirements for their digital infrastructure.

  • New business models & (digital) competitors: Companies like Amazon.com or financially strong Chinese companies occupy logistics based on their B2C platforms and business models.

  • Limited financial and human resources: Particularly in the areas of digitization and AI, there is a lack of human resources, and, due to low margins in logistics, in-house developments in this area are limited to the bare essentials.

No single company by itself has sufficient motivation, market power, or resources to succeed on its own in the logistics of a Silicon Economy. Open, federated, and strong consortia from business and science, in which technologies, de facto standards, and new business models are quickly brought together and developed, would be able to create the basis for economic use of AI solutions with new services, technologies, and applications in logistics and supply chain management and enable decisive participation for (German) SMEs. It is necessary to create open and federated platforms that all can benefit from.

The Emergence of Supply Chain Ecosystems

The leitmotif of the change toward a Silicon Economy is a new type of cooperation in global, digital ecosystems. Today’s rigid and well-defined value chains are being replaced by flexible, highly dynamic and globally connected value networks. The availability and transparency of relevant data are a key prerequisite for this [8] and a decisive driver of innovation and growth. In this context, data sovereignty—understood as the ability of a natural or legal person to decide in an exclusive and sovereign way on the use of data as an economic asset—plays a key role. On the one hand, data sovereignty acts as an enabler for the use of AI applications and thus automation and autonomization in supply networks. On the other hand, it represents a basic prerequisite for the cooperation or connection of previously separate value chains and networks. The silo-like, discontinuous vertical linking of companies along the value creation process, which is currently mostly dominated by producers of an end product (OEM), can be expanded to include horizontal and spontaneous or situational cooperation between chains or networks that were previously separate or in competition.

Supply chains will be connected at all levels—autonomously and in real time. Logistics services will be traded, scheduled, and supervised via platforms. Devices will negotiate and pay autonomously. The control loops of logistics planning and scheduling will be closed. Supply chains will plan, organize, and optimize themselves autonomously. Consequently and finally, an autonomous logistics ecosystem will emerge.

Synergy potentials that clearly exceed the potential of an isolated and optimized chain are the result. These include the reduction of emissions through optimization and consolidation of transports with a simultaneous acceleration of throughput times through transport networks; the reduction of logistics costs and the vulnerability of transport chains to errors (increase in resilience); the setting of impulses for an ecologically and economically sustainable, cycle-based economy; and much more.

3 Silicon Economy inside

3.1 Big Picture/Vision

The “big picture” of the Silicon Economy shows the complete data chain: from data generation in the Internet of Things (IoT Broker) to the trading and booking of data (Blockchain Broker) to the organization of (logistical) processes (Logistics Broker) with the all-connecting secure data space (International Data Spaces (IDS)) and the platforms above it for the realization of new digital business models (see Fig. 16.2).

Fig. 16.2
figure 2

An open and digital ecosystem as a digital infrastructure for autonomously acting and highly dynamic supply chains (© 2020, Fraunhofer IML)

This digital infrastructure enables end-to-end transparency in value networks and creates trust along complete supply chains—from raw material suppliers to end customers—perhaps the most important prerequisite for the participation of all companies. Many of the technologies required for the “big picture” are already available. Starting with logistics, this comprehensive vision could be successively translated into products and business models. The key to realizing this vision is to combine the following key areas and lines of action into a holistic solution in the Silicon Economy sense.

Integration and Connectivity of Infrastructures

The basic constitution of the technical infrastructure must be based on European values. Data protection, IT security, and data sovereignty must take a central role. This can be achieved in a Silicon Economy by using the components of International Data Spaces [9] to create secure data spaces that ensure data exchange between a network of companies while maintaining data sovereignty (trust anchor, trusted platform, data usage control, and no transfer of ownership rights) as a central criterion of sovereign management of data.

Realization of Open and Federated Digital Infrastructures and Platforms

Participation in the digital ecosystem of the Silicon Economy should take place by means of open and barrier-free access to all basic technologies (open source; see opensource.org). The goal must be to minimize the entry thresholds into the Silicon Economy for companies and developers. These, in turn, are free to build new data-driven business models or adequate services, etc. The open basic technologies also include methods of AI.

Capabilities for Real-time Connection of Things

The basis of all digital business models or services is made possible by current developments, particularly in the field of information and communications technology. By developing components for networking devices of an industrial Internet of Things with open and federated platforms, a technological basis for new services and process models will be created.

Smart Services

Companies that understand how to use data as a basis for creating unique customer offerings are among the most successful companies in the world: on the list of most valuable companies, Alphabet (Google), Amazon.com, and Facebook are at the top positions with their data-driven business models, and 80% of the approximately 260 Unicorns existing in 2018 had data-driven business models [10]. This includes, for example, new solutions for digitally negotiated contracts (smart contracts), receipts and payment models by using distributed ledgers—for example, for booking and billing logistical services (transport, handling, storage), or also platforms and digital environments for autonomous planning and scheduling processes.

3.2 Silicon Economy Architecture

The architecture of the Silicon Economy can be characterized firstly by the central architectural patterns used. Secondly, it is characterized by its essential architectural components. Both architectural patterns and components will be briefly presented below.

3.2.1 Architectural Patterns

An architectural pattern is a general, reusable solution to a commonly occurring problem in software architecture. Central architectural patterns of the Silicon Economy include microservices, self-contained systems, application containers, application container orchestration, and event-driven communication.


A microservice architecture [11] is a version of a service-oriented architecture (SOA). The target system combines a set of small-scale services (“micros”) that allow for easy, independent distribution, as well as independent changes and extensions. Each microservice has a high degree of autonomy and isolation and can be developed autonomously and deployed in its own Docker container (see also descriptions of the Application Container architectural pattern). Each microservice can be implemented using a different technology; they communicate with each other using lightweight protocols (fast, data-efficient protocols such as REST).

The goals of this pattern are reuse, high cohesion, low coupling, separation of concerns, single responsibility, and information hiding. Its advantages are modularity and maintainability, as well as faster adaptation to changing requirements (scaling). Furthermore, these goals are supported by the use of the additional architecture pattern called self-contained systems.

Self-contained Systems

Self-contained systems (SCS) [12] divide a system into independent web applications, in this case built with microservices. They communicate preferably via asynchronous application programming interfaces (API). Here, the preferred architecture is based on the Independent Systems Architecture (ISA) best practice guidelines [13].

Application Container

In the Silicon Economy, a microservice is always delivered and executed in exactly one application container. Containers do not only include the application or the microservice itself but also all the required dependencies. These include, for example, runtime environments and system libraries. In contrast to virtual environments or virtual machines (VM), containers use core functionalities of the underlying operating system and are therefore more lightweight in comparison.

Application Container Orchestration

The use of the architecture patterns described above leads to a large number of containers. This results in a high effort for the management of the containers. This is exactly where application container orchestration comes into play [14, 15]. Orchestration solutions, such as Kubernetes, perform the following tasks, for example:

  1. 1.

    Managing resources, such as storage

  2. 2.

    Management of nodes on which individual containers are run

  3. 3.

    Allocation of resources, such as memory and network

  4. 4.

    Scaling containers based on redundancy requirements

  5. 5.

    Monitoring containers for functionality and resource usage

Event-driven communication

Software components are loosely coupled via known interfaces. In a purely event-oriented system, this knowledge is no longer necessary, since events can simply be triggered and assigned to receivers via certain criteria (e.g., topics). This enables asynchronous or event-based communication, in which the sending and receiving of data takes place asynchronously and, for example, waiting for a response from the recipient does not block the process. Events can be triggered both from outside the system, e.g., by user input or by sensor values, and internally by the system itself.

There are several implementations of this architectural pattern. In [16, 17], different asynchronous messaging protocols with wide distribution are compared and referred to the respective standards and technical description documents of the protocols. In the Silicon Economy architecture, MQTT [18, 19] and AMQP [20, 21] are used.

3.2.2 Architectural Components

International Data Spaces

The International Data Spaces (IDS) address the design of a reference architecture model and associated reference implementations for industrial data spaces. The basis is built by the so-called IDS connectors. The functionality and security of these connectors are based on the three topics of trust anchor, trustworthy platform, and data usage control. One of the fundamental principles of IDS is to maintain sovereignty over one’s own data. This principle excludes the transfer of ownership rights to any central entities or providers. IDS provides a generally applicable technical infrastructure for the exchange of any kind of data and has no direct technical reference to the logistics application field.

IDS connectors establish connectivity between the individual platforms while maintaining data sovereignty. They are used to communicate securely with the outside world.

IoT Broker, Blockchain Broker, Logistics Broker

Central to the concept of the Silicon Economy are so-called brokers.

IoT Brokers are important data sources of a Silicon Economy. They connect cyber-physical systems (CPS), such as smart containers and pallets, the same way they securely connect smart machines via 5G technology, NarrowBand IoT, or conventional networks and offer data over the Internet. An IoT broker encapsulates IoT devices and their low-level protocols (data is typically sent in binary representation) and also real-time capable protocols and transforms the messages into open standards (e.g., HTTP(S), AMQP, or MQTT(s)) and open data format JSON, except for visual data (e.g., images, point clouds/3D sensor data).

Blockchain Brokers offer integrated and standardized blockchain solutions for horizontal and vertical networking in value networks. The IT architecture required for this is provided by the Blockchain Broker setup and is integral to the Silicon Economy ecosystem. Contracts (i.e., smart contracts) can be signed via Blockchain Brokers. One of the central building blocks of the Blockchain Broker is a component for smart-contract-based billing of services. Payments via crypto tokens and micropayments are also among the services offered by the brokers. Performed transactions are announced, immutably chained, and validated. Current developments in e-money and cash-on-ledger are taken into account. A framework will be created that considers the requirements of international trading operations. An integrated payment system acts as an enabler for new and disruptive business models in Industry 4.0, supporting instant payments at the value-added and enterprise level on the one hand and micro-transactions at the system level between individual CPSs on the other.

Logistics Brokers provide connectivity between services in the Silicon Economy that run on different platforms. Logistics services and their execution are organized via Logistics Brokers. They connect providers of logistics services with customers and users. This applies equally to both internal logistics/facility logistics and external logistics, e.g., the internal transport or picking of a customer order as well as the (road or rail) transport and handling of goods. In addition, the Logistics Broker is responsible for orchestration, i.e., combining several Silicon Economy services to form a meaningful business process. This means that even complex IT service business processes can be automated.

Silicon Economy Services

Silicon Economy services (SE services) are developed and operated on the basis of the abovementioned infrastructure and architectural patterns. From a technical perspective, SE services consist of several microservices. Cross-company use of services and brokers takes place via IDS. Generally, independent and exchangeable web applications (SE services with web user interface as self-contained systems) are developed for specific use cases. These consist of individual software as well as standard products. For each SE service, it can be decided individually which system platform is to be used, although there are basic specifications that must be met (IDS, Web User Interface technology, programming language portfolio, database system portfolio, tool portfolio). Developer guides and style guides provide the necessary standardization. Viewed from the outside, an SE service forms a decentralized unit that communicates with other SE services (as asynchronously as possible) only via IDS. The business logic is usually implemented as microservice. Due to the clear, isolated functional scope, an SE service can be developed, operated, and maintained by one team.

3.3 The Role of Open Source

The abovementioned developments present a challenge for traditional logistics service providers. An already competitive market, in which profits are made through standardization efforts, is put under pressure by the requirement of increasing integration into complex, digital supply chains. Consequently, logistics service providers are facing a conflict between offering their traditional services and providing and developing new, digital products and services to meet the (digital) requirements of their customers. At the same time, already established platforms such as Amazon or Uber are increasingly entering the B2B market, followed by startups that act as fourth-party logistics providers, for example, by decoupling technologically driven, smart services and solutions from the actual logistics service [22, 23].

Platforms, through their inherent characteristics of strong network effects and the ability to incorporate complementary goods, offer traditional companies the opportunity to expand their product portfolio and value proposition. By including other companies in a platform-based ecosystem, platform service providers can achieve integration into their customers’ supply chains in addition to their core logistics tasks. Until now, B2B platforms are mostly still very specialized and hardly benefit from strong, indirect network effects [24]. Moreover, the integration of complementary providers rarely succeeds and is associated with high costs. In particular, the challenge of trust relationships and the question of orchestration are crucial for platform building [25]. Since logistics, which is in any case a link between the individual supply chain partners, must establish trust and orchestrate processes and tasks, it can be attributed a central role here in building a B2B platform economy.

Open source developments in particular play a decisive role here. On the one hand, open source is a driver of a federated platform economy, as the open provision of processes and implementations enables integration into further platform and offerings from other partners and thus contributes to the growth of the ecosystem. On the other hand, open source in combination with collaborative software development is by definition a good way to work collaboratively, openly, and transparently, thus increasing the trust relationship between the partners involved.

That is why the crucial aspects of a coming federated platform economy are linked to strategies such as open source, open innovation, and collaboration. No company (in logistics) has sufficient motivation, market power, or resources to implement the “big picture” of a Silicon Economy on its own (see above). Only together open and federated platforms can be developed and thus technologies, de facto standards, and new business models can be established quickly. Consequently, it is about a “Linux for logistics” and thus about the joint foundation of a European Open Logistics Community as a driver of open developments of a Silicon Economy. Through the joint development and use of open source software and hardware, efficiency and participation are to be achieved in equal measure. Common standards, tools, and services are created, which in turn enable successful commercial use in companies, act as growth drivers for the industry, and become the starting point for new products and services that can be generated from them.

Design of an Open Source Concept for the Silicon Economy

The core component of the Silicon Economy ecosystem is a repository in which components for infrastructure (platforms and brokers) and applications (Silicon Economy services) are provided. These Silicon Economy components are mostly not a finished program or a finished software platform. They provide a reusable, common structure and (AI) algorithms for applications and devices and are generally developed with the goal of multiple use in a wide variety of logistics areas. Brokers provide a framework through which companies can connect Silicon Economy applications (e.g., web services or service platforms such as freight exchanges), services, or devices (e.g., IoT and blockchain devices). Components are developed by the open source community. They are made available as open source. Companies build their own applications on top of them and extend them in such a way that they meet their specific requirements. In sum, a logistics operating system is created, a “Linux for logistics.”

Initially, this “operating system” will primarily cover existing logistics services and components that are either shared by a large number of companies or form the basis for individual implementations. This will create, use, further develop, and continuously improve a common source code basis. Previously very different IT implementations can converge both technically and functionally by using the common basis and thus realize greater interaction with less integration effort. Typical logistics use cases, such as Track&Trace or the integration of freight forwarders into corporate IT, can be easily used and reused via existing components from a repository (see Fig. 16.3).

Fig. 16.3
figure 3

Silicon Economy Repository (© 2020, Fraunhofer IML)

4 Conclusion

The world is no longer divided into East and West, but into digital and non-digital. The motto is: Whatever can be digitized will be digitized. Supply chains will be networked independently and in real time at all levels (link + virtualize). Logistics services will be traded, planned, and controlled via platforms (trade – plan – control). Devices will negotiate and pay independently (smart contracting + blockchain). The control loops of logistical planning and scheduling will close (closed loop), and supply chains will independently schedule, organize, and optimize themselves (plan – organize – optimize). Due to secure communication and data spaces, this will happen without losing sovereignty over data. All in all, an autonomous logistics ecosystem will emerge—in short: the Silicon Economy. This is too complex an undertaking for one company alone, so that open source developments and open innovation (must) take on a central role.