Internet of Things

Regarding to a lack of transparency, expert interviews have shown that many activities in load-carrier-management in companies are performed manually and are prone to errors. The consequences are overstock or more severe understock of load carriers. This article presents the concept of the data-based service “automated monitoring of stock levels”. The service is based on the integration of technologies for identification, communication and localization into conventional load carriers and the generation of data during the logistic process. The data are processed in a cloud-based service-system that triggers supply chain events in case of potential understock of load carriers to prevent supply chain disruptions.

In many industries, the transformation of value creation is accelerating as a result of the fundamental change from pipelines business to platform business. For taking part in the new platform business companies need to develop innovative business models. The framework developed for designing digital platforms includes the type of value contribution (what?), the actors involved (who?), the reason for the value contribution (why?) and the way of value creation (how?). The design hints derived from the framework are subsequently used to develop B2B platforms to illustrate their relevance and impact.

Whilst bIoTope should be understood as a highly flexible and dynamic ecosystem capable of seamlessly integrating arbitrary proprietary IoT platforms, it nevertheless builds upon several core components, which provide essential functionality. Those functionalities referenced in this article, are agnostic to other ecosystem-external systems. Inside of the ecosystem, a micro-service-architecture MSA approach is applied. Using O-MI for technical Interoperability and O-DF for syntactic and semantic interoperability are foundation pillars of the ecosystem.

The Industrial Internet of Things (IIoT) radically changes value creation in the manufacturing industry. Dynamic environmental factors, technical complexity, and limited resources cause many companies to be left behind. As a principle borrowed from strategic planning, business ecosystems can provide new ways of interorganizational collaboration and thus dynamize efforts to internalize specific IIoT knowledge. Especially in an early phase of trend scouting and idea generation, ecosystems can be seen as a highly effective venturing concept.

Blockchain technology - a distributed ledger in which transactions of all kinds of values can be carried out securely, transparently and consensually without trust in a central authority - is a socio-technical innovation that has the potential to enable new business models and make existing business models obsolete. While most applications today exist in the financial sector (e.g., payment processing), the technology is suitable for various industrial applications, especially in the area of Internet of Things (IoT) applications. This article uses three propositions and various concrete examples to shed light on the possible applications of blockchain technology for new IoT business models.

Securing the IoT poses specific challenges which are still only partially solved. These include the often very limited computing, storage, and energy resources of wirelessly connected IoT devices, the high demands on the user-friendliness, as well as the high-cost pressure. Upon this background, the research project PROPHYLAXE (“Providing Physical Layer Security for the Internet of Things”), funded by the BMBF, pursued a novel approach in which the physical properties of the wireless transmission link between two devices are exploited. Based on these properties, cryptographic keys can be generated that are known only to the communicating devices and can be used to secure and authenticate a connection.

The ever increasing volatility in market conditions push the requirements of industrial companies for operational intelligence far beyond the capabilities of common business intelligence solutions. An integrated and comprehensive software solution was missing. Enterprise Operational Intelligence (EOI) closes the gap. EOI focuses Big Data on company strategies and (strategy-)compliant value chains. This incorporates a structured approach for all data to deliver value, including manufacturing-related data, Internet of Things and real-time processing, somewhat analogous to computer tomography. EOI allows leaders and managers to continuously adjust business processes to keep them in line with business strategies. Operations can be adjusted to new realities rapidly and the consequences of interventions can be understood quickly and comprehensively.

Intensified networking and digitalization of systems affect an increasing number of sectors. At the same time a great variety of different concepts, ideas, expectations as well as fears have emerged around Industry 4.0. A look into the newspapers is enough to understand that the profound connection of critical structures does also hold profound dangers. For the future it is crucial to consider a way of using the new mass of data and information to protect these structures. Evaluating big data and transforming it into smart data with support of Artificial Intelligence will be a significant security factor in the future.