Volume 60, Issue 3 - Industry 4.0 / Guest Editor: Harald Schöning, Host Editor: Richard Lenz

Digital Twin technology is increasingly gaining importance for digitizing industry. Even if digital twin technology still needs fundamental research, its significance for industry has become very visible, as digital twin technology is a key success factor to professionalize Cyber-Physical Systems (CPS) applications. Industrie 4.0 as an initiative to increase industrial added value is based on establishing connectivity between CPS and to enable communication between CPS. The industrial application scenarios for Industrie 4.0 aim at vertical and horizontal lifecycle integration concepts where digital twin technology plays an important role. This contribution explains major concepts of Industrie 4.0 and derives the role of digital twin technology. Furthermore, digital twin approaches are presented and as well as the proposal to use the STEP-technology (Standard for the Exchange of Product Model Data, ISO 10303) as a fundamental basis for a comprehensive digital twin technology.

The advent of Industry 4.0 is expected to dramatically change the manufacturing industry as we know it today. Highly standardized, rigid manufacturing processes need to become self-organizing and decentralized. This flexibility leads to new challenges to the management of smart factories in general and production planning and control in particular. In this contribution, we illustrate how established techniques from Business Process Management (BPM) hold great potential to conquer challenges in Industry 4.0. Therefore, we show three application cases based on the DFKI-Smart-Lego-Factory, a fully automated “smart factory” built out of LEGO ® bricks, which demonstrates the potentials of BPM methodology for Industry 4.0 in an innovative, yet easily accessible way. For each application case (model-based management, process mining, prediction of manufacturing processes) in a smart factory, we describe the specific challenges of Industry 4.0, how BPM can be used to address these challenges, and, their realization within the DFKI-Smart-Lego-Factory.

Industrie 4.0, the transition from static, centrally controlled production to dynamic, decentralized manufacturing, is aiming to increase product quality, customization and efficiency. Autonomous production systems and flexible inter-organizational cooperation are key challenges. Blockchain seems to be a natural fit to cope with these challenges. This article derives major challenges of I4.0 and compares them with benefits of blockchain technology. Use cases demonstrate where challenges and benefits match. Also, well known blockchain issues and possible solutions are addressed.

Abandoning fossil and nuclear energy sources in the long run and increasing amount of renewable energies in electricity production causes a more volatile power supply. Depending on external realities, renewable energy production emphasizes the need for measures to guarantee the necessary balance of demand and supply in the electricity system at all times. Energy intensive industry processes theoretically include high Demand Response potentials suitable to tackle this increasing supply volatility. Nevertheless, most companies do not operate their production in a flexible manner due to multiple reasons: among others, the companies lack know-how, technologies and a clear business case to introduce an additional level of flexibility into their production processes, they are concerned about possible impacts on their processes by varying the electricity demand and need assistance in exploiting their flexibility. Aside from fostering knowledge in industry companies, an IT-solution that supports companies to use their processes’ Demand Response potential has become necessary. Its concept must support companies in managing companies’ energy-flexible production processes and monetarize those potentials at flexibility markets. This paper presents a concept, which integrates both companies and energy markets. It enables automated trading of companies’ Demand Response potential on different flexibility markets.

An increasing amount of information is collected in industrial production processes. In many cases, this data is only accessible to the vendor of the machines involved in the production process. In the government-funded research project BigPro, we propose a flexible and fast Big Data platform that allows detection and reaction to incidents and anomalies in the production process in near real-time.

Since 2011 the Java runtime library uses a Quicksort variant with two pivot elements. For reasons that remained unclear for years it is faster than the previous Quicksort implementation by more than 10 %; this is not only surprising because the previous code was highly-tuned and is used in many programming libraries, but also since earlier theoretical investigations suggested that using several pivots in Quicksort is not helpful. In my dissertation I proved by a comprehensive mathematical analysis of all sensible Quicksort partitioning variants that (a) indeed there is hardly any advantage to be gained from multiway partitioning in terms of the number of comparisons (and more generally in terms of CPU costs), but (b) multiway partitioning does significantly reduce the amount of data to be moved between CPU and main memory. Moreover, this more efficient use of the memory hierarchy is not achieved by any of the other well-known optimizations of Quicksort, but only through the use of several pivots.