Enhancing transparency in production processes, especially in shared manufacturing, relies heavily on sharing data. Information asymmetries and coordination problems between parties with conflicting interests pose a challenge in this multi-stakeholder interaction. Blockchain technology with smart contracting can be a solution due to its immutable data and decentralised data storage features. Designing and executing blockchain in industrial applications is a highly intricate task that requires extensive testing, expertise, and proficiency. This paper is the first to propose a holistic simulation model for evaluating the impact of smart contracting on shared manufacturing, including a novel approach to simulated smart contracting in time-lapse for Ethereum-based networks. The introduced model guides the design and implementation process of blockchain applications in shared manufacturing to address this challenge. A systematic literature review establishes ten design process requirements and ten smart contract functions. The implementation is developed based on the design benchmarks of three Ethereum-based frameworks to investigate the simulation model's respective feasibility and scalability. The simulation model validation demonstrates our approach's suitability for simulating smart contracting in shared manufacturing within a hybrid production. It enables fast and scalable simulations, offering an innovative approach to extensively testing blockchain applications before their introduction to ongoing industrial operations.

Reversing the digital twin concept to control the physical entity with its virtual entity is an emerging field of research to ensure that the physical components of a production system mirror the simulation precisely. We present a novel concept for the integration of blockchain-based smart contracting into the reverse digital twin of a hybrid production. In this approach, a simulation model monitors and controls the physical system to increase transparency and to enable dynamic simulation of scenarios. To further increase transparency in the digital twin, the financial flow of the production processes is integrated by smart contracting. This ensures tamper-proof documentation of processes and can decrease information asymmetry between the multiple stakeholders interacting within a hybrid production. To validate the concept, we conduct a case study with the implemented digital twin to show its capabilities.

A minimum requirement of feasible order picking layouts is the accessibility of every storage location. Obeying only this requirement typically leads to a vast amount of different layouts that are theoretically possible. Being able to generate all of these layouts automatically opens the door for new layouts and is valuable training data for reinforcement learning, e.g., for operating strategies of automated guided vehicles. We propose an approach using answer set programming that is able to generate and select optimal order picking layouts with regards to a defined objective function for given warehouse structures in a short amount of time. This constitutes a significant step towards reliable artificial intelligence. In a first step all feasible layout solutions are generated and in a second step an objective function is applied to get an optimal layout with regards to a defined layout problem. In brownfield projects this can lead to non-traditional layouts that are manually hard to find. The implementation can be customized for different use cases in the field of order picking layout generation, while the core logic stays the same.

In this paper, we apply answer set programming (ASP) to the task of planning warehouse layouts within the logistical domain. Warehouse layout planners have to take into account a vast number of aspects to come up with feasible layouts, especially the placement of logistical elements under specific conditions like the accessibility of warehouse elements. Also optimization criteria, e. g., minimizing the pathways, have to be considered. Out of a generally large number of feasible layouts, the layout planner has to decide which layout fits best. As this can be quite difficult and time-consuming, we propose an interactive modelling environment which creates all feasible layouts for a given planning problem by exploiting a logistical knowledge base as well as specific case data provided by the user, who can interactively review all optimal layouts and look for the most suited one. It allows for taking soft constraints and vague expert knowledge into account while leaving room for proposing novel ideas for layouts.

Combining a line production with a matrix production into a hybrid production is a vivid field of research to cope with the challenges of mass personalization. Nevertheless, to implement a hybrid system, several challenges have to be overcome. For a line production, a predefined sequence of products is necessary in order to handle variants at all. In a matrix production, workstations can be skipped or variants have different processing times that result in non-deterministic sequences. This leads to the main challenge in such a hybrid system, to control the output sequence of the flexible system in order to continue the production in a line segment.

The speed of adopting new technologies in industrial automation depends on two factors, reliability and ease of integration. CNN-based object segmentation is one of those technologies that are well developed in research and other industries but still not well established in industrial automation. It is an essential processing step for robotic grasping. Nevertheless, most of the grasping in the industry is still computed by classical non-learning algorithms or based on simple manually programmed hypotheses. The traditional setup in most research related to the object segmentation problem is to have a finite number of objects/classes. While this is suitable for some other problems, it is the hurdle stopping the ease of integrating CNN object segmentation in the industry. A more practical approach is to use object class-agnostic segmentation, where a CNN is used to segment objects in an image without classifying them. Then classical feature extractors can be used for the classification process. This method would avoid the need for manual tailoring of CNNs for each individual setup/environment. In this work, we propose an image processing pipeline that is general and invariant to setup. We also show the feasibility of the class-agnostic segmentation, discuss the feasibility of using purely synthetic data for the CNN training and its results when deployed and tested on our real setup.

Industry 4.0, digitization, and Internet of Things enable companies to react quickly and flexibly to market changes. The objective is the production of customized products at the cost of a mass-produced product. The widespread entry of cyber-physical systems enables a networking of all entities. The introduction of cyber-physical systems in production and logistic systems dissolves existing rigid structures and as a result versatile cyber-physical production systems occur. To support the flexibility of the cyber-physical production systems, the production supply needs to be adapted. For this reason, this paper proposes a novel universal production supply concept. This concept introduces a decentralized controlled supply. It is executed by several cyber-physical system entities, which are represented by software agents. These agents negotiate autonomously with each other. The novel concept was implemented in a research lab and evaluated quantitatively and qualitatively. For the quantitative evaluation, the efficiency of the novel concept is evaluated and compared with a kanban supply. An innovative key performance indicator system called process status indicators evaluates the efficiency. The result of this indicator system states that the novel concept is more efficient than the kanban supply.

Staging experiences and providing optimal customer experience has become the new battlefield within the marketing segment, since the introduction of experience economy. The modern customer has mult ...