Reporting flock patterns

Data representing moving objects is rapidly getting more available, especially in the area of wildlife GPS tracking. It is a central belief that information is hidden in large data sets in the form of interesting patterns. One of the most common spatio-temporal patterns sought after is flocks. A flock is a large enough subset of objects moving along paths close to each other for a certain pre-defined time. We give a new definition that we argue is more realistic than the previous ones, and by the use of techniques from computational geometry we present fast algorithms to detect and report flocks. The algorithms are analysed both theoretically and experimentally

An Approach For Analysis Of Human Interaction With Worker Assistance Systems Based On Eye Tracking And Motion Capturing

Human behavior in production systems influences productivity, product quality, work safety and overall process performance. To guide human behavior, digital worker assistance systems can be used to support cognitive decision tasks and sensory perception tasks. In doing so, the design of the assistance systems affects user experience and work results. To optimize and develop human-centric productions systems, data on human behavior and interaction with manufacturing equipment must be collected and analyzed. This analysis is expected to yield benefits regarding process monitoring, quality assurance, user experience and ergonomics. In addition, the results could be used for training purposes to monitor skill improvements. This paper presents a framework for data acquisition and analysis of human interaction with digital worker assistance systems. In addition to the overall system architecture, the individual development steps are discussed. An eye tracking device and a motion capturing camera are used for data collection and provide live information about human behavior in conjunction with a digital worker assistance system. The data is stored in a database and analyzed by custom analysis algorithms. The results are displayed in a dashboard application and show that the presented framework with eye tracking and motion capturing is suitable for the analysis of human interaction with worker assistance systems

Traceability System’s Impact On Process Mining in Production

From the perspective of manufacturing companies, data handling is gaining more attention as it is becoming a strategic resource in digital ecosystems. Market forces such as rising amounts of product variants and decreasing batch sizes lead to higher complexity in manufacturing processes. Therefore, production management’s demand for data-based process transparency is growing continuously as well as the number of companies turning to process mining to address these challenges. The increased use of process mining has uncovered many documented data quality issues that hamper output quality. In response to data usage and quality problems, research in the field of Big Data has turned to sophisticated data value chains as a promising approach to optimize data usage. This paper presents the application of the data value chain concept on a manufacturing use case, delivering an assessment of traceability systems and their effect on data quality issues. This assessment reviews commonly known quality issues and investigates how traceability systems can influence and facilitate better data quality. The results support manufacturing companies in their use of traceability systems to improve the reliability of their process mining input data and, hence, their output performance indicators to meet the demand for more data-based process transparency

Integrating Assessment Methods in the Development of ML-based Business Models for Manufacturing

The use of machine learning promises great potential along the entire value chain of manufacturing companies. Many companies have already recognized the resulting opportunities for increasing enterprise value and are developing their machine learning applications for the production environment. However, despite these efforts, many of the solutions developed fail in the market. Especially small- and medium-sized enterprises have difficulties developing suitable business models for their technical applications. These difficulties arise because companies do not evaluate their business projects sufficiently during the development phases. As a result, unpromising projects are not recognized until late in the development process and thus cause high sunk costs. This paper presents an approach for integrating assessment methods into developing machine learning- driven business models for production. Due to the diametric evolution of information availability and uncertainty during the business model development process, various methods and tools can be used for the assessment depending on the current phase. For this purpose, existing assessment methods are evaluated and contrasted regarding their suitability concerning machine learning-based business models for production. Afterwards, three approaches for the different planning phases of business model development (strategic, tactical, operational) are presented in this paper

Towards efficient production of highly optically pure d-lactic acid from lignocellulosic hydrolysates using newly isolated lactic acid bacteria

This study presents the production of D-lactic acid with high enantiomeric purity using lignocellulosic hydrolysates from newly isolated lactic acid bacterial (LAB) strains. Six strains, 4 heterofermentative and 2 homofermentative, were investigated for their ability to grow and produce lactic acid on sugar beet pulp (SBP) hydrolysates, containing a mixture of hexose and pentose sugars. Among the strains tested, three were isolates designated as A250, A257 and A15, all of which belonged to the genus Leuconostoc. Only strain A250 could be reliably identified as Leuconostoc pseudomesenteroides based on cluster analysis of Maldi-ToF spectra. All strains produced D-lactic acid in the presence of SBP hydrolysates, but with varying optical purities. The homofermentative strains achieved higher D-lactic acid optical purities, but without assimilating the pentose sugars. Co-cultivation of the homofermentative strain Lactobacillus coryniformis subsp. torquens DSM 20005 together with the heterofermentative isolate A250 led to the production of 21.7 g/L D-lactic acid with 99.3 % optical purity. This strategy enabled the complete sugar utilization of the substrate. Nanofiltration of the SBP hydrolysate enhanced the enantiomeric purity of the D-lactic acid produced from the isolates A250 and A15 by about 5 %. The highest D-lactic acid concentration (40 g/L) was achieved in fed-batch cultures of A250 isolate with nanofiltered SBP, where optical purity was 99.4 %. The results of this study underline the feasibility of a novel isolate as an efficient D-lactic acid producer using lignocellulosic hydrolysates

Towards a Data-driven Performance Management in Digital Shop Floor Management

Key performance indicators (KPIs) are crucial for measuring and managing the performance of industrial processes. They are used to detect deviations in processes, enabling opportunities to improve manufacturing processes within the three dimensions time, quality, and cost. In this context, the timeliness of information plays a decisive role in the success of measures since delayed information availability can leave decision makers with no time to react. With the introduction of digitization and industry 4.0, increasing amounts of data become available. They can be used to accelerate problem detection and shortening reaction times to define appropriate actions. This paper presents a data-driven performance management approach integrated in digital shop floor management (dSFM). If a deviation is detected in one process, KPIs of subsequent processes (horizontal level) as well as subordinate levels (vertical level) are checked for correlations and, if present, the associated team is notified by an automatic warning through the dSFM system. Based on the identified correlations, the team discusses the deviations and defines suitable countermeasures. The aim of this approach is to identify deviations more quickly and to quantify their impacts, thus giving shop floor managers the ability to react in time

Knowledge Graphs for Data And Knowledge Management in Cyber-Physical Production Systems

Cyber-physical production systems are constituted of various sub-systems in a production environment, from machines to logistics networks, that are connected and exchange data in real-time. Every sub-system consumes and generates data. This data has the potential to support decision making and optimization of production processes. To extract valuable information from this data, however, different data sources must be consolidated and analyzed. A Knowledge Graph (KG), also known as a semantic network, represents a net of real-world entities, i.e., machines, sensors, processes, or concepts, and illustrates their relationship. KG allows us to encode the knowledge and data context into a human interpretable form and is amenable to automated analysis and inference. This paper presents the potential of KG in manufacturing and proposes a framework for its implementation. The proposed framework should assist practitioners in integrating raw data from multiple data sources in production, developing a suitable data model, creating the knowledge graph, and using it in a graph application. Although the framework is applicable for different purposes, this work illustrates its use for supporting the quality assessment of products in a discrete manufacturing production line

Building a Knowledge Graph from Deviation Documentation for Problem-Solving on the Shop Floor

The description of deviations on the shop floor includes information about the deviation itself, possible causes and countermeasures. This information about current and already processed deviations and problems is a valuable source for future activities in the context of problem-solving and deviation management. However, extracting information from unstructured textual data is challenging. Furthermore, the relationships among the heterogeneous data are hard to represent. This paper proposes a framework to extract the knowledge contained in the deviation documentation and store it in a knowledge graph as triples. The proposed knowledge graph can then be used for the decision support system in production and will support more application scenarios in shop floor management in the future

Supporting the Transformation to Climate Neutral Production with Shop Floor Management

The European Green Deal proposes the transformation to climate neutrality by 2050. Especially for manufacturing companies and their production sites, this transformation is a big challenge. Every aspect of the value stream needs to be re-evaluated and adjusted to reach the new target state of climate neutral production. In the last decades, many companies used lean management methods to improve production in the dimensions of time, quality, and cost. However, a growing number of studies show that lean methods can also be used to drive sustainability goals (with the target state being climate neutral production). This paper analyses the suitability of shop floor management, a popular lean method, in the context of climate neutral production. To this end, a literature research has been conducted to summarize the goals of shop floor management and the success factors for the transformation to climate neutral production. Then the results are contrasted and overlaps are analysed to identify possible shop floor management tools to accelerate the transformation to climate neutral production. Finally, the findings are briefly discussed and summarized in a matrix. The paper closes with specific recommendations for further research in this area

Developing GAIA-X Business Models for Production

The manufacturing industry is in the midst of a digital transformation. As part of the increasing internal and external integration of manufacturing companies, ever more significant volumes of data are being exchanged in order to meet the challenges of a globalized production world. The European initiative Gaia-X aims to establish a federal data infrastructure based on European law to ensure data sovereignty in the resulting digital value creation ecosystems. Under the conditions thus created, it will be possible for manufacturing companies to develop entirely new business models. Within the scope of these business models, the benefit of data sharing in the sense of added value will come into focus. The following paper presents opportunities for the development of disruptive digital business models for manufacturing companies in the context of Gaia-X. The paper focuses on how data sharing can be used to create value. Furthermore, it highlights how the transition from technological use case to monetizable value creation can be made with data-based, digital business models in the context of Gaia-X. Finally, the state of work in business model development in the Gaia-X project EuProGigant is presented for discussion and exemplified by two use cases