Data Innovation Community “Industrie 4.0”

Industrie 4.0 is a powerful driver of large data growth and directly connected with the “Internet of Things”. Through the Web, real and virtual worlds grow together to form the Internet of Things. In production, machines, as well as production lines and warehousing systems, are increasingly capable of exchanging information on their own, triggering actions and controlling each other. The aim is to significantly improve processes in the areas of development and construction, manufacturing and service. This fourth industrial revolution represents the linking of industrial manufacturing and information technology – creating a new level of efficiency and effectiveness. Industrie 4.0 creates new information spaces linking ERP systems, databases, the Internet and real-time information from production facilities, supply chains and products.

The Data Innovation Community “Industrie 4.0” wants to explore important data-driven aspects of the fourth industrial revolution, such as proactive service and maintenance of production resources or finding anomalies in production processes.

The Data Innovation Community “Industrie 4.0” addresses all companies and research institutions interested in conducting joint research with regard to these aspects. This includes user companies as well as companies from the automation and IT industries.

DIC leads

Plamen Kiradjiev
kiradjiev@de.ibm.com

IBM

plamen-kiradjiev
tilman-becker

If you are interested in working with the Data Innovation Community or visit one of its meetings, please contact the DIC leads via e-mail at industrie@sdil.de.

Members can access the internal forums at SAP Jam.

Featured Projects

  • STEP

    Smart Technician Mission Planning (STEP)

    The research project “Smart Technician Mission Planning ” (STEP) aims to simultaneously increase the efficiency of technician assignments and the availability of machinery. Several project partners will work on the simulation model that allows to evaluate individual measures quantitatively based on real dispatching operation data.

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  • SDSC BW: Sawing with the SDSC experts

    During the planning and production of sawn timber a wide range of data accumulates: from quality data of the wood to the data generated at the saw line and the sales data. In a joint project with the SDSC-BW, the sawmill Karl Streit from the Black Forest optimized its approach to the planning of round timber incisions.

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  • SDSC BW: Precise planning of production processes

    Sedus Stoll AG is a full-service provider of office equipment and workplace concepts. For the analysis project of the SDSC BW, Sedus provided data from the production of office chairs, which are available in a large number of configuration variants.

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  • SDSC BW: Plant growth by means of image recording

    The da-cons GmbH sells products for the determination of a variety of plant properties. To this end, it has developed the PhenoScreen system, which supports the seed industry in plant breeding and is based on sensors such as cameras, hygrometers and luxmeters. As part of the potential analysis, the SDSC BW examined the question of whether it is digitally possible to reliably detect plants in the image files.

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  • SDSC BW: Dynamic Machine Planning with Smart Data Technologies

    During the production process at the automotive supplier Erdrich Umformtechnik GmbH, various data accumulate at different times. These come, for example, from the machine used, their equipment or the origin of a component (manufacturer, batch). For the SDSC BW experts, the company provided the data of the whole process that are generated during and after the production from MES and ERP over a period of 1.5 years at the Thuringian production site.

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  • SDSC-BW: Smart data analyzes on sensor data support the production of packaging solutions

    HHard film products are produced via so-called calenders, large heated or cooled rolls, through which the raw material is passed. In this investigation, the focus was on the data of such calender systems, equipped with rotation and temperature sensors on the rollers. For the smart data analysis of the Smart Data Solution Center Baden-Württemberg (SDSC-BW), the hard-film manufacturer Bilcare collected high-resolution sensor values from different calenders over a period of six months and made these available to the data analysts.

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  • billiger.de

    Analyze of user behavior based on web protocol data using the example of billiger.de

    Statistical statements based on website visit logs such as the click-through rate, that means the number of clicks on listed offers in proportion to the total visits on the website, are strongly distorted by the dominating bot-share among the users. Price comparison sites such as billiger.de need to segment users of their website into homogeneous groups to calculate precise and reliable business metrics from those groups.

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  • SDSC-BW: Smart data analyzes reduce maintenance intervals for milling machines

    The Hermle AG develops systems that record the machining center as a central parameter, providing information on the condition of the components. This information is analyzed and evaluated accordingly. This can help prevent downtime and precisely determine the need for maintenance. For a smart data analysis of the Smart Data Solution Center Baden-Württemberg (SDSC-BW) data from several machines were provided for a period of 12 months. The initial analysis focused on classifying the state of the axes of the processing center and thus identifying potentials for automated remote maintenance. The second step involved the evaluation by means of supervised learning methods (for example decision trees). The aim of the SDSC-BW experts was to use the data for the prediction of machine problems (predictive maintenance).

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  • Trelleborg_SDIL

    Condition monitoring and prediction of sealing systems

    Trelleborg Sealing Solutions carries out numerous, fully instrumented, tests of these seals and sealing systems. Measured variables, such as pressures, temperatures and velocities, are measured at various points in a very high frequency. The tests carried out and planned represent an extensive data base, which offers great potential for the application of big data analytics.

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  • ibm-logo

    Optimization of product quality at OSRAM Schwabmünchen

    An important goal in the implementation of an industry 4.0 strategy is the optimization of production to further increase the quality of the produced product. Using data analysis of the production parameters, sensor data, test protocols and their interdependencies forecasts and rules for the production can be created.

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  • SDSC-BW: Smart data analysis for component manufacturing

    Smart data analyzes support the scheduling of component production at Herrenknecht AG. Within a customer order, it is necessary to produce various components. The core components are generated in individual production orders at the corporate headquarters in Schwanau. Component manufacturing includes cost, planning, production and quality data.

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  • john-deere-traktor

    Optimization of the production processes at John Deere

    The project mainly aims at the reduction of the rework and the avoidance of errors during the production of tractors at the John Deere factory in Mannheim. These two objectives are realized through a data analysis of the error information, the test protocols and their interdependencies. Based on the results of the data analysis, we can make prognoses and rules for the production planning that help the company to take one step further in the process of self-optimization.

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  • ibm-logo

    All-Time Parts Prediction (ATP) Demo

    ATP predicts the demand for service parts (especially in the automotive industry) for so-called long-time-buy or all-time-buy decisions. This future demand may cover the next 10-20 years and is difficult to estimate, which often leads to buying way too much. Consequently, after many years of sitting in the warehouse, at the end, huge amounts need to be scrapped. This causes high inventory and warehousing costs, which can be significantly reduced by more accurate demand predictions. The IBM ATP Solution has been developed to do exactly that: to predict all-time demand with high accuracy.

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  • SDSC-BW: Smart Data supported campaign analysis for marketing

    With the speed in which IT topics are now being pushed forward, even media companies have to adapt more quickly and become flexible. In particular, this includes marketing and sales activities in order to keep the customers satisfied and to raise further potential. For the analysis project of the Smart Data Solution Center Baden-Württemberg (SDSC-BW), the Huber publishing company provided anonymised information about the concluded contracts of their services. The contracts and customer data, as well as the related marketing activities, were collected over a period of 72 months. In total, information from 943 database tables was processed and a quarter of a million data sets were analyzed and evaluated.

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  • SDSC-BW: Smart data analysis to predict the state of industrial process water

    Water plays an important role in many industrial processes. A smooth running of complex process water systems is the requirement for a functioning cooling process. Different sizes and measured values are decisive. These include, among others, the pH value, the redox value or the conductivity of the system water. In order to monitor these values, a wide variety of sensors capture and make data available.

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  • rolfbenz-banner

    SDSC-BW: Potential Analysis for Leather-Cutting Optimization

    The cutting optimization of natural leather is a highly sophisticated process that already factors in the different quality characteristics of the leather hides while placing the cutting templates. Not only the underlying natural product leather but also the furniture manufactured from it – individually and just-in-time – are available in many variants. For this reason, a flexible and individual consultation regarding Smart Data was especially important for Rolf Benz as an individual manufacturer.

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  • kit-logo

    GPU + In-Memory Data Management for Big Data Analytics

    The global rollout of Smart Meters opens a new business paradigm for utilities with data collection/transaction at such a high volume and velocity. In this project, we develop a toolchain based on In-Memory Data Management and Parallel Data Processing in the GPU. Our aim is to use the processing power of the GPU and the high-throughput and low-latency features of In-Memory databases to develop an adequate Big Data analytics platform. Although the project is primarily concerned with the use case of Smart Meter data, the tool chain is also applicable for Big Data analytics in other domains.

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  • conditionbased_maintance

    Condition-Based Maintenance

    The enterprise “TRUMPF Machine Tools” is the global leader in the production of machine tools for sheet metal forming (laser, punching, and bending machines). At specified, but irregular intervals a “digital image” in the form of a data collection of logging and configuration information is created in a TRUMPF machine tool. Using these data, the project being planned aims at detecting deviations (anomalies) from the so-called “normal operation” and revealing correlations to yet unknown factors.

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  • sap-logo

    Association Rule Mining for High Dimensional Master Data

    Traditionally, organizations are using rule-based approaches to discover defects in Master Data. The definition of these rules is expensive for organizations and constrained by the availability of resources with the right domain expertise. The aim of this project is to evaluate with recourse to suggested validation rules the applicability of approaches like association rule mining as a way to support Master Data domain experts.

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  • SDI-X: Smart Data Innovation Processes, Tools, and Operational Concepts

    The BMBF-funded project “Smart Data Innovation processes, tools and operational concepts (SDI-X)” explores appropriate tools and best practices. The aim is to not only facilitate extensive data analysis projects between different research and industry partners but also enable their prompt implementation. The results of the projects will be fully integrated into the SIDL and its projects.

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  • sdsc-project-image

    Smart Data Solutions for Producing SMEs in Baden-Württemberg

    The project is funded by the Ministry of science, research and art Baden-Württemberg (MWK) as part of the Smart Data Solution Center Baden-Württemberg (SDSC BW) to explore the use of suitable Smart Data technologies for producing SMEs. The project’s aim is the research of a simplified access to Smart Data technologies to facilitate the use of these technologies for SMEs. The results of the conducted Smart Data analyses of real industrial data sets are published in the form of public success stories.

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  • SmartFactoryKL

    Predictive Maintenance Data Analysis on SmartFactoryKL-generated Data

    The joint research project sought by SDIL and SmartFactoryKL is strongly related to the topics “Industry 4.0” and “Internet of Things (IoT)”. Modern machinery is characterized by a large amount of sensors that continuously offer such status information that is relevant to the production process. The intelligent monitoring, storage, and analysis of sensory data can have multiple positive consequences. Predictive maintenance will be one of the key issues for the future development of highly modular, multi-vendor production systems.

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  • abb-logo

    Association Rule Mining for Data-driven Services based on Industrial Logs

    Today’s industrial plants continuously produce log data on the references of measurements, error reports, and documented user interventions. The existing solutions show some constraints, though. The main focus of this project is to use the potential that sources in the analysis of log files in relation to the system level along the entire production or process context. The identification of cause-effects relationships at the system level would allow an optimization of industrial plants and corresponding processes.

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