Enhancing manufacturing by using predictive data
A German SME offers a software tool that consolidates data from available data sources and streams the data to the on-premise server in real-time using unique, machine learning architectures. It can be used to decide based on data, decrease scrap rate and help workers on the line. The company is looking for industrial partners in new sectors to further develop the software within the scope of a technical cooperation agreement or a commercial agreement with technical assistance.
Technical cooperation agreement: - Industrial partner (SMEs or larger companies) with a production process which can be described as series production - Metal sector, food (w/o chocolate), B2C articles manufacturer, Cosmetics industry - Industrial partner should be willed to share current challenges in his manufacturing process and provide manufacturing data to be analysed by the SME, Willingness for improving the manufacturing site by using data analysis, Providing manufacturing process as proof of application for real-time integration of the analysis software Commercial agreement with technical assistance: - Industrial partner (SMEs or larger companies) with a production process which can be described as series production - Compounder, Sealing manufacturer, chocolate manufacturer - Sharing production data for a Proof of concept with the SME and willingness to improve manufacturing site based on data, Willingness to integrate software in decision-making process It is expected from the partner to work together with the SME and its software solution on room for improvement in the manufacturing site by using data-driven approaches. Therefore, a strong cooperation is envisaged to further develop the software with the input of the cooperation partner (mainly manufacturing data). At the same time, the cooperation partner will benefit from the results of the data analysis.
Modern manufacturing sites collet big amounts of data, which is often due to complexity not efficiently used. The German SME is focussed on simplifying complex production processes by generating automated insights in that data. The SME was founded in 2018 and has currently several customers in the automotive OEM (Original Equipment Manufacturer), rubber compounding, chocolate and sealing producer sector. The software consolidates data from available data sources and streams the data to the on-premise server in real-time. Using unique, machine learning architectures, the analytic engine autonomously identifies anomalies, interlinks those to other process parameters within the production process, and ultimately predicts evolving failures. The described company has reached TRL6 and is currently live in manufacturing sites in compounding and chocolate factories. Use-cases are the identification of root-causes for quality fluctuations and supporting quality assurance as well as production engineers in their decision process. Companies will profit from ready to use data consolidation software and can directly visualize all available in the manufacturing environment. With out-of-the box machine learning algorithms, root causes of quality failures can be tracked back. The system is already proven in real-time application in the compounding sector. The SME is interested in applications in already proven sectors (compounding, chocolate, body-in-white manufacturing), but strives for applications in new sectors. Together with the technology partner the SME wants to evaluate where is room for improvement in the manufacturing plant and how this can be addressed with the existing software. They want to further develop and implement the software with partners from new sectors and therefore envisage a technical cooperation agreement or a commercial agreement with technical assistance.
Advantages and innovations
- Real-time monitoring of all available data streams in the manufacturing environment - Possibility of using dashboards to deliver the information in real-time - Predicting the quality status of a part without measuring it (based on machine parameters) - Automated analysis of data without the need to hire data-scientists - From machine-learning to traditional SPC functionalities, but faster than traditional SPC tools due to real-time and automated analysis - Structured analysis method with the use of the industry-wide proven CRISP-DM model
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