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REACH - The Core of Smart Factory


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- Balázs Bojkó - Specialist for Machine Learning in Manufacturing Engineering at Mortoff IT Consulting and Services Ltd. -

Manufacturing operations mostly rely on traditional best practices established in several years or decades. Launching the REACH-I4 platform at any company challenges this conservative approach and requires a fresh and cooperative mindset from workers, engineers and managers. Besides combining unique Big Data and engineering know-how, we also help our partners to see the big picture, define the proper road map and monetize their data.

IVSZ | EuDEco project
Data Economy Conference
Budapest, 2018. 01. 31.

Published in: Data & Analytics
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REACH - The Core of Smart Factory

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  2. 2. www.reach- Industry 4.0 – from the viewpoint of data Biggest winners of Industry 4.0 will be companies, that find out rapidly, how to turn their data into real business benefits.
  3. 3. www.reach- Introduction Balázs Bojkó | Industry Analytics Expert Mechanical engineering & numerical simulation background Automotive Industry, Hungary & Germany Machine Learning modelling, now developing use cases for Industry 4.0 REACH Solutions Ltd. Building Smart Factories and creating business value from their data Highest number of enterprise-level Industry 4.0 implementations in Hungary  How to build complete I-4.0 systems  How to reach tangible business benefits with I-4.0  How to help customers define a long-term IIoT and I-4.0
  4. 4. www.reach- What kind of resource is data? © Is it really the new oil? IoT and data are transforming businesses and distribution channels But manufacturing industry is different! Business models are not directly and fundamentally affected Companies concentrate on the same core business Improve quality and efficiency, reduce costs, boost innovation and increase profits ➝ support lean manufacturing Not a fast and spectacular revolution
  5. 5. www.reach- Amounts of data Data volume estimation Incredible amount of data is generated around the clock At one of our manufacturing partners, 1 manufacturing machine produces ~2G / day data There are 1000+ machines that produce data in the whole factory and will be included in the data collection process 600 TB/year: This is Big Data on a large scale, which requires experience and tools to handle Data from a manufacturing machine 300+ sensors, 0.1s sampling rate Data is available in a completely new way Event-based data monetization in real-time Enough performance to analyze complex data in depth Enabling further improvements in lean manufacturing
  6. 6. www.reach- “Complex event space”
  7. 7. www.reach- How is data used in manufacturing industry? Our experience: Most manufacturers don’t have clear ideas what to use data for Huge challenge and great opportunity at the same time
  8. 8. www.reach- Practical use cases Energy costs are a major factor in the manufacturing industry. Energy efficiency Logistics lacks real-time monitoring and basic prediction about material levels. Business decisions | Logistics Management of raw material levels in reservoirs Energy consumption 0 20 40 60 80 100 120 140 160 0:00 0:20 0:40 1:00 1:20 1:40 2:00 2:20 2:40 3:00 3:20 3:40 4:00 4:20 4:40 5:00 Patterns of high consumption
  9. 9. www.reach- Practical use cases “Standard” stops 4-5 times a day due to raw material sticking on tool surface, 15-30 minutes downtime each. Lean manufacturing Quality testing line Manual data processing: Incredible amount of human work necessary, low throughput. Scaling complications. Quality issues pop up too late. Quality management Die-casting machine
  10. 10. www.reach- Change your ways Standard and long standing processes, traditional technology and practice: Little room for improvement. Technology shift + mind-set change Building a technology platform that enables monetizing IoT data in a user-friendly way: REACH – Real-time Event-based Analytical and Collaboration Hub Industry 4.0 solutions require new approaches and new technology. Data analytics and Machine Learning can reveal new information.
  11. 11. www.reach- Real business results Energy saving: Data analysis helped recognizing patterns of large consumption. Real-time reporting enabled saving unnecessary costs. Key: Pattern recognition Energy cost savings: 10% Investment return: 6 months Raw materials logistics: Even a basic prediction algorithm and real-time messaging via email helped to make business decisions much faster. Key: Predictive decision making support Logistics cost savings: 7% Investment return: 11 months Test line: Quality indicators are calculated and provided in real time. Issues can be tracked and followed up during production. Immediate response and intervention is possible. Key: Real- time alerting Cost savings in quality management: 5% Investment return: 15 months Die-Casting: Reducing “standard” downtime by 30% by using a Machine Learning predictive model, productivity is improved by over 2% ➝ investment is returned within less than a year. Key: Predictive maintenance
  12. 12. www.reach- A proven Industry 4.0 architecture Edge computing Cloud computing  Process data where it is generated  Low latency – take immediate actions  Cyber security  High computational power  Accessible from any location  Flexible storage options
  13. 13. www.reach- A proven Industry 4.0 architecture Why not have both advantages? Smart Factory level – on-premise cluster Corporate level – private cloud  High computational power, still low latency  Real-time data analytics, Machine Learning  The real Smart Factory functionality  Long-term, high-volume storage of filtered data  Corporate analytics (business) “Fog computing”
  14. 14. www.reach- Industry 4.0 agile approach Framework Establishme nt Use Case Exploration Data Preparation Analytical Modeling Model Evaluation Business Insights Laying out the framework for the PoC by harmonizing best practices and implementation experience with local technical and business demands and vision. Assessment and understanding of Use Cases and data sources regarding their volume, velocity, variety and quality. Loading data from different sources into REACH, conducting data cleansing when necessary and preparing it for further analysis Data analysis, and implementing advanced machine learning models (both supervised and unsupervised learning) Fine-tuning and evaluation of the developed Machine Learning models, and implementing it in the production environment Analytical dashboards and reporting for various end-users, presentation about insights, and discussion of potential business improvements3 weeks 1 week 3-4 iterations (3 weeks per iteration) Continuous consultation with experts
  15. 15. www.reach- Take off at www.reach- Shall we begin?
  16. 16. www.reach- Thank you!