Workshop: Predictive Analytics Industry 4.0 for Practitioners and Decision Makers with Christian Spindler
Wednesday, 8 May, 2019
9:00 am – 5:00 pm
Holiday Inn Munich City Center
Included are two coffee breaks, lunch and soft drinks during the conference
The workshop places are limited – secure your space now!
- Production Heads and Maintenance Managers who seek concrete advice how their particular problems can be addressed with data mining, predictive analytics and machine learning, and how to start
- C-level managers who want to understand how Industry 4.0 creates value in real cases and how organizations arrived at that point
- Data Scientists who want to familiarize with the particularities of industrial processes and production data
- Engineers and Product Managers who are interested in how predictive analytics and artificial intelligence can improve and transform their products and services
- Understand the data science value chain in a production environment
- Learn best-practices in setting up data-driven solutions in production, and how to assess and improve the quality of such solutions over time
- Understand why predictive analytics projects fail – and learn to avoid the pitfalls
Data mining and predictive analytics are key topics in Industrie 4.0. Data mining brings data together from various sources within a production hall, a company or even a global network of production sites. It harvests the fuel for all data-driven processes that finally lower cost and increase effectiveness of the production. Predictive analytics on this data is the engine that drives those effectiveness gains and even enables new business models.
Significant value propositions in mind, it is not a straightforward task to start engaging with data mining and predictive analytics. The data science value chain in a production environment can be complex, involving a heterogeneous landscape of data sources and formats. Developing effective models and keeping their quality and return-on-invest up over time is also not an easy task. Finally, understanding how to set up data science projects in manufacturing is crucial to bridge the gap from proof-of-concept to a productive solution.
The one-day workshop “Predictive Analytics Industry 4.0 for Practitioners and Decision Makers” offers a use-case and demo-based guideline how to draw most value from data science for the shop floor. At the end of the day, you’ll be able to independently conceptualize data-driven use cases for your organization, understand the steps to bring those cases to live, and know how to assess the value they bring for your company. Equipped by this knowledge you are able to optimize business outcome, while addressing risks and uncertainties as early as possible.
|9:00 am||Welcome & introduction, sharing expectations of the workshop|
|9:15 am||Presentation: The state of data science and predictive analytics in Industry 4.0|
|9:45 am||Presentation/Demo: Data Mining. The value of raw data transparency in a heterogeneous production landscape|
|10:30 am||Coffee break|
|11:00 pm||Presentation: Data Science: What are proven steps to generate analytics models in an industrial context?|
|11:45 am||Practice: Successful predictive analytics use cases in Industry 4.0 and how they were built|
|12:30 pm||Lunch break|
|1:15 pm||Demo: Machine Learning: The top machine learning use cases in Industry 4.0: Time-series analysis, anomaly detection and computer vision. What is their business value and how are they implemented, monitored, and certified?|
|3:00 pm||Coffee break|
|3:30 pm||Practice: Why do predictive analytics projects fail – and how to avoid the pitfalls|
|4:15 pm||Presentation: Exploiting digital twin data for flexible raw data access and seamless predictive analytics|
|4:45 pm||Q&A and open feedback session, wrap-up|
|5:00 pm||End of workshop|
With his industrial analytics consultancy DATA AHEAD ANALYTICS, Christian Spindler supports companies in their digital transformation to analytics-driven organization with consulting and solution development in all data and analytics aspects. His approach to data and analytics workshops is to involve the audience into actively testing and playing with concepts and solutions, to maximize the learning process and foster ideation. Christian Spindler sums up more than 10 years of data science and industrial experience.
Prior to Data Ahead, Christian Spindler was Senior Manager at the global consultancy PricewaterhouseCoopers, where he led the IoT & machine learning activities within the data analytics department. Before, he got extensive training in predictive maintenance and industrial analytics during his role as Senior Researcher at the global electro-technology company ABB. Christian Spindler has a quantitative education in physics (PhD) and earned an M.B.A. from St. Gallen University.