This talk will describe how leading pharmaceutical companies have become data driven in their decision-making and why this trend is the key to enabling analytics, reporting and meeting regulatory compliance, in combination with the goal to deliver products that are safe and effective to their market as fast as possible.  This talk will focus on the benefits of an enterprise wide data infrastructure and knowledge management systems.  New technologies provides new opportunities to improve product quality, to boost compliance and to improve the economy gains in for example improved overall equipment effectiveness, better asset and maintenance management, and energy management.  Petter will give real world case studies of how such enhanced analytics are becoming increasingly valuable to improve throughput, lower costs, shorten time to market and improve compliance.

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Students need to be prepared for their careers in the emerging Biotechnology industry which is becoming increasingly digitized, automated, robotic and data rich.  This talk will describe the Industry 4.0 world that students will need to navigate.  It will also address other important trends seen in industry and technology changes such as single-use unit operations, parallel microreactors, continuous bioprocessing, advanced sensors and analytics, real-time process monitoring and control, which are all changing the biotechnology field rapidly.  Joining these all together, we are now offering student labs courses with new technologies and real-time analytics able to sense, collect, analyze, visualize and share large amounts of time-series data from multiple sources to show the students what is happening throughout a series of experiments.  Students can now take their process data and make them more immediately useful. Furthermore, the use of open data infrastructure lets our students see the results of their own experiments and those of the other teams in the room as they happen in real time.   With the results of experiments happening in the room, students can adjust their experiments in real time – both in the lab and from home, depending on the adjustments that are needed.  We will talk about how this data-driven approach is generating cultural changes in how students learn individually and as a multi-disciplinary team.  Examples of academic programs designed for students interested in manufacturing or process development will be presented.

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In this session, learn how PI System technologies can be used to gather, structure, process and deliver data to meet the needs of different audiences throughout your organization, ensuring that anyone can access, understand, and trust the data.

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Applying Data Science Tools and Techniques to Solve Your Business Problems will allow the audience to extract insights from time series data with data science tools and techniques. Erica will highlight some work used by university partners that leverages Jupyter Notebook and Python to demonstrate analytics using time-based data.  The dataset for the example will be fermentation data, where analytical examples will include prediction of the apparent degree of fermentation, cooling curve prediction, and PCA for visualization and outlier detection.  Erica will also mention some challenges and point future ideas.  This work is based on the OSIsoft Cloud Services platform.

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In this closing talk, we will recap the full PI System stack as part of the overall enterprise IT landscape, and how digital transformation evolves from digital enablement and business process transformation.  And, we review its role as the enterprise infrastructure for IoT and industrial sensor time-series data - including process data (SCADA, PLC, …), machine condition data (vibration, oil analysis etc.), and others –  to provide the mapping and data for numerous line-of-business systems, in production planning, equipment reliability, inventory, quality etc. 

Next, with a silica-gel/molecular-sieve based feed dryer use case, we will explore how a “layers of analytics” approach and the PI System self-service tools help subject-matter-experts prepare context and metadata enriched time-series datasets for your machine learning tools/data scientists. Lastly, we will touch on new offerings – OCS (OSIsoft Cloud Services), EDS (edge data store), OMF (OSIsoft Message Format), and others.

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