HAL HENDERSON – EVENT DIRECTOR - IQPC

‘Digital Transformation’ is a phrase can become a buzzword, and products that promise rapid digital transformation do not always consider the practical realities of modernizing the lab. A buzzword perspective on digital transformation is all well and good, but it must come alongside a tangible plan for re-wiring of an organizations processes without large scale disruption to the lab.

Ultimately, digital transformation is necessary but creating a roadmap that is practical, achievable and above all recognises the unique requirements of each lab is vital. This means not just pulling out the wires and starting again, but ensuring older systems are adapted. Not just innovating with AI but ensuring that technicians and scientists have the skill sets to feel digitally capable. Not just buying new tools but ensuring they can be implemented in a cost-effective manner.

Join this cross-industry presentation as we discuss:

·       Adapting legacy systems that cannot be removed to fit new digital ecosystems

·       Ensuring new tools are implemented innovatively to maximise their cost-efficiency

·       Building interconnectivity into older systems and introducing adaption layers of digital communication

·       Upskilling technicians and generating buy-in to ensure your workforce is digitally capable

·       Creating realistic and believable transformation plans that prioritise the efficiency of the upgrade

Geoff Parker

Co-Founder
Scimcon

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Brian Eichelberger

Director of Quality
SPL

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Duncan Coffey

Global Director Science & Innovation IT Global Director Science & Innovation
DuPont

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Mads Torry Smith

Director, Digital Transformation, Applied R&D
Novonesis

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Scientific data has the scope to deliver maximum value, both directly to your team as well as the wider organisation. Adopting a centralised, accessible platform with a forward-thinking approach will support your operations and potential of current and future projects. Join this discussion to better understand how you can maximise your data, use it to its full potential, and leverage organisation-wide operations.

•       Building automation workflows for analytical data

•       Retaining full control over workflows through simpler, faster automation

•       Considering automation capabilities best suited to your individual analytical datasets

Whether you have barely started the journey of digital transformation, or you are ready to take the automation plunge, building a solid foundation for change sets you up for success in the future. Digitalization is an ongoing process, and whether you are at dipping your toe in the water of digitalization or planning a future with lights out labs, a measured approach will be vital.

Join this plenary to discuss the difficulties of journey planning, hear strategies for digital transformation and listen to a case study from a leader in the industry

·       Standardising data collection from differing systems to provide clean and clear data and metadata

·       Closing the Gap between Industry innovation and the future of lab 4.0

·       Creating Technology champions to ensure that change can be built from the ground up

·       Building Analytical models to ensure site leadership has everything they need to track transformation

Parag Dave

Head of Data, Digital, and Technology
Takeda Pharmaceuticals

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All lab work results in data. Each instrument, experiment and platform provides a wide stream of data, that must be probably analyzed, organized and clarified in order to see its full value. The only way to do this is to continuously improve the way the data flows. To analyze and re-analyze every stream that flows to the data lake, and to have a platform that can improve these flows, by understanding how the meta data of work has been collated.

Join this innovative presentation to understand:

·       Building partnerships between dataflow platforms and operational workflows

·       Using advanced analytics to translate data into actionable insights

·       Continuously improving your understanding of the data to perfect operations

The benefits of Machine Learning are clear; accelerated product discovery and research targeting are rapidly transforming company workflows and increasing the speed of work. Many large biopharma’s have already leveraged ML to accelerate drug discovery processes and increase the accuracy of patient risk prevention. However, these processes are not just for biopharma companies: Machine Learning and Deep Learning can revolutionise product design by quickly uncovering potential new areas of research or finding more efficient solutions to quality and workflow challenges. With this industry first panel, join ML and AI leaders as they discuss their successful implementations, how they identify external partners, and outline how ML could revolutionise the workflows of your lab.

The discussion will consider:

•     taking the first steps in ML utilisation and establishing use cases appropriate for your specific needs

•     Establishing infrastructure and scaling ML from a part of a lab to an entire process

•     Identifying blind-spots, navigating ML governance and harmonising ML strategies with senior leadership

•     Discussing lessons learned, and looking to the future of automation

Yves Fomekong Nanfack

Executive Director, Head of End-to-End AI Foundations
Sanofi

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Sandip Tiwari

Head of Technical Services, Pharma Solutions R&D
BASF

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As cloud data storage becomes necessary for digitalization, R&D and Quality leaders are faced with a mounting set of challenges. Data governance, improving data quality and data interoperability can be difficult when data becomes enclosed within a vendor’s cloud. Change management is challenging when different solutions and platforms are not designed to communicate with each other. Planning for future automation becomes impossible when private data must be shifted to a vendor’s system to train a model.

To solve these problems, lab leaders must construct data eco-systems that they can control, where data is storage is directly owned, standardized and interoperable between departments, and even countries. Join this presentation to focus in on:

·       Remaining compliant with long-term archiving in human-readable formats

·       Using on-premises data storage to centralise and standardise both data and meta-data

·       Building modular systems to enable easy integration with new vendor tools in the future

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