webinar register page

Webinar banner
Embracing “Eleventh Hour” Updates: Just-In-Time Scheduling for Pilot Plants (EU)
Pharmaceutical pilot plants are integral to drug development and commercialization, providing critical data for scientists and producing material for early- and mid- stage clinical trials. Today, these facilities struggle to meet the highly varied demands for capacity, as well as last-minute changes to schedules.

In this webinar, we’ll look at how a new approach to scheduling – “Just-In-Time” or JIT Scheduling – is allowing Pilot plants to react in minutes to changes to project setups, prioritizations and equipment availability. This method of scheduling - based on other “JIT” approaches from other disciplines - allows teams to rapidly evaluate changes to capacity and automatically re-optimize across the entire line-up of work. Join us to see how this exciting new technology can solve the critical need for agile, adaptive pilot plant capacity in the pharmaceutical industry.

3 Key Take-Aways
- JIT Scheduling benefits including 10+% increases in projects per month and a reduced ‘frozen window’ for orders from 3 weeks to just 3 hours ahead of project start
- Specific ways pilot plants can use JIT Scheduling to quickly manage changes to priorities
- Practical steps for how to implement the JIT Scheduling technology in a pilot plant
* Required information
Loading

Speakers

Rick Johnston, PhD
Senior Director
Dr Johnston is a senior technical leader within Applied’s pharmaceutical business unit. He has more than 15 years of experience in building software that solves big data, machine learning, scheduling, forecasting and advanced analytics problems in pharma. Software built by Dr Johnston is used by more than 90% of the world’s largest pharmaceutical companies. He has a Ph.D. in operations research and bioinformatics from UC Berkeley.
Lisia Dias, PhD
Senior Engineer
Lisia leads the Pharma Scheduling Solutions team at Applied Materials. She has extensive experience in mathematical optimization, machine learning, digital twins, and automation of planning and scheduling decisions. She has led the development and implementation of advanced scheduling solutions to improve throughput in QC Labs, biomanufacturing, cell and gene and packaging sites. She has a Ph.D. in Chemical Engineering from Rutgers University.