BEGIN:VCALENDAR PRODID:-//Microsoft Corporation//Outlook 16.0 MIMEDIR//EN VERSION:2.0 METHOD:PUBLISH X-MS-OLK-FORCEINSPECTOROPEN:TRUE BEGIN:VTIMEZONE TZID:W. Europe Standard Time BEGIN:STANDARD DTSTART:16011028T030000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=10 TZOFFSETFROM:+0200 TZOFFSETTO:+0100 END:STANDARD BEGIN:DAYLIGHT DTSTART:16010325T020000 RRULE:FREQ=YEARLY;BYDAY=-1SU;BYMONTH=3 TZOFFSETFROM:+0100 TZOFFSETTO:+0200 END:DAYLIGHT END:VTIMEZONE BEGIN:VEVENT CLASS:PUBLIC CREATED:20240306T142013Z DESCRIPTION:Planning for Uncertainty – Using Modeling and Simulations and predictive maintenance techniques\n \nDigitalization is progressing at a rapid pace in the life science industry\, although it is lagging behind ot her industries that use data to optimize processes. Observations show that most pharma companies collect vast amounts of data without leveraging it for efficiency gains\, cost savings\, and more robust processes. Many comp anies often do not know how to use digital technologies and whether this d igital strategy will have added value once it is deployed. Making use of c ollected data with modeling & simulation techniques and tools proves to be very useful when it comes to making data-driven decisions in our biopharm aceutical field. Modeling & simulation techniques are used\, where uncerta inty manifests itself in various forms\, such as unplanned equipment failu res\, unavailability of materials\, and stochastic demand\, to name a few. Data-driven predictive maintenance plans can play a decisive role in incr easing reliability\, improving safety\, and extending equipment lifespan w hile reducing maintenance costs and unplanned downtime\n\nNiranjan Kulkarn i\, Ph.D.\, and Nicolas Bahler will introduce the latest Modeling & Simula tion techniques and discuss when and how these techniques can be applied. They address the data needs required\, pitfalls to avoid\, and generally a ccepted study methods. In conclusion\, the development of a condition-base d predictive maintenance plan is demonstrated in the context of a current case study.\n\n\n\nLearning Objectives\n\n- Learn how equipment downtime\, planned or unplanned\, has a direct impact on throughput and overall site capacity.\n\n- Understand the overwhelming advantages of predictive maint enance\, allowing operators to know in advance the likelihood of equipment failure and maintaining the equipment before the failure occurs.\n\n- Dis cuss development and implementation of a predictive maintenance plan using one of the previously presented techniques for a case study of a filler w hich was already acknowledged to be the line bottleneck.\n \n \nSpeaker\nN iranjan Kulkani\, Senior Director\, Consulting Services\, CRB\nNicolas Bah ler\, Bioprocess Engineer SIM\, CRB Germany GmbH\n \n DTEND;TZID="W. Europe Standard Time":20240613T160000 DTSTAMP:20240306T142013Z DTSTART;TZID="W. Europe Standard Time":20240613T153000 LAST-MODIFIED:20240306T142013Z LOCATION:Frankfurt Messe\, Hall 8.1. Symmetrie 2 PRIORITY:5 SEQUENCE:0 SUMMARY;LANGUAGE=en-us:Planning for Uncertainty – Using Modeling and Simu lations and predictive maintenance techniques\, Niranjan Kulkani & Nicolas Bahler TRANSP:OPAQUE UID:040000008200E00074C5B7101A82E0080000000040E6AA72D96FDA01000000000000000 01000000084193365749B6D43AD5221DF616BB58B X-ALT-DESC;FMTTYPE=text/html:

Planning for Uncertainty –\; Using Modeling and Simulations and predictive maintenance techniques

 \;

Digitalization is progressing at a rapid pace in the life scienc e industry\, although it is lagging behind other industries that use data to optimize processes. Observations show that mos t pharma companies collect vast amounts of data without leveraging it for efficiency gains\, cost savings\, and more robust processes. Many companie s often do not know how to use digital technologies and whether this digit al strategy will have added value once it is deployed. Making use of colle cted data with modeling &\; simulation techniques and tools proves to b e very useful when it comes to making data-driven decisions in our biophar maceutical field. Modeling &\; simulation techniques are used\, where u ncertainty manifests itself in various forms\, such as unplanned equipment failures\, unavailability of materials\, and stochastic demand\, to name a few. Data-driven predictive maintenance plans can play a decisive role i n increasing reliability\, improving safety\, and extending equipment life span while reducing maintenance costs and unplanned down time

Niranjan Kulkarni\, Ph.D.\ , and Nicolas Bahler will introduce the latest Modeling &\; Simulation techniques and discuss when and how these techniques can be applied. They address the data needs required\, pitfalls to avoid\, and generally accept ed study methods. In conclusion\, the development of a condition-based pre dictive maintenance plan is demonstrated in the context of a current case study.



Learning Obj ectives

- Learn how equipme nt downtime\, planned or unplanned\, has a direct impact on throughput and overall site capacity.

- U nderstand the overwhelming advantages of predictive maintenance\, allowing operators to know in advance the likelihood of equipment failure and main taining the equipment before the failure occurs.

- Discuss development and implementation of a predic tive maintenance plan using one of the previously presented techniques for a case study of a filler which was already acknowledged to be the line bo ttleneck.

&nb sp\;

 \;

Speaker

Niranjan Kulka ni\, Senior Director\, Consulting Services \, CRB

Nicolas Bahler\, Bioprocess Engineer SIM\ , CRB Germany GmbH

 \;

X-MICROSOFT-CDO-BUSYSTATUS:BUSY X-MICROSOFT-CDO-IMPORTANCE:1 X-MICROSOFT-DISALLOW-COUNTER:FALSE X-MS-OLK-AUTOFILLLOCATION:FALSE X-MS-OLK-CONFTYPE:0 BEGIN:VALARM TRIGGER:-PT5M ACTION:DISPLAY DESCRIPTION:Reminder END:VALARM END:VEVENT END:VCALENDAR