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Optimize performance and product quality, and limit asset downtime -- with analytics |
A Daimler and IBM case study discussion
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Wednesday, June 25, 2014
From 11:00 AM - 12:00 PM ET
Identifying if and where something went wrong on a complex production line can result in wasted materials and production time, as well as added costs.
Join us to hear two case studies that exemplify how predictive maintenance is enabling companies across industries to predict what is likely to happen next so they can optimize their manufacturing processes and improve product quality, while lowering costs.
Hear how Daimler is using analytics to gain a more granular understanding of the factors that cause quality problems in its manufacturing process. By being able to precisely identify the complex patterns in machine settings, material temperatures and equipment maintenance activities, they have been able to: - Improve productivity by 25%
- Shorten the ramp up process by more than 50%
- Enable rapid adjustments by monitoring the process in near real time
Hear how IBM is using Predictive Maintenance and Quality in its own microelectronic production line to: - Drive an expected 150% ROI in one year
- Optimize operating conditions and significantly reduce costs to ensure smooth production flow
- Resolve problems quickly to drive better business outcomes
- Gain insight into root cause of a fault to identify underlying defects, including environmental factors, that impact product quality
Register today and learn how IBM predictive maintenance solutions help you identify patterns, predict outcomes, reduce costs, increase productivity, and improve product quality.
Speakers:
| | Matthieu Lirette-Gelinas Business Analytics Engineer, IBM |
| | Anuj Marfatia Program Director, Predictive and Business Intelligence Solutions Marketing, IBM
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| | Eric Paradis IT Strategy, IBM
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| | Thorsten Burdeska SME PMQ, thought leader predictive analytics, IBM
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