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Sitech's industrial analytics journey
Available on demand
 
Today’s manufacturing facilities depend on experts who can help them find new ways to optimize production processes and increase profitability through analytics insights. In this webinar, Marc Pijpers of Sitech will share 3 real-life use cases that show how Sitech used (predictive) industrial analytics to contextualize asset performance with process data.
 
Watch this webinar to learn:
1. Why traditional model-based analytics were not enough
2. Why Sitech chose self-service analytics for AspenTech IP21 historian data
3. How the self-service solution unlocked new insights into process performance
4. How Sitech solved previously "impossible" cases with ease
5. And how this helped Sitech's customers to accelerate their digital transformation.
Sitech-webinar
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Why watch this webinar?
Learn how to take your predictive maintenance to the next level
 
The journey
Avoid the pitfalls and fast forward to the future. Gain from hearing the experiences and challenges that Sitech encountered, and how you can avoid them in your own journey to digital transformation.
 
The results
Hear real use cases where analytics were key. Marc will share how they are now able to tackle increasingly difficult cases, solve cases more quickly and more thoroughly, and how an easy to use tool provides new performance insights.
 
The road ahead
Bringing analytics to the subject matter expert. Learn how Sitech plans to continue helping its customers on the road to transformation, and how it could help you speed up your journey to success.
Marc-Pijpers
Presented by: Marc Pijpers
Principal Process Control Engineer, Sitech
 
Marc Pijpers has over 15 years’ experience in the field of process control as well as in designing and implementing basic and advanced process control solutions. Over the last two years, Marc’s activities have extended to analytics, where he is supporting predictive maintenance and operational improvement projects. A true supporter of the process industries, Marc is an evangelist for helping manufacturers get more from their data.
 
“We try to get the data analytics skills of the data scientist in the hands of the process engineer, without transforming the process engineer into a data scientist…. The more we can do that, the more we can put the data scientists’ time on the most complex cases.”