Forbes Technology Council – July 2020, by Thomas Dhollander: Analytics for Operating in the New Manufacturing Normal. Recently, many process manufacturing companies have started or are progressing with their digitalization journeys. But due to the Covid-19 pandemic, many of them are facing a new truth. Some companies have accelerated their production, others have modified their product lines and a third group have had to stop production due to lack of demand. In this article Dhollander explains how, in the new manufacturing normal, making a digital transformation is mission-critical.
Oilfield Technology – July 2020, by Nicholas Woodroof, Assistant Editor: Data analysis softens the impact of current crisis. Julian Pereira from TrendMiner explores how the use of data analytics can help Upstream Oil & Gas companies move to next levels of operational excellence and face volatile market pressures. Self-service industrial analytics shows rapid adoption and an increasing array of use cases. In this article some upstream cases engineers have worked on are discussed:
- Well surveillance and optimisation
- Offshore compressor monitoring
- Unexpected flare flow issues
AutomationWorld – June 2020, by Jeanne Schweder: By Seeing Patterns, Analytics Software Improves Process Control. Translating raw data into usable information has been a difficult task for industrial companies. Using TrendMiner, a self-service industrial data analytics software, Sitech was able to overcome this problem. Read the full digitalization journey and the success Sitech achieved:
“Adapting to new software like TrendMiner often asks for an organizational shift: a new way of looking at technology and data. It enforces alternative thinking and a new way of looking at operational performance using data,” says Marc Pijpers, principal process control engineer at Sitech.
Plant Engineering – May 2020, by Edwin van Dijk: Introduce analytics into industrial environments. Put analytics in the hands of the process experts who understand the data best. | What is the best way for a company to collect and analyze the data needed to make data-driven decisions that support good business outcomes? Let’s look at some of the best ways to successfully implement advanced analytics for multiple operational stakeholders.
Control Global – May 2020, by Jim Montague: Cornerstone cracks the data analytics case. | End users are using faster, more capable analyses to make better decisions and optimize operations. Aubyn Chavez, process improvement engineer, Process Improvement Group, Cornerstone in Waggaman, La., near New Orleans advises that software like TrendMiner can enhance or accelerate process knowledge. She proves her point with a few practical use cases and concludes with: “Analytics software is better than spreadsheets because it lets us work faster by highlighting process details better and lets us disprove our traditional prejudices.”
Forbes Technology Council – April 2020, by Thomas Dhollander: Data Scientists Or Self-Service Analytics? Discovering A Winning Approach To Manufacturing Analytics | Today’s manufacturing companies are investing significantly in analytics as part of their digital transformation programs. But analytics is a broad topic on its own, with many different factions and beliefs, and many companies are struggling to understand which approach is the better one. In this post, we’ll focus on two conflicting approaches: the belief that the data scientist should take the lead when it comes to analytics, and the notion that the business user should be in the driver’s seat (often referred to as self-service analytics). So what is the winning approach?
Processing Magazine – April 2020, by Julian Pereira: Reduce Emissions by Improving Sulfur Recovery Unit’s Performance with Self-service Industrial Analytics | Oil & Gas companies are continuously striving to optimize overall equipment effectiveness, performance and profitability within a highly volatile and regulated environment. Several of those regulations are coming from an increasing industry effort towards the reduction of emissions that affect both health and the environment.
In this article Julian Pereira explains in detail how process experts were able to improve the Sulfur Recovery Unit’s Performance with use of TrendMiner.
Industrial Process Products & Technology – April 2020, by Edwin van Dijk: The Benefits of Utilizing Advanced Analytics in Chemical & Process Manufacturing. | Self-service analytics eliminates potential cost-intensive data models because it supports plug-and-play functionality and user-friendly interface for process experts allowing immediate value and ROI. The benefits and competitive advantage self-service analytics bring to a process manufacturing company far outweighs the minimal investment needed to set it up and deploy it. The article includes a range of practical use cases from Trendminer customers.
Pump & Systems – March 2020, by Jasper Rutten, Global Advanced Analytics Manager Huntsman: Advanced Analytics Speed Up Continuous Improvement Cycle. | In many companies, operational data is still kept in separate silos, adding an extra challenge to improve operational excellence by use of advanced analytics. Through various uses cases addressed with TrendMiner, Huntsman was able to increase stable and therefor safer production as well as increasing control over the product quality. This article includes practical use cases:
- Develop a soft sensor on product quality
- Control batch processes with finger printing
- Product Quality Improvement using DMAIC
Hydrocarbon Engineering – March 2020, published by Callum O’Reilly, Senior Editor: Doing more with data | These days, there is no escaping the phrase ‘digital transformation’. There is no denying that the rise of Industry 4.0 is impacting day-to-day environments. Digital transformation is challenging traditional approaches and can open up new opportunities, if the willingness to adopt is there.
Forbes Technology Council – March 2020, by Thomas Dhollander: Considerations For Analytics-Driven Decision-Making At Scale | To make analytics-driven decision-making possible at scale in an organization, the data needs to be available, accessible and digestible for users. When using a historian, many criteria are already met. The next step is to provide an analytics tool that is tailored to the needs of the users. To make a new tool successful companies must put a set of key success factors in place for enabling successful data-driven decision-making at all levels of the company. Read the article how TrendMiner customers drive for success at scale, among which LANXESS.
Power Magazine – February 2020, by Edwin van Dijk: Using Self-Service Analytics to Improve Power Plant Efficiency | There is a widespread movement to reduce carbon emissions around the world. One way to do so is by improving plant efficiency, but that can be easier said than done. However, self-service analytics could play a role in the process. Advanced technology can help subject matter experts spot areas for improvement faster and more effectively than was previously possible. Includes practical use cases:
- Increased Efficiency of a Power Generation Plant
- Control Energy Consumption within the Cooling Water Network
IIoT Connection – Published the article Using Self-Service Analytics to Improve Power Plant Efficiency in their February 2020 issue.
Industry Week – January 2020, by Petty Ferry: Seeking Digital Supremacy. LANXESS invests in added analytics capabilities in quest to become digital leader.
“Thus far, Lanxess has significantly increased its capacity utilization, optimized resource efficiency and reduced maintenance costs. The digital transformation also serves to further develop employees”, according to LANXESS CDO Jörg Hellwig. “Competences in the field of digital data analytics will be essential for chemists and chemical engineers in the future,” he says.
Processing Magazine – January 2020, by Nora Villarreal: 5 Ways You Can Improve Your Process Operations with Self-service Analytics | New platforms help process engineers make the most of their data. With real live use cases about how to:
- Reduce cost and waste
- Improve product quality and yield
- Avoid unplanned downtime
- Reduce energy consumption
- Share information across teams and shifts