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  1. Machine Learning (ML) is a top priority for most manufacturing organizations— and for good reason. Machine Learning models can simulate processes and predict outcomes, drive continuous improvement cycles, and enable manufacturers to proactively maintain and optimize equipment. That’s a big deal; it can mean millions of dollars in savings and efficiencies in a short amount of time. However, leveraging Machine Learning in manufacturing settings is not as simple as finding the right algorithm or hiring data scientists. The engineers and plant operations personnel that understand their processes and the associated sensor data are critical participants in any effort to apply ML in an industrial context. They’re the folks that must understand and trust the predictions. They will want to carefully vet changes and then verify results. In these scenarios, Information Technology (IT) personnel like sysadmins and data scientists bring the algorithms and software knowledge – often including programming skills. Operational Technology (OT) employees like chemical engineers and operations managers bring the real-world manufacturing and process knowledge. The last necessary piece is an environment where they can collaborate effectively. Otherwise, promising ML initiatives can end up as frustrating failures. Seeq provides key components to enable IT/OT teamwork for successful Machine Learning efforts: data access, cleansing and contextualization; algorithm development and iterative workflow; collaboration and knowledge capture; publishing of Machine Learning models with simple point-and-click, workflow-centric interfaces for the OT personnel; and now a rich ecosystem of community-based ML solutions for manufacturing environments via the Seeq Open Source Gallery. View the full article
  2. Process control engineers focus on optimizing the automatic control of the process operation, designing control strategies, specifying controller tuning, implementing advanced process control and real time optimization applications, and troubleshooting sources of abnormal process variation. In contrast to process engineers, control engineers focus much more on dynamic operational responses and signal trajectories. Control engineers often want to identify and analyze transient, non-steady modes of operation (e.g., setpoint ramps, startups, batch sequences, etc.)—for which Seeq’s extensive contextualization capabilities (capsules and conditions) were purposely built. Process engineers and control engineers typically work in different roles within the process automation pyramid. While they both derive analytics value from Seeq’s contextualization and calculation tools, there are several capabilities that control engineers can specifically utilize for their unique roles. View the full article
  3. For electric utility organizations, prioritizing maintenance and replacement strategies across fleets of complex assets is critical to avoiding costly downtime. But without the right combination of subject matter expertise, data connectivity, and analytics tools, these organizations are left with a number of questions and roadblocks. View the full article
  4. Technology investments, regardless of industry, come with risk, and startup investments are often met with even more hesitation. While startups promise to bring greater innovation, major organizations fear these young, unestablished companies could bring even greater risk to mission-critical projects. View the full article
  5. Facilities large and small rely on machinery (rotating equipment) to make their operations tick. Reliable and efficient machinery operation is instrumental in ensuring facilities meet their safety, environmental, and production targets. World-class machinery performance is the result of good design practices, effective and skilled maintenance, and operation within appropriate design conditions (which, depending on operating requirements, can be challenging). View the full article
  6. For many years, finding insights in data using analytics—and data collection and storage strategy—have been considered a package deal. This is because operations data is often siloed, has limited analytics options, and is in difficult-to-reach places. As a result, some companies embark on huge data strategy and migration efforts, often lasting months or even years. They believe data must be centralized to begin extracting value with advanced analytics technologies. And in the meantime, companies miss out on valuable insights that can optimize assets and processes, minimize waste, and prevent incidents. View the full article
  7. Sustainability is critical to the health and survival of our planet, and now, more than ever, companies are prioritizing it as part of corporate social responsibility initiatives. According to a recent McKinsey survey, more than 50 percent of executives believe sustainability is very or extremely important for brand reputation and overarching corporate strategies. Unfortunately, only 30 percent of those executives believe their companies actively invest in sustainability or have implemented measures to reach goals. While many companies set goals, the ability to affect change often fails to trickle down the front-line employees who can make the most difference. View the full article
  8. In the process manufacturing world, there’s been a significant increase in the accessibility into operational and equipment data. Teams now have visibility into both historical and near real-time data from their operation, and can even monitor this as it’s happening at remote locations. But the problem with this is that teams are drowning in data—”DRIP”—data rich, information poor. View the full article
  9. When I graduated with a degree in chemical engineering, I was excited to join the workforce and make a positive contribution. Being part of different engineering teams at various companies made me believe that mining the many years’ worth of big data stored in the process manufacturing historians could be a gamechanger. There were many opportunities to contribute to sustainability, reliability, and profit maximization goals that would result in happier customers and a more environmentally responsible industry. View the full article
  10. Seeq began offering our advanced analytics applications as Software-as-a-Service (SaaS) in 2018. SaaS offers many benefits to end users including higher performance, better supportability, and lower overall costs. However, there can be a perceived risk associated with SaaS offerings because end user data is in the cloud on computing resources managed by a vendor. The “Security, Availability, Processing Integrity, Confidentiality, and Privacy” of customer data largely depends on the controls used to operate both the vendor’s organization and specifically the controls used to operate the SaaS service. View the full article
  11. Seeq recently conducted a poll of chemical industry professionals—process engineers, mechanical and reliability engineers, production managers, chemists, research professionals, and others—to get their take on the state of data analytics and digitalization. Some of the responses confirmed behaviors we’ve witnessed first-hand in recent years: the challenges of organizational silos and workflow inefficiencies, and a common set of high-value use cases across organizations. Other responses surprised us, read on to see why. View the full article
  12. In batch processing operations, the combination of numerous concurrent and independent steps can lead to bottlenecks, causing the process to pause and wait for a downstream operation to finish before the preceding steps can move forward. This introduces latent time to the cycle and lengthens the time required to complete each batch. View the full article
  13. Data integrity is of prime importance to companies in the process industries because data is valuable and therefore must be managed and protected. This encompasses information security (infosec), which considers issues like authentication and authorization to ensure that only the right people can get into the system and see the proper data. View the full article
  14. Things of quality have no fear of time. - Author Unknown There’s this thing that crisis does. It makes us reevaluate, think about what brought us to this point and what can we do about it. It’s an opportunity to reset, reframe, and redefine what’s possible. Crisis activates disruption which sets the stage for innovation, the next act. View the full article
  15. Seeq customers know Seeq drives significant value for their organizations and they also know the ROI of their time spent using Seeq is quite high. However, simply finding time to develop high value insights using Seeq can be challenging because process engineers are so busy running their plants. View the full article
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