Marketing

ARC wrote a white paper about Seeq and stated: Devon Energy has implemented advanced data historian and analytics technologies to transform its data into actionable insights that support data driven decisions. This required close cooperation between the company’s operations technology (OT) and information technology (IT) groups. White Paper Summary: Like many other energy companies, Devon Energy, a leading independent oil and natural gas exploration and production company in North America, generates huge volumes of data. The company’s SCADA system monitors 6.5 million data points from multiple sites, with more than 10,000 updates per second. As we learned at a session at the recent ARC Industry Forum in Orlando, Devon Energy has implemented advanced data historian and analytics technologies to transform all this data into actionable insights that support data-driven decisions. This required close cooperation between the company’s operations technology (OT) and information technology (IT) groups. Don Morrison, Real Time Data Engineer at Devon Energy, shared his experience working with newer real-time data and analytics technologies. Devon Energy is based in Oklahoma City with on-shore operations in the US and Canada. The company’s 2017 portfolio was evenly distributed with 46 percent oil, 37 percent natural gas, and 17 percent natural gas liquids (NGL). It has a large inventory of future projects planned, mostly in western Oklahoma, southeastern New Mexico, and the Delaware basin. To continue its successful track record as an industry leader in technology, the company has nurtured a culture of innovation. This includes establishing groups to implement innovative new technologies that provide business value.

ARC wrote: Seeq Corporation recently briefed ARC Advisory Group about new enhancements and features of the company’s advanced analytics applications that enable insights on manufacturing data, increasing data discovery for collaboration and data-driven decision making. Seeq applications are transforming the industry with some unique capabilities and enhancements already proven across multiple use cases. The white paper summary: Advanced analytics is a key innovation for digital transformation. Most companies want to find solutions to empower their employees to quickly find insights, rather than waste a lot of time searching for, filtering, and cleansing the available data. They want smarter, easier-to-use tools and solutions that can help their employees – including subject matter experts (SMEs), engineers, and managers - find, under-stand, and take actions to solve their day-to-day problems. While many industrial companies are rolling out pilots and enterprise analytics projects, it is im-portant for users to understand the features and capabilities of the analytics offerings. The analytics technology suppliers are improving and enhancing capabilities and taking advantage of newer tech-nologies; open source, time-series databases; Big Data and machine learning; better connectivity; and new platforms to improve ease of use. But, as a user, how do you know what features and capabilities are important? Can you connect to the data sources you need easily? Can you easily repair or cleanse data that is bad? What algorithms do you need? What questions should you ask? Perhaps most importantly, does the solution apply to the specific needs of process manufacturing industries?

Abstract From Data to Direction. Scientists and engineers at the R&D, pilot, and production scales need a new way to harness the power of the data gathered, a new way to drive innovation through an enhanced stata strategy that leverages Seeq®. Crucial elements for enabling pharmaceutical process innovation include: understanding the key physical situation of the process, identifying the right process analytical technology to obtain the required data, connecting disparate data sources, and implementing data analysis and visualization applications that make it easy to analyze and make changes, to improve quality and quantity of medicines. Companies often capture all the data they need to improve operations within their data historians and other databases. However, creating insight from this information can be difficult, expensive, and time-consuming using traditional approaches or limited data analytics tools. For example, it is important to ensure that your data management applications provide: A strong connection with all data historian(s) and other important databases Automatic indexing of the sensor names/tags in the historian to make them easy to search and access related data A comprehensive connection to other data sources Rapid visualization of the time-series data over a designated period of time Streamlined future analysis in a way that also facilitates collaboration With a facile data visualization strategy, the world looks different. Imagine having the ability to easily search and interact with past and present time-series data in a "Google-like" fashion and collaborate in real-time. Imagine being able to make business critical decisions with more confidence simply because you have the data in hand. In this presentation, we focus on a specific case study example in upstream bioprocessing that illustrates how to drive innovations through an effective data strategy using Seeq®. Using an enhanced data aggregation and visualization strategy, factors that affect product quality ca ben rapidly identified, thus further enabling definition of key performance indicators in early development. Leveraging historical data allows small-scale model verification and insights into challenges of scale up.

Process manufacturing organizations run on data—from a manufacturing, operations, and business perspective. The data generation and collection strategies at the center of their manufacturing processes have evolved dramatically, especially in recent years. These organizations now collect and store huge volumes of data across their operations, both on and off premise, across multiple geographic locations, in an increasing number of separate data silos. These advances have coincided with the proliferation of connected sensors and increasingly inexpensive storage, leading to an Industrial Internet of Things (IIoT) projected to generate more than 4 trillion gigabytes of data per year by 2020, according to IDC Research. New advanced data analytics have a huge positive impact on the growing volumes of data in many sectors, from retail to financial. So why aren’t all these new analytics widely leveraged in process manufacturing? With so much data and the promise of so many new technologies, why is it so difficult to apply these technologies to process manufacturing and gain the same benefits as other sectors? Why do these organizations still feel like they have too much data and too little insight? At Seeq, we believe this gap—between the data these organizations have and the insight they want—exists because existing data analytics solutions fail to completely grasp the unique challenges and opportunities presented by process manufacturing. When we talk about data analytics, we mean any software enabling process engineers or scientists to: create a cleansed, focused data set for analysis through assembling, aggregating, or wrangling data from various sources, including data historians, offline data, manufacturing systems, and relational databases investigate operations data using “self-service” tools to rapidly analyze alarm, process, or asset data for ad hoc or regular reporting publish or share insights and reports across the organization to enable data-driven action, or enable predictive analytics on incoming data Many data analytics solutions claim to offer some or all of these things—with the goal of finally closing the gap between data and insight. But are they successful, and how do you evaluate if they are? In this paper, we propose five questions we believe every process manufacturing buyer should ask when evaluating an advanced analytics solution.

This Insight explains how Abbott Nutrition is using advanced technologies for new insights and faster decision making in two pilot manufacturing applications. The technology eliminates hours of manual work by collecting and integrating the data in OSIsoft’s PI System and using Seeq’s analytics tool to improve production. Abbott’s nutrition business, a division of the global healthcare company, manufactures a wide variety of science-based nutrition products. These range from Similac brand infant formula to adult nutrition brands such as Ensure. Recently, ARC had the opportunity to speak with James Li, an engineering manager in the Abbott Nutrition division. We discussed how the company uses Big Data and analytics to improve manufacturing productivity.

As we look ahead in 2017, there is an important issue we need to address first: most firms need to catch up to the technology opportunities available today. Enthusiasm and the potential benefits of IIoT are not being realized by many end users, with the opportunity still ahead of them, instead of being already recognized in bottom-line results. The Industrial Internet of Things (IIoT) has progressed from dream to hype to reality. Today, the basic deployment scenarios of the IIoT solutions we implement for our end user manufacturing customers include: Greenfield deployments, which are primarily found in “smart” solutions related to advanced monitoring and visibility Brownfield upgrades, which are the introduction of IIoT technologies and approaches to existing facilities to expand asset and process visibility and analytics New asset-monitoring services from vendors who are leveraging IIoT to provide remote predictive analytics capabilities for their assets installed at customer sites.

ARC Insights by Janice Abel Avangrid Renewables is a subsidiary of AVANGRID, Inc., and part of the IBERDROLA Group. Spain-based Iberdrola S.A. is the largest wind energy company in the world. As ARC Advisory Group recently learned, the company collects a wide variety of data from many different sources. However, in the past it faced challenges when it came to gaining useful insight from these data. Particularly problematic, was the difficulty determining and documenting lost generation across its wind turbine fleet time due to voluntary generation curtailment to meet contractual obligations. Inability to do so, can lead to lost revenues. Avangrid Renewables owns and operates nearly 60 plants in the US. The company is the second largest owner of wind energy projects in the US, with more than 6,000 MW of owned and controlled renewable generation assets, which includes 3,000 wind turbines. It also has 636 MW of combined cycle gas turbine generation, 50 MW of solar generation, plus 55 MW of controlled biomass generation. The company has more than 750 employees in the US. According to company executives, it is focused on operational excellence and selective growth. Avangrid Renewables collects a wide variety of data from many different sources. However, in the past it faced challenges when it came to gaining useful insight from these data. Particularly problematic, was the difficulty in determining and documenting lost generation across its wind turbine fleet time due to voluntary generation curtailment to meet contractual obligations. Inability to do so, can lead to lost revenues.

Automation.com by Stephany Romanow, Industry Consultant The oil & gas (O&G) industry is under constant pressure due to fluctuating commodity prices, shifting regulatory policies, antagonistic political pressure and other factors. In recent years, O&G companies have also had to contend with volatile commodity prices, a shrinking pool of experienced workers, and shareholder demands for increased profitability. To navigate and prosper under these conditions, leading O&G operators are focusing on technological advancements and modernizations to solve operational issues and sustain profits.

At ARC Advisory Group’s 20th Annual Industry Forum in Orlando, Florida, Shawn Anderson, Senior Research Specialist for Fisher Valves, a division of Emerson Process Management, gave a presentation on how the company is leveraging the Industrial Internet of Things (IIoT) to help end users reduce valve-related unplanned downtime.