Seeq® is an advanced analytics solution for process manufacturing data that enables organizations to rapidly investigate and share insights from data in historians, IIoT platforms, and database web services—as well as contextual data in manufacturing and business systems. Seeq’s extensive support for time series data and its inherent challenges enables organizations to derive more value from data already collected by accelerating analytics, publishing, and decision making. With diagnostic, monitoring, and predictive analytics powered by innovations in big data and machine learning technologies, Seeq’s advanced analytics solutions help organizations turn data into insights to drive process improvement and increase profitability. 

5 Questions To Ask Before Selecting A Process Data Analytics Solution   Leveraging Predictive Analytics: A Case Study    

5 Questions To Ask Before Selecting A Process Data Analytics Solution


Leveraging Predictive Analytics: A Case Study




When using Seeq, teams can easily create automated SPC control charts which can empower data driven decisions.

Workbench, Organizer, and Data Lab are powered by Cortex, which enables Seeq calculations at scale, data connectivity, and administration features. 

Organizer is Seeq’s application for engineers and managers to assemble and distribute Seeq analyses as reports, dashboards, and web pages.

Workbench is Seeq’s application for engineers engaged in diagnostic, descriptive, and predictive analytics with process manufacturing data.


Seeq Corporation

1301 2nd Avenue Suite 2850

Seattle, WA 98101





  • This webinar will explore some of the most common challenges companies have faced regarding skill up and how to navigate using datapoints derived from 100+ successful Seeq analytics training rollouts.

  • Advanced analytics software offers a solution to chemical manufacturers who rely on statistical process control charts for process monitoring.

  • New advanced data analytics have a huge positive impact on the growing volumes of data in many sectors. Learn how to leverage these new analytics in process manufacturing.

  • Explore chemical industry professionals' take on the state of data analytics and digitalization. 

  • Process manufacturing 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.

  • Multivariate analysis, which allows operators to model processes with several signals, can afford manufacturers a holistic, interconnected view of their operations in near real-time.

  • Advanced analytics tools are putting power and usability in the hands of engineers and subject matter experts using cloud-based SaaS and simple interfaces designed with scalability in mind.

  • Across water and wastewater organizations, engineering decisions are too often made based on subjective judgements. Considering how inexpensive and easy modern automation makes it to generate and collect massive amounts of process data, the propensity to make decisions by gut feel may seem far-fetched to a bystander. For plant personnel, however, the struggle to improve upon instinct is often all too real.

  • Chemical companies that aren’t tapping into multivariate analytics are likely working with compressed sources of data. This information tends to omit key features because legacy data processing tools often compress data, and historical analysis tools (namely spreadsheets) force them to down sample or aggregate data (hourly or daily) as opposed to processing the raw data frequency due to row and memory limitations. As a result, chemical plant managers are missing many opportunities for process optimization and the proactive nature of predictive and prescriptive diagnostics.

  • Some chemical plant managers avoid the topic of predictive analytics because they believe data must be migrated to the cloud and that only data scientists can create data insights. However, this simply isn’t the case. With the proper tools and a live connection to the source data system, an existing workforce can generate advanced analytics and reap significant benefits.

  • Watch this webinar to learn how a leading chemical manufacturer is tackling the complexity of Big Data and working to make their solutions sustainable.

  • Learn how to leverage data to implement proactive approaches to manufacturing issues through the use of predictive analytics.

  • 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.

  • At many process manufacturing operations, bearings fail exponentially and with little notice, leading to downtime that can become expensive and making scheduled maintenance difficult. System interdependence often means that a failure of one bearing results in the subsequent failure of other system bearings. Being able to prepare for and prevent the first bearing failure can reduce the costly and harmful effects of unplanned bearing failures.

  • Manufacturing sites can have hundreds, or even thousands, of automatic controllers, but most sites don’t have insight into how these controllers are actually performing. 

  • Performing a mass balance on manufacturing sites process units (or the overall plant) is critical for identifying a number of issues, including leaks, faulty sensors, meter calibration issues, process inefficiencies, and more. Unfortunately, the plants that do perform mass balances likely use a method that is difficult to maintain and does not update as new data is available for continuous monitoring.

  • There are several challenges to effectively analyzing CIP operations. Seeq Tools help create a process model that can be applied across cleaning circuits and amended with circuit-specific data.

  • Abbott’s nutrition business manufactures a wide variety of science-based nutrition products. Here we review how the company uses Big Data and analytics to improve manufacturing productivity.

  • Batch manufacturing of chemicals entails many distinct phases. Learn how one developer overcame its struggle to analyze batch phase times for process improvement. 

  • A large molecule pharmaceutical manufacturer was struggling to predict batch quality results in near real-time. The solution created a better way to predict batch quality and enabling process optimization.

  • In the chemicals industry, it is critical to control the finished product properties (such as product density, viscosity and reagent content) to maximize product quality, and as a result, profitability. Chemicals manufacturers test their finished products, placing them into different quality ranges based on how they perform in a series of laboratory tests. The highest quality product sells for the highest price, so maximizing the amount of the highest quality product will maximize the revenue of a particular process unit.

  • In the power utility industry, independent system operators (ISOs) are quasi-governmental organizations that serve as neutral market-makers between power generation companies and consumers to help ensure the power grid performs at maximum efficiency and reliability. While they don’t own generation, transmission, or distribution assets, nine ISOs play an important role to balance the supply and demand for power in North America.

  • In the process of selecting a SOx Reduction Additive, refinery engineers must be able to easily compare the effect of several different additives on environmental performance. They must be able to develop a model to understand how different additives and operating parameters will affect SOemissions. With the right additive and an accurate environmental model, refiners can incorporate environmental planning and compliance into operational plans and targets, allowing for the optimization of both environmental performance and operating costs. Without the proper analytics support however, development of an accurate model can be extremely time-consuming.

  • Valves are one of the most common assets in the process industry, spanning all verticals. Chemicals, refineries, and petrochemicals, however, will find improved valve health diagnostics useful for critical valves and controllers in their plants, while upstream and midstream oil and gas companies may be focused on much larger, critical valves like pipeline or subsea valves. Using Seeq, process manufacturers are able to implement a condition-based monitoring analysis to monitor valve health across an entire fleet. Engineers can utilize the historical data to accurately create a predictive maintenance forecast and preemptively detect valve failures before they occur.

  • How Seeq allows navigation to past production runs to find past production settings and visibility into the relationship between the production settings and key process KPIs, like quality or production rate.

  • See how a manufacturer gained insight into the leading causes of production losses by finding times when equipment was not running at capacity and categorizing the loss by reason.

  • Highly-efficient and improved facility operations require the management of chemical and energy usage to ensure that both air and water quality meet goals while minimizing cost. 

  • Increased visibility into unproductive process time is necessary to reduce inefficiencies. With the ability to increase production opportunities when reducing waiting times, overall profitability can also increase. 

  • It is important for IT professionals to support the efforts of driving operational excellence to improve quality and safety in production operations. 



  • Despite the availability of advanced software, spreadsheets are still the default data analytics tool for operations managers at many municipal water systems and water distribution companies. However, an investment in analytics technology can pay for itself quickly by providing a relatively easy method to extract process data from various sources and then by performing an analysis to provide answers to previously difficult questions.

  • Advanced analytics is a key innovation for digital transformation. While many industrial companies are rolling out pilots and enterprise analytics projects, it is important for users to understand the features and capabilities of the analytics offerings.

  • Are you challenged with managing the severity of reactor operation on a fixed-bed reactor and planning catalyst regeneration or replacement? It is important to analyze the catalyst activity and predict the end-of-useful life for the catalyst in order to optimize near and long-term economics. This process requires the calculation of normalized weighted average bed temperature, selecting historical data to “train” the correlations, and auto-updating with new data.

  • Predicting end-of-cycle (EOC) for a heat exchanger due to fouling is a constant challenge faced by refineries. Proactively predicting when a heat exchanger needs to be cleaned enables risk-based maintenance planning and optimization of processing rates, operating costs, and maintenance costs. Read more to learn how utilitzing the Seeq Formula Tool to monitor heat exchanger performance in the place of time-consuming spreadsheets will eliminate weeks of work for engineers, freeing them up to perform other valuable company tasks.

  • Unmanned well sites in remote locations present operational challenges. Data must not only be collected, but it also must be monitored to uncover any discrepancies, and ideally predict any problems before they occur. Advanced analytics software, coupled with a sophisticated data collection system, can address these issues, and also provide additional benefits.

  • The oil and gas industry, like many others, is collecting and storing ever larger volumes of data. Although, there is value in this data, it is often difficult to unearth using conventional analysis tolls such as spreadsheets. To address this issue, new data analytics software platforms are being introduced specifically to deal with time-series data.

  • The data generation and collection strategies at the center of manufacturing processes have evolved dramatically, especially in recent years. Process manufacturers now collect and store huge volumes of data throughout their operations, both on and off premise, across multiple geographic locations, in an increasing number of separate data silos. In this paper, we propose five questions we believe every process manufacturing buyer should ask when evaluating a data analytics solution.

  • Often the first notification of a spill comes from a member of the public, hours and sometimes days after the first spill. This can intensify public health and environmental impacts and the cost of clean-up efforts. Following a sewer spill at an environmentally significant site at Midway Point in August 2017, TasWater sought a way to reduce the likelihood and impact of spill events occurring in the future.

  • Businesses rely on process units meeting or exceeding their operational plans. To ensure that operational plans are achieved, it is important that equipment operates as designed (i.e., delivers the required performance) and continues to operate in an optimum manner (i.e., remains reliable, in a good condition). The most common causes of missing operational plan targets are equipment failure, which results in unplanned downtime, and low quality or yields from production processes.

  • The prime reason most industrial plants still have internal, on-site maintenance staffs is to reduce repair times and unplanned downtime, which negatively impact revenue, customer satisfaction, cost, and other key business metrics. In most plants today, contracting with the equipment manufacturer for maintenance usually results in unacceptably long periods of downtime for critical equipment while waiting for a technician to arrive – particularly with the typical two passes required for inspection and repair.

  • 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.

  • In batch processing operations, the combination of numerous concurrent and independent steps can lead to bottlenecks. Learn how to find the root cause and solution for every operational delay.