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




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.


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


Pharmaceutical companies must thrive in a challenging business environment defined by regulations, competition, and operational excellence. Learn how advanced analytics can help pharmaceutical and life sciences companies gain insight to important data. This webinar will demonstrate how Seeq enables the integration of myriad data sources for rapid investigation and insight to drive improved decision-making.

Improve pharmaceutical technical transfer and accelerate product approval.


Seeq Corporation

1301 2nd Avenue Suite 2850

Seattle, WA 98101


Phone: 206-801-9339

Contact: Jennifer Bentzel




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

  • 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.

  • 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.

  • Seeq Corporation, a leader in manufacturing and Industrial Internet of Things (IIoT) advanced analytics software, announced today it has closed a $50 million Series C funding round, led by global venture capital and private equity firm Insight Partners. The round includes participation from existing investors Altira Group, Chevron Technology Ventures, Cisco Investments, Saudi Aramco Energy Ventures, and Second Avenue Partners. This round brings Seeq’s total funding since inception to approximately $115 million.

  • Manufacturing sites have many automatic controllers (typically in the hundreds or even thousands for large facilities). These controllers are designed to run in automatic mode without operator intervention. Most sites don’t have insight into how these controllers are actually performing. 

  • Seeq Corporation, a leader in manufacturing and industrial internet of things (IIoT) advanced analytics software, announces a new packaging of Seeq features and applications as Seeq Team and Seeq Enterprise editions.

  • 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.

  • This use case demonstrates a solution that empowers users by connecting to all relevant data sources to visually represent batches and perform analytics with process data.

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

  • A biotechnology company specializing in the design and manufacture of proteins was struggling with the incredibly time-consuming process of comparing the quality and yield of batches based on the chromatography peaks in Excel. A solution enabled them to capture quality and yield of chromatography peaks in addition to have the ability to share all the analyses \throughout the organization with auto-updating dashboards to track the column integrity, process yield, and quality in near real-time.

  • In order to maintain optimum production, it is critical to find a way to reduce the time and expense it takes to introduce a new pharmaceutical product to the marketplace. Replacement and validation of a new control system is both costly and time-consuming. It is typically performed by high-cost experts such as control systems engineers, validation specialists, and management. Seeq helps users to save time and increase efficiency with a tool allows users to identify a statistically-good control scheme based on the actual process variable (PV) and to detect deviations.

  • Batch manufacturing of chemicals entails many distinct phases. A large chemical manufacturer was struggling to analyze batch phase times for process improvement using Excel spreadsheets. Utilizing Seeq, process engineers can use process variables, formulas, and string signals to separate each batch into phases. This enables them to Identify the “best” cycle time for each phase and tune production variables (such as adjusting pump speeds or adding catalysts sooner) to replicate the ideal cycle.

  • Predicting the quality of a batch has traditionally been a challenge for drug manufacturers. The usual process is to take samples while a process is running and send it to the lab for analysis then waiting on the results. A large molecule pharmaceutical manufacturer was struggling to predict batch quality results in near real-time. They found a solution that gave them a better way to predict batch quality and enabling process optimization.

  • 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.

  • All manufacturing industries suffer a variety of different performance losses including production losses, product quality losses, energy losses, raw materials losses, environmental/regulatory losses and others. These losses can negatively impact profitability, environmental stewardship, and even license to operate. A manufacturer needed a way to gain insight into the leading causes of production losses, finding those times when equipment was not running at capacity and categorizing the loss by reason. 

  • In order to raise efficiency and production standards, it is important to be able to analyze the performance of batch processes and identify time spent in each of the different process phases. Increased visibility into unproductive process time (for example during cleaning and maintenance and shift v shift differences in manual re-cleaning events) is necessary in order to enable users to take actions to reduce them. With the ability to increase production opportunities when reducing waiting times, overall profitability can also increase. 

  • Engineers and subject matter experts within operations settings are tasked with driving operational excellence to improve quality, safety, and throughput in production operations. The new technologies proliferating as part of Industry 4.0 initiatives are raising and expanding performance expectations. Rapid, data-driven insights are becoming critical to balance resiliency and agility with efficiency. As an IT professional, supporting these efforts is imperative as operations leaders grapple to get the most out of investments in technologies that generate and use large volumes of data.

  • Seeq Corporation, a leader in manufacturing and industrial internet of things (IIoT) advanced analytics software, and an AWS Industry Software Competency Partner, announces expanded support for Amazon Web Services cloud services.

  • Looking at the human aspect of Pharma 4.0 may be the most crucial part of how you ready yourself and your teams to take full advantage of industry 4.0-based manufacturing concepts.

  • The need to analyze data more quickly with continuous manufacturing requires a robust data collection and integration strategy across your entire organization.

  • 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.

  • For clinical or commercial scale manufacturing in the pharmaceutical and biotech industries, just finding the right data to analyze can consume significantly more time than it does to perform the analysis. Having the right applications to store and contextualize the data is imperative, but being able to obtain business value by accessing and interpreting it quickly and effectively can have a huge impact on decision-making and, ultimately, the business’s bottom line.

  • Digitization offers the promise to connect everything on the plant floor but will also bring challenges such as storing, capturing, contextualizing, visualizing and analyzing the tremendous volumes of data. 

  • This pharmaceutical manafacturer's goal was to minimize the traditional scale-up challenges when moving from pilot production to commercial manufacturing. Read how they are utilizing the OSIsoft PI System data infrastructure and piloting Seeq’s analytics to optimize its product and processes to support continuous manufacturing.

  • During a commercial campaign, a small-molecule pharmaceutical company needed to investigate the cause of crystallization deviations at the end of its batch processing. The manufacturer’s engineering team was experiencing difficulty in trying to get to the root of the problem using spreadsheets. By implementing a root cause analysis to determine the changes that might explain the circumstances surrounding the slow-filtering batch they were able to dramatically shorten the analysis time for the engineering team through integrative calculations and data analytics.

  • A pharmaceutical company was finding it difficult to aggregate data and perform analytics across multiple assets, as well as monitor KPIs for continuous pharmaceutical processes in near-real time. The company needed a dashboard to track and monitor the process parameters and identify deviations when they occur. This solution allowed the company’s quality engineers to create a continuously updating operational dashboard to monitor KPIs.

  • A major pharmaceutical manufacturer needed to improve the QbD modeling process it used in R&D, enabling it to avoid failed batches and deviations in production. A solution allowed them to analyze a continuous pharmaceutical drug product wet granulation step with a Design of Experiments (DOE) to determine a multivariate QbD process model. The goal was to apply the multivariate design space to commercial production for process monitoring and identification of deviations.

  • A large molecule pharmaceutical company was finding it difficult to reduce the time involved in pinpointing pilot drug batches that showed the best opportunity to be produced commercially. Implementing a solution that allowed them to combine lab and pilot plant data from different historians to visualize trends and perform advanced analytics resulted in faster process development, which meant the pharmaceutical company was better able to meet clinical timelines.

  • 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.