ABOUT SEEQ

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

 

 

FEATURED PRODUCTS

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.

VIDEOS

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.

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

SEEQ WEBINARS

View this on-demand webinar to learn how advanced analytics applications help pharmaceutical and life sciences companies integrate and investigate important data for improved decision-making.

Improve pharmaceutical technical transfer and accelerate product approval.

CONTACT INFORMATION

Seeq Corporation

1301 2nd Avenue Suite 2850

Seattle, WA 98101

UNITED STATES

SEEQ SOCIAL MEDIA

        

FEATURED ARTICLES

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

  • 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.

  • Improving the collection and analysis of the data a drug manufacturer produces is key to driving innovation. Novel technology solutions safeguard scale up and optimize processes.

  • 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.

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

  • 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. 

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

  • How a biotech captured quality and yield of chromatography peaks and created the ability to share column integrity, process yield, and quality metrics throughout the organization in near real-time.

  • Seeq helps users to save time and increase efficiency with a tool that allows users to identify a statistically good control scheme based on the actual process variable and to detect deviations.

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

  • 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.

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

  • 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. 

     

     

  • 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.

  • 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. 

  • 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.

  • 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.

  • How a root cause analysis enabled a pharma company 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. 

  • 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 drug company needed to optimize CIP processes to improve efficiency while maintaining quality. To characterize the CIP process, the company needed to identify where it was spending time on CIP and to create process models directly from the data. Read how the team developed process models for each of the company’s CIP units and was able to identify excessively long procedures that represented overcleaning events.

  • Delayed lab results made it difficult for a pharmaceutical manufacturer to optimize process inputs to control the batch yield. A solution allowed the scientists to build a model of process quality. 

  • A pharmaceutical company was better able to meet clinical timelines by combining lab and pilot plant data to visualize trends and perform advanced analytics resulting in faster process development.

  • 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.

  • 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.