FEATURED ARTICLES

• Realizing The Benefits Of CPV With Advanced Analytics

  When implemented correctly using advanced analytics solutions, CPV can afford companies insights that enable increased productivity, reduce warehouse and materials costs, and support iterative process improvements.

• 5 Questions To Ask When Selecting An Advanced Analytics Solution

  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.

• The Value Of Multivariate Analysis And Hybrid Modeling For Pharma

  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.

• Shortening Time-To-Market Through Improved Data Analytics

  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.

• Improve Water & Wastewater Operations With Advanced Analytics

  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.

• Predictive Analytics 101

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

• Control Loop Performance Monitoring

  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. 

• Analysis Of Clean In Place (CIP)

  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.

• Faster R&D Of Pharma Production Lines

  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.

• Improving Manufacturing Productivity At Abbott Nutrition

  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.

• Chromatography And Transition Analysis

  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.

• Control System Validation

  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 Tracking & Cycle Time Analysis

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

• Seeq For Operators - Find Production Settings

  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.

• Loss Monitoring And Categorization

  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. 

• Asset Utilization (OEE) Monitoring

  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. 

• Enabling Engineers With Data Science

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

   

   

• The Human Side Of Pharma 4.0

  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.

• Use Advanced Analytics To Usher In The Continuous Manufacturing Era

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

• How Data, Analytics Improve Chromatography, Batch Comparisons

  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. 

• Pharma Manufacturer Uses New Data Tools To Help Increase Efficiencies

  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. 

• Pharmaceutical Manufacturer Leverages Data And Analytics To Support New Continuous Drug Manufacturing Processes

  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 And Situational Awareness For Manufacturing Data

  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.

• Drug Manufacturer Crystallization Deviation Investigation

  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.

• Continuous Manufacturing Of Pharmaceuticals

  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. 

• Continuous Drug Product Quality By Design (QbD) Modeling

  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.

• Clean In Place Optimization For Pharmaceutical Manufacturers

  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.

• Batch Quality Prediction In Pharmaceutical Manufacturing

  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. 

• Accelerate Biopharmaceutical Scale-Up

  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.

• 5 Questions To Ask Before Selecting A Process Data Analytics Solution

  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.

• Leveraging Predictive Analytics: A Case Study

  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.

• Quantifying Latency In Batch Processing

  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.