TQS Integration announces launch of their latest software release, ODIN, empowering the connectivity of real time data with cloud-based systems, machine learning and AI platforms in an easy, secure, and affordable way. This newly developed software enables TQS to help their customers achieve process manufacturing advances and propel towards industry 4.0.

Equipped with an easy to use, web-based interface, cloud data transfer through ODIN allows for real time and on-demand data egress to enable machine learning and supports multiple source and destination platforms. ODIN is compatible with both OSIsoft PI and Wonderware historians, allowing efficient and secure transfer of valuable information from multiple historians to cloud-based destinations including Amazon S3, Microsoft Azure Data Lake Gen 2, IOT Hubs, Event Hubs, SQL Server and more.

Cloud Data Transfer

"We are very excited about what ODIN can do for our customers to provide an easy-to-use tool to solve the need for automated data extracts and remove the time-consuming process of extracting and contextualizing data. ODIN will allow that valuable time to be refocused back to data science and empower analytics and Industry 4.0.” says Emmett O’Connor, Global Head of Product Development at TQS."

The complex nature of cloud data transfer is now made easy as ODIN automatically aggregates, filters, contextualizes and delivers formatted real time information efficiently via a user-friendly GUI (Graphic User Interface) that enables ease of monitoring job success. In-built auditing and distributed agent’s nodes also ensure configurations are monitored for compliance, while transfers are seamless, secure, and scalable.

ODIN can help manufacturing companies to prepare compliance and regulatory reports, run predictive advanced analytics, integrate operations information with business information, ultimately gaining valuable insights to make optimized business decisions and cost savings. This tool allows TQS to deliver competitive advantage in digital transformation for their clients.  

"ODIN enables companies to make key business decisions and operational improvements through the utilization of existing data – it brings Industry 4.0 and digital strategies to the next level. This further strengthens our position as the industry leader in data intelligence. TQS has seen significant expansion in recent years, more than doubling staff numbers over the past two years, and we are planning to add an additional 50 jobs in the next 12 months to support our continued growth" comments Korie Gleeson, COO at TQS.

About TQS Integration

TQS Integration is a global data intelligence company providing turnkey solutions in system architecture and application design, engineering, system integration, project management, commissioning and 24x7 “follow the sun” support services to valued customers. TQS is at the forefront of data intelligence for over 20 years, working with an extensive client base in the Pharmaceutical, Life Science, Food & Beverage, Energy and Renewables industries. As the go-to partner for data collection, cloud data transfer, contextualization, visualization, analytics, and managed services, we are the main drivers in the world’s leading companies — helping them become leaders in Industry 4.0.

For information, please contact us.

Multivariate Analysis (MVA) is a well-established technique to analyse highly correlated process variables. It is well known in batch, but also successfully applied in discrete or continuous processing. In comparison to single variable applications, for example statistical process control, MVA has shown to be superior in the detection of process drifts and upsets. In practice, the implementation of MVA requires two different data structures or models:

Event Frames are usually autogenerated from the batch execution system (BES) and reflect the logical\automation sequences for recipe execution. Both AF Elements and Event Frames are  being used to create MVA models and calculate statistics. Below is an example of a multivariate model that combines the autogenerated Event Frame “Unit Procedure” and process variables in the Element: “Bio Reactor 0”:

This type of analysis is  typically used for batch-to-batch comparison (T2 and speX statistics) and batch evolution monitoring in the pharmaceutical, biotech and chemical Industry.


One of the shortcomings of using automation phases is that they  seldom  line up with time frames that are critical for the underlying process evolution (process phases). Often there is a mismatch in the granularity, process phases are either longer or shorter in duration compared to the automation phases. Also start and end might be based on specific process conditions, for example temperature, batch maturity, online measurements and others. The mismatch between automation and process phases causes misalignment in the MVA model and a broadening of the process control envelopes. . The resulting models are often not optimal.


SEEQ has developed a platform that excels in creating time series segments as well as time series data cleansing and conditioning. The platform provides several different approaches to define very precise start and end condition.  The following show the definition of a new capsule based on a profile search that solely focuses on the process peak temperature:

These capsules can be utilized in other applications through an API and blended with other PI data models to create very precise multivariate models:


Multivariate Analysis is a powerful method to analyze highly correlated process data. It depends on  equipment\process models and time series segments. OSIsoft PI provides data models for both. And typically time segments are automatically populated from a BES or MES systems. SEEQ provides new capabilities to create highly precise time segments called capsules, that refines the MVA analysis and creates meaningful process envelops. The integration is seamless since both systems provide powerful API’s to their time series data and models. The resulting MVA models target specific process phases that can be used to create improved process control limits or regression analysis.

Please contact us for more information.

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