Life Sciences Manufacturing and Quality

whitePaper | July 13, 2020

There are different tracking technologies typically used in a production facility. Depending on the amount of data transmitted and the complexity of implementation each have corresponding price points. The manual manipulation of hand scanners requires time for processing and allows for error in recording. The cost of implementation is low, but systems require consistent investment of time devoted to ensuring accuracy. The cost to serialize assets for tracking is low, but the data can be damaged and capacity/flexibility for data storage is limited.

whitePaper | June 7, 2022

Manufacturing organizations benefit from the automation brought by the Third Industrial Revolution, but struggle to extract value from and manage the massive volumes of data created by 20th Century process innovation. The Fourth Industrial Revolution (Industry 4.0) - now present - promises to offer tools to unlock the value of these massive quantities of data with the use of technologies such as Data Analytics, Machine Learning & Industrial IoT. However, manufacturing companies still struggle to convert this data value into business outcomes due to the proliferation of data silos, and the overwhelming prevalence of complex and unwieldy data supply chains which leaves data in an unmanageable state.

whitePaper | July 25, 2022

Traceability, the ability to identify and trace the product, process, operator, context, and detailed quality and component supplier and lot information for every item that moved through manufacturing, is a must-have for manufacturing success. A robust traceability framework that envelopes the total process, product, and material traceability, is a critical component for manufacturers facing stringent regulations while seeking to reduce the overall cost of compliance.

whitePaper | November 2, 2022

This white paper entitled "Artificial Intelligence in Manufacturing" reports the use of Artificial Intelligence (AI) in European manufacturing, and its potential to enhance competitiveness and technological leadership in the future. The paper is based on research conducted by the Artificial Intelligence in Manufacturing NETwork (AIM-NET), which represents the viewpoint of the manufacturing community on the achievements and challenges of AI solutions. The document covers topics such as AI in manufacturing processes, robots, machines, and operations support in manufacturing, and AI in manufacturing systems. It also includes discussions on cross-cutting aspects such as regulation, education, systems engineering, and data augmentation. The paper also presents the maturity achieved TRL levels of existing AI-based applications in manufacturing, typical KPIs used to assess performance, and barriers and limitations to wide adoption. It also provides a roadmap for future work on AI, with efforts needed in short-term and long-term time horizons. Overall, the paper provides a comprehensive overview of the current status and future vision of AI in manufacturing for policy makers, practitioners, and researchers.

whitePaper | June 3, 2022

Instantly appears in many people’s minds is of a production plant run entirely by robots. But robotics is only part of the overall technology stack that will enable today’s factories meet the demands of tomorrow’s markets. In just a few decades, industrial manufacturing has changed dramatically in the continuous effort to keep pace with ever increasing market demands. Basically, this transition has occurred in three distinct phases, each with its own pathway to success.

whitePaper | August 5, 2022

New technologies have woven their way into the manufacturing industry in recent years, particularly spurred by the advent of the Industrial Internet of Things (IIoT). Just as consumers have been able to integrate technologies such as smart thermostats and doorbells into their homes, American manufacturing firms have leveraged new technological tools to make their workflow more automated and interconnected—improving efficiencies and output.