June 2012
By Bill Lydon – Editor
This article about using data, modeling and other automation technology for Intelligence-based Manufacturing, is the sixth and final article in a series covering the recent annual Pharmaceutical Automation Roundtable (PAR).
I had the privilege of attending the Pharmaceutical Automation Roundtable as an observer in November 2011. This PAR was hosted by Johnson & Johnson in Spring House, PA, with Dave Stauffer, Terry Murphy, and Joel Hanson of Johnson & Johnson participating.
Lead automation engineers from various parts of the world attended the invitation-only, two-day event. This is the most knowledgeable group of automation professionals gathered in one place at any one time focused on discussing automation issues. A range of companies participated including Abbott, Amgen, Biogen Idec, BMS, Genentech, Genzyme, Glaxo, Imclone, Johnson & Johnson, Eli Lilly, Lonza, NNE Pharmaplan, Novo Nordisk, Pfizer, and Sanofi-Aventis.
The PAR was founded about 15 years ago by Dave Adler and John Krenzke, both with Eli Lilly and Company at the time, as a means of benchmarking and sharing best practices for automation groups among peer pharmaceutical companies. The group specifically does not discuss confidential or proprietary information, cost or price of products, price or other terms of supply contracts, or plans to do business or not do business with specific suppliers, contractors, or other companies.
The individual PAR group members have a wealth of practical knowledge and knowhow to share with other participants, truly learning from each other.
Topics are agreed upon prior to the meeting and a member with make a presentation on their organizations views and approach to the topic. After this presentation others comment on their organizations situation.
The presentation started with a discussion of intelligence based manufacturing concepts focused on harnessing the complementary power of data, modeling, engineering and IT infrastructure in order to create a game changing paradigm by transforming data into knowledge and ultimately intelligence. The goal is to move from responsive and reactive actions to preventative and proactive manufacturing strategies. Ideally this will lead to a holistic system shifting from stand-alone and isolated unit operations towards integrated e-manufacturing infrastructure at process, plant, and enterprise level. The PAR member presenter defined intelligence as the ability to accommodate uncertainty in data and the adaptability to cope with prevailing conditions and risks.
Intelligence-based manufacturing is based on a combination of a number of elements including process measurements (inputs and outputs), soft sensors (derived values), process models (simple to AI models), process simulation (dynamic or static), and process optimization algorithms.
The presenter offered a roadmap for implementation:
Generation & Accessibility of Critical Operational Data
Transformation of Data into Process Intelligence
Integration of Intelligence from Different Sources
Predictive, Adaptive, Multi-Scale, and Multi-Unit Control Strategies
Integrated Intelligence-based Planning, Scheduling & Operations
Skilled, Engaged, & Enabled Workforce Making Technology Savvy Decisions
It was also observed that intelligence-based manufacturing is the technology backbone enabling high process capability including Lean and Agile manufacturing.
The presenter pointed out that data from all sources needs to be used productively. This encompasses supply chain to production for multiple sites. Modeling capabilities at different levels are needed as well as integration of intelligence from different sources leveraging supporting technologies including predictive, adaptive, and advanced control. Another challenge is getting the right information to the workforce and having them make decisions based on real-time data.
“We have been talking about it for a long time but are just now getting to the point of predictive and proactive type activities.”
There is interest in analytics and optimization at a high level of management and they are looking for something that is available today that can help reduce costs, increase yields, and profits. This PAR member’s company has a process analytics sciences group leading an optimization initiative with people that are research oriented and don’t get intimately involved with plant sites as much as they should. The philosophy behind it may be in the right direction but they haven’t yet included and asked the right people. We look at this optimization system and many in the automation group feel much of this should be in the automation controller. Also, the quality group has concerns about advanced software changing setpoints dynamically.
Maybe in a few years they will get to the point that manufacturing will be based on a model developed under QbD (Quality by Design). “I just don’t see it right now,” said the PAR member.
He also described local efforts, “Every automation engineer in every site is doing something novel, something innovative using historical data and SCADA systems to make life easier for the operators.” Historical data is primarily used for predictive maintenance or operational improvements. It is important to find further ways to encourage sharing and collaboration between site engineers.
The following comments came out of the discussion by PAR members on this topic:
Intelligent-based Manufacturing in many ways appears to be the extension of “front office” business intelligence into manufacturing and with a site-based focused. In the context of business intelligence (BI), it refers to computer-based techniques used in identifying, extracting, and analyzing business data. Business intelligence technologies provide historical, current and predictive views of business operations with common functions including reporting, online analytical processing, analytics, data mining, process mining, complex event processing, business performance management, benchmarking, and predictive analytics. Marrying business intelligence methods with automation systems appears to be the next logical step in the evolution manufacturing landscape.
Your thoughts and comments are welcomed.