Putting Global Manufacturing Data at Your Fingertips
Implementing a new IT paradigm for on-demand data is as much an organizational issue as it is a technological one.
By Justin O. Neway, PhD, Aegis Analytical Corp.
Like the rest of the industry, this company faced pressure on manufacturing support to do more (or at least the same) with less. The amount of work to be done doesn’t go away, just because fewer people are working on the tasks.
Management knew that its scientists were spending more than 80 percent of their time gathering data with less than 20 percent of their time left to analyze it and make process improvements. The goal was to reverse those percentages. No one goes to a university to get an advanced degree in chemistry or engineering and thinks, “I want to work at a large pharmaceutical company and spend 80 percent of my time gathering data.” People found their tasks boring and difficult because it was hard to get data, and often, they didn’t end up using the data because they didn’t trust what they found.
The company wanted to facilitate quarterly rather than annual product and equipment reviews. Today, after deployment of the on-demand data access and analytics platform, it has realized a greater than 90 percent reduction in the amount of time required to gather data for the preparation of annual product reviews (APR). The system had to provide validated data sets that could be used in GMP reporting. Global data sharing was essential, since it had the same processes at multiple sites and needed to compare data and view the process from raw to bulk to finished goods in a single view. With faster access to data, the company wanted to move incident resolution from weeks to hours when a batch was held up by a deviation in the process.
Taking batch genealogy automatically into account was also one of Manufacturing’s priorities. One of its processes has more than 15 different process steps, each with different splits and recombinations, so looking at over a thousand combinations of process pathways manually was impossible if its scientists wanted to see what effect conditions in the first step had on process outcome. Users needed traceability of intermediates and bulk products from sites of manufacture to finished goods sites.
The company needed a system in which it could have line of sight for all end users from manufactured materials back through to process development. In this case, it put systems in place in manufacturing first and has plans to make the connection back to process development as a next step, using the same on-demand data access and analytics environment.
Manufacturing was replacing an in-house developed system that was managed by a staff of 20 IT people. It needed to replace it to combine data integration and analysis in a validated GMP environment that would end the “spreadsheet madness” required for investigational analysis. Previously, every scientist was using Excel spreadsheets with data scattered around on everyone’s desktop, from which they pulled reports. If the company made hula hoops or potato chips, that would suffice. But it needed something more robust that could stand up to a GMP audit.
The considerations for a new system included: ease of implementation, ease of use, ease of maintenance and GMP readiness. An independent consulting firm helped the IT department evaluate available COTS systems based on these requirements. It ultimately chose a commercially available, enterprise-class process intelligence platform for integrated on-demand data access and analytics.
One of the outcomes of this process was the realization that the data aggregation and analytics offerings of commercially available systems like data historians, ERP systems, enterprise application integration buses, transactional data buses and stand-alone statistics packages were not properly designed for the task. What was needed, and what is currently rolling out to its global operations, is a layer above these systems that provides integrated on-demand and scheduled data access to the operational data stores of these systems integrated with analytics that enable a much higher level of end-user functionality.
Implementation: Change Management and User Retooling
When the global manufacturer rolled out the new system, it started with a proof of concept stage using a few sites—adjusting as necessary. Site readiness factors included:
- An influential, enthusiastic site sponsor. This was important because a business person (vs. IT) can more readily see the value in having data at their fingertips.
- Site functions that understand the importance of data.
- Site S95 Architecture components in place.
- Batch context in process historians or provided from other readily available sources. You need to know what batches ran when and how.
- Central IT group project funding and coordination. This helps the business side welcome the solution.
The company’s central IT organization provided training, consulting and strategic planning, continuing to work with users to discover possibilities of the system and extract value from the investment. Eventually, it would like to move ownership of the system from IT to the business and use even more of the systems capabilities for process understanding.
Replacing an existing business process for managing data is a tough journey, but the destination is worth it. It doesn’t happen overnight, but the long term benefits of having data in context at your fingertips is enormous—although difficult to quantify in concrete financial terms. It sometimes comes down to the one or two batches that you save that wouldn’t have been saved otherwise.
When you start working with your data, you can sometimes find out that your data is not as properly organized as you thought it was. A side benefit this manufacturer saw was uncovering data or systematic issues, giving people a higher level of consciousness about data integrity, and offering the opportunity to find and correct issues before they become audit findings.
Finally, organizational change is much harder than the technology implementation. Change is seen as difficult by Process Automation teams because their natural way of thinking comes from their experiences with changes to validated systems, the paperwork for which can take months. Moving users to a new system and a new paradigm requires gaining their trust and buy-in that the payoff is worth the time commitment. Only then will they see the benefits and use the new system everyday for their investigations, bottom line process improvements and GMP reporting.
About the Author
Justin Neway, Ph.D., is executive vice president and chief science officer at Aegis Analytical Corporation. He has more than 25 years of experience in pharmaceutical and biotechnology manufacturing, and in the development and application of software solutions to quality compliance and operational efficiency in life science manufacturing.