2018 PDA Manufacturing Intelligence Workshop Commands a Crowd
Implementing big data within pharmaceutical
manufacturing will require extensive
collaboration. Fortunately, a 2018
PDA workshop suggests this is possible.
Last March, the 2018 PDA Manufacturing
Intelligence Workshop explored efforts
to advance the use of big data in manufacturing
and supply chain management.
Approximately, 100 participants attended
this workshop, which immediately followed
the conclusion of the 2018 PDA
Annual Meeting.
PDA’s Manufacturing Intelligence Task
Force helped coordinate the 2018 PDA
Manufacturing Intelligence Workshop.
This task force falls under the
PDA Manufacturing Science and
Operations ProgramSM (MSOP).
The workshop provided thought-provoking
keynote speakers from Amgen,
Siemens, Disney and Biogen, introduced
some background information on big data and reviewed the goals of PDA’s big data
task force. Breakout sessions focused on
three areas: the Manufacturing Information
Model (MIM), big data analytics
for process robustness and challenges to
implementing big data in a regulated
industry. Each of these three sessions
included group activities to help PDA’s
Manufacturing Intelligence Task Force
develop strategies to grow big data knowledge
within parenteral manufacturing.
Manufacturing Information Model
In this track, participants acknowledged
that the move to Industry 4.0 requires
managing large amounts of manufacturing
data, confirming the need for this
body of work as a backbone of PDA’s
Manufacturing Intelligence Task Force.
A standard, open-source MIM will allow
the industry to make manufacturing data
findable, accessible, interoperable and reusable
over extended periods of time and
throughout the organization.
Standard open-source information models
have been developed in many industries,
such as the construction and process
industries. In the pharma industry, the
Allotrope Foundation develops data standards
for scientific laboratory data.
The Manufacturing Intelligence Task
Force has identified the need for an
MIM for pharmaceutical manufacturers
similar to other industries’ information
models. Such an MIM would provide
a standard information architecture
for manufacturing data. It would use
standard terminology and be productagnostic.
An MIM enables insights to be
drawn across multiple locations (internal
and external suppliers) and provides
faster insights from manufacturing data.
Brainstorming during the session resulted
in a robust list of business use cases where
an MIM would bring value. Participants
agreed that increased process understanding
leads to process robustness and greater
yield, stability and reduction in cycle time.
This track also highlighted the challenges
of implementing an MIM that included
data challenges—enabling context for
historical data, ownership and governance,
common data definitions and sponsorship
and validation.
Potential next steps identified included
communication of "data lake" best practices
along with alignment on a fit-for-purpose
validation approach.
Big Data and Process Robustness
In this track, 25 participants from various
pharmaceutical companies, consulting
firms and technology firms focused on
what process data to compile, which
process data can be used, and how process
data should be used for monitoring and
improvements. The session consisted of a
case study and group exercise followed by
discussion.
A case study on the evolution of a large
molecule process monitoring program was
presented. Merck has adopted the Pipeline
Pilot software to analyze large-scale
manufacturing data and report real-time
process performance across its network
and product portfolio.
As a group exercise, participants identified
and collected the process robustness parameters
from each of the unit operations
within a typical biopharmaceutical drug
substance and drug product manufacturing
process. The group universally agreed
on the need to gather and analyze all the
data to improve process understanding
and robustness. Yet participants expressed
a concern that there remains lack of a
clear understanding on how global regulatory
agencies consider the use of big data
for continuous improvement.
The track concluded with the consensus
that industry needs to continue to gain a
better understanding of the challenges of
using big data to enable process robustness.
All agreed that a position paper
providing case studies and best practices
in process robustness improvement for the
industry would be a valuable document.
Big Data Meets Regulated Industry
When it comes to Industry 4.0 in a
regulated environment, how should companies respond? In the third breakout
session, participants reviewed the collective
obstacles facing pharma in the new
Industry 4.0 paradigm. These include lack
of robust data governance, data lifecycle
challenges and associated regulatory
expectations.
The track then delved more deeply into
Industry 4.0 concepts with a use case on
applying data analytics in biomanufacturing.
Key takeaways from the discussion
included:
- Decision-making should be married
to the data
- If the data paradigm shifts, the quality
paradigm must shift as well
- Industry should look to experience
from process and product monitoring
to serve as examples
The track concluded with a lively debate
on whether a data lake needs to be
validated. Participants brought multiple
views from the areas of manufacturing,
business, IT and quality, such as, the data
lifecycle needs to be clarified, and there is
no one-size-fits-all solution when it comes
to the validation approach, as it depends on use of that data. Clarity is needed on
roles (balancing control and ownership
in the most efficient way). And there is a
lack of common practice when GMP and
non-GMP data is mixed.
[Editor's Note: Learn more about big data in an “On the Issue” video featuring
coauthor Aaron Goerke.]
Following these interactive breakout
sessions, the remainder of the workshop
involved participants regrouping to reflect
on what they learned from the earlier
sessions. This led to a discussion on what
additional opportunities exist in the
manufacturing intelligence space that are
not currently being explored by the PDA
task force.
Potential use cases for the application
of modern data techniques were also
presented. Afterward, attendees were split
into ten groups to select one of ten areas
in need of deeper exploration. Recommendations
were collected using online
polling software. Each group spent some
time identifying the risks and value
propositions associated with each opportunity.
At the end, each group was asked
to rank the ten opportunities based on the
following criteria (1= highest priority/10 =
lowest priority):
- Relative value in a cross-company workstream (value proposition)
- Desire to have PDA sponsor a workstream in this area
- Personal interest in participating in a cross-company workstream
in that topic area
The clear preference, from the results shown in Table 1, was the
Predictive Process Monitoring workstream. In addition to the
intrinsic value of this opportunity, in post-workshop discussions,
it was realized that this workstream could also take advantage of
the work being done by the three existing task forces, combining
their output in a way that could significantly impact the industry.
Synergies with the other workshop breakouts were clear, and the
task force plans to align its next steps across the workstreams as a
key output.
Table 1 Opportunities for a Deeper Dive
In closing, the significant number of participants, their active
interest and participation, the robust discussions and identification
of specific use cases/outcomes to be further pursued all underscore
the timely opportunity presented by the Manufacturing Intelligence
Task Force. A roadmap will be published shortly with specific
information on where additional team participation is needed
most to make this a successful industry-wide collaboration.
Want to help direct the future of
manufacturing? PDA’s Manufacturing
Intelligence Task Force seeks volunteers from
other pharma companies to offer their input. If
interested, email PDA’s Volunteer Coordinator.
About the Authors
Aaron Goerke, PhD, has more than 14 years
of industry experience in supporting bio/
pharmaceutical companies in process
development, manufacturing and quality
systems.
Michele D’Alessandro is currently Vice President
and CIO for the Manufacturing Division with
Merck & Co., Inc. In this role, she provides
strategic leadership, oversight and delivery of
information technology and digital solutions
for the Merck Manufacturing Division.