BIOTECH SUPPLY
CHAIN
ACADEMY
October 8-9, 2012
Crowne Plaza, Foster City, CA
Leveraging Technology to Transform
the Clinical Trial Supply Chain
Leon Wyszkowski : Fisher Clinical Supplies
David Northrup : Accenture
2
Top ranked clinical trial supply chain pain points
Source: Tufts CSDD
Short lead times
Poor Clinical & supply team communication
Protocol readiness / accuracy
Inaccurate forecast
Poor visibility
Technology in Clinical Development is evolving
Fisher Clinical Services perspective
Current biopharmaceutical supply chain landscape
New ways to think about data visibility and collaboration
Available technologies and an example
What are we going to talk about today
“The sexy job in the
next 10 years will be
statisticians”
Clinical Development is recognizing the strategic
role of the clinical trial supply chain
In an Accenture survey of Development Directors and Study Managers:
100% agreed clinical supply would be on their development critical path 100% highlighted supply as an important pre-marketing differentiator
90% cited drug supply as a key enabler of their development goals over the next 3 years
In many instances, the Clinical Trial Supply Chain is facing up to these pressures from a position of weakness:
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Industry wide opportunities
Cottage Industry Not exploiting the
power of collaboration Expected to be
available on-demand High level
of wastage Disparate systems
Fragmented marketplace
Acceleration in Clinical Development
transformation is driving a change in clinical
trial supply chains
Invest in system infrastructure Understand the customer and
the patient
Provide increased supply
flexibility with reduced process lead-times, as the norm
Deliver into ever expanding markets
Achieve higher performance expectations
Outsource, consolidate and integrate with the wider supply-side operation
Focus on efficiency and standardization
Adhere to stringent & evolving regulatory requirements
The Industry’s Strategic Focus...
Analytics Collaboration Recruitment Patient focus Personalized medicines Globalization Quality & Compliance Technology
Data & information capabilities
Complexity and Data Variability
Supply Chain Systems Current State
API/MFG SAP Oracle JD Edwards Others Self-Developed Inventory SAP Oracle JD Edwards Others Self-Developed Clinicopia Clin-Apps Packaging SAP Oracle JD Edwards Others Self-Developed Clinicopia Clin-Apps Distribution IVRS (Multiple Providers) Site
IVRS & E-Pro Systems
PROCESS
Number of systems becomes more complex as innovators use both in-house and external capacity across supply chain
Supply Chain professionals spend more time gathering data rather than analyzing it
Broader trends on site performance, patient recruitment across therapeutic areas, and protocols are harder to detect
SYSTEM
Current Solution
Challenges:
Not easy to bring on new systems
All require a level of software development to connect to a data warehouse
Some data is not transferred or system architecture does not allow for integration – rules for data structure are not standard
Inventory Packaging Distribution Dispensing Returns Consumption Customer System
Visibility to some parts of the Supply Chain to
Traditional view of my network
Brand owner
Logistics Services Providers
Suppliers Customers
Extending beyond the 4 Walls
Brand owner
Logistics Services Providers
Suppliers Customers
Randomization
External Supplier
Taiwan China Hub
PO #4553245 External Supplier Comparator BL# HJC 554545 Item LIMS Pooled Supplies Russia Argentina Market Vendor Plant Import hold FDA Batch Record Quality data Amsterdam US Customs Dose Instructions Grant Supplies Kit number Quality Release Complaint Manual intervention Stability date Invoice 425775 Manufacturing Plant
Data management might get a little more
complicated
Future Information Solution
How would data visibility with a single version of
the truth impact your day-to-day life?
Example of technology solutions available today
CMO Collaboration
Analytics
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Pfizer Moves
Supply Chain
to the Cloud
Financial Times, Sept 11, 2012
Over the past 18 months…
a single version of the truth against
which all
stakeholders operate
..allows supply chain network participants to be added or removed
Access to data creates opportunities
for analytics…
When the data size and
performance requirements
“become significant design
and decision factors for
implementing a data
management and analysis
system.”
Roger Magoulas & Ben Lorica of O’Reilly Media
•
Data sets that are big enough to obscure
underlying meaning
or for which
traditional methods of storing, accessing,
and analyzing are breaking down (or
simply getting too expensive).
•
Practically speaking
big data is also about
adding unstructured or semi-structured
data to the mix
•
Or creating hybrid environments that
perform while controlling cost.
Setting the Stage: What is
Big Data?
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Analytics capabilities drive improved
…which provides opportunities for improved
business outcomes
Big Data is top of mind for virtually every industry, impacting core business processes
Upstream Oil & Gas companies monitor 40K sensors per asset (combined with 4d seismic imagery) to drive real-time production operations and
maintenance & reliability programs.
USPS applies unique barcodes so it can seamlessly induct and account for postage. This results in ~1B pieces per day, scanned multiple times throughout the supply chain.
Electronic health records, home health monitoring, telehealth, and new medical imaging devices drive data deluge in a connected health world.
Pioneers in Big Data, Capital Markets firms continue to innovate around low latency systems to unlock trading arbitrage opportunities.
Mobile usage data for Service Providers unlock new business models and revenue streams from Outdoor Ad placement to medical adherence. Emerging location based data, group purchasing and online leads allow Retailers to continuously listen, engage and act on customer intent across the purchasing cycle.
Resources Health
Public Sector Retail
Financial Services Communications
Summary
To deliver enhanced patient outcomes, key components of business transformation emerge
Outcomes Visualization Mobility Analytics Data Integration Clinical Data (via Collaborations) Key Components
Data visibility:Increasing amounts of data will be available (e.g. via EMR, patient communities, digital aids, etc.), making data sourcing and preparation an essential foundation for the model.
Data Integration: Raw data from many different sources will need to be consolidated and connected (e.g. via Master Data Management).
Analytics: Predictive analytics must be developed to leverage data for proactive insights
Mobility: Data and insights must be accessible to consumers, when and where they need it.
Visualization: Data and insights are meaningless if they cannot be understood; Effective visualization is
required to support insight development.
Outcomes:All supporting components and capabilities must enable relevant, actionable insights in order to deliver improved patient outcomes.
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