CONTRIBUTORS
INCORPORATING COLD CHAIN
How AI is improving the
pharma supply chain
By Gary Hutchinson, president, Modality Solutions
A
Artificial intelligence (AI) will transform the pharmaceutical cold chain — not in the distant,
hypothetical future, but in the next few years.
s the president of a company that
has been actively involved in the
creation of an application that will
utilise machine learning to generate
predictive data on environmental hazards
in the biopharmaceutical cold chain
cycle, I've seen first-hand the promise of
this technology.
When coupled with machine
learning and predictive analytics, the AI
transformation goes much deeper than
smarter search functions. It holds the
potential to address some of the biggest
challenges in pharmaceutical cold chain
management.
Here are some examples:
ANALYTICAL DECISION-MAKING
Most companies capture only a fraction
of their data’s potential value. By
aggregating and analysing data from
multiple sources — a drug order and
weather data along a delivery route, for
example — AI-based systems can provide
complete visibility with predictive data
throughout the cold chain. Before your
cold chain starts, you can predict hurdles
and properly allocate resources.
Analytical decision-making relies on
companies having actionable data
and real-time visibility throughout the
cold chain. Just-in-time delivery of
uncompromised drug product relies on
predictive data analytics. With the help
of analytical decision-making, cold chain
logistics and overall drug cost, patient risk,
and gaps in the pharmaceutical pipeline
will be significantly reduced.
For example, BenevolentAI in the
United Kingdom is using a platform
of computational and experimental
technologies and processes to draw
on vast quantities of mined and
inferred biomedical data to improve
and accelerate every step of the drug
discovery process.
Just-in-time
delivery of
uncompromised
drug product relies
on predictive data
analytics.
SUPPLY CHAIN MANAGEMENT
(SCM)
A 2013 study by McKinsey & Company
detailed a severe lack of agility in
pharmaceutical supply chains. It
noted that replenishment times from
manufacturer to distribution centres
averaged 75 days for pharmaceuticals
but 30 days for other industries,
and reported the need for better
transparency around costs, logistics,
warehousing and inventory. Assuring drug
efficacy, patient identity and chain of
custody integrated with supply chain
agility is where the true value of AI lies for
the drug industry.
DataRobot is an example where the
agile pharmaceutical supply chain can be
implemented with an AI platform powered
by open-source algorithms that are able to
model automation by using historical drug
delivery data. Supply chain managers can
build a model that accurately predicts
whether a given drug order could be
consolidated with another upcoming order
to the same location or department.
INVENTORY MANAGEMENT
Biomarkers are making personalised
medicine mainstream. Consequently,
pharmaceutical companies must stock
many more therapeutics but in much
lower quantities. AI-based inventory
management can determine which
product is most likely to be needed
(and how often), track exactly when
it's delivered to a patient, and provide
delivery time and delays or incidents
that might trigger replacement shipment
within hours.
OptumRx increasingly uses AI/ML to
manage data it collects in a healthcare
setting. Since becoming operational, the
AI/ Machine Learning (ML) system is able
to continuously improve itself by analysing
data and outcomes, all without additional
intervention. Early results indicate that
AI/ML is adding agility to the cold chain
already by reducing the number of
shortages or excess inventory of drug
products needed.
WAREHOUSE AUTOMATION
AI is at a turning point. In the next decade, it is expected to contribute a massive amount of
money to the global economy.
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Integrating AI into warehouse
automation tools speeds
communications and reduces errors in
‘pick and pack’ settings. At its simplest,
AI predicts which items will be stored the
longest and positions them accordingly.
With this approach a cold-chain food
supplier increased productivity by
20%. In another example, AI positions
high-volume items so they are easily
accessible while still reducing congestion.
Historically, pharmaceutical companies
have been slow to adapt to disruptive
technologies because of the important
oversight role played by the US Food
and Drug Administration (FDA). However,
the FDA realises AI’s potential to learn
and improve performance. It already
has approved AI to detect diabetic
retinopathy and potential strokes in
patients, and updated regulations are
expected soon to help streamline the
implementation of this important tool.
GAIN A COMPETITIVE EDGE
For pharmaceutical companies looking to
implement AI into their cold chain, here
are some steps to take to become an
early adopter:
Prepare your data, and
ensure you own it
You need a strong pipeline of clean
data and a mature logistics ecosystem
with historical data on temperature,
environmental conditions and packaging,
as well as any other data you collect
during your cold chain. If you don’t
have clean data stored, start collecting
it now. If you think you have the data,
verify that you own it. Some vendors
claim ownership of the thermal data their
systems generate and don’t allow it to
be manipulated by third-party software.
In that case, it can’t be combined with
other data sources for AI analysis. Either
negotiate ownership or change vendors.
Define your area of need
Where do you need a competitive edge?
Start small with one factor that makes a
measurable impact on your cold chain.
That may be inventory control, packaging
optimisation, logistics, regulatory strategy
or patient compliance. Track metrics and
tie them to business value.
Assemble the right people and
verify your internal capabilities .
Implementing or supporting an AI/ML
strategy requires skills that IT personnel
typically lack. Consider upskilling your IT
team or adding an AI skills requirement for
your next new hires.
AI is at a turning point. In the next
decade, it is expected to contribute a
massive amount of money to the global
economy. In the life sciences market
alone, AI is valued at USD902.1 million
(R13.4-billion) and is expected to grow
at a rate of 21.1% through 2024. As
part of this growth, I believe AI will also
make significant contributions to the
pharmaceutical supply chain. CLA
Source: Forbes
COLD LINK AFRICA •
March/April 2020