DHL Supply Chain – Excellence. Simply delivered.
With a dynamic regulatory environment, increased global
competition, and the need to constantly innovate and
develop new products, pharmaceutical and medical device
manufacturers surely have more to consider than their
supply chains?
If that was ever really true, it no longer is. The reality is
that the supply chain can be as important in addressing
the challenges of regulation, competition and innovation
as any other part of the business.
Like other high-margin businesses, life sciences companies
have been better able to absorb the cost of supply chain
inefficiencies than companies in lower-margin industries,
such as consumer-packaged goods. However, with the
pressure to control growing healthcare costs, that ability is
eroding. The supply chain represents a significant
component of total costs—25 percent of pharmaceutical
costs, according to McKinsey and Company—and even
incremental improvements can free up valuable capital for
other parts of the business.
Integrating supply chain analytical technologies into life sciences businesses could deliver substantial competitive advantage.
Yet cost savings are just one of the opportunities that exist
within the life sciences supply chain. An even greater
opportunity resides in the ability to capture and use supply
chain data to anticipate and even predict the future and in
so doing, get ahead of their business and direct their global
operations accordingly. This is the potential of the predictive
supply chain, the next major evolution in supply chain
management.
Establishing the Foundation
When the Council of Supply Chain Management, which
advocates improving the portfolio of supply chain metrics as
key to achieving supply chain excellence, analyzed the
progress of pharmaceutical companies progress on the use
of metrics, they concluded the industry was “stalled.” This
puts life sciences organizations at a disadvantage when it
comes to the opportunities offered by the predictive supply
chain. The principle of which is the application of analytics—
data mining, statistics, modeling, and artificial intelligence—
to supply chain data to make predictions about the future
both within the supply chain and beyond
SUPPLY CHAIN INSIGHTS
UNLOCKING THE POTENTIAL OF PREDICTIVE ANALYTICS IN THE LIFE SCIENCES SUPPLY CHAIN
DHL Supply Chain – Excellence. Simply delivered.
The Path Forward
To achieve the holy grail of a predictive enterprise an
organization must change its perspective on the value of
the supply chain. The 2014 Chief Supply Chain Officer
Report, a survey of more than 1,000 supply chain
executives, found that only 39 percent of pharmaceutical
respondents see the supply chain as equal in importance to
other parts of the business, such as research and
development and sales and marketing, compared to 68
percent in consumer packaged goods.
According to Harrington, “The days of viewing the supply
chain as strictly a cost center have passed in most
industries, but this attitude still persists in the life sciences
sector. This view precludes companies from tapping the
true power of supply chain analytics and intelligence –
power that drives business opportunity and creates
sustainable market advantage.”
This shift in perspective is essential because the key to
what Harrington calls the predictive enterprise is breaking
down the organizational silos that prevent data from
being consolidated across the supply chain for analysis.
“Life sciences organizations must tackle the difficult task
of organizational change. They must break down the
internal and external organizational barriers that get in
the way of sharing data and collaborating to realize a
more predictive business model. This takes senior
management commitment and buy-in, and requires a
deeper understanding in the “C-suite” of the value of the
supply chain to the enterprise.”
Ultimately this is not just about having a better supply
chain. This is about having a smarter enterprise.
Years of cost insensitivity has left life sciences companies
with less sophisticated supply chains than exist in many
other industries. But before they can leverage the
transformative power of the predictive supply chain, they
must ensure they have the foundations in place in the
form of the descriptive supply chain.
The descriptive supply chain refers to the ability to collect
and use supply chain data to better understand what is
happening and respond to change. Descriptive analytics
comprise business intelligence systems, such as supply
chain dashboards and scorecards, as well as data
visualization and geographic mapping tools. With these in
place, companies can manage the day-to-day operation of
their supply chain to become more agile and cost-
effective.
These tools, and the data collection that supports them,
are well established in many industries; however, their use
in life sciences is lagging. The lack of visibility that results is
perpetuating major issues in the life sciences supply chain:
lack of coordination across the business and inefficient
inventory management.
Organizations in this position need to move forward
aggressively to implement first a descriptive supply chain,
which will yield efficiency improvements and cost savings
in the near-term while creating the foundation for
predictive analytics. One opportunity to begin this journey
in life sciences may be the new serialization requirements.
These will address this challenge to a degree by requiring
visibility into product as it moves through the supply chain.
This could enable life sciences companies who lead the
way in this new legislative requirement, to evolve rapidly
in supply chain sophistication, but it won’t happen
automatically.
Understanding the Predictive Supply Chain
According to Lisa Harrington, a Senior Research Fellow at
the Supply Chain Management Center at the University of
Maryland, “The predictive supply chain enables
organizations to shift from reactive to proactive
management. Today, management is being asked to make
strategic decisions using historical data, which is like
driving a car using only the rear-view mirror. Predictive
analytics expands their visibility to include seeing what’s
coming – looking out the front windshield as well as the
rear-view mirror.”
Studies of organizations that have used data effectively
have documented numerous benefits, including higher
revenue, improved customer service, more successful
product launches and higher quality products. Most
significantly, companies that do a better job predicting
demand can improve margins by 1-2 percent.