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.