UNLOCKING THE POTENTIAL OF PREDICTIVE ANALYTICS IN THE LIFE SCIENCES SUPPLY CHAIN
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.
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.
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.
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.