The Pharmaceutical Industry and the Digital Supply Chain

Industry 4.0 Digital Transformation of Pharma
Implementation of DSCSA mandates has provided an opportunity to transition from a complex traditional pharmaceutical supply chain to a digital pharmaceutical supply chain.  By executing on the DSCSA vision, a system of electronic drug product traceability will improve the speed and accuracy of track and trace, saving the lives of patients across America.

The incredible volume of data involved with manufacturing, selling, storing, handling, transporting,  distributing and regulating pharmaceutical drug products is staggering.  With the ascent of data-driven technologies, innovative tools are capturing this valuable data and transforming it into actionable insight to better manage the vast network of pharmaceutical manufacturers, distributors, pharmacies and PBNs.

End-to-end pharma supply chain visibility is a major challenge, especially because it can often involve dealing with trading partners across the globe.  Digital technologies have not been fully embraced across the pharma supply chain yet.  Moving to a digital pharma supply chain will eventually eradicate error-prone, manual processes which waste time and money.  Without having access to the best tools and data, how can the best decisions be made about products that directly impact the health of consumers?
To enact a digital supply chain schema, data must be collected from both inside and outside the sources.  This involves not only using newer technologies but also breaking down silos that occur across organizations and enterprises, between trading partners, entire networks and beyond.  Gathering and consolidating all this data including information involving operations, processes, regulations, supply chain risks, social media, trends in patient health and much more helps to provide a more unified picture, a new perspective that is more realistic and impactful.  Imagine the power of unifying all that data…

As an example of this, German drug manufacturer Merck KGaA uses harmonized, real time data consolidated from a myriad of data sources.  This data is collected from sensors set up throughout its supply chain to gather insight on inventory distribution practices and availability of every SKU as well as data from the company’s ERP.  Facilitating greater visibility has enabled Merck to improve its forecast accuracy.  Merck previously used traditional tools and humans who were engaged in 80% of predictions.  Using the data for end-to-end visibility also produced the benefit of reducing order processing time by shifting materials or production to alternative locations as needed.

Pharma M&A Activity Provides Key Impetus for a Digital Supply Chain

In contrast to previous decades, mergers and acquisitions in the pharmaceutical industry are no longer dominated by manufacturers but rather have become vertical in nature.  Examples of this include the CVS decision to buy Aetna.  This provides new opportunities, synergizing health insurance services with retail offerings.  Combining two businesses of most types is problematic but in the pharmaceutical industry, it is doubly challenging due to the nature and complexity of their respective supply chains and networks.

To facilitate end-to-end visibility across a new combined enterprise, a digital transformation is essential.  This helps to harmonize data across multiple supply chains and facilitate greater flexibility.

Transforming Pharma:  Moving to a Digital Supply Chain

Artificial Intelligence, Big Data, Predictive Analytics and Cloud

A digital pharmaceutical supply chain can provide the data needed to analyze performance at each step throughout the processes of testing, manufacturing and distribution.  This will aid in developing more accurate forecasting and will provide the ability to react in real time to demand changes.  Tools such as predictive analytics and big data can play a critical role in enabling digital operations. In addition, the adoption of cloud and big data technologies can enhance end-to-end data visibility for internal and external stakeholders.  This will improve real time decision making and facilitate speed in operations.

For example, a large wholesaler implemented artificial intelligence-driven cloud software and uses it to collect data from across its enterprise including at multiple subsidiaries and storage facilities.  This is used to track hundreds of thousands of pharma product SKUs which are distributed to customers.

Gaining insight into enterprise-wide pharma operations can be done by employing advanced data analytics.  Key metrics such as manufacturing absorption, demand, volume and gross margin can be utilized in pilot projects to provide insight on a specific business question or issue as well as in analytics experiments.  Using advanced analytics can help the pharma industry remove and replace legacy systems, unleash innovation and remove roadblocks in operations.

Key metrics can easily be monitored and visualized using digital dashboards and customized reports which can be sent automatically to stakeholders and decision makers.  According to a report by McKinsey, advanced data analytics can be employed across the entire pharma value chain to generate returns from 10-50% in areas such as research and early development to manufacturing and supply chain and to enable functions.

Benefits of Developing a Digital Pharma Supply Chain

  • Increases visibility across enterprise supply chain operations
  • Facilitates integration of supply chains
  • Improves and expedites operational processes
  • Enables rapid response and flexibility
  • Aids in improving planning accuracy
  • Improves identification of suspect and illegitimate drugs
  • Speeds up product recalls, improving consumer safety


Challenges to Developing a Digital Pharma Supply Chain

  • International market with companies operating across multiple regions and timelines
  • Increased regulation in countries across the world which is not uniform
  • SKU proliferation
  • Pharmaceutical product lifecycle management issues
  • Complexity of supply chain and number of pharma supply chain partners
  • Absence of integrated supply chain planning
  • Necessity to keep costs under control through efficiency and effective supply chain management
  • Increasing trend toward personalization of healthcare and prescription drugs and therapies
  • Risk of counterfeit drugs
  • Constant need for quality control

AI and the Digital Pharma Supply Chain

According to Crunchbase, in the third quarter of 2017, approximately $1.165 billion was invested in U.S. artificial intelligence company startups. Already beyond mere speculation, artificial intelligence shows the promise of being able to transform pharmaceutical supply chains though its ability to process vast volumes of data and provide intelligent insight and recommendations.

Using machine learning algorithms, AI is capable of learning and refining information in real time, even as it crawls data sets from internal and external sources.

With its robust cognitive automation abilities, AI can be applied to examine and improve processes across the supply chain including supply and demand forecasting, manufacturing performance, assessments of supplier reliability and inventory optimization.

Optimizing Pharma Inventory

Because of the tendency for pharmaceutical companies to carry excessive inventory to ensure 100% fulfillment, it has proven challenging to effectively manage inventory.  Converting to a digital supply chain is projected by McKinsey to recover $25 billion through inventory reduction alone.  For serious chronic medical conditions, often there is only one or at least very few suppliers.  Because of the value of human life, pharma companies do not want to risk a stock out and sometimes end up discarding millions of dollars’ worth of inventory upon the drug’s expiration date.

Another major issue is long cycle times.  Overall cycle times in the pharmaceutical industry often extend into the hundreds of days.  This is often in addition to the four or more months that It takes for drug products to move through to the distributor then to dispensers and then to the patient.  Having information visibility across the supply chain is critical to optimizing inventory, avoiding overages and shortages and reducing cycle time.

AI Improves Available-to-Promise Accuracy

Traditionally, available-to-promise functionality relies on a rules-based calculation using theoretical lead times and highly volatile, variable allocation rules.  Relying on these data points for ATP calculations often results in inaccurate ATP dates. Artificial intelligence significantly improves ATP accuracy by automatically producing a ‘supply chain map” which highlights details about an order.  By including key metrics such as expected delivery data and allocated quantity, AI then distributes accurate predictions and suggestions that are based on data science and machine learning rather than simplified rule-based ATP system calculations.

Supply Chain Collaboration

As developing economies continue to grow and the number of consumers increasingly expands, pharma supply chains also broaden their capacity and reach.  Factors including an aging population, increased consumer empowerment and desire for personalized health plans and other factors are driving up the use for prescription drugs.  With economics improving across the globe, pharma companies are continually expanding operations into emerging markets to reduce cost.  This makes supply chain collaboration even more challenging.

Having a digital pharma supply chain can improve the speed and accuracy of operations, making real time visibility a reality with minimal overhead.  With the cost of innovative technologies decreasing, more pharma companies are starting to embrace digitization and digitalization with more vigor.  Moving to a Industry 4.0 model enables pharma companies to shift to a demand-driven supply chain.  This minimized latency and maximizes value, reduces or eliminates fragmented decision making, decreases lead times and harmonizes the network of supply chain trading partners.


At a time when U.S. consumers are more concerned than ever before about the cost of prescription drugs and their healthcare system, the evolution of the digital supply chain is poised to make a major impact on the supply chain planning process, delivery times, speed, cost and drug security.  Embracing the digital transformation of the supply chain promises to have a transformative effect on the pharmaceutical industry.

Using artificial intelligence, predictive analytics, machine learning, IoT and other innovative supply chain technologies, the evolution of the digital supply chain is accelerating and poised to unleash significant positive impact on the value chain.  Real time supply chain visibility alone, whether across an enterprise or the entire digital supply chain can help to optimize inventory so that drug product shortages and outages can be avoided.

Get ready for it.  The digital supply chain is here to stay.

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