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Big Data and AI will bring value beyond compliance
With the US and EU about to implement the next stage of their ‘track and trace’ legislation to combat illegal drugs in the pharmaceutical supply chain. Whether illegally diverted from the supply chain through theft or introduced into the supply chain via counterfeits, every company must prepare to ensure compliance. This is a challenge. It’s also a major opportunity. Artificial Intelligence (AI) will allow companies to use their ‘track and trace’ programs to deliver new levels of transparency and visibility to their supply chains.
Counterfeit goods are a major issue in every industry. Analyst firm PWC estimates that counterfeit products have cost almost $1.6 trillion in the 2010s, more than tripling from the decade before. With annual sales of over $1 trillion, prescription drugs lead the list of products counterfeited. 
The effects of these drugs can be devastating. It’s estimated that one million patients die each year through toxic or ineffective counterfeit product and that there are as many as 450,000 preventable malaria deaths annually from counterfeit pills.
No one in the life sciences industries is opposed to measures to help ensure the integrity of the pharmaceutical supply chain. Properly implemented, track and trace should provide a “virtuous cycle” that benefits government, the pharmaceutical companies and patients. If we can always prove that drugs are what they say they are then the entire industry can drive improvements in patient outcomes worldwide.
Track & trace: A catalyst for change
In November 2018, pharmaceutical manufacturers will need to deliver lot-level traceability with product serialization at an individual unit level to comply with the Drug Supply Security Act (DSCSA). Mandated details include product identifier, serial number, lot number and expiration date. By 2023, there must be complete product traceability from manufacturer to dispenser.
The EU Falsified Medicines Directive (EU FMD) brings similar track and trace requirements to all EU countries in 2019. This activity sits within a global environment where more than 40 countries already have or are implementing a track and trace regime.
The pharmaceutical sector is an increasingly global business. Pharmaceutical manufacturers have operations throughout the world. The production of drugs often involves processes taking place in multiple different locations. The shape and nature of the global pharmaceutical supply chain is changing. Contract manufacturers and contract packagers are becoming more prominent and the supply chain is based on collaboration rather than distribution.
The linear supply chain operation from manufacturer to patient is being replaced by a networked environment where supply chain partners work more closely together and role definitions blur (Fig. 1). For instance, major manufacturers are acting as distributors to other manufacturers in some territories and pharmacists are acting as wholesalers to other pharmacists where it makes sense.
Track and trace means that you need to know at a granular level exactly where the product is and the condition it’s in at every stage in the supply chain. It’s important to remember that track and trace is not just about product information. It’s also about supply chain information such as where it currently is, where it’s going and when it’s likely to get there. Ideally, this is best served by transparency between supply chain partners and visibility – for both the manufacturer and for government – across the entire supply chain.
Supply chain visibility is not something that the pharmaceutical industry has been traditionally good at, but it is now a necessary component of whatever track and trace solution that a company implements to comply with global regulations. It is also the basis for improving supply chain operations is areas such as sales and demand forecasting, inventory management, supplier performance management and business continuity.
This drive for supply chain visibility is implicit in, at least, the US DSCSA legislation. Commenting recently on how the legislation enhances drug distribution security, the FDA’s Commissioner, Scott Gottlieb said: “We want to create a system that flags illegitimate drugs in the supply chain…A fully digitized supply chain can also help develop predictive analytics to reduce healthcare fraud, waste and abuse. It can allow regulated industry and regulators to more easily manage and avoid the costly or dangerous supply disruptions.” 
Achieving Gottlieb’s goals requires the application of new technologies such as artificial intelligence and analytics. Stephen Meyer, research director, life sciences at Gartner Group recently told Supply Chain Dive that, for Pharmaceutical supply chain managers, “visibility is hands down the biggest challenge and the one most likely to be solved with technology.” For Meyer, technologies such as AI and blockchain can help automate, sort and analyze the data needed to deliver visibility and improve supply chain security. 
Of course, track and trace is hardly a new concept. Pharmaceutical companies have been tracking product through their supply chains for years but this is often manual or spreadsheet-based. Where business has been conducted digitally, the files exchanged have been transactional in nature and have not allowed for anything to be tracked. There has been no way to analyze that supply chain data to address any of the counterfeiting, diversion or supply chain performance issues that companies are faced with.
The track and trace legislation forces every company to address this situation. The first step is to realize that you are not just handling the product, you’re handling the product data. To move any product along the supply chain means accommodating a complex set of requirements on how data is created, managed, stored and accessed as the product passes from manufacturer to patient – and you have to be able to do this at scale.
There’s often a range of unintended consequences that fall out of government regulation. In this case, it’s the sheer volume of data that a track and trace solution creates in the supply chain. It’s highly unlikely that those regulators that created the legislation had a clear understanding of the huge data sets, transaction volumes or network interaction required for effective track and trace compliance.
Nor could they foresee the way that technologies advance to meet business requirements. For example, chemical taggants are being used to protect both the product and the packaging. Smart labels are appearing that enable much more information to be added above the identifiers – such as prescribing guidance or adverse side effects.
The result of which is that the track and trace solution must be able to accommodate not just the volume of data but also the variety – both structured data, such as label information, and unstructured data, such as adverse side effects.
With this Big Data challenge, effective compliance with track and trace regulations will be difficult to achieve through existing infrastructure and approaches. The pharmaceutical industry is no different than any others in that it has built up silos of information in its ERP, MES and warehouse management systems. Customization of these systems is unlikely to work and will prove challenging to maintain as regulatory requirements change of over time.
In addition, the levels of supply chain visibility required for track and trace need high levels of collaboration and data exchange between supply chain partners. In an industry that jealously guards its data for competitive advantage, the track and trace solution needs to find a way to make the right data available in a way that can be accessed in a secure way that doesn’t add risk to intellectual property.
The answer is likely to be a track and trace solution that’s based around an enterprise data management platform that can contain all the relevant data and enable secure access across the supply chain – and for regulators if required.
The platform can deliver the end-to-end visibility required. In fact, OpenText operates the world’s largest business network with over $7 trillion dollars of business transacted each year. It enables the automated processing of key business documents and transactions between trading partners. 
The interesting point is that, often, companies on our business network use it to analyze elements of their business as the network is the place where they can gain the visibility into their transaction. It’s a good example of how a global data management platform facilitates the use of AI to deploy the predictive analytics necessary to achieve supply chain improvements.
AI: Driving the business benefits of track and trace
While it’s difficult to find research into the specific benefits that an effective track and trace solution can bring, a McKinsey report from 2012  estimated that a pharmaceutical manufacturer with 25 packaging lines and $4 billion in revenues could see the following benefits from track and trace:
These are benefits that supply chain managers wish to achieve from their track and trace programs. PWC found that 80% of companies expected an anti-counterfeiting solution to offer additional capabilities beyond pure brand protection. The same capabilities that achieve counterfeit detection can also drive supply chain efficiencies.
A new generation of AI-powered analytics can help transform the pharmaceutical supply chain by processing the vast amounts of data within your track and trace solution. As Figure 2 shows, the top use case for AI technologies are predictive analytics and simulation/modelling both of which are critical not only to manage risk and product quality but also harmonize and optimize supply chain management.
The challenges are evaluating and classifying structured/unstructured data to automate and refine processes. Together with an enterprise data management platform, AI-powered analytics can help companies use track and trace data to make information supply chain decisions while facilitating real-time data availability across the supply chain. It enables you to blend your track and trace data with data from a wide range of sources including the enterprise applications of organizational ecosystem, social media, weather conditions, traffic patterns, disease incidences and the like.
Life Sciences companies are increasingly involved in extended partner ecosystems, working closely with CROs, suppliers, customers, academic bodies and regulators. Managing the supply chain becomes more important as companies rely more heavily on—and spend more with—a wide range of suppliers. AI-powered analytics can help improve supply chain automation and visibility. It allows for real-time management of supplier relationships and performance, as well as improved contingency planning and business continuity. It can use a combination of internal and external data to help reduce unforeseen shortages and supply chain disruptions affecting customer service levels and sales revenues. By breaking down information silos and providing “good data” for analysis, AI can help apply actionable insight to all supply chain processes, such as forecasting, inventory management, procurement automation and production optimization.
Within the next few years, over three quarters of all the world’s prescription medications will be protected by legislation. While some companies are taking a wait-and-see approach to how the legislation develops, it is more sensible to begin to build a digital track and trace platform that can be amended to support the global regulations that affect your business. This platform will allow you to apply AI-powered analytics to improve the ability to protect against counterfeit drugs as well as provide a basis for the supply chain visibility needed to transform supply chain operations.
ABOUT THE AUTHOR
Based in DC, Jaleel is the Life Sciences Industry Strategist at OpenText with over 25 years of experience working with and for a diverse set of global organizations in life sciences, healthcare, academia and government. Jaleel is a Six Sigma Green Belt, and a member of the DIA, ISPE, and RAPS. He earned his BS in Biology from Tufts University and MBA from University of New Haven.