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Big Data’s Unique Role in the Pharmaceutical Supply Chain

In this special guest feature, John Hogan, Senior Vice President of Engineering at TraceLink, discusses the pharmaceutical supply chain, and the challenges it is facing regarding big data and interoperability. John leads product design, development, and testing of TraceLink products and solutions, ensuring the seamless processing and tracking of high-volume, highly distributed events, and facilitating shared customer value up and down the supply chain. He joined TraceLink in January 2018 from Boston-based cybersecurity startup Barkly, where he served as VP of Engineering and was responsible for product development and DevOps building out the company’s unique approach to endpoint protection. At TraceLink, he is instrumental in helping grow and manage a rapidly expanding team that is building products that benefit customers worldwide.

Historically, the pharmaceutical industry has been slow to adopt when it comes to cutting edge technology, and hurdles like heavy compliance and regulation make it even more difficult. Despite past obstacles, technologies like the Internet of Things (IoT), Artificial Intelligence (AI) and Machine Learning (ML), and Blockchain are playing a role in the digital transformation happening within the supply chain.  

How does each contribute to the digital transformation and the shift toward a more patient-centric supply chain?

The Internet of Things (IoT)

Cold chain monitoring is the most viable use case when it comes to IoT in the pharmaceutical supply chain. A failed cold chain poses serious challenges, particularly by opening the door to potentially unsafe products being delivered to patients. With over $35 billion lost per year due to failed cold chains, industry leaders are looking to IoT to address the associated challenges, particularly with the growing industry focus on precision medicines.

Environmental excursions pose a big threat to medicines, and historically, typical methods for monitoring environmental excursions have involved proprietary devices and sensors, siloed data sets, and error-prone manual processes.

IoT sensors are critical for automating temperature monitoring and safeguarding the cold chain. They can also help with things like preserving efficacy and safety of pharmaceutical products, eliminating opportunities for human error, increasing real-time transparency and preventing costs associated with loss of product due to unmet quality standards.

Artificial Intelligence (AI) and Machine Learning (ML)  

Data science in general has the potential to have a profound impact on the growth of the pharmaceutical industry. AI combined with predictive analytics and ML will transform the pharma supply chain, enabling a more intuitive and data driven approach to pharmaceutical supply chain management. The more visibility companies have into the ebb and flow of pharmaceuticals within their supply chain, the better equipped they are to proactively manage critical business challenges through complete visibility, allowing for business to predict issues before they occur, avoiding major supply chain disruptions.

Real-time visibility and data related to a drugs movement through the supply chain, from manufacturer to patient would be instrumental in identifying and predicting challenges related to inventory management, cold chain management, drug shortages, and drug diversions to name a few.

Today, the pharmaceutical supply chain is linear and this is what presents a challenge to visibility. But to avoid the negative impact associated with the above challenges, pharma supply chain entities must collaborate and share information in a way that improves efficiencies and ensures that medicines reach patients safely, on time and in full without compromising their business interests. One way this can become a reality is for supply chain stakeholders to use AI and ML to share intelligent data signals with each other so that they can proactively make fast, real-time decisions on inventory and medicine availability and significantly reduce the risk to patient lives, as well as the costs associated with massive disruptions.

Blockchain

Blockchain has become a buzzword ever since the initial Bitcoin boom. The result is a convoluted understanding of realistic applications in all industries. Blockchain is a secure, distributed, immutable ledger – attributes which make it well suited for some use cases within the pharmaceutical supply chain, and not so great for others.

First and foremost, it’s integral to consider what you’re putting on the chain and whether it violates blockchain’s primary rules. For example, things like patient data exchange and pricing would not be well suited for a blockchain use case. If there is a potential need to delete or move information, it is physically impossible to do so on a blockchain platform.

Where blockchain is generally a good option is when you have parties that need to communicate regularly, and the communication between them requires high levels of integrity. For instances like the pharmaceutical supply chain, it can cut out a lot of miscommunication and misdirection when it comes to things like drug tracking and tracing, custom therapies, and temperature modulation. For example, if a wholesaler has invested a large amount of research and development into a certain therapy and they’ve paid a manufacturer a significant amount of money to ship it from point A to point B, they’re going to want to see where that product is at every point along the supply chain. Blockchain is a viable option for this type of communication and as specialty medicine becomes more prominent, this is something we’re likely going to see more and more.  

While the pharmaceutical industry has been historically linear and slower to digitally transform, legacy processes are finally starting to blend with modern technology to create safer and more efficient operations. Despite heavy regulations, a streamlined approach that combines sophisticated technology with enhanced collaboration and transparency among supply chain stakeholders could be the answer to the many complicated moving parts of the pharma supply chain.

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