Advanced Supply Chain Visibility Technologies to Save Money and Ensure Quality Products

Supply chain visibility has garnered additional importance and scrutiny with the advent and increase in the number and complexity of next-generation biologics, and mRNA vaccines, as well as cell and gene therapies. Each of these emerging therapy types has its own supply chain complexities, with one commonality, they are all much more sensitive to temperature deviation than traditional pharmaceutical products. As stated in a recent article by Richard Ettl,¹ “the transport of temperature-sensitive pharmaceutical products often failed to fully satisfy the necessary quality requirements. The study showed that temperature deviations resulted in complete loss of goods in about one-third of cases, leading to additional costs along the entire pharmaceutical transport chain.” Additionally, personalized therapies have the added complexity of managing the collection of patient-specific material that must seamlessly integrate into complex manufacturing processes despite significant restrictions on collection and transport times and duration.

Companies have been responding to these challenges in a number of ways. Firstly, many have instituted near real-time tracking and data monitoring to enhance the visibility of the transportation of these therapies. Secondly, multiple supply chain orchestration platforms have been launched with various companies or through internal development to assist in patient scheduling and just-in-time manufacturing. Thirdly, temperature-controlled packaging has been continuously upgraded and enhanced to minimize temperature excursions and deviations due to logistics challenges. While these advancements have improved overall performance over time, they still haven’t addressed all of the challenges associated with the collection, distribution, and manufacturing of these therapy classes.

In fact, in some cases, the rise of disparate systems has increased workload and management of supply-chain considerations through the utilization of multiple data platforms, staging of various equipment profiles at clinical sites, as well as difficulty in collecting, integrating, and assessing the growing number of data platforms. The inability to fully integrate the ecosystem has limited leveraging of this critical data to improve processes and outcomes. Moreover, there are still multiple product management steps and processes that are not currently factored into the current data strategies when managing the supply chain of these therapy classes.

The complexities of managing fragile and extremely valuable advanced therapy medicinal products (“ATMPs”) have effectively exposed many of the weaknesses of historical supply chains, but still haven’t solved many of the challenges associated with product movement of materials that cannot be replaced. Much of this is due to the fragmentation of the data streams being collected to support the chain of custody and chain of conditions. Collating this data into a readily actionable format is critical to continued progress in the space. Moreover, leveraging data formats and interfaces to facilitate ease of use will be vital for long-term adoption and scalability. How can this be effectively accomplished? Fortunately, every company involved in the collection, transportation, storage, manufacturing, and fulfillment activities is collecting significant amounts of data; however, much of this data is collected and stored in different systems and cannot be cross-referenced for analysis.

So how do we leverage these technologies and data streams to facilitate improvement in overall supply chain management over time? We have identified four areas that are opportune for optimization.

1. Data collection: Data is collected from a multitude of sources such as the clinical site of collection, transportation equipment and logistics, biostorage and fulfillment activities, manufacturing and product release testing, CRM and other enterprise management platforms, as well as supply chain orchestration platforms. The industry needs to determine a consensus format across all data platforms such that data can be shared seamlessly between platforms as well as visualized effectively. ISBT 128 is an important start as it establishes globally standardized terminology, coding, and labeling for products of human origin.² While ISBT improves the visibility of product origin and manufacturing specification, it does not address the broader supply-chain data continuum.

2. Data storage and analysis: The creation of “data lakes” that collate and store all data associated with a unique patient or sample identifier, clinical study, transport, or process step needs to be established where it can be analyzed for trends, performance, as well as any deviations from approved collection, storage, manufacturing, fulfillment, and transport regardless of source. The ability to analyze data collected throughout the entire supply chain is critical to improving the process.

3. Data visibility and accessibility: A consistent challenge associated with supply chain visibility is the accessibility of the data collected for analysis. There are often five to seven different data interfaces associated with the supply chain activities in relation to pharmaceutical manufacture and distribution. The ability to visualize this data and access it easily is a deficiency within the space today. Moreover, the industry needs to establish data accessibility through devices such as smartphones, iPads, etc. An example of such a data interface is shown in Figure 1. While this interface supports logistics and warehouse monitoring, over time we must upgrade such systems to provide access to all data associated with product supply chain processes.

4. Analysis and learning: Long-term improvement in supply chain processes will require not only the ability to collect and visualize data effectively but it will also the ability to leverage this data to conduct in-depth, real-time analysis using artificial intelligence and other machine learning technologies to proactively assess and address risk. Risk can come from raw materials management issues, transportation challenges, manufacturing, and scheduling challenges, as well as fulfillment and outcomes data, and any potential impact of the supply chain on product efficacy.

Now that we have a fundamental understanding of the elements to be addressed, how do we approach these challenges? Firstly, we need to look outside the industry for inspiration and direction. Why is it that Amazon can show you in near real-time the location and anticipated delivery schedule down to as little as 30 minutes on your phone for a simple package, but we are challenged to demonstrate the same responsiveness in the pharmaceutical space? We obviously have additional challenges associated with maintaining confidentiality, HIPAA compliance, as well as 21 CFR Part 11 data security, but none of these barriers should prevent the implementation of near real-time accountability. Amazon has been successful because it controls and has visibility on all aspects of its supply chain.

The ability to receive actionable information quickly is the first critical step in improving the process. This includes all stakeholders in the process, whether it’s the clinic, transportation partner, manufacturer, or storage and fulfillment company. Companies such as Cryoport, Trackcel, Controlant, and some of the specialty couriers in the space have applications that support near real-time reporting, but these typically do not proactively push notifications to all end users via readily accessible mobile applications and are only solving the distribution and manufacturing scheduling aspects of the process. None of them allow for changes in real-time to rapidly address an issue without manual intervention. These improvements will only become commonplace if companies take a “data first” approach as recommended by thought leaders at Amazon³ among others.

A data-first strategy requires robust data collection, storage, and analysis capabilities. All of these are predicated on a standardized format for data collection and archival. We all need to be speaking the same language, similar to ISBT 128 for standardized labeling. Namely, the industry needs to develop and launch a standardized data format consensus that dictates what data needs to be collected, how it should be collected, and the format in which it is stored and accessed. Migration away from manual data integration to automated machine learning processes should take place as soon as possible. In addition, the implementation of new technology such as 2D barcodes and RFID will allow more timely, accurate, and authoritative information. Another advantage of today’s more advanced data carriers is that one-dimensional barcodes can contain about 20 characters of information, but 2D barcodes, such as QR codes (quick-response codes), can hold more than 7,000 characters of data and can provide access to more detailed information such as features, product specifications, transport considerations, packaging and handling instructions, and marketing.⁴

Data accessibility will be the next challenge to be addressed. Currently, companies involved in the supply chain and manufacturing of temperature-controlled therapies are very guarded when it comes to providing accessibility to the data they are generating. We need to get past these constraints and share non-confidential data that will enable machine learning and artificial intelligence to provide valuable feedback to address many of the supply-chain limitations we have today within the space. This includes predictive analysis on transportation issues due to weather, maintenance, etc., equipment performance, environmental monitoring of all the steps related to the physical management of therapies from storage, manufacture, and labeling, as well as correlating such data to long-term outcomes research to determine the impact of product management on efficacy. Data accessibility will also be addressed over time through supply chain consolidation, where data can be aggregated easily when fewer entities are responsible for many of the supply chain considerations related to a given process flow for a client.

Data mining and analysis is the last critical element that is required for improving supply chain visibility and responsiveness. Having the ability to leverage a single stream of standardized data throughout the supply chain may lead to profound impacts on efficiency and performance. By leveraging advanced algorithms and data analytics, artificial intelligence/machine learning technologies enhance the accuracy of demand forecasting, enabling companies to anticipate market trends, fluctuations, and customer preferences with unprecedented precision. This is particularly important for therapies utilizing patient-specific starting materials. This predictive capability facilitates efficient inventory management, reducing excess stock and minimizing stockouts, thus optimizing working capital. In addition, artificial intelligence/machine learning-driven algorithms optimize logistics, streamlining transportation networks and reducing costs.⁵ Additionally, artificial intelligence can assist proactively in dealing with logistics disruption, patient scheduling variability as well as manufacturing optimization. Companies need to, however, work within the limits of the technology and facilitate optimization through processes that provide the ability to be flexible. A noteworthy example of this would be to cryopreserve the inbound patient starting material of autologous cell therapies, which would provide the ability to increase manufacturing success without impacting the efficacy of the product.

Data and technology that improve supply chain visibility are both critical components in the future of pharmaceutical manufacturing and distribution. While the industry tends to move slower than the consumer product space, the implementation of technologies and processes that leverage real-time tracking, next-generation software platforms, as well as utilization of artificial intelligence in assessing and leveraging an integrated data stream will provide a significant opportunity to drive efficiency and performance in the space. We must as an industry embrace the spirit of collaboration and enable standardization and collection of data across the entire supply chain to make it a reality.

References

1. https://www.pharmaceuticalcommerce.com/view/reshaped-priorities-pave-cold-chain-path 
2. https://www.aabb.org/news-resources/resources/cellular-therapies/isbt-128-for-cellular-therapy#:~:text=The%20COI%20includes%20uniform%20names,labeling%20and%20 product%20description%20codes. 
3. https://aws.amazon.com/blogs/supply-chain/2024-supply-chain-management-predictions-navigating-the-future-with-advanced-technologies/ 
4. https://www.technologyreview.com/2023/09/20/1079519/moving-data-through-the-supply-chain-with-unprecedented-speed/ 
5. https://hbr.org/2023/09/using-technology-to-improve-supply-chain-resilience

Author Details

Mark Sawicki, PhD - President and CEO of Cryoport Systems and Chief Scientific Officer, Cryoport, Inc.

Publication Detail

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