The FDA recently issued Final Guidance for Industry Standards for Securing the Drug Supply Chain. The publication recommends unique identifiers in the form of a serialized National Drug Code (sNDC) on prescription drug packages. It also aligns the FDA sNDC with the same GS1 product serial number definitions embraced by similar agencies in Brazil and Europe, and points toward an emerging global consensus on how 100% traceability and product authentication can be achieved.
One constant theme is emerging: The need for product authentication delivered using codification and mass serialization.
Manufacturers working towards compliance with new serialization requirements or seeking improved product traceability for brand protection or supply chain optimization will have to retrofit existing production equipment with the proper printers, vision systems and ID readers.
In order to avoid derailing production when implementing a serialization strategy amid evolving regulatory and compliance environments, we offer these ten essential steps that must be considered.
1. Internal assessment: Conduct a thorough internal assessment. Determine why you need traceability. Do you want to achieve greater inventory visibility? Improve the returns process? Detect and eliminate counterfeiting? Detect and reduce “gray” market activity? Have access to more timely and accurate sales data? Speed up and reduce the cost of the recall process? Achieve more accurate order fulfillment and shipping? Enhance reputation with customers and the public? Comply with regulatory requirements? There are a wide range of uses for traceability information, and the first step is figuring out what you want accomplish with it. Is the scope of the traceability objective limited to product integrity on the production line to track production parameters and ensure product quality for risk management? Is it part of a broader initiative that includes brand authenticity and return validity assessment? Will serialization be the foundation for optimizing business practices where the traceability information will be used for automated routing and inventory control? Or, do you want to maintain channel integrity, protect against counterfeiting, detect diversion and fraud, or comply with regulations?
2. Project ownership and budget: Determine who owns the traceability project and budget. Don’t ignore that serialization can involve change that may require new methodologies and processes that will need to be defined and validated. Most traceability projects require input from multiple teams such as manufacturing, engineering, validation, operations, information technology and marketing. It may cover, for example, packaging and design, data management, warehouse execution systems, line configuration and other areas.
3. Stakeholder buy-in: Get input from all the various stakeholders to develop a strategic approach that covers not only how traceability will be implemented, but also to organize where various parts of the program and development responsibilities fall.
4. Data requirements: First, figure out who is going to consume the traceability data once it has been generated. Is it marketing? The quality department? Production tracking? Inventory control? Engineering? Once you know who will be consuming the data, determining the data requirements is more straightforward. This is a critical step because everything in a traceability project, such as code design, label design, IT infrastructure design, and all the rest, tends to be configured around the data requirements.
5. Code Design: Today, all manufacturers uniquely code each lot or batch to identify time and location of production and make recalls more efficient and less costly. But this is not sufficient for full traceability where the code must be unique for each product, especially if the time stamp goes to the minute and the production rate is 200ppm. Serialization is a lot more complex than just generating numbers and printing them on a label. To avoid derailing production, the information will have to be encoded into a barcode, Data Matrix, or some other machine readable data carrier in order to automate data capture during production. Another factor to consider when implementing code design for a traceability system is that serialized packaging or product marking has to fulfill a defined life cycle from generation to allocation on the product and beyond. That means to create the unique serial number requires permission to generate it and permission to distribute it.
6. Label design: At some point the code will have to be added to a label or in some cases etched onto the product itself. Along with location and size of the code, the longevity and readability of the code also needs to be taken into consideration. For instance, if traceability is going to be used to audit production samples after they’ve been shipped to the end user, then one must make sure the code is located in a location free from abrasion during shipping and that the code printing material is resistant to the environment to which it will be exposed.
7. Technology Audit: Serialization will require many facilities to re-tool printers, vision inspection systems, as well as ID scanners and readers. This will either require a recipe\software change of existing equipment that meets the new requirements, or the installation of new equipment. Assess the production line’s capability to produce and read the traceability marker. If Data Matrix is used, then make sure printers and readers on the line are capable of marking and reading it at the highest production rates. In some cases, the right technology might be in the wrong place. For example, you may want to begin tracking your product as raw material entering the plant but there is only an end of line label printer before packaging that can apply the correct traceable mark. If the correct technology is not in place there will be some capital expenditures needed to get the traceability initiative off the ground. It’s good practice to, wherever possible, leverage existing investment in technologies and processes such as site level vision systems and printers. However, ultimately, it’s best to go with the software, printers, vision systems and ID readers that meet performance requirements and are easy to install, while requiring the least amount of resources to maintain. For example, when retrofitting existing equipment and production lines, new smart camera vision systems can be less expensive to install, less complex to validate, and less costly to maintain than traditional PC-based inspection systems. Also, a distributed model using smart cameras minimizes the effect of equipment failures as the failure of one camera will not bring down the entire system.
8. IT infrastructure: Perhaps one of the greatest challenges is managing and administering serialization, primarily due to the sheer scale and complexity of the problem. It’s not thousands of numbers, or even tens of thousands of numbers. It is millions or billions of numbers. Plus, it may require integration to multiple systems for cross-site, multi-discipline coordination of serial number generation. The traceability mechanisms require an IT infrastructure that can handle the required bandwidth, capture the data in some style of database and insure the data is accessible. Serialization can be extremely complex when you are looking at generating and managing master data sets and data flows. Data volumes are typically large (terabytes) and manufacturing performance must not be hindered by data handling. This is not a solution for a pilot over a very small number of products. Once past pilot phases serialization has strategic implications at a regional and global level. How does the information integrate within offsite processes and onsite processes? Keep in mind that for ePedigree applications, the serial numbers will have a highly complex aggregate relationship at all the different packaging levels from individual items to bundles, cartons, cases and pallets. Flexibility is key to future-proof the system amidst moving legislation and requirements. It’s about creating a sustainable and scalable solution that will work over many years in a compatible and in a continuous way.
9. Data access and security: How will the consumers of the data to interface with the database? How will they pull the right information from manufacturing or enterprise systems? Will they request it from IT, or will there be an online interface to a shared web portal for access? How will the database be searched? Also security of data at rest and in transit is of paramount importance because it will likely be the target for counterfeiters. Securing the data means protecting it from loss, corruption or tampering. Security may likely require 21 CFR Part 11 compliance. All of the various elements must be traceable within internal environments, as well as throughout the supply chain and at the point of dispensing for authentication. Consequently, the data must be tested periodically to verify viability to ensure that as each unique item moves through the value and supply chain data integrity is maintained and relates to a particular drug with specific batch and expiry date.
10. Validation: As with any system under regulatory control, a plan must be in place for verification and validation. This includes exploration of failure modes such as data integrity, label accuracy, data path in the case of connectivity loss and error detection. A series of FMEAs that draw experiences from multiple disciplines will help to identify potential weaknesses in the system early on so that preventative measures work their way into the overall plan. Technology selection may also play a large role here as custom equipment will have more involved validation requirements. For example, an off-the-shelf smart camera vision system is simpler to validate than a custom PC-based system. Smart cameras are generally classified as GAMP class 2 or 3, which means only the configuration and the calibration need to be controlled. In contrast, because PC-based vision systems generally require a greater degree of custom computer code, these applications typically fall under GAMP class 3 (best case), class 4 or even class 5 (100% custom). GAMP 3 or higher requires supplier audits, life cycle plan for code. GAMP 4 or higher require code audits and validation of all code on a line by line basis.
With the tremendous amount of work that will be required, it’s not too early for drug manufacturers to actively engage with equipment and software suppliers to map out their traceability requirements. Now is the time to learn about data carriers, coders and markers, labelers and printers, ID readers and vision systems, and to start planning to implement the software infrastructure required to support data sharing with trading partners.
About the Authors
John Lewis is market development manager at Cognex. Formerly a technical editor for an engineering magazine, he has been writing about packaging technology, machine vision, factory automation, and other technology topics since 1996. He has published hundreds of articles in dozens of trade journals and holds a B.S. degree in chemical engineering from the University of Massachusetts at Lowell.
Josh Capogna is currently the Manager of Advanced Machine Vision Design at McRae Integration. Formerly, he was Manager of Vision Engineering at ATS Automation Tooling Systems and the Associate Director of Engineering at Metrigenix, a manufacturer of MEM's based microarray DNA\RNA assay devices for high throughput pathogen detection. Josh has also been active as a consultant for the development of medical devices. He holds a MASc in Biomedical Engineering and a B Eng in Engineering Physics from McMaster University.