Practical Strategies for Digitization and Integration in Medtech Manufacturing

Data science continues to evolve to support increasing demands for predictive insights and dynamic systems. The innovation in this field is welcomed and is changing all industry sectors, including medtech manufacturing. Forward-thinking companies are mindful of the need to develop capabilities to compete in a digital economy. That doesn’t mean it is always clear how to adopt these innovations—or easy, for that matter. It is essential for organizations to outline a digital adoption roadmap to achieve their digitization goals and maintain future relevance.

This article will provide a model for digital maturity that manufacturers can compare themselves against, as well as outline some of the current trends with digitization in medical device manufacturing. It will include some practical strategies to increase digital capabilities and some real use cases to illustrate how medtech manufacturers can realize tangible benefits from digitization, specifically through integration with innovative software and manufacturing tools.

Moving Up the Manufacturing Digital Maturity Spectrum

Digital maturity is a state where companies adopt the technologies that enable them to establish a seamlessly connected, optimized, and error-free manufacturing operation. It can also describe the attitude behind said adoption as companies make their way toward full digital maturity. A digital maturity spectrum that encompasses four main tiers can help manufacturers determine where they are in their digital transformation journey and where they need to be, as well as motivate continued progress.

1. Manual manufacturing systems consist primarily of highly manual processes and use paper to manage them. Required documentation is physical, narrative-based, and must be stored. It can be nice to follow familiar workflows, but paper-based operations often lead to good documentation practice (GDP) and human errors that digital systems eliminate.
2. Digital manufacturing systems incorporate digital data capture and maybe some degree of automation. They are a vast improvement over paper-based systems and establish a solid foundation for progressive digital maturity because they can enforce data integrity. For example, operators completing a form cannot fat-finger an ID that must have five digits or leave a field blank—simple things that paper cannot do. The degree of digitization and connectivity within or without the system can limit its efficiency and scalability. Any remaining challenges with siloed production data, offline disconnects, and human errors can be resolved with a truly connected system.
3. Connected manufacturing systems offer the most promising outcomes because they incorporate robust applications that are currently available. Enterprise (or platform) software solutions fit here and make it possible for stakeholders and operators to share real-time information across their organization and coordinate simultaneous efforts. Connected systems can leverage integration capabilities to further expand functionality and operations. With cloud-based electronic batch records (EBRs), for example, operators can combine production data with shop floor data and make truly informed decisions in real time. All of this data is likewise accessible to quality personnel. It is important for organizations to take a value-driven approach over a tech-driven one to successfully move beyond the “pilot” phase of digitization into connectivity.¹ This requires buy-in from all stakeholders and careful planning.
4. Intelligent manufacturing systems can be built upon connected ones with embedded intelligent code. Truly intelligent systems are relatively few and take time to build but are quickly becoming a priority for leading manufacturers because of the deep analytical and predictive insights they can offer, as well as highly responsive automation. These systems are ideal for increasingly popular precision products but remain largely inaccessible for digitally immature companies.

Competitive Digitization in Medtech Manufacturing

According to McKinsey, it is notoriously difficult for manufacturers to scale up digitization efforts to take advantage of Industry 4.0, but those that do are able to outpace competitors and see irresistible returns. By successfully implementing advanced technology, “it is not uncommon to see 30%-50% reductions in machine downtime, 10%-30% increases in throughput, 15%-30% improvements in labor productivity, and 85% more accurate forecasting."²

One recent study focused on the current levels of digitization in life sciences manufacturing cited that most manufacturers have achieved some level of digitization.³ For example, over 50% of manufacturers claim they are using a manufacturing execution system (MES)/digital manufacturing solution in some instance. However, when taken in aggregate, most manufacturing lines and facilities remain manual or disconnected. As if to add insult to injury, while 65% of manufacturers are striving to achieve a more competitive (i.e., connected) level of digitization over the next five years, the majority of manufacturers situate themselves well behind their peers when self-assessing their current level.

Delaying Digitization in Medtech Manufacturing

The implications for manufacturers that remain manual or disconnected are these:

• They will continue to struggle to optimize day-to-day operations and move beyond them to reach competitive levels.
• They will miss out on the advantages of data-rich and data-connected systems and other efficiencies of digitization—including automation, integration, and laying adequate foundations for intelligent systems.
• By refusing to level the playing field when connected digital solutions are still affordable and in stride with current technology trends, they risk taking themselves out of the game entirely. The time is right, but times will change.

Practical Steps to Jump-Start the Digital Transformation Journey

While the thought of digitization in manufacturing operations may feel overwhelming, the reality is that many companies have found practical strategies to move forward. In fact, there are several simple steps that companies can take to dip their toes in.

Start Collecting Equiment-Specific Data

An easy way to start optimizing daily operations is to select one class of equipment in a facility and automatically collect relevant equipment-specific data4 that informs operators about the reliability and reproducibility of experimental results. For many companies, IoT sensors are a cost-effective way to gather this information and connect processes. For example, balances are a critical piece of equipment used to gather mass data throughout workflows and collecting information from them during the manufacturing process improve efficiency and prevent lost time and material. Equipment monitoring and alerting by leveraging IoT devices has been shown to increase reproducibility, while also protecting against losses.

Implement an EBR Application

Another way to start digitizing is with a purpose-built EBR software application designed to automate production processes and maintain compliant production records. EBRs reduce documentation burdens and can signal operators when values are out of range and adjustments need to be made. EBRs increase “right-first-time” metrics and can yield high-quality products much more rapidly.

Integrate Equiment-Specific Data with an EBR Application

Now it’s time to put steps one and two together by implementing an EBR application that can automatically capture values from manufacturing equipment. (See Image 2.) This significantly shortens production cycles, not to mention eliminates manual data entry errors that occur when an operator transcribes data values from equipment into EBR software. Imagine the time and money saved by knowing a piece of equipment is down (viewable on the same screen as an EBR) before even starting a workflow. Quality teams can also access electronic data needed for audits and validation across the product life cycle. This approach has resulted in significant time savings and can reduce the number of ruined batches (valued anywhere from $20,000 - $2.5 million and up).⁵

Leverage Unified Data

After automatically collecting data from equipment and populating EBRs, you can now examine the data from the equipment and EBR together. Form a committee to evaluate the data and determine gaps or areas where process improvements can be made. If a manufacturing team isn’t ready for truly intelligent digitization, this is a great intermediate step to gain insights and adjust. Include key team members in this process to get essential buy-in and drive further digitization efforts.

What to Expect from Digitization Efforts in Manufacturing

After completing the initial digitization and integration steps outlined above, medtech manufacturers will achieve greater speeds and reduce errors. This is only the beginning, however. Digitization opens a new world of production insights and business intelligence that is not readily knowable in manual systems. Manufacturing teams can use these insights to optimize their processes and innovate—ultimately with more ease and lower costs. These teams will become the leaders who drive operational excellence and forge the frontier of digital economies.

One final thought: While reaching the intelligent manufacturing stage of digital maturity may still seem far away, it is important to keep it in sight. There are exciting developments in artificial intelligence (AI), including natural language processing (NLP) and machine learning (ML) that are unlocking new capabilities and helping teams make real-time data-driven decisions. The only thing holding manufacturers back is choosing to digitize and connect their data first. All the more reason to get started sooner rather than later. Good luck in your digital transformation journey! Just getting started is the key.

References
¹ “Capturing the true value of Industry 4.0,” Ewelina Gregolinska, Rehana Khanam, Frédéric Lefort, and Prashanth Parthasarathy, McKinsey, April 13, 2022.
² Supra note 1.
³ “Research Report: The State of Digital Maturity in Pharma and Medtech Manufacturing,” MasterControl, 2023.
⁴ “How Operational Metadata Unlocks Mystery and Hastens Discovery,” Sal Savo, Elemental Machines, December 22, 2022.
⁵ “Operationalizing Batch Analytics,” By Emily Johnston and Synjen Marrocco, SeeQ, November 8, 2033.

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Brian Curran has more than 25 years of experience leading the direction for enterprise and entrepreneurial software development companies. He joined MasterControl in 2002 to direct MasterControl's product development efforts. Brian began his career with IBM, where he spent five years leading consulting engagements with Fortune 500 companies such as MCI, BellSouth, and many others. After this, he held several director and vice president-level product management and marketing positions for entrepreneurial software development companies in the telecommunications and web analytics sector.

Salvatore (Sal) Savo is the CTO of Elemental Machines and co-founder of TetraScience. He has more than 8 years of experience digitizing the life science industry using IoT. Sal obtained a B.S. and an M.S. degree in Electrical Engineering. Prior to co-founding TetraScience, he completed an international Ph.D. program in Photonics and Metamaterials and spent five years working as a scientist in several top-notch academic institutions, like Harvard University, Boston University, and Southampton University in the U.K. During his tenure in academia, Sal published several highly cited papers on high impact journals like Nature Materials and Advanced Optical Materials. Prior to that, Sal also worked as a process data analyst engineer for the world leader in the semiconductor industry Micron Technology.