Most of our life science clients have a vision for using Manufacturing 4.0 — the use of “smart” connected devices to gain more complete, accurate and real-time intelligence about production processes, customers and markets. In life sciences, the goals of Manufacturing 4.0 include speeding delivery of treatments to market, improving production yields, increasing quality and ensuring regulatory compliance.
The benefits are real: One of our clients says it has increased revenue by improving intermediate stages in its production processes and establishing tighter closed-loop controls around process variables. Through faster and more complete analysis of data from its connected production equipment, the client also reports faster batch completions and increased production throughput.
But many life sciences manufacturers struggle to balance the long-term investments in skills, technology and processes required for Manufacturing 4.0 with their immediate needs to get drug products to market. In the rush to quickly bring new facilities online, there’s the risk of creating siloes of incompatible process equipment, data and information technology. If this technology and data is replaced later in the program, rather than deploying and connecting it correctly in the first place, it can result in crippling costs and delays.
Evolving While Producing
Through our experience, we’ve identified four ways life sciences organizations can future-proof their technology and data strategies so they can more quickly and efficiently achieve Manufacturing 4.0 benefits while meeting this quarter’s, this month’s or this week’s production demands.
- Carefully define the business case. Investment funds are always scarce, especially for complex, longer term programs. To gain budget buy-in, managers should carefully assess and present the business benefits to senior leadership, and set expectations for fast-failure, rapid deployment and other innovation approaches.
Rather than deploying technology for its own sake, be sure initiatives are linked to prioritized, outcome-driven projects with quantifiable value, such as:
- Increased revenue (more batches produced each year)
- Reduced costs/increased productivity (improved yields or fewer resources needed per batch)
- Improved process robustness or reliability, such as more resilient supply chains, more reliable equipment processes and reduced downtime
- Reduced CapEx by eliminating the purchase of new equipment or parts through preventive maintenance
- Clarify your data requirements. Gleaning new insights from previously unavailable data is at the heart of Manufacturing 4.0. That why it’s important for life sciences companies to clearly identify data requirements with their vendor, in addition to their process, mechanical and functional needs. By designing in this requirement from the start, businesses can avoid the need for added work, additional costs and delays that can result from continually needing to understand the data needed by other systems, such as analytics, enterprise resource planning (ERP) or supply chain platforms. With careful planning, data scientists will be able to deliver the most impactful results most quickly.
Data about equipment performance or reliability is often first on the requirements list. But this priority shouldn’t overshadow data about production processes. This data also needs to be properly formatted so it can be used to maximize yields and quality.
Capture the data with as much context as possible. Answer the question: What was happening in the production process at the moment of data capture? For each data point, this context might include batch information, unit operations details or operator details, as well as the start times or durations of related steps. The more completely decision makers can analyze raw data in context, the better they can act on it to increase yields and quality while cutting costs.
Finally, when setting purchase requirements, make sure everyone understands that data and insights are just as important as the functional attributes of a piece of equipment. Remember, as recently as 10 years ago, the importance of operational shop-floor data was underappreciated. Make it clear how costly it would be to capture and process the required data later. This will go a long way toward encouraging stakeholders to consider data analytics earlier in the purchase cycle.
- Standardize and reuse. By definition, industrial equipment and processes are uniform and consistent, which helps deliver increased output, efficiencies of scale and consistent levels of quality. For this reason, the tools and processes that analyze and act on Manufacturing 4.0 data should be uniform and consistent, as well.
One immediate standardization step is to increase awareness and help shape standards such as the PROFIBUS (Process Field Bus), Fieldbus and Ethernet IP industrial hardware and communication protocols. Doing so will ensure you’re not left with “stranded” equipment that must be replaced as standards change.
When a production facility or process reaches a particularly high level of Manufacturing 4.0 effectiveness, replicate the facility design and processes to other production facilities to multiply the benefits. Consider creating communities of practice, centers of excellence or technical support groups that can work across facilities to define facility designs and best practices and extend them to other production sites.
If you haven’t already done so, select one data analytics platform for common use. This could be either a global platform that all production sites are expected to use, or a segmented approach based on the type of production site or division within a multinational. This platform may include everything from data warehouses and data lakes, to analytics frameworks and the presentation layer that delivers insights to users.
While it might be necessary vary the design or deployment schedule based on local needs, failing to impose some level of standardization will result in added costs of supporting many platforms, overlap among them or the creation of homegrown solutions that are neither scalable nor supportable in the longer term.
Creating a comprehensive data strategy as part of the overall basis of design (BoD) helps ensure that the equipment, networks, information technology (IT) and operational technology (OT) platforms can work together to deliver the information needed to maximize yields and quality. By designing this strategy as part of the BoD, the business can ensure it understands the types of data generated across the manufacturing landscape, and how that data must be managed and analyzed for maximum benefit.
- Standardize on strategic partners. When manufacturers procure equipment from multiple suppliers for various facilities, they’ll need to make design changes from plant to plant, as well as in the software needed to run them. This increases operational complexity, as well as training and support costs.
By standardizing manufacturing process equipment purchases with strategic partners, manufacturers can reduce complexity and cost. In addition, the more that’s purchased from a single vendor, the more likely that needed data will be accessible from their equipment.
Life sciences manufacturers that follow these four best practices can pave the way to Manufacturing 4.0 while meeting this quarter’s (or this month’s or this week’s) urgent challenges.
Cognizant’s Ray Lockard, Life Sciences Manufacturing Senior Manager, and Madhukar Saboo, Associate Director of Business Development for Life Sciences, also contributed to this blog post.
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