Smart manufacturing, digital supply chains may help pharma boost value

By Laks Pernenkil, Ph.D., principal and practice leader, Deloitte Consulting LLP

Imagine driving a car with an opaque windshield…and the only way to move forward is by looking backward through the rearview mirror. This is how some biopharmaceutical engineers might feel as they monitor the production of a product using legacy manufacturing processes. The technology might only allow them to view production as it is taking place rather than seeing where it is going. Artificial intelligence (AI) and other smart technologies can use historical and real-time data to predict production challenges and outcomes, which can help human operators make better decisions as the process moves forward.¹

Smart manufacturing combines a range of digital technologies (e.g., AI, robotics, Internet of Things, sensors, data analytics, digital twins). Every process is automated and grounded in value.² This is a departure from the more traditional, simpler, and less costly analog manufacturing model that relies heavily on human labor and human decision-making.³ A smart factory is designed to quickly adapt to schedule and product changes with minimal intervention. However, the transition from legacy manufacturing to smart manufacturing typically requires an organizational and cultural shift from linear thinking to knowledge-rich, complex systems thinking.⁴

Smart manufacturing and digital supply chains

Some biopharmaceutical companies are beginning to transition from legacy manufacturing processes to smart manufacturing and digital supply chains to help maximize efficiencies, improve output, optimize energy consumption, and reduce waste while also enhancing quality and safety of their products. Despite the potential of digital technologies, a survey of more than 100 biopharma executives, conducted by the Deloitte Center for Health Solutions earlier this year, found that some digitized processes have not yet led to anticipated returns on their investments. About half of respondents reported partial improvements in key areas due to digitalization. Specifically, this includes better risk-sensing (50%), enhanced yields (50%), warehouse efficiencies (48%), and cost-effective sourcing (47%). (See Digitized supply chains are essential to biopharma's future.) We have seen some biopharma companies experience challenges in scaling, deploying, expanding, and achieving value from smart manufacturing.

Four drivers of smart manufacturing performance 

Retrofitting existing systems with digital technologies can frustrate end users and business leaders, especially when the updated systems fail to improve day-to-day work processes and add value to the bottom line. Although many companies are incorporating 21^st century technologies, they should not ignore 20^th century manufacturing processes.

Digitizing manufacturing processes could help biopharmaceutical companies meet the changing demands of a market that is still recovering from supply chain disruptions, economic fluctuations, and ongoing workforce shortages related to the COVID-19 pandemic.⁵ We have identified four performance drivers that can help smart manufacturing processes unlock value for biopharmaceutical companies:

• Process performance: Predictive analytics can be used to identify quality and productivity trends. An analysis of process performance can identify human, machine, or environmental causes of quality or productivity issues. Switching to smart manufacturing processes can help reduce scrap rates and lead times, minimize defects and recalls, improve fill rates and yield, and help companies become more productive and competitive.
• Asset performance: Pharmaceutical manufacturers tend to create terabytes of rich data that is often not used to its potential. With sensors and enabling infrastructure in a smart manufacturing setting, manufacturers can digitize analog data. AI and machine learning can be used to sense issues or constraints in real time and either make (or even autonomously take) remediation actions to resolve the constraints as they emerge. Continuous analysis of data can help companies identify areas for improvement, such as reducing downtime and improving capacity.
• Network performance: Optimized processes can help reduce operational variability, create more predictable inventory requirements, and increase product quality while lowering warranty and maintenance costs.
• Human performance: While most biopharma manufacturing processes are automated, legacy processes tend to rely on human intervention. Smart manufacturing can help reduce error rates on the production floor while more effectively distributing talent to improve efficiencies. Technologies such as digital twins and virtual/augmented reality can make it possible for employees to collaborate regardless of location.

Upskilling the workforce

Over the past five years, the demand for digital roles in life sciences has surged. Job postings for data engineers and data scientists has increased by 69% and 16% respectively, according to Deloitte’s analysis of labor market data (see Pharma’s supply chain workforce). Biopharma companies often must contend with a shortage of professionals who have experience with emerging digital technologies. And it can be challenging to retain digital talent. Upskilling might be one way to enhance existing talent.

Combining external staff with Generative AI (GenAI) is another strategy that could help bridge the talent gap, remove inefficiencies, and reduce costs. External contract employees should have experience in smart manufacturing technologies, data analytics, and quality. The combination of skilled contract workers and cutting-edge technology could help organizations remove inefficiencies and reduce costs without the burden of building and managing these capabilities in-house.

Workers across different functions should be involved in discussions related to the transition to smart manufacturing. Company leaders should have a robust understanding of who will be using the technology, their incentives for using it, and what could lead to frustrations. As technology is tested and deployed, and as processes are updated, it is important that the workforce understands the value of the new capabilities. Smart manufacturing should become part of the fabric of the manufacturing plant. Employees might be even more willing to use new technologies if they understand the value.

Conclusion

The transition from legacy manufacturing to smart manufacturing and digital supply chains can present significant opportunities for the biopharmaceutical industry to help enhance value. A smart factory can operate with minimum manual intervention and high reliability via automated workflows and synchronized assets. The result is typically greater yield, uptime, and quality, along with reduced costs and waste. However, integrating these technologies with existing systems can be challenging, often requiring organizational and cultural shifts. Biopharmaceutical companies can benefit from collaborating with experienced partners to help navigate the complexities of smart manufacturing implementation and leverage their experience in managed services for a more successful digital transformation.

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Endnotes:

^{1_{Artificial intelligence discussion paper, Center for Drug Evaluation and Research, US Food & Drug Administration, 2023}}

^{2_{How the digital twin drives smart manufacturing, Automation World, February 3, 2021}}

^{3_{Pharma's smart factory future is now, pharma Manufacturing, November 14, 2023}}

^{4_{Using ecosystems to accelerate smart manufacturing, Deloitte Energy, Resources & Industries, October 13, 2020}}

^{5_{As biotech recovers, venture firms’ preferences appear to shift, Biopharma Dive, June 6, 2024}}

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