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But before we dive into how AI can be used; what does it truly mean? Artificial Intelligence aims at generating systems that can carry out tasks which would typically need human intelligence; systems that can learn, reason, solve problems, respond to questions and comprehend natural language. Such systems can analyse data, perceive their environments and make decisions – functions typical of human intelligence.

AI with a Human Touch 

Conversation context and intent understanding are among the important areas of AI evolution—just like in human interactions. Imagine asking a question not just to retrieve data but to get an answer that considers the context of the inquiry—this is where AI is going.

Modern AI systems, for instance, have the ability to interpret language subtleties, leading to more meaningful and accurate interactions. In recent years, AI has progressed from merely processing inputs to understanding the intent and history behind those inputs. Earlier on, tools such as ChatGPT could give direct responses based on preprogrammed information alone. Today, they grasp the meaning of questions and provide answers that better align with user requirements. This capability is especially exciting for quality management professionals because having knowledge about contextual flow and history of data means making decisions that can be more accurate and efficient.

AI in Quality Management: Current State, Challenges and the Future

• In our recent webinar poll conducted with life sciences quality and IT professionals, we discovered that 2/3 of the audience were already utilizing AI in their work. AI-enabled Quality Management System (QMS) can revolutionize quality management in the life sciences sector by leveraging advanced capabilities to support high standards of quality and compliance. These systems now offer a wide range of capabilities including:
  
  – Automatically generating text summaries based on quality event records
• – Objectively deciphering complex quality data sets, reducing human bias
• – Reducing duplicate investigations and cycle time for complaint resolutions
• – Quickly identifying potential high-risk events and correlated quality records
• – Verifying CAPA effectiveness to help prevent recurrence of quality events
• – Enhancing adverse trend identification across sites and product lines

However, as we integrate new technologies in quality management, it’s essential to balance the benefits with what’s considered ethical. Data privacy, the correct use of AI, and the transparency of AI systems—often referred to as the “black box” problem—are major concerns. In strictly regulated industries such as life sciences, ensuring that all systems are reliable and trustworthy is crucial. This involves not only validating systems but also continuously monitoring their performance to maintain compliance and mitigate risks.

But we are just at the beginning of integrating AI-driven innovation with quality management. As we get deeper into this integration, one big question is: What’s next?

To learn more about how AI can help with quality management, check out our recent On-demand with Fabrizio Maniglio as he shares his insights, real-world examples, and talks about how AI is making quality management better. Watch Now

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The life sciences industry is facing unprecedented levels of change and transformation. On the bright side, the global biotechnology market surpassed $1 trillion in 2021 and is expected to grow 13.9% annually from 2022 to 2030. The troubling news is that quality and compliance events continue to plague the industry, with 25% to 30% of pharmaceutical manufacturing costs drained by quality issues.

More than 300 drugs and medical devices have been recalled in the past two years. That’s a large cost for the companies doing the recalls, but the cost of poor quality is more than recalled products. There’s also the cost to patients as well as the danger posed to their health and safety. That’s the paramount reason why the life sciences industry must focus on quality; patients depend on it for safe and effective medical products at the right time. Another major cost to the industry is reputational risk. Life sciences companies limit their ability to deliver their innovations and achieve market growth—and improved clinical outcomes—if patients don’t trust their products.

Rise Of Quality 4.0

We can address these challenges by embracing Quality 4.0. It’s an approach that blends traditional quality assurance methods with emerging Industry 4.0 technologies—such as artificial intelligence, big data, automation and IoT—to improve quality, reduce costs, ease compliance and increase the efficiency of quality operations.

Quality 4.0 is driven, in large part, by the growing amount of data being generated along our increasingly complex supply chains and manufacturing operations. Digital transformation is a crucial component of Quality 4.0 because it enables companies to capitalize on their newfound sea of data. The key is to make the data actionable and derive insights to power both human decision-making and real-world automation.

How can life sciences organizations connect the “data dots” to deliver on the promise of Quality 4.0 and realize the value it can bring to their companies, customers and patients? One way is by enabling a connected life sciences ecosystem that provides a closed-loop feedback process of quality, manufacturing, supplier and regulatory data across the product life cycle. This vision rests on three connected elements that all have patients at the center.

The first element is proactive quality, which is enabled by digital, end-to-end management of critical quality processes. Proactive quality involves the harmonization of people, culture, technology and processes to identify and correct issues early on in near real time. It’s about cultivating a proactive—and, ultimately, predictive—mindset inside and outside of your organization.

The second element is intelligent operations. This includes capabilities like batch automation, control and visualization to drive quality and manufacturing excellence in a connected plant. Intelligent operations rely on quality and operational data being accessible and actionable—easy to get, integrate and analyze—all in near real time.

The final piece of the puzzle is an integrated ecosystem. The key here is more secure, end-to-end supply chain visibility from the lab to the patient. This integrated ecosystem is essential for improving product availability and integrity and ensuring patient safety.

Quality 4.0 is a journey, not a destination. Here are three ways to help make it a smooth one.

1. Check Data Readiness

It takes more than technology to realize the value of Quality 4.0. Your data must be ready for digital transformation. Check that it’s accurate, current, complete, concise, relevant, accessible and available. Additionally, make sure your teams understand the why behind your 4.0 initiatives and the behaviors you want to drive, such as improved productivity and the ability to make data-driven decisions.

2. Focus On Outcomes Over Technology

Don’t do digital for digital’s sake. Start with the end in mind. Consider the business and patient outcomes you want to achieve and look for solutions that can provide optionality and interoperability that fit your infrastructure and technology environment. To assess performance against desired outcomes, it’s important to measure key performance indicators (KPIs) based on areas like supplier quality. One example in this area is the supplier risk score, which can help you better manage and evolve your supplier network based on objective qualitative and quantitative factors.

3. Don’t Spread Yourself Too Thin

Analyze your most pressing business problems with a scope that is feasible based on your resources and capabilities. Look for partners that have the depth of domain and capabilities to enable you to hit the ground running. Start small, learn, pivot and scale quickly. Solving big problems matters, but starting with a realistic scope can provide a quick win you can continue to build upon. The key is picking the right problems from the outset and making them both meaningful and manageable.

Quality 4.0 In Action

Keeping up with the pace of change requires effort, but the payoff can be massive. Imagine the possibilities when a business can draw on data from across the company and the ecosystem, deliver aggregate reporting with the press of a button and more.

Almost two-thirds of respondents from a BCG survey say that Quality 4.0 will have a significant impact on their operations by 2024. Respondents identified important manufacturing use cases as predictive quality, machine vision quality control and digital standard operating procedures (SOPs).

We’re seeing this impact today. We worked with a medical device manufacturer that was struggling to manage tens of thousands of complaints each month. It needed to improve efficiency and effectiveness without sacrificing accuracy and compliance, so it turned to Quality 4.0 and AI to automate its complaints management process.

The manufacturer can now predict the reportability of post-market complaints nearly 100% of the time, allowing it to accelerate identification and throughput. This means the team can focus its attention on high-value activities like addressing critical records and effective root-cause analysis, resulting in 48% more high-priority complaints closed.

The value of transformational quality management and its impact on life sciences and patients is significant. It’s a digital race to deliver safe and effective products and services, faster. That’s why now is the time to take the first step toward Quality 4.0.

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The quality management maturity spectrum checklist outlines the required capabilities in six key areas:

1. Policy and Decision Making: What is the level of governance in your quality systems, internal quality oversight and external supplier oversight?
2. People/Talent: Have you established a true culture of quality within your company where employees understand their impact on quality and take steps to improve it?
3. Process Optimization: Have you optimized processes to reduce complexity and variability and drive greater simplicity and standardization? Does quality permeate throughout your organization with the right quality professionals in the right places at the right times with the right knowledge/skills to drive improvements?
4. Integration: Is quality tightly integrated with all other departments/functions, do all stakeholders operate collaboratively within a common, electronic platform and does this integration extend out to external business partners?
5. Data: Do you have a single source of accurate, complete and comprehensive quality data? Have you digitized quality processes and data in order to leverage Industry 4.0 technologies (e.g. AI, ML)?
6. Actionable Business Insights: Can your quality team easily perform advanced analytics? Do you have a balanced scorecard with key performance indicators (KPIs) focused on all quality elements: product quality, safety, efficacy, continuity of supply and compliance? Are your quality KPIs closely aligned with your broader corporate objectives?

When reviewing this assessment, consider your current level of maturity in each of these areas and whether your capabilities align with where you want to be in your quality management evolution.

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