EB23 Inspirationarticle Charlotte Øbakke
Data enablement and digital business models

Find the true cost of poor quality with data enablement

With Charlotte Øbakke, Head of Quality, Europe, NNIT

By analyzing their quality data, pharma companies can find the weak points in their value chain and optimize their processes. Charlotte Øbakke, Head of Quality, Europe at NNIT explains how digital tools and strong data models can be used to improve quality across the business.

While all pharma companies make efforts to ensure the quality of their products, the full impact of quality-related issues can be difficult to determine. How much does it cost to recall a product? What is the total value of products that fail quality control? Have delivery issues affected consumer trust and brand image?

Finding the true cost of quality-related issues not only prevents losses due to waste, regulatory fines and product recalls; it can also enable pharma companies to improve processes that may not be problematic but are still sub-optimal.

Essentially, the objective should be to ensure that quality management is integrated across the entire organization and that all employees understand how it impacts the company’s reputation, product quality, patient safety and the ability to compete. Data from Gartner shows that employees make 85 percent more mistakes on average in companies with weak quality cultures, compared to companies with a strong quality culture.

– By using a modern quality management system (QMS), data analytics and strong data models, pharma companies can gain a better understanding of the true cost of poor quality, improve quality culture and implement preventive actions and predictive maintenance to reduce costs over time. Ultimately, this can help improve the company's bottom line, while also ensuring the quality of its products, says Charlotte Øbakke, Head of Quality, Europe at NNIT.

Recurring patterns reveal the root cause of issues

A good place to begin when examining the cost of quality-related issues is a comprehensive analysis of existing quality data. This allows pharma companies to identify recurring patterns that might be related to critical issues. A solid and reliable data model, coupled with robust data governance is an important prerequisite, as it ensures that the data used for the analysis are reliable and up to date.

– You have to look for patterns across data in your quality management system (QMS) to anticipate future results and take proactive measures to prevent errors and failures. For instance, analyzing your QMS data can expose underperforming processes and forecast future non-compliance issues, audit findings, production complications, and other concerns. To identify these patterns, you need a good data model and the ability to link data from your QMS to your business operating framework, says Charlotte Øbakke and continues:

– By analyzing data across your QMS, predictive maintenance algorithms can identify when certain steps in your process are likely to fail, and maintenance can be scheduled before the failure occurs. This approach can help to minimize unplanned downtime, reduce costs, and extend the life of equipment and machines, for example. But it requires a process-oriented approach. If you have not linked those processes to your QMS, you will miss the insights.

Improve quality through the use of AI

Once you have identified the root causes and determined the financial impact, it is easier to prioritize which measures will yield the best return on investment. And there are multiple ways pharma companies can utilize digitalization and data enablement to improve their business processes.

One example is using advances in Natural Language Processing (NLP) and Artificial Intelligence (AI) to analyze and interact with large volumes of unstructured data, such as deviation reports, batch records, and customer complaints, and extract meaningful insights and patterns. This can help companies identify potential issues and root causes of problems and establish mitigation and corrective actions.

Another example could be to analyze historical audit and inspection data along with other relevant data sources, and thereby enable organizations to identify and prioritize high-risk zones in operations, where they can focus their efforts on reducing non-compliance risk and improving their inspection readiness.

Overall, the right technology can help companies get better insight into their weak points and take the right actions to achieve business excellence.

Run on-demand training and remote audits with Mixed Reality

Training and learning management can be another root cause of quality issues. Here, multiple digital tools are available that can be used to improve learning outcomes and create a right-first-time culture.

One of the most promising examples is Mixed Reality, which is often associated with the HoloLens 2 device that makes it possible for a person to see and interact with digital elements in the real world. For example, trainees can use Mixed Reality to practice manufacturing processes, troubleshoot equipment malfunctions, or perform quality control checks.

In addition, training can also be conducted using Virtual Reality (where you interact with a fully digital environment via VR Headsets) and Augmented Reality (where you interact with digital content placed in the real world via a flat screen on a phone or tablet). All three technologies have different training applications that can improve learning experience and outcome, but it is important to use them for the right use-cases to reap the full benefit of these technologies.

– We have seen a large number of use cases for pharma training using Mixed Reality. It provides trainees with realistic, engaging learning experiences that improve knowledge retention and create a right-first-time culture by doing the training interactively instead of just read & understand. It can also be used for audits and other remote inspections, which saves time and reduce cost for both pharma companies and regulators. The FDA has already issued a draft guidance for remote regulatory assessment, so this is definitely an area that has not yet reached its full potential, says Charlotte Øbakke.

Mixed Reality can also facilitate remote training by enabling trainees using Mixed Reality headsets to participate in live, interactive training sessions with trainers and other trainees, getting the “in person view” of the trainer, with new and better insights. This also helps pharma companies reduce their environmental impact, as it eliminates the need for travel.

Read more in this article about how to link your business systems with the newest technologies and maximize the effectiveness of your learning environment.

Want to know more about how your organization can work with data enablement, digital business models and processes, and new agendas?

Read the other articles where NNIT experts share their concrete experience and advice for different aspects of the life sciences industry.

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