Mai 03, 2023

To maintain a competitive advantage and deliver high-quality pharmaceutical products over the long term, careful management of total cost of quality is essential. AI-powered proactive quality management can play an important role in keeping the total cost of quality under control.

Quality management strives to continuously improve product quality and reduce failures by monitoring all areas of the production process. This comes with a cost (of quality) that companies need to invest in to assure product safety, comply with regulatory requirements while maintaining financial performance. According to the America Society for Quality Management, the cost of quality can make up to 15-20% of sales revenue. 
The total cost of quality comprises of four factors: internal and external failures, appraisal costs and preventative costs. Internal failure costs occur with failures that are discovered before the customer receives a product or service. External failure costs occur for failures that are discovered after the customer has received a product or service. Assessment costs are costs associated with determining the degree of conformance to quality requirements, and prevention costs are incurred to avoid quality problems.

Preventing failures requires controlled production processes with supplier audits, audits of production sites, evaluation of raw materials and finished products, as well as an overall assessment of risks and potential impacts on customers. These activities are labor intense, require manual effort and laborious collection of data. 

In addition, manual analysis of quality problems using traditional quality methods (e.g., 5-Why, Fishbone, FMEA) is no longer effective because they are increasingly intertwined with hardware and software. Customization and tailored configuration of products has vastly increased the types of quality issues. This leads to huge and diverse amounts of data with new failure patterns that are difficult to analyze.

Powerful machine learning algorithms can help analyze complex quality data to provide valuable insights for product defect detection, analysis, and prevention. The following application areas can boost your quality cost performance:

• Prevent incidents: Use AI models to identify quality trends and predict potentially high-risk issues before quality problems or product complaints occur.
• Prioritize impact: Use predictive analytics to categorize and prioritize quality issues based on issue types, potential risks, and impact on customers.
• Optimize workflows: Streamline the tracking of quality issues and create digital workflows to prevent recurrence, track corrective actions, and measure effectiveness.
• Automate monitoring: Use advanced robotics and AI image analysis to automatically monitor production processes and inspect and test raw materials all the way to the finished product.

Thanks to the in-depth analysis of quality data and the introduction of advanced manufacturing control technologies, quality costs can be significantly reduced. This allows companies to dedicate valuable resources to proactive quality, drive innovation, and reach the next level of quality maturity.
Feel free to contact us to learn more about the use of AI in quality management, our experts Dr. Human Riahi Asl (Enterprise Quality Management Consultant) and Dr. Matthias Rüdiger (Head of AI) will be pleased to answer your questions.