UK product recalls are rising and their costs are staggering, though  manufacturers can reduce the risk of recalls with AI and supplier oversight.

The Growing Impact of Product Recalls in the UK

In the last five years, 77% of UK manufacturers have experienced product recalls. Recalls can have severe financial implications, disrupt operations across the organization and can cause significant, lasting brand damage.

The Financial Cost of Recalls

The cost of product recalls in the UK is high: 50% of UK organizations said rectifying the most recent product recall costs between 8 million and 39.9 million pounds (GBP), according to The Pulse of Quality in Manufacturing 2024, a survey by ETQ.

Operational Disruptions and Brand Damage

Along with the high direct cost of product recalls, there are hidden additional costs to consider as a result of the disruption caused by recalls across organizations. Operational disruptions can have severe long-term consequences, including delayed product introductions, decreased customer satisfaction and reputational damage.

In the worst case, product recalls can directly impact workers’ jobs. According to the ETQ survey, 30% of businesses said recalls led to plant shutdowns and 26% said recalls led to layoffs.

Common Causes of Product Recalls in the UK

To prevent future recalls, manufacturers need to examine the leading causes of product recalls within their organizations.

Supplier-Related Issues

Poor supplier quality management and defective parts are major contributors to product recalls. In fact, the survey revealed that 52% of respondents claim that up to one-half of product recalls can be attributed to supplier issues.

There are many reasons why so many organizations are struggling to implement effective supplier oversight. Supply chains are increasingly complex, and in many cases, suppliers are in multiple locations around the globe. This introduces language barriers and time zone differences, which means that different suppliers are typically subject to different regulatory requirements.

Many suppliers also do not provide real-time data, which can make it difficult for manufacturers to figure out delivery schedules. A lack of transparency may cause compliance challenges later on. To complicate matters further, many supply chain departments are understaffed, which can make it difficult to monitor supplier relationships.

Internal Quality Management Failures

Unfortunately, internal mismanagement of quality processes and inadequate quality control systems can lead to higher rates of product defects.

One of the main reasons behind this is that many manufacturers do not establish clear policies and procedures for monitoring quality, and they do not perform frequent inspections. In many cases, quality metrics are vague and poorly defined. That means defects and variations are difficult to identify, which makes them more likely to slip through. When a defect is identified, manufacturers often fail to conduct root cause analysis to find out how the defect occurred.

For some organizations, especially those with a high turnover rate, employees may have received inadequate training. As a result, it’s common for them to use machinery incorrectly or to make errors that impact quality.

Leveraging AI for Recall Prevention

AI can play an integral role in preventing product recalls by predicting potential issues before they arise. This is becoming a key solution for manufacturers aiming to reduce the cost of product recalls in the UK.

Predictive Quality Analytics for Early Detection

Increasingly, manufacturers are using AI-driven predictive quality analytics to monitor production data in real-time. This can help flag potential defects before they result in recalls.

Many organizations are already set up to collect significant amounts of data by continuously monitoring parameters, including temperature, pressure and humidity. This data feeds into AI systems, which are trained to recognize patterns.

AI then analyzes this data and flags any deviations before they cause issues. For instance, this could include early signs of machine wear and tear that may result in defective parts. If necessary, AI can also adjust deviations in the environment before they lead to issues.

Automating Quality Checks and Enhancing Accuracy

Manufacturers can use AI to automate quality management processes and increase both the speed and accuracy of detecting issues.

One of the most effective uses of automated quality checks is in conducting more accurate visual inspections. Computer vision systems can conduct inspections much faster than humans, and they can detect minor defects that are invisible to the human eye. Unlike humans, who can be subjective in their judgments, AI can apply the same criteria to each product to reduce the chance of defects slipping through.

AI can also monitor supplier data to search for trends and anomalies. This can help manufacturers detect potential issues with incoming materials and decide whether further inspections are necessary. AI can also assign scores to suppliers based on the data it has analyzed.

Strengthening Supplier Quality Management

Rigorous supplier quality management is vital to minimizing the risk of recalls. One of the most effective ways for manufacturers to ensure consistent product quality across the organization is to manage supplier relationships effectively.

Establishing Supplier Performance Metrics

One of the most effective ways for manufacturers to monitor supplier quality is to set key performance indicators (KPIs).

KPIs provide a clear set of benchmarks for suppliers to meet. They provide both manufacturers and suppliers with transparent targets to work toward and reduce the risk of miscommunication. This also means that performance reviews are more accurate, as they provide manufacturers with a resource they can use to assess supplier performance objectively.

Manufacturers can choose to monitor many metrics in this way, including defect rate, on-time delivery rate, order accuracy rate, supplier response time and supplier risk rating.

Using AI to Monitor Supplier Quality in Real Time

AI tools allow manufacturers to track supplier performance in real-time by providing increased visibility into potential quality issues, rectifying them before production is affected.

Suppliers can use AI systems to gather data from various sources. These sources include the Internet of Things (IoT), enterprise resource planning (ERP) systems and their own databases. Then, they can send this data to manufacturers.

Manufacturers can use AI-powered dashboards to centralize information from different suppliers and confirm that all materials meet an agreed-upon set of standards and can be used in production.

AI tools can provide a transparent overview of the entire supply chain by tracing raw materials from the very beginning. This can help to maintain compliance and meet sustainability initiatives.

Best Practices for Reducing Recall Risk

Manufacturers can take many steps to reduce the risk of recalls, from using AI to prevent defects from reaching the market to enhancing supplier oversight to focusing heavily on internal process improvements.

Implementing AI-Powered Quality Management Systems

One way that manufacturers can prevent defects from reaching the market is by integrating AI-based systems into their quality management operations.

This involves identifying issues with current quality management systems and setting clear objectives for improvement. AI can integrate with sensors and production lines to collect data about environmental conditions and automate visual inspections.

When identifying defects, AI and machine learning algorithms can automate the root-cause analysis process by analyzing this data to pinpoint patterns and recommend corrective actions.

Building Stronger Supplier Relationships

Open communication with suppliers is essential for addressing quality issues and preventing them from reoccurring later. It helps to build trust and foster joint responsibility, which means suppliers are likely to be more open to working collaboratively when issues do arise.

Using performance data can help manufacturers hold suppliers accountable for product quality and compliance. This approach ensures that evaluations are fair and based on concrete data rather than subjective opinions. Additionally, it helps pinpoint specific issues, allowing for improvements that prevent the same problems from recurring in the future.

Reducing UK Product Recalls Using AI

UK manufacturers can prevent costly recalls by adopting AI solutions across their organizations and improving supplier management practices. Doing so reduces the risk of recurring issues and drives improvements in product quality.