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AI models company of Datrix group
  • 22 April 2022

AI applied to Cost of Quality

AI applied to Cost of Quality

AI applied to Cost of Quality 800 450 3rdPlace

Every industrial context has to face new challenges every day, but the Cost of Quality remains a key indicator for company performance. The goal is to meet customer needs, while reducing costs.

The Cost of Quality (CoQ) takes a relevant part of the company’s total costs and it directly impacts on profits. For this reason it should be carefully measured, analyzed and managed. In detail, the capability of measuring essential KPIs in real time – such as the Supplier Defect Rate (evaluation of raw material) and Customer Complaints and Returns Rate (evaluation of returned products) – is a real competitive advantage.

Cost of Quality stands for about the 30% of the company’s total costs

Typically, most of the costs are absorbed by the Cost of Non-Quality, since failure to solve problems in the first phases of the process leads to much higher costs across the board.

Through AI (Artificial Intelligence), Advanced Analysis and Machine Learning, we can take action in the early stages of the process, drastically reducing the Cost of Quality as a whole.

In the following stages, Artificial Intelligence helps companies to automatically identify and manage product defects in a smart way; at the same time it can perform predictive maintenance on all the machines through all the production chain. This virtuous path allows to reduce waste and product recalls, it minimizes machine downtime, protecting the reputation and profitability of the brand.

Solutions to reduce Prevention Costs

Prevention costs represent the smallest part of the total Cost of Quality, but being the first line of defense for a company, they offer the largest room for improvement through AI.

In the prevention phase, the analysis of information on materials, machines and production processes can help predict the resulting quality output and support root cause analysis, defining and planning effective corrective actions.

Thanks to Machine Learning algorithms we can improve the production process to avoid deviations and minimize the occurrence of non-conformities. Improving prevention measures is the greatest opportunity to prevent poor quality products, thus reducing higher internal and external costs down the line.

Solutions to optimize Control Activities

Appraisal costs include the inspection and testing of raw materials, processed and finished products, plus all the tests performed all along the production process. The entire procedure, which aims to identify defective materials or products, is usually performed manually, therefore it could lead to errors due to sampling.

With the most advanced computer vision techniques, which exploit Artificial Intelligence, we can automate and improve inspection processes both in terms of granularity and accuracy, in all the different processing stages.

Solutions to minimize internal non- conformities

Internal cost occurs when quality defects are detected before the product reaches the customer. Managing production non-conformities generates high costs, including management activities, rework activities, waste and downtime.

Using Machine Learning algorithms, companies can get early warnings and guide the production process so that it does not undergo deviations and minimizes the occurrence of non-conformities both on the machines and on the product.

In particular, predictive maintenance can be used to monitor the health of critical machinery in a smarter way, planning better scheduled and preventive maintenance. Finally, with computer vision algorithms you can support the management and resolution of non-conformities while minimizing costs.

Solutions to minimize external non- conformities

External costs occur when customers discover defects after receiving the product, resulting in higher costs for the company. These can be associated with defective products, but also with poor quality products as a whole.

Thanks to Artificial Intelligence, we help companies better manage devices in the field, we enable predictive maintenance for products, extending their life cycle. Even for external non-conformities that still occur, computer vision algorithms can support their management and resolution minimizing their cost.

In this context, we provide an advanced monitoring solution to measure the Customer Complaints and Returns Rate (evaluation of returned products) and detect trends in the short and long term.

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