Planning audits using Quality 4.0

The use of Quality 4.0 in audit refers to artificial intelligence (AI) methods used to represent, structure and model data. This includes large quantities of unstructured data collected before, during and after the audit process, which is then analysed by machine to give more accurate predictions and inference before reporting the audit findings.

The era of Quality 4.0 came fast, and has led to something of a knowledge gap in the workplace, because of the varying levels of age and expertise of people in the workplace, and more diverse approaches to problem-solving with data-driven IT solutions. This means that audit professionals must keep abreast of key skills and knowledge in data analytics and industry awareness, as well as interpersonal and communication skills.

Planning audits

Planning of audits using Quality 4.0 requires key questions to be asked: what problem are you trying to solve; what data is needed; what data is available, is accuracy or interpretability more important, and what are the types of data required?

During audits, the scope should focus on assessing compliance, and the effectiveness of how risk is managed and helping to drive continuous improvement.

To assess the effectiveness of how risk is managed, we must first look at where risk lies in any process. Things to consider include the complexity of the process and product, the product’s criticality and the process location, its history and how long employees involved have been working on the product.

“Planning of audits using Quality 4.0 requires key questions to be asked: what problem are you trying to solve; what data is needed; what data is available, is accuracy or interpretability more important, and what are the types of data required?”

Divine Ashu, IRCA Lead Auditor and Technical Director at CIAT Consulting in Cameroon

Processing data

During audits, data obtained from computer analysis of robust production chain and processes within the company’s systems can be analysed using Quality 4.0 concepts to determine any deviations from the product’s quality objectives. The data produced by this systematic evaluation should be accurate, complete, and maintain integrity.

So how do we formally integrate risk impact on a company’s product quality into the audit programme?

Key ways include: identifying noncompliance data (that is, any data that does not reflect and align with the objectives of the process); looking at the frequency of occurrence and gravity of impact in the process; letting the risks found dictate the actions taken (ie, the outcome of the evaluation will give the direction of the actions taken to find the solution); establishing a feedback loop in the risk management programme, distributing a risk report to interested parties to raise awareness.

When raw data is collected from the audit process, it is converted into computer language, which tells the computer what to do and how to do it. This comprises low-level, high-level and specialised languages, which can be further categorised by their functions and use.

Doing this means we can model complex non-linear relationships using the data. We can also process large volumes of data and unstructured data, such as text and images, to establish the relationship between linear and structural relation representation, making it easier to interpret.

Once this analysis has been carried out, if there any nonconformities or corrective actions are proposed, these can be performed, offering opportunities for continuous improvement.

The digital opportunities created by Quality 4.0 can help auditors to manage risk throughout an audit, assisting with the audit frequency, sample size and the complexity of the audit plan, through to any corrective action plans, effectiveness verification, special training required, and in assessing the risk management programme.

Summary

Product data gathered during auditing when employing Quality 4.0 methods has an integral role to play in sales, inventory, forecasting, marketing, financial forecasts, and supply chain management. There is no doubt that good quality data is essential to providing excellent customer service, as well as in making operations efficient, ensuring compliance with regulatory requirements, engaging in effective decision-making, and conducting effective strategic business planning.