Managers of food businesses make responsible decisions every day. These decisions affect food safety, product quality, economic efficiency and the reputation of the company.
Good decisions are always based on good information. A manager needs to see not only what is happening in the operation, but also the direction in which individual processes are changing. This requires data, continuous data analysis, experience, the ability to recognise connections and consistent thinking.
Artificial intelligence can provide significant support in this area. It helps turn data into useful management information.
During the operation of a food business, data is generated continuously. This may include production data, temperature values, quality control results, complaints, storage information and documentation records. However, these data alone do not always support management effectively.
Data becomes truly valuable when it is transformed into understandable information.
Artificial intelligence may be able to review large amounts of data quickly. It can help identify recurring patterns. It may indicate when a process differs from the usual operation. It can also point out connections that may be difficult to notice in time with the human eye.
This does not mean that artificial intelligence takes over the manager’s decision-making responsibility. Rather, it means that it can provide a better basis for decision-making.
Digital systems are capable of monitoring processes. This allows businesses to respond more quickly to emerging problems.
The working time of an employee is naturally limited. A person cannot monitor data with the same level of attention 24 hours a day, every day of the year.
Artificial intelligence, on the other hand, can operate continuously. It may be able to monitor processes in the background. If it detects a change, it can send a signal to the manager or the quality assurance specialist.
This is particularly important in the food industry. Even a small deviation may lead to a more serious problem if it is not noticed in time. A temperature fluctuation, a recurring technological deviation or an increasing number of complaints can all be signals that require management attention.
Artificial intelligence can help ensure that these signals are not lost in daily operations.
In food safety, prevention is always more important than corrective action after a problem has occurred. One of the greatest opportunities offered by artificial intelligence lies in prediction.
If a company collects and organises its data over a longer period of time, this data can be used for learning. It can become visible under which conditions a particular risk increases. It may also become clear in which processes deviations occur more frequently.
Artificial intelligence can help predict when the current operation differs from previous safe patterns. This gives the company an opportunity to intervene in time.
In this way, the manager does not only react to problems. Instead, they can manage operations with a preventive approach.
A manager often has to make decisions quickly. However, a quick decision is only good if it is also well-founded. For this, accurate information is needed.
Artificial intelligence can help organise the knowledge required for decision-making. It can summarise the key data. It can highlight risk points. It may also present different decision options together with their expected consequences.
This can be a major help for plant managers, quality assurance managers and company executives.
Artificial intelligence is especially useful in situations where a large amount of data must be turned into a clear picture within a short time. This may include preparing for an audit, investigating a complaint or evaluating an internal deviation.
It is important to emphasise that artificial intelligence does not replace human decision-making. In food safety, professional responsibility, experience and legal compliance remain essential.
Artificial intelligence creates real value when it works with good-quality data. It is also important that its results are evaluated by a competent professional. A signal, a summary or a prediction is not a decision in itself. It is only part of the decision-preparation process.
The final decision is still made by the responsible manager.
Dr. András Tóth
