The "Make or Buy" decision and the supplier selection are critical steps for the efficient operation of companies' supply chains. Safety and security are paramount considerations, especially in industries like logistics, where supply chains are vulnerable to external threats and disruptions. In this scientific article, we present a novel Artificial Intelligence (AI)-assisted supplier selection method that significantly enhances the safety and security of suppliers. During our research project, we have created an expert system and a corresponding knowledge base with the relevant rules to support supply chain decision-makers in selecting logistics service providers for warehousing services. The foundation of the AI-assisted supplier selection method is advanced data analytics and the application of expert systems, enabling companies to evaluate potential suppliers in detail from a safety and security perspective. The applied expert systems can identify potential risks and make predictions about supplier performance in the future. In the turbulent environment of the global supply chain, selecting long-term partners like warehousing services providers is critical for the success of the organization. A wrong supplier selection can hardly be reversed in warehousing services, as the cost of change is usually high. The article demonstrates the practical application of the expert system-assisted supplier selection method in a real-world supply chain environment and thoroughly analyzes the achieved results and advantages. The results show that the expert system-assisted method not only increases supplier safety and security but also contributes to improving the efficiency and sustainability of the supply chain. This article provides valuable guidance and solutions for companies looking to enhance their supplier selection using expert system technologies, thereby increasing the safety and security of their supply chains.

The paper aims to highlight the lack of usage of knowledge-based expert systems in purchasing decisions in the context of hybrid corporate reality. We use the transdisciplinary approach in our work, which is essential to examine the problem that occurs in the reality. While reviewing publications containing the keywords “Cognitive bias” and “Supplier selection”, we focused on the methods used. The examined methods in the pooled papers are mainly based on arithmetic and rank the possibilities without considering the available expert knowledge then and there. Afterwards, we propose a solution beyond analyzing the data measured in the past; in addition, the decision-maker, their mental model, and their knowledge is considered. We assume that the effects of cognitive biases are more readily identifiable when using expert systems in considering the decision-maker’s opinions in connection with the actually applied rules in making decisions. In addition to seemingly objective solutions, in our experiment, we propose that by using past cases with known results, complex rules, which are based on the expert’s knowledge, can be simplified without changing the results of decisions in purchasing.