The specificity of the business of agro-food companies is that their products have little or no impact on the environment. However, environmental pollution of these products is caused by the use of packaging. Therefore, it is necessary to apply the principles of the circular economy in the business of companies. Applying green packaging that has little or no impact on the environment helps in preserving the environment. Companies usually purchase packaging from suppliers and therefore, it is necessary to choose the right supplier from which to purchase green packaging to support the implementation of the circular economy. The aim of this research is to select a green packaging supplier for company X in order to influence the development of a circular economy in the company’s business. Based on this, the following research question is considered in this paper: how can the selection of a green packaging supplier influence the implementation of a circular economy at company X? The research covers ten criteria used in this selection, with which eight suppliers were observed. Because every decision-making process in the economy is characterized by risk and insecurity that affects the uncertainty in decision-making, an intuitionistic fuzzy set (IFS) was used. Determining the importance of weights was performed directly based on the ratings of the decision-maker (DM) and the steps of the SiWeC (Simple Weight Calculation) method, as well as using the Entropy method. The compromise results of these methods showed that the most important criteria for assessing the life cycle of packaging are transparency and ethics in business. The ranking of suppliers was carried out using the TOPSIS (Technique for Order of Preference by Similarity to Ideal Solution) method and its results showed that supplier 5 is the first choice for establishing long-term cooperation in the procurement of green packaging.

The increasing adoption of servitization has led many manufacturing companies to operate simultaneously in manufacturing and service activities, creating dual-sector business models characterized by heightened organizational complexity. Although prior research acknowledges that both internal capabilities and contextual conditions shape organizational outcomes, fewer studies have examined these variables within the same empirical model in companies operating under both manufacturing and service logics. Drawing on the resource-based view and contingency theory, this study examines the effects of organizational culture, organizational commitment, knowledge management, environmental uncertainty, and employee retention on organizational performance in dual-sector companies, while also assessing whether these relationships vary by company size. Survey data were collected from 433 employees working in dual-sector companies and were analyzed using confirmatory factor analysis, covariance-based structural equation modeling, and supplementary hierarchical regression analysis. The findings indicate that environmental uncertainty and employee retention did not receive empirical support as independent direct predictors in the structural model. Organizational commitment, knowledge management, and two dimensions of organizational culture—consistency and adaptability—are significant positive predictors of perceived organizational performance. The moderation analysis does not provide strong evidence that company size changes these relationships, although the interaction suggests that environmental uncertainty may be more consequential in large firms. This study contributes to research on servitization by showing that, in dual-sector companies, performance is most strongly associated with internal capabilities that support coordination, shared meaning, and knowledge integration across manufacturing and service activities. For managers, the results highlight the importance of strengthening commitment, adaptive coordination, and cross-domain knowledge processes rather than relying on retention efforts alone.

This paper explores the importance of integrating risk into financial models to improve the decision-making process. Through the analysis of various methods such as Monte Carlo simulations, stress testing and scenario modeling, the paper shows how the integration of risks enables more accurate forecasts and better management of uncertainties. Examples from practice, such as the experiences of global financial institutions and corporations, illustrate how by applying these techniques companies can reduce losses and optimize financial performance. The paper further analyzes the challenges and limitations that arise when implementing these models. In conclusion, it is emphasized that the integration of risks is crucial for making sustainable and informed financial decisions in the modern business environment.

Predictions suggest that artificial intelligence will lead to the reduction of 300 million jobs in Europe and the US. Accountants are professionals who are most exposed to the potential impact of artificial intelligence on their work. Does this also mean the end for experts in financial modelling? Keeping this in mind, how can those of us involved in financial modelling ensure that we remain relevant in this constantly changing world? The answer lies in those who learn and adapt to changes will continue to thrive. Artificial intelligence brings advantages, not just threats; let's leverage them. How can artificial intelligence help in finance and financial modelling? By helping us understand the vast amounts of data we encounter every day and uncovering connections between them. This information will help us improve our business processes, facilitate decision-making, and enhance forecasting. Companies should leverage artificial intelligence to improve competitiveness, mitigate differences caused by human errors, and automate repetitive tasks as much as possible. Through our work, we will attempt to showcase use cases of artificial intelligence in financial operations and financial markets. We will try to demonstrate the benefits of artificial intelligence for financial modelling and forecasting as an efficient way to gain advantages in this field by processing complex tasks that ordinary people cannot perform and automating them. The automation process is essentially the key advantage of AI for financial modelling.