In today’s rapidly evolving technological landscape, effective management of information technology (IT) projects is paramount for organizational success. However, ensuring sustainability in IT project management remains a persistent challenge. This paper proposes a novel approach centered on data-driven strategies to enhance sustainability and excellence in IT project management. By harnessing the power of data analytics, organizations can derive actionable insights to optimize resource allocation, mitigate risks, and streamline project workflows. The integration of data-driven decision making processes enables project managers to make informed choices, fostering adaptability and resilience in the face of dynamic project environments. This research explores the utilization of IT software project performance metrics and historical project data to fuel analysis of how strategies drive business sustainability. Moreover, this paper examined these dimensions with the help of advanced analytics techniques such as multiple linear regression. By aligning IT project management practices with business sustainability objectives, organizations can enhance project success rates, while minimizing adverse environmental impacts and resource consumption. Prospectively, this kind of research should expand to determining factors for the adoption of data-driven strategies to empower organizations to achieve sustainable IT project management excellence, ensuring long-term competitiveness and value creation in a digitalized world.

Efficient traffic management in urban areas represents a key challenge for modern cities, particularly in the context of sustainable development and reducing negative environmental impacts. This paper explores the application of artificial intelligence (AI) in optimizing urban traffic through a combination of reinforcement learning (RL) and predictive analytics. The focus is on simulating the traffic network in Belgrade (Serbia, Europe), where RL algorithms, such as Deep Q-Learning and Proximal Policy Optimization, are used for dynamic traffic signal control. The model optimized traffic signal operations at intersections with high traffic volumes using real-time data from IoT sensors, computer vision-enabled cameras, third-party mobile usage data and connected vehicles. In addition, implemented predictive analytics leverage time series models (LSTM, ARIMA) and graph neural networks (GNNs) to anticipate traffic congestion and bottlenecks, enabling initiative-taking decision-making. Special attention is given to challenges such as data transmission delays, system scalability, and ethical implications, with proposed solutions including edge computing and distributed RL models. Results of the simulation demonstrate significant advantages of AI application in 370 traffic signal control devices installed in fixed timing systems and adaptive timing signal systems, including an average reduction in waiting times by 33%, resulting in a 16% decrease in greenhouse gas emissions and improved safety in intersections (measured by an average reduction in the number of traffic accidents). A limitation of this paper is that it does not offer a simulation of the system’s adaptability to temporary traffic surges during mass events or severe weather conditions. The key finding is that integrating AI into an urban traffic network that consists of fixed-timing traffic lights represents a sustainable approach to improving urban quality of life in large cities like Belgrade and achieving smart city objectives.

This paper is a result of its author's work, whose goal was to examine the role of globalization in the change of organization models and intensification of its competitiveness, following trade liberalization. In addition, the goal was to define competitive advantages, which the companies have to develop in order to survive in a highly competitive global market. The study has shown that market expansion does not mean that only big business subjects will be able to make a profit. Also, the analysis has shown that there is no correlation between a big market and success of big businesses. In other words, globalization rewards the companies which are innovative and competitive, regardless of the company's size and country of origin.

Innovations are one of the most important factors in the competitiveness of the enterprises in modern business conditions. Innovation is defined as the process of creating new (and differentiated) value for customers in the market, which can create a sustainable competition advantage. The goal of innovation is to create more successful future of the company, but this success does not happen by itself, but a purposeful implementation of the innovation process is required - which requires, in the first place, the planning of all phases and sub-processes.