Research Question: This paper presents a simulation model aimed at improving total adequacy calculation for performance indicators of the first phase of production management. Motivation: Defining a set of adequate performance indicators that are specific and highly important for the observed planning phase has a significant impact on the effectiveness of the production process management. The result of the developed model is the value of selected performance indicators adequacy. Consequently, it can be concluded whether defined performance indicators are essential for the observed phase or other performance indicators should be selected. The monitoring of defined performance indicators should provide complete information about the observed phase and improvement of the planning phase management. Idea: The main idea of this paper is to develop a user-friendly simulation model, in accordance with the basic principles of spreadsheet engineering, for improving the selection of adequate performance indicators. This model is intended for use in Small and Medium Enterprises (SMEs) for the improvement of production management. Data: The data used in the paper are grounded on the scientific articles, reviews and empirical studies, related to the performance and performance indicators in production management. The set of most adequate performance indicators based on a relevant literature review and used in the model, represents performance indicators for the planning phase that are the most suitable for SMEs. Tools: The simulation model is developed in a spreadsheet environment. The use of spreadsheet applications enables simple data entry, processing, editing, analyzing and output reports compiling. Findings: Simulation results show the overall efficiency of the developed model in the determination of adequate performance indicators. They influence overall adequacy of performance indicators on the observed phase. Contribution: The main contribution of this paper lies in the model developed to maximize the adequacy of the performance indicators, suitable for use in the production management of SMEs.

Introduction/Objective. The purpose of the article is to analyze the efficiency of primary health care centers (PHCCs) in the city of Belgrade, using key performance indicators (KPIs). The main objective is to present the potentiality of the application of KPIs for improving primary health care services, in order to increase efficiency. Methods. As a tool for measuring the efficiency of PHCCs in Belgrade, this article defines a set of KPIs. Based on defined KPIs, a comparative analysis of PHCCs? efficiency is conducted. Results. According to the values of the overall average efficiency rating according to all observed KPIs, the best-rated, i.e., the most efficient PHCC in Belgrade is Rakovica, and the lowest, i.e., the least efficient is the PHCC Zvezdara. It was noticed that the PHCCs Novi Beograd and Vracar are among the least efficient. Conclusion. The efficiency of primary health care can be measured by applying KPIs, and the observed results can be used as a basis for increasing the efficiency of health care services in the PHCCs in Belgrade. Based on the results, recommendations to PHCCs to improve the efficiency of health care services are: appropriate distribution of patients to selected physicians, measuring patient satisfaction, improving internal processes by engaging professional managers, increasing the ability and opportunities to apply new technologies and new knowledge, increasing the accuracy of the data used for detailed analyzes, motivate physicians to raise the level of awareness of their patients about the importance of preventive examinations.

The purpose of this paper is to analyze the efficiency of healthcare services in healthcare institutions in the years before and after the pandemic COVID-19 by application of Key Performance Indicators (KPIs). The aim of this paper is to show the possibilities of improving the efficiency of healthcare services in healthcare institutions by applying KPIs. For the comparative analysis, 12 KPIs divided into six groups are defined, monitored, measured and compared, as a tool applied to the analysis of specific data of one of the largest healthcare institutions in the Republic of Serbia, Clinical Hospital Center “Dr Dragiša Mišović - Dedinje” in Belgrade, from six-month reports for the years before and after the pandemic COVID-19, i.e. 2019 and 2022. The results observed from the comparative analysis indicate that in the post-COVID year, at the institution level, 11 applied KPIs decreased in value, and only one KPI had an imperceptible value increase. The results of the conducted analysis can be applied to increase the efficiency of healthcare services in healthcare institutions. Recommendations for improving the efficiency of healthcare services are: Reduction of waiting lists for specialist examinations and operations created during the pandemic COVID-19; Online consultations by application of digital technologies; Coordination of activities, employees and all types of examinations; Alignment of available material and non-material resources and providing timely interventions; Capacity adjustment of the specialist examinations to the number of post-COVID patients; Additional training of employees, especially those who started working in the COVID regime.

Industry 4.0 presents a revolution in the manufacturing sector by integrating advanced technologies such as the Internet of Things, artificial intelligence, augmented reality and others. This integration sets the foundation for autonomous, effective and highly efficient manufacturing processes, increasing the levels of digitization and automation. The purpose of this paper is to present examples of the implementation of Industry 4.0 in manufacturing companies. The aim of this paper is to analyze the key aspects of digital transformation through Industry 4.0 and present concrete examples of its implementation in manufacturing companies in the Republic of Serbia. The intention of this paper is to indicate the importance of the implementation of Industry 4.0, identify key challenges in the development of Industry 4.0 and define recommendations for improving production management in manufacturing companies, based on examples of Industry 4.0 application from the practice. Industry 4.0 creates an intelligent production environment with greater efficiency, effectiveness and adaptability.

The main objective of this paper is to identify key determinants that impact the intention to adopt the most widely used digitalization and automation technologies in manufacturing companies—namely, the Internet of Things, Artificial Intelligence, and Big Data. An Expanded Unified Theory of Acceptance and Use of Technology (UTAUT) model was used, based on which tailored strategies for specific technology adoption can be defined. The data were collected through a survey conducted in manufacturing companies in the Republic of Serbia and analyzed using Structural Equation Modeling. The results show that the main drivers of intention to use these three technologies are performance expectancy, facilitating conditions, and the current level of digitalization and automation. All three determinants exhibit statistically significant and positive effects on adoption intentions. The main findings are valuable for decision-makers and practitioners, offering insights into the factors that enable technology adoption and providing support for the development of effective implementation strategies based on the key determinants. Also, these results are relevant for other emerging and post-transitional economies in the Balkans and Eastern Europe, as they face similar infrastructural, institutional and digital challenges, as well as similar levels of industrialization and technological development.