Amanda Aljinovic

Due to the increasing heterogeneity of consumer needs and preferences, manufacturing companies are forced to expand their product range to maintain market share while avoiding cost increases. However, increasing product variety increases the complexity of assembly systems and complicates planning, design, and production management. The quantification of manufacturing complexity and its impact on key performance indicators remains a subject of debate. To examine the relationship between assembly complexity, assembly line balance, and productivity from an operator-oriented perspective, an empirical complexity indicator for mixed-model assembly workstations is proposed. This indicator is based on experimentally collected data and analysis of working time variability. The proposed indicator is evaluated through controlled experimental case studies conducted in a learning factory environment. The results indicate that the relationship between complexity and productivity is not linear. Instead, within the investigated experimental boundaries, the observed trend suggests a turning point beyond which further increases in complexity are associated with decreased productivity, while line balance continues to improve. This finding suggests that integrating the proposed complexity indicator into production planning and management may support decision-making related to assembly line balancing and complexity management in manual assembly systems.

Modern production systems face the challenges of increasing personalization of products, growing structural complexity, and the need for sustainability. In this context, it is necessary to include the human dimension in the optimization of production processes, especially in line with the principles of Industry 5.0 and the circular economy. In this paper, a complexity index is proposed that integrates the objective characteristics of the product and the subjectively perceived workload of the operator during assembly. The proposed index was used in the assembly line optimization process using linear programming to find a compromise solution between two often-conflicting objectives: maximizing output and minimizing complexity. In the analysis, two approaches to the initial balance of the assembly line were considered—by assembly time and by complexity of work elements—which were used as inputs to the optimization model. The results show that an approach that considers complexity from the operator’s point of view contributes to a more even load distribution but also can lead to higher overall performance. Such an approach confirms the importance of integrating the human factor into optimization processes and thus contributes to the creation of efficient, sustainable, and human-centric production systems of the future.

Today’s economic systems are characterized by overproduction, rapid changes in consumer preferences and the intensive exploitation of natural resources. For this reason, the idea of the circular economy has emerged in recent years as a key strategy for tackling environmental, social and resource problems. At the same time, manufacturers are increasingly trying to fulfill customer requirements, so that products are becoming ever more personalized. This increasing focus on individuality is leading to greater variability in design, while at the same time the complexity of product structures and components is increasing, which poses major challenges for production and assembly processes. Understanding this complexity helps in finding the most effective ways for the disassembly process to enable reuse, repair and high-quality recycling, which are among the key principles of the circular economy. This not only supports environmental and resource sustainability, but also contributes to long-term competitiveness and climate neutrality in manufacturing. This paper outlines how complexity is defined and how this parameter can be used to obtain an optimal solution for minimizing product complexity and maximizing the number of disassembled parts. This problem was modeled using linear programming, where the optimal disassembly sequence was defined taking into account variables and constraints such as the time available within a working day and the complexity of the sub-assemblies. The results showed that the process can be significantly optimized if clear variables and targets are defined.

New trends in industry emphasize green and sustainable production on the one hand and personalized or individualized production on the other hand. Introducing new manufacturing technologies and materials to integrate the customer’s specific requirements into the product, while keeping the focus on environmental footprint, becomes a serious challenge. As a result, new production paradigms are developed to keep up with new trends. The most known Industry 4.0 paradigm is oriented towards new technologies and digitalization. Recently, Industry 5.0 appeared as a supplement to the existing Industry 4.0 paradigm, oriented to sustainability and the worker. A multidisciplinary approach is necessary to address these challenges. The Industry 5.0 paradigm’s main pillars—human centricity, resilience, and sustainability—are also pillars of the multidisciplinary approach used in this research. A human-centric approach includes workforce reskilling and acquiring new technologies to ensure that technology serves to enhance human work, while creating a supportive and inclusive work environment and prioritizing employee engagement and wellbeing. Resilience as a second pillar is related to the ability of manufacturing systems and processes to adapt to changing conditions to remain robust and flexible, and sustainability is an important and long-term requirement of this multidisciplinary approach. Based on the research part of the Erasmus+ ExCurS project, particularly research focused on application and training related to digital twins, an advanced concept of organizational sustainability is presented in this paper. The concept of organizational sustainability is realized through the usage of key digital twin technologies aligned with human-centric approaches. A new prototype of a digital twin that optimizes an assembly system based on a developed algorithm and humanoid decision-making is provided as a proof of concept. The human-centric digital twin for industrial application is presented through a case study of personalized products.

A comprehensive statistical analysis was conducted to investigate the causes and prioritization of failure modes within a production line manufacturing leather covers for automotive interiors. The study was grounded in a Process Failure Mode and Effects Analysis (PFMEA), with a dual emphasis on evaluating the traditional Risk Priority Number (RPN) approach and the more contemporary Action Priority (AP) methodology, which has been increasingly adopted to enhance risk assessment sensitivity. Failure modes were classified and prioritized using both approaches, revealing notable differences in the ranking outcomes. To further elucidate the underlying contributors to these failure modes, causal factors were systematically categorized in accordance with the 5M+1E framework—Man, Machine, Method, Material, Measurement, and Environment—commonly employed in quality and reliability engineering. A cause-and-effect diagram was constructed to visualize the distribution of root causes across these categories. Descriptive statistics and correlation analyses were employed to quantify the relationship between each category and the prioritized failure modes. Particular attention was paid to examining the interdependencies among the core PFMEA parameters—Severity, Occurrence, and Detection—in order to determine their respective contributions to the variability in failure mode rankings. It was found that Severity exerted the most substantial influence on the prioritization outcomes under the AP model, while Occurrence was more dominant when the RPN method was applied. These findings suggest that the choice of prioritization method significantly alters the interpretation of risk and resource allocation for corrective actions. The integration of 5M+1E categorization with PFMEA metrics offers a structured pathway to enhance the diagnostic capability of reliability assessments and improve decision-making in failure prevention strategies. This approach is proposed as a more robust alternative to traditional analysis, enabling more precise targeting of corrective and preventive measures in high-precision manufacturing environments.

The current industry trends increasingly impose the need for personalized products to meet customer needs. Following these trends, manufacturers have to recognize the needs of the customers and adjust their production systems to realize the required products. They also should be able to help customers to define their preferences and product characteristics in the simplest possible way. The interaction between manufacturers and customers should be clear, fast and efficient. To achieve this, the procedure for sustainable product development can be very useful for manufacturers. With this procedure, manufacturers can offer customers an appropriate product that will meet on the one side the needs of the customer and on the other side enable manufacturers to realize the product sustainably and efficiently. Therefore, this paper proposes a procedure for sustainable product development in the Learning factory environment, so that students or other participants can use it and further develop it. The procedure is based on a hybrid way of selecting the appropriate product variant with the help of different decision making methods, thus providing support in the interaction between the customer and the manufacturer, with the aim of mutual satisfaction.