Maintenance management plays a key role in many industries, as maintenance determines the availability of systems, influences their lifespan, impacts customer satisfaction, and as a result affects overall investment profitability. In this context, the aviation industry seeks models to improve efficiency. Researchers seek to provide conceptual models that help to shape the industry’s operations. Spare parts management plays a fundamental role in aviation, considering the predominance of planned maintenance. In this study, we analyzed the impact of the distribution network design for spare parts management and the fixed and dynamic planned maintenance intervals on the overall efficiency of an aircraft fleet. We present a conceptual model considering a variety of topics, such as distribution network design, that have been managed to a limited extent based on maintenance management. A simulation model was developed by applying the conceptual model for the aviation industry considering an aircraft fleet over its whole life cycle. The simulation model provides results concerning the impact of the distribution network, maintenance intervals, and other key factors on the efficiency of the aircraft fleet. The simulation enables a comparison of different distribution networks and maintenance strategies to decide which of them is the best fit for each spare part. The approach we propose enables companies and managers to make decisions informed by a centralized tool with all the relevant factors concerning the maintenance management of an aircraft fleet over its life cycle. As a result, managers are provided with a conceptual and simulation model for the assessment of future what-if scenarios based on aggregated databases from multiple sources without delays and with a dynamic vision of the relevant relationships between factors.

Forecasting is the basis for planning. Good planning is based on a good prediction of what is going to happen to prepare a company, a department, and their environments for certain future developments and their intermediate states. In this context, resources are allocated to these future states in the most efficient way, given a certain set of resource conditions. Although market volatility demands the high adaptability of companies’ operations, dynamic planning is still not widespread. As a result, the alignment of planning processes with potential scenarios is not given, leading to a lack of solution preparation in the long term, suboptimal decision-making in the medium term, and corrective measures in the short term, with higher costs and a lower service level. Therefore, the aim of this research is to propose a predictive approach that will help managers develop sales and operations planning (S&OP) with higher accuracy and stability. For this purpose, a methodology combining demand scenarios, statistical analysis of the demand, forecasting techniques, random number generation, and system dynamics was developed. The goal of this predictive S&OP is to predict the supply chain system’s behavior to generate plans that prevent potential inefficiencies, thereby avoiding corrective measures. In addition, to assess the methodology, the model is applied in the software Vensim, for an automotive producer´s supply chain, to compare the predictive S&OP model with a classical approach. The results show that the proposed predictive approach can increase a manufacturer’s efficiency by increasing its adaptability through the identification of potential inefficiencies and can also be used to prepare solutions.

Matching supply capacity and customer demand is challenging for companies. Practitioners often fail due to a lack of information or delays in the decision-making process. Moreover, researchers fail to holistically consider demand patterns and their dynamics over time. Thus, the aim of this study is to propose a holistic approach for manufacturing organizations to change or manage their capacity. The viable system model was applied in this study. The focus of the research is the clustering of manufacturing and assembly companies. The goal of the developed capacity management model is to be able to react to all potential demand scenarios by making decisions regarding labor and correct investments and in the right moment based on the needed information. To ensure this, demand data series are analyzed enabling autonomous decision-making. In conclusion, the proposed approach enables companies to have internal mechanisms to increase their adaptability and reactivity to customer demands. In order to prove the conceptual model, a simulation of an automotive plant case study was performed, comparing it to classical approaches.

The current global market situation pursues high adaptability, but why? Complexity due to mass customization is greater than ever. Globalization is no longer a theory but a fact that makes disruptions in the globalized supply chain a major risk for operations. In this context, customers demand novelty and unique experiences. These are the main drivers for market success. Therefore, existing products are now in continuous states of change with shortened product lifecycles. The purpose of this article is to analyze the impact of new market entries and product changes along the lifecycle as well as supply chain disruptions in supplier inventory levels. The goal is to minimize costs by achieving a given service level with a market-oriented procurement planning model. The model pursues minimizing the time needed to align the system with the market and, therefore, the adaptability of the system. The research compares classical inventory management models with the new proposed approach by means of simulation with the system dynamics methodology. The results show how the proposed model increased the delivery service level, reduced inventory costs, and increased the utilization of resources due to lower demand uncertainty. Therefore, the developed model is able to plan the inventory supply with a low risk of stock outages. The conclusion proposes a differentiated forecasting and inventory strategy depending on the product lifecycle stage. The developed market-oriented procurement planning model provides guidance for inventory managers regarding how to optimize their operations as an opportunity within the fourth industrial revolution to develop information technology (IT) systems to gather and utilize demand and inventory data with real-time efficiency.

At the moment, many engineer-to-order manufacturers are under pressure, the overcapacity in many sectors erodes prices and many companies, especially in Europe have gone into recent years in bankruptcy. Due to the increasing competition as well as the new customer requirements, the internal processes of an ETO company play an essential role in order to achieve a unique selling proposition (USP). Therefore this paper exposes how the production planning and control of an engineer-to-order manufacturer can be designed in order to increase its OTD (order-to-delivery) rate as well as decrease the WIP (work-in-progress) and the production lead times. To prove the optimized planning logic, it was applied in a simulation case study and based on the results; the conclusions about its potential are derived.

Many improvement techniques, methods, and technologies have been the driver of the development of supply chain systems. However, many managers and companies are focused only on new technologies without considering a comprehensive evaluation, and therefore lacking a real need and purpose. As a result, practitioners are often confused with regard of how to integrate improvement strategies and new technologies, as well as how to evaluate their convenience. Thus, this research aims to develop a model for the assessment for each manufacturing capability. This assessment aims to enable a continuous business transformation aligned with organizational goals; thus, a dynamic maturity assessment is chosen. Based on this, the study seeks to provide an integration model for relevant improvement strategies and new technologies that can be applied to any organization. As a result, the paper develops a sequence model, the GUVEI-Model, for the application of Industry 4.0 related technologies for continuous improvement in five different clusters. Furthermore, the research develops an evaluation scheme of optimization alternatives. Based on this conceptual development, a simulation model is built for specific use cases, such as additive manufacturing or virtual reality. The results show how the use cases along the GUVEI-Model application improve relevant indicators significantly, with the first two steps, obtaining and using data, acting as enablers of the three subsequent optimization steps that allow the virtualization, expansion, and improvement of capabilities and a higher impact on the target indicators than the first two steps. Finally, a discussion is presented about the utility of digital twin models for dynamic maturity level assessment and for simulating project improvement impacts before, during, and after their implementation.

Vertical mobility, as a commercial service, has been strongly focused on the scheduled volume and long-distance mobility services. Thus, limiting its potential coverage, flexibility, and adaptability with high investments and centralized mobility hubs, called airports. In this context, a customized and on-demand air mobility concept providing high flexibility in location combinations and time schedules appears as an unexplored challenge for regional mobility needs. As a result, the aim of this research is to provide a generic framework for various mobility means as well as to design a holistic air mobility management concept for electric vertical mobility for profitable and sustainable operations by providing a service to society. A system dynamics simulation case study applies the conceptual model for an on-demand air mobility network of electric aircrafts in a regional area considering capacity constraints in vertiports, aircrafts, charging, and parking stations. Thus, bottlenecks and delays can be quantified by using a digital twin tool for customized scenarios. Simulation results show how an optimal maintenance management and redistribution of aircraft units improve service indicators in passenger quantity and customer order lead time as well as reduce aircraft on ground time. As a result, a digital twin air mobility network model with simulation capabilities is a key factor for successful operations.

Over time, the satisfaction of needs and the ability to meet them have consistently increased. However, the world of the 21st century is one in which the basic needs of millions of human beings are still not satisfied. Why? To an extent, nonprofit organizations such as charities play essential roles in the needed improvement of this situation. In this regard, the human factor within an organization is key influence in organizational performance and societal impact. Human beings within organizations make decisions based on their own motives, so the ethical values of each person are significantly important. Therefore, it is necessary to use analyze the potential of the human factor in the fourth industrial revolution and to analyze its influence in the previous industrial revolutions. This research was aimed to conduct such analyses for a nonprofit charity. Moreover, the authors of this paper also analyzed the industrial revolution potentials of the charity case study using system dynamics. The relevance of the presented paper was ensured by the aforementioned combination of topics. The results showed how greater impacts, higher expenses, and higher stocks were not necessarily able to quantitatively satisfy food needs in a timely manner if the human factor and global effectiveness and efficiency were not optimized. When these aspects were optimized, our hypothesis was proven, as the models set for further industrial revolutions were shown to provide better results in the satisfaction, efficiency, and economic indicators with a lower financial need; therefore, this model can be used to satisfy other needs of Maslow’s pyramid. In conclusion, this proposed approach empowers welfare organizations to increase their CSR consideration, thus enabling them to use internal mechanisms to secure viability in the pursuit of a high-performance CSR approach.