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.

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.

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.

The industrial revolutions and their impact on production systems have increased productivity and quality in manufacturing over time. Lean methods have been the driver of the development of production systems from the 1990s to the rise of the fourth industrial revolution, or Industry 4.0. However, many different approaches and methodologies have been described, applied, and discussed for achieving improvements in production systems. As a result, organizations are often confused in regard to the order, the convenience, and the outcomes intended by the different improvement strategies and techniques. This paper provides a systematic sequence of process optimization steps that can be applied to any organization. A conceptual model was built based on the systematic sequence. In addition, a simulation model was built with the goal of representing and quantifying the sequential steps of the conceptual model. The results of the simulation model show a clear improvement in quality, performance, and economic indicators, with the first two steps in the optimization sequence providing critical initial information, while the three last steps served as net contributors to a global production system improvement for demanding market scenarios. Finally, we analyzed the impacts of Industry 4.0 on production systems and developed a methodological sequence to design, select, implement, and control projects, even those that include Industry 4.0 technologies.

Historically, manufacturing system researchers and managers have often failed to consider all the areas, factors, and implications of a process within an integrated manufacturing model. Thus, the aim of this research was to develop an integral modeling approach for manufacturing processes in order to assess their status and performance. For this purpose, a novel conceptual model consisting of an integral definition of areas and flows is applied. As a result, manufacturing systems can be modeled, considering all related flows and decision-making options in the respective areas of production, maintenance, and quality. As a result, these models serve as the basis for the integral management and control of manufacturing systems in digital twin models for the regulation of process stability and quality with maintenance strategies. Thus, a system dynamics simulation model is developed for a metallurgical process. The goal of the simulation model is to provide a digital manufacturing system regulated with different maintenance, quality, and production strategies in order to secure quality and delivery service. The results show how the monitoring of all flows together with the optimal strategies in the quality and maintenance areas as a result of a regulated system can enable companies to increase their profitability and customer service level. In conclusion, the applied simulation case study allows better decision making, ensuring continuous optimization along the manufacturing asset lifecycle and providing a unique selling proposition for equipment producers and service engineering suppliers, as well as for production and assembly companies.

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.