Musculoskeletal disorders (MSDs) that result from poor ergonomic design are one of the occupational disorders of greatest concern in the industrial sector. A key advantage in the primary design phase is to focus on a method of assessment that detects and evaluates the potential risks experienced by the operative when faced with these types of physical injuries. The method of assessment will improve the process design identifying potential ergonomic improvements from various design alternatives or activities undertaken as part of the cycle of continuous improvement throughout the differing phases of the product life cycle. This paper presents a novel postural assessment method (NERPA) fit for product-process design, which was developed with the help of a digital human model together with a 3D CAD tool, which is widely used in the aeronautic and automotive industries. The power of 3D visualization and the possibility of studying the actual assembly sequence in a virtual environment can allow the functional performance of the parts to be addressed. Such tools can also provide us with an ergonomic workstation design, together with a competitive advantage in the assembly process. The method developed was used in the design of six production lines, studying 240 manual assembly operations and improving 21 of them. This study demonstrated the proposed method’s usefulness and found statistically significant differences in the evaluations of the proposed method and the widely used Rapid Upper Limb Assessment (RULA) method.

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.

This paper presents a study of hole quality and energy consumption in the process of drilling a thermoplastic polymeric material, polyether-ether-ketone, reinforced with 30% glass fibers (PEEK-GF30). PEEK-GF30’s capacity to be machined has focused on turning operations. Studies of drilling involving thermoplastic polymeric materials have considered materials with other types of matrices, or reinforcement. In this study, quantities such as maximum and mean surface roughness, delamination, maximum thrust force, maximum momentum, and energy required during the process were determined for three types of drill bits, and the most influential factors for each variable were identified using an ANOVA multifactor analysis. The highest quality and lowest energy consumption were achieved for a drill bit rotation speed of 7000 rpm and a feed rate of 400 mm/min with a tungsten carbide (WC) drill bit coated with titanium aluminum nitride (TiAlN). Although a WC drill bit with a diamond point reduces delamination, the roughness increases, thus, the choice of the drill bit type depends on the roughness allowed. A WC drill bit coated with TiAlN produces a good surface finish that can eliminate subsequent operations and requires less energy; thus, this type of drill bit is the most attractive of the types evaluated.

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.