The last few decades have seen rapid growth in additive manufacturing (AM) technologies. AM has implemented a novel method of production in design, manufacture, and delivery to end-users. Accordingly, AM technologies have given great flexibility in design for building complex components, highly customized products, effective waste minimization, high material variety, and sustainable products. This review paper addresses the evolution of engineering design to take advantage of the opportunities provided by AM and its applications. It discusses issues related to the design of cellular and support structures, build orientation, part consolidation and assembly, materials, part complexity, and product sustainability.

I4.0 promotes a future in which highly individualized goods are mass produced at a competitive price through autonomous, responsive manufacturing. In order to attain market competitiveness, organizations require proper integration of I4.0 technologies and manufacturing strategy outputs (MSOs). Implementing such a comprehensive integration relies on carefully selecting I4.0 technologies to meet industrial requirements. There is little clarity on the impact of I4.0 technologies on MSOs, and the literature provides little attention to this topic. This research investigates the influence of I4.0 technologies on MSOs by combining reliable MCDM methods. This research uses a combination of fuzzy DEMATEL and fuzzy TOPSIS to evaluate the impact of I4.0 technologies on MSOs. The fuzzy theory is implemented in DEMATEL and TOPSIS to deal with the uncertainty and vagueness of human judgment. The FDEMATEL was utilized to identify interrelationships and determine criterion a’s weights, while the fuzzy TOPSIS approach was employed to rank the I4.0 technologies. According to the study’s findings, cost is the most critical factor determining MSOs’ market competitiveness, followed by flexibility and performance. On the other hand, additive manufacturing (AM) is the best I4.0 technology for competing in the global market. The results present an evaluation model for analyzing the relative important weight of multiple factors on MSOs. They can also assist managers in concentrating on the most influential factors and selecting the proper I4.0 Technology to preserve competitiveness.

Manufacturing is transitioning from traditional and mass manufacturing to mass personalization, fast, and intelligent production. Through full automation in various fields and data sharing, Industry 4.0 (I4.0) contributes to the digitalization of manufacturing by enhancing industrial flexibility and product customization. I4.0 is being utilized as a strategy for advanced manufacturing to counter global competitiveness. A company’s manufacturing strategy outputs (MSOs) are critical to its ability to move forward and remain competitive. Despite their importance, I4.0 technologies have received less attention in the literature, and it is unclear how they influence MSOs. Thus, this study aims to build a powerful hybrid MCDM method for ranking the influence of I4.0 technologies on MSOs by adopting a combination of AHP and fuzzy TOPSIS. The application of fuzzy set theory has addressed the ambiguity in comparing various I4.0 technologies. The AHP was used to calculate the weights of criteria and sub-criteria, and the fuzzy-TOPSIS method was utilized to rank the I4.0 technologies. The results revealed that the cost criterion is the most critical factor when implementing I4.0 technologies. In contrast, additive manufacturing (AM) is the most suitable I4.0 technology for countering global competition.

Industry 4.0 (I4.0) has increasingly been adopted as an advanced manufacturing strategy to counter global competition. The capability of a company to compete on various manufacturing strategy outputs (MSOs) such as cost, quality, delivery, flexibility, performance and innovativeness play a significant role in motivating the whole company to take advantage of its competitors. I4.0 is comprised of various technologies, and how I4.0 technologies can influence the MSOs are still unclear, and it took less interest in the literature. This article aims to analyze the influence of the I4.0 technologies on MSOs to achieve market competitiveness from the perspective of academic and industry experts. To do so, the influence of the adoption of various I4.0 technologies on the MSOs was investigated. Expert opinions were gathered on the relationship between manufacturing competitive capabilities and I4.0 technologies. The identification of the influential relationships provides arguments for the proposed I4.0 technology selections, which will allow companies to gain a highly competitive advantage. The results showed that the performance (regarding the MSOs) had a high potential for integration with different I4.0 technologies.

The turning, which consists of the removal of metal from the outer diameter of a rotating cylindrical workpiece, is one of the most common techniques for cutting, especially when finishing the product. The values of the cutting parameters, such as feed rate, cutting speed, and depth of cut, in a turning operation must be selected carefully to improve the profit of operations by enhancing productivity and reducing the total manufacturing cost for each component. A high vibration leads to poor surface finish and reduced productivity and shortens the tool life; therefore, this parameter should be controlled. In this study, an experiment is conducted to investigate the effects of these cutting parameters in the turning process of a workpiece, composed of AISI 1040 steel, using the response surface method. Statistical tools were used to design the experiments. These parameters are optimized by using analysis of variance, regression, and optimization techniques to achieve the condition of minimum vibration and chip frequency, therefore improving the surface roughness after the turning process.

Globalization has created a highly competitive and diverse market, an uncertain and risky business environment, and changing customer expectations. An effective manufacturing strategy reduces complexity and provides organizations with a well-organized manufacturing structure. However, existing research on manufacturing strategies appears scattered, lacking systematic understanding and finding no causal relationship between manufacturing strategies’ outputs (MSOs) and their importance. Therefore, this study is a pioneer in identifying the influential factors of MSOs in the adoption of Industry 4.0 (I4.0) technologies utilizing the decision-making trial and evaluation laboratory (DEMATEL) approach. This method is considered an effective method for identifying the cause-effect relationship of complex problems. It evaluates interdependent relationships among MSO factors from the perspective of academic and industry experts. Identifying cause and effect factors leads to increasing the market’s competitiveness and prioritizing them. To deal with the vagueness of human beings’ perceptions, this study utilizes fuzzy set theory and the DEMATEL method to form a structural model. Results show that customer satisfaction, cost per unit produced, and the number of advanced features are the main factors influencing MSOs.