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In recent years, manufacturing industries have been expected to achieve servitisation—namely, a shift from product sales to product-service systems—in order to achieve sustainable production and consumption patterns. In order to achieve servitisation, manufacturing firms should grasp the business environment and encourage organisational learning to develop the knowledge for servitisation in their environment. The existing knowledge management studies enable the empirical acquisition and reuse of knowledge from past case studies and make efforts to support organisational learning. However, they do not cover the guiding of firms engaged in servitisation to learn appropriately for their business environment. The learning required for manufacturing firms engaged in servitisation is learning that focuses on questioning and modifying existing product-oriented premises—double-loop learning. This paper proposes a method to support strategic double-loop learning within manufacturing companies engaged in servitisation. This method evaluates the compatibility between the implicit premises that manufacturers refer to as the rationale for their decision toward servitisation and the external environment and enables to formulate a practical strategy for double-loop learning. The proposed method was applied to the case of a cassette tape music player to demonstrate its usefulness. This study suggests theoretical foundations for future research into knowledge management for traditional manufacturing companies’ decisions concerning servitisation, and suggests that these should be carried out dynamically according to the business environment.

This study seeks to explore the strategic alternatives for the many small and medium-sized manufacturing firms in China that play an OEM role in the global value chain. Declining margins due to rising production costs and intense competition from emerging manufacturing industries in Southeast Asia necessitates the transformation of China's traditional manufacturing industry. The result is opportunities for the creation and manufacturing of domestic brands. A multiple-case study methodology is used with primary data collection through in-depth interviews, supported by secondary data on the subject firms. The findings reveal an awareness of the opportunities afforded by the trend towards the development of domestic brands as well as the vision, strategy, tactics, and forecasting related to a transition to brand creation. This chapter could be of interest to those with an interest in manufacturing transition, brand development, and providing direction to OEM SMEs in China that seek to develop a strategic roadmap during the imminent industrial evolution and transformation.

Artificial intelligence has led to the automation of traditional manufacturing and industrial processes and practices. The use of artificial intelligence improves customer experience and it's a proven fact that consumers who enjoy their shopping experience end up making more purchases. Retailing is one of the sectors that has seen drastic changes after the inception of artificial intelligence. This transformation can be seen in the supermarkets like Amazon Go store, Alibaba Hema store, IKEA, and many others. The objective of this chapter is to study the impact of artificial intelligence on Indian retail customers. Primary survey was conducted for the study and it was found that retail organizations emphasizing store design/layout and adoption of technological innovation to ease the consumer buying process were more successful in creating loyal customers for their stores. It can be concluded that India is still lacking in the adoption of IT systems in the retail sector and serious efforts are required in this direction.

This chapter describes with the comparison of the most used back propagations training algorithms neural networks, mainly Levenberg-Marquardt, conjugate gradient and Resilient back propagation are discussed. In the present study, using radial overcut prediction as illustrations, comparisons are made based on the effectiveness and efficiency of three training algorithms on the networks. Electrical Discharge Machining (EDM), the most traditional non-traditional manufacturing procedures, is growing attraction, due to its not requiring cutting tools and permits machining of hard, brittle, thin and complex geometry. Hence it is very popular in the field of modern manufacturing industries such as aerospace, surgical components, nuclear industries. But, these industries surface finish has the almost importance. Based on the study and test results, although the Levenberg-Marquardt has been found to be faster and having improved performance than other algorithms in training, the Resilient back propagation algorithm has the best accuracy in testing period.

Due to their unique advantages over traditional manufacturing processes, metal additive manufacturing (AM) technologies have received a great deal of attention over the last few years. Using current powder-bed fusion AM technologies, metal components are very expensive to manufacture, and machines are complex to build and maintain. Wire arc additive manufacturing (WAAM) is a new method of producing metallic components with high efficiency at an affordable cost, which combines welding and 3D printing. In this work, gas tungsten arc welding (GTAW) is incorporated into a gantry system to create a new metal additive manufacturing platform. Design and build of a simple, affordable, and effective WAAM system is explained and the most frequently seen problems are discussed with their suggested solutions. Effect of process parameters on the quality of two additively manufactured alloys including plain carbon steel and Inconel 718 were studied. System design and troubleshooting for the wire arc AM system is presented and discussed.

AbstractHybrid manufacturing processes are known for combining the advantages of additive manufacturing and more traditional manufacturing processes such as machining to create components of complex geometry while minimising material waste. The trend towards lightweight design, especially in view of e-mobility, gives aluminium materials an important role to play. This study examines the use of aluminium alloys in laser metal wire deposition (LMWD) processes with subsequent subtractive machining, which is considerably more difficult due to the different process-related influences. The investigations are focussed on the influence of the differently controlled laser power on the shape accuracy, the microstructure, and the hardness of the AlMg5 test components after the LMWD process with subsequent subtractive machining by turning. The long-term goal of the investigations is to increase the stability of the hybrid production process of AlMg5 components with defined dimensional accuracy and mechanical properties.

Abstract The goal of this work is to study the way student designers use design for additive manufacturing (DfAM) rules, or heuristics. It can be challenging for novice designers to succeed at creating successful designs for additive manufacturing (AM), given its differences from traditional manufacturing methods. A study was carried out to investigate the way novices apply DfAM heuristics when they receive them at different points in the design process. A design problem was presented to students, and three different groups of student participants were given a lecture on DfAM heuristics at three different points in the design process. The novelty and quality of each of the resulting designs was evaluated. Results indicate that although the DfAM heuristics lecture had no impact on the overall quality of the designs generated, participants who were given the heuristics lecture after the initial design session produced designs that were better suited for 3D printing in the second phase of the design activity. However, receiving this additional information appears to prevent students from creatively iterating upon their initial designs, as participants in this group did not experience an increase in novelty between the two sessions. Additionally, receiving the heuristics lecture increased all students' perceptions of their ability to perform DfAM-related tasks. These results validate the practicality of design heuristics as AM training tools while also emphasizing the importance of iteration in the design process.

The research presented in this paper aim at identifying research commonalities between shipbuilding, offshore fabrication practices and manufacturing. As part of an exploratory effort a literature review and a case study of two offshore structures projects were performed. Research concerning shipbuilding and offshore fabrication, together with literature from other industries in construction, larger engineering projects and traditional manufacturing was reviewed. The two offshore structures projects were analyzed by means of interviews and complemented by direct observations and document reviews. The study concludes that there are gaps in the research concerned with holistic perspectives on the fabrication and installation phases of shipbuilding and offshore projects. The number of actors involved in any project of this magnitude increase barriers and communication interfaces. The dynamic nature of these types of projects was also observed and the changeability should always be a accounted factor when dealing with projects of this sort. The interviews held as part of the verification of observed phenomena in literature was limited to two projects and a single company and actors perceptions. However the collected data served well in being complementary to the literature review. It could be the task of academia to patch the gaps for overall project success, in the cases where single industry actors simply cannot see the benefit or do not have the recourses to fill them themselves. This study combines findings from traditional manufacturing industries, shipbuilding, offshore structures fabrication and large engineering projects in general.

Recently, the competitive environment is very tough in shipbuilding industry and under these circumstances, manufacturing a ship in a shorter time becomes significantly important in order to meet the customer demands. Therefore, it is hard to do that by using traditional manufacturing techniques. The shipyards located in Turkey usually have functional locations for the machines and this situation often causes longer production times. Instead of this, assembly lines should be redesigned as workshops in the shipyard. Prefabrication work unit is a good example in which an assembly line is needed to be designed. In this study, an assembly line design for prefabrication work unit was performed. For this, assignments of work operations to work shops were carried out by using Largest Set Rule Algorithm and some alternatives were created according to compare the different values of cycle time. These alternatives were simulated by using a production simulation program and the most appropriate assembly line design was presented.

Aiming at the selection of modern auto parts design schemes, an evaluation method of automobile parts design scheme for intelligent manufacturing is proposed. Considered the environment-friendly factors and traditional manufacturing factors, the evaluation index system of automobile parts design scheme was established, which taken technology, environment, economy and quality reliability as the first level evaluation index, and 12 second level indexes under the first level index. The game theory method was used to comprehensively consider the proportion of subjective and objective weights to determine the composite weight of each index. Aimed at the certainty and uncertainty of the evaluation index, the set pair analysis relative closeness method was used to calculate the composite weight and established the model. Three sets of automobile panel design schemes of a company were selected, and the mathematical model was used to calculate them. With the help of C language program, the advantages and disadvantages of the three design schemes were evaluated, and the optimal scheme was obtained. The calculation example proves that the method is true and effective, conforms to the background of modern intelligent manufacturing, and the calculation is simple, and has good feasibility and practicability.