Variety management is a cross-domain issue in product family design. In the real field, the relationships across the domains are so complex for most of the existing product families that they cannot be easily identified without proper reference architecture. This reference architecture should provide the cross- domain mapping mechanisms in an explicit manner and be able to identify the proper units for management. From this perspective of cross-domain framework, this paper introduces development architecture (DA) to describe the relationships between elements in market, design, and production domains and to give insights for the cross-domain variety management in the product development stage. DA has three parts: (1) the arrangement of elements in each domain, (2) the mapping between elements, and (3) the identification of management sets and key interfaces which are the proper units for variety management. The proposed development architecture framework is applied to the case of front chassis family of modules of an automobile.
As mass customization becomes a major challenge for manufacturing companies, assembly systems should have the capabilities to accommodate a high variety of products. Since the assembly system operations become much more complex with the increased variety, the firms are in need of a measure which is general and practical enough to correctly estimate the complexity of an assembly system. This paper proposes a new model which measures the complexity based on the modeling of production architecture. Production architecture describes how a product is processed in an assembly system. Then, the complexity is measured by checking which tasks are processed at each station since the differences among tasks are regarded as the main cause of complexity. The model has been applied to a case, a simplified version of real data from an electronics manufacturer. Applying this approach, the complexity induced from task differences can be measured from each station to an entire assembly system, and one can reduce the overall complexity by controlling the tasks which are allocated to stations. Consequently, the firms can get fundamental insights into the operation of complex assembly systems with the help of production architecture from which the complexity measure is derived.
One of the major challenges in variety management of modular product families is to prevent continuously generated variants of design elements. This paper aims to provide guidance on how manufacturing companies can reduce a large number of variants that have already increased from the existing product architectures. This paper introduces a new concept of architecture named variation architecture ( VA) in which relationships between variants in the market, design, and production domains are arranged explicitly for planning variety of a modular product family. Since the VA includes two perspectives which are domain mapping and variant-level planning, it can help companies to systematically establish complex relationships between variants across the domains. This paper describes elements of the three domains, relationship types between elements, and four categories of relationship rules called management rules at the variant-level planning. A framework is proposed for reducing variants through the VA to demonstrate its applicability. In the case study, we apply the framework to a front chassis family having a large number of variants and show that the VA significantly reduces unnecessary variants compared to the currently being produced.
As a result of current trends, a level of SW⋅AI competency for digital innovation has become a necessity, not a choice, SW-AI competency has also developed as a purpose not only for training specialist personnel, but also for providing universal SW⋅AI basic education. However, to increase the effectiveness of universal SW⋅AI basic education, it is advisable that we systematically educate SW⋅AI basic education in stages.</br>A university offers various elective courses related to SW⋅AI for all students. Schools have expanded the opportunities for students to participate in SW⋅AI basic education after they have completed the required elective courses. However, expanding the opportunities for SW⋅AI basic education does not automatically strengthen their skills. Therefore, educators are aiming to strengthen the learners’ problem-solving skills by developing evaluation tools for core competencies in SW⋅AI elective courses and by applying these skills in algorithm courses.</br>A performance assessment tool was developed in SW⋅AI basic education to enhance the core competencies of higher-order thinking and for the utilization of information and technology. The tool was developed through the following stages: clarifying class goals, developing core challenges, developing evaluation criteria, developing and applying assessment tools, and analyzing the evaluation results. The tool was applied in an algorithm course for problem solving and helped learners understand what to focus on when performing challenges. It also provided them with guidance for learning to enhance their various competencies through feedback. Indeed, a survey of self-assessment by learners at the beginning and end of the semester showed that the necessary competencies for solving core challenges had improved. On the other hand, students who were not majoring in SW⋅AI basic education stated that having to program in order to solve problems and being responsible for programming in team projects was a burden for them.</br>In this study, we found that if the learner’s ultimate competencies are concretely embodied as behavioral indicators and challenges are set based on this criterion, the learner can concentrate more on practical competency enhancement. However, in regards to the SW⋅AI basic education subject, we confirmed that there is a need for us to clarify the core challenges and evaluation criteria for evaluating core competencies. In the future, we will apply various core competency performance evaluation tools to various SW⋅AI basic education subjects to identify areas for improvement. Through this process, we will continuously improve the effectiveness of SW⋅AI basic education and systematically enhance competencies thereby.
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