Integrating optimal process and supplier selection in personalised product architecture design

Author(s):  
Changbai Tan ◽  
Kira Barton ◽  
S. Jack Hu ◽  
Theodor Freiheit
2003 ◽  
Vol 2003.13 (0) ◽  
pp. 224-227
Author(s):  
Yutaka NOMAGUCHI ◽  
Satoshi IDAKA ◽  
Atsushi OHNUMA ◽  
Kikuo FUJITA

Author(s):  
Mike J. Van Wie ◽  
Palani Rajan ◽  
Matthew I. Campbell ◽  
Robert B. Stone ◽  
Kristin L. Wood

Product architecture is the transformation of function to layout. Like much of conceptual design, it is a highly dynamic process whereby engineers must consider a deluge of information in terms of both function and form. One shortcoming of current engineering practice is the absence of representations or abstractions used to aid in developing, refining, and exploiting alternative layout solutions. The purpose of this paper is to present a representation for product architecture that sufficiently captures the design factors relevant to product architecture design which are not taken into account in current practices. An example is given to illustrate the technique, and results of a validation experiment are shown.


Author(s):  
Masato Toi ◽  
Yutaka Nomaguchi ◽  
Kikuo Fujita

Abstract This paper proposed a design support method based on structuralization and analysis of various design candidates of product architecture design. The product architecture is a basic scheme that assigns the function of the product to physical components. In the conventional modular design method, a concise model, i.e., a graph or a matrix, is used to express the interactions of the system’s components and aims to support the designer grasping the system behavior. The Design Structure Matrix (DSM) is a representative model of system architecture and enables quantitative evaluation of design candidates. While various design candidates are generated through mathematical operations, it is difficult to understand their relationships from simple comparisons because of discrete behavior and the size of the problem. It must be a critical issue at the stage of selecting and interpreting the design candidates. In the proposed method, the design candidates are classified and structuralized as a dendrogram by the hierarchical clustering method. The comparison of clusters of each branch of dendrogram clarifies the system leverage points. The information of the system is summarized into the hierarchical tree-shaped graph that corresponds to the dendrogram. The designer can explore the design candidates with such a graph-based based interpretation of underlying structures effectively.


Author(s):  
Olga Sankowski ◽  
Kevin Otto ◽  
Seung Ki Moon ◽  
Dieter Krause

AbstractThe field of design research has been expanding into a wide diverse range of multidisciplinary topics. It takes substantial time for young researchers to attain a cumulative overview of state of the art on ever more complex methodologies. Teaching doctoral candidates in summer schools is an approach being taken by the design society to support them attaining an immersed understanding of a chosen research field as well as to help them formulate their own line of research. The aim for a new researcher is to form exchanges and collaborations with other researchers. The 'International Summer School on Product Architecture Design - PAD 2018' was such an effort, where 17 international PhD researchers and three international faculties met for a week and explored research in product architecture through hands-on exercises. We surveyed the researchers for effectiveness of the summer school and found that structure and concept of the summer school was effective for providing a background baseline of state of the art. We found there was a significant but less impact on individual participant´s research. We have yet to understand if the creation of collaborations among participants will occur.


2021 ◽  
Vol 51 (1) ◽  
pp. 9-25
Author(s):  
John Heiney ◽  
Ryan Lovrien ◽  
Nicholas Mason ◽  
Irfan Ovacik ◽  
Evan Rash ◽  
...  

Due to its scale, the complexity of its products and manufacturing processes, and the capital-intensive nature of the semiconductor business, efficient product architecture design integrated with supply chain planning is critical to Intel’s success. In response to an exponential increase in complexities, Intel has used advanced analytics to develop an innovative capability that spans product architecture design through supply chain planning with the dual goals of maximizing revenue and minimizing costs. Our approach integrates the generation and optimization of product design alternatives using genetic algorithms and device physics simulation with large-scale supply chain planning using problem decomposition and mixed-integer programming. This corporate-wide capability is fast and effective, enabling analysis of many more business scenarios in much less time than previous solutions, while providing superior results, including faster response time to customers. Implementation of this capability over the majority of Intel’s product portfolio has increased annual revenue by an average of $1.9 billion and reduced annual costs by $1.5 billion, for a total benefit of $25.4 billion since 2009, while also contributing to Intel’s sustainability efforts.


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