A combined Model design for developing and optimizing product platform architecture considering parameters of DFV, DFC, DFSC, and DFAv, case study: phased array radar

Subject Areas : Design of Experiment

Masoud Merati ¹ , Mahdi Karbasian ² , Abbas Toloie Ashlaghi ³ , Hasan Haleh ⁴

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Received: 2023-03-07 Accepted : 2023-08-20 Published : 2024-09-01

Keywords: Phased Array Radar, : Product Platform, Design for Variety (DFV), Design for Cost (DFC), Design for Availability (DFAv), Design for Supply Chain (DFSC),

Abstract :

Developing a robust platform architecture can give companies a competitive edge and enhance product future generations and customer satisfaction. However, in order to develop a product platform architecture, there is a need for some kind a product variety design that concurrently manages costs and the supply chain process, and focuses on ease of use and improved availability to components. In this research, the design for variety (DFV) approach and two indices, generational variety index (GVI) and coupling index (CI) are used to measure a product architecture. Using the quality function deployment (QFD) and design structure matrix (DSM), design indices for product diversity are identified and ranked. Additionally, the design for variety approach is modeled simultaneously with the concepts of design for cost (DFC), design for availability (DFAv), and design for supply chain (DFSC) to yield a practical mathematical model for the development of the product platform architecture, which aims for product diversity, improved availability, reduced costs, and supply chain management. The case study of the current research is a phased array radar, which is optimized using the latest techniques (genetic algorithm) and MATLAB software to solve the problem. After implementing the model, considering four objectives including total cost, availability, supplier evaluation score (competency) and replaceability (variety), and seven main parameters of the model, sensitivity analysis and other comparisons and results are presented, which analyzes the relationships between objectives, the impressment and affectability of objectives and model parameters on each other. Regarding the comparison of objectives, the results generally show the inverse relationship between the total cost objective and the other objectives, and the direct relationship between the other objectives with each other. Additionally, the results of the sensitivity analysis performed indicate that the availability objective had the highest effect and replaceability (variety) and the evaluation score of suppliers (competency) and total cost also took next place.

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