Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers' requirements of product specification combinations. To better facilitate decision-making of modular product design, correlations among specifications and components originated from customers' conscious and subconscious preferences can be investigated by using big data on product sales. This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data. The correlations of the product specifications are determined by analyzing the collected product sales data. By building the relations between the product components and specifications, a matrix for measuring the correlation among product components is formed for component clustering. Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster. A case study of electric vehicles illustrates the application of the proposed method.
Jian Zhang
,
Bingbing Li
,
Qingjin Peng
,
Peihua Gu
. Product Specification Analysis for Modular Product Design Using Big Sales Data[J]. Chinese Journal of Mechanical Engineering, 2023
, 36(1)
: 17
-17
.
DOI: 10.1186/s10033-023-00841-5
Big data on product sales are an emerging resource for supporting modular product design to meet diversified customers' requirements of product specification combinations. To better facilitate decision-making of modular product design, correlations among specifications and components originated from customers' conscious and subconscious preferences can be investigated by using big data on product sales. This study proposes a framework and the associated methods for supporting modular product design decisions based on correlation analysis of product specifications and components using big sales data. The correlations of the product specifications are determined by analyzing the collected product sales data. By building the relations between the product components and specifications, a matrix for measuring the correlation among product components is formed for component clustering. Six rules for supporting the decision making of modular product design are proposed based on the frequency analysis of the specification values per component cluster. A case study of electric vehicles illustrates the application of the proposed method.
[1] K T Ulrich, S D Eppinger. Product design and development. New York: McGraw-Hill Education. 2004.
[2] Q L Xu, S K Ong, A Y C Nee. Function-based design synthesis approach to design reuse. Research in Engineering Design, 2006, 17(1): 27-44.
[3] J C Aurich, C Fuchs, C Wagenknecht. Life cycle oriented design of technical product-service systems. Journal of Cleaner Production, 2006, 14(17): 1480-1494.
[4] M B Beverland, M T Ewing, M J Matanda. Driving-market or market-driven? A case study analysis of the new product development practices of Chinese business-to-business firms. Industrial Marketing Management, 2006, 35(3): 383-393.
[5] W Kuncoro, W O Suriani. Achieving sustainable competitive advantage through product innovation and market driving. Asia Pacific Management Review, 2018, 23(3): 186-192.
[6] P Gu, M Hashemian, A Y C Nee. Adaptable design. CIRP Annals-Manufacturing Technology, 2004, 53(2): 539-557.
[7] Y Koren, S J Hu, P H Gu, et al. Open-architecture products. CIRP Annals-Manufacturing Technology, 2013, 62(2): 719-729.
[8] J Zhang, G Xue, H L Du, et al. Enhancing interface adaptability of open architecture products. Research in Engineering Design, 2017, 28(4): 545-560.
[9] J Jiao, T W Simpson, Z Siddique. Product family design and platform-based product development: A state-of-the-art review. Journal of International Manufacturing, 2007, 18(1): 5–29.
[10] T J Marion, H J Thevenot, T W Simpson. A cost-based methodology for evaluating product platform commonality sourcing decisions with two examples. International Journal of Production Research, 2007, 45(22): 5285-5308.
[11] T W Simpson. Product platform design and customization: Status and promise. Artificial Intelligence for Engineering Design, Analysis and Manufacturing, 2004, 18: 3-20.
[12] J Zhang, D Xue, P Gu. Adaptable design of open architecture products with robust performance. Journal of Engineering Design, 2015, 26(1-3): 1-23.
[13] J Zhang, P Gu, Q Peng, et al. Open interface design for product personalization. CIRP Annals-Manufacturing Technology, 2017, 66(1): 173-176.
[14] H J Thevenot, T W Simpson. A comprehensive metric for evaluating component commonality in a product family. Journal of Engineering Design, 2007, 18(6): 577-598.
[15] J Bonvoisin, F Halstenberg, T Buchert, et al. A systematic literature review on modular product design. Journal of Engineering Design, 2016, 27(7): 488-514.
[16] J Zhang, B B Li, A Simeone, et al. Design decision based on dependencies among product specifications. Proceedings of the 13th International Conference on Axiomatic Design, Sydney, Australia, October 18-20, 2019: 30100012.
[17] Z Y Liu, S G Zhou, J R Tan, et al. Multi parameter correlation analysis and prediction method of product performance based on multi criteria correction. Journal of Mechanical Engineering, 2013, 49(15): 105-114.
[18] J Sangjin, A Oyku, T W Simpson. A method to evaluate direct and indirect design correlations between components in a product architecture. Research in Engineering Design, 2018, 29(3): 507-530.
[19] J Zhang, A Simeone, P Gu, et al. Product features characterization and customers' preferences prediction based on purchasing data. CIRP Annals–Manufacturing Technology, 2018, 67(1): 149-152.
[20] O Kramer. On missing data hybridizations for dimensionality reduction. In: Hybrid metaheuristics. Berlin Heidelberg: Springer. 2013.
[21] I H Witten, E Frank, M A Hall, et al. Data mining: Practical machine learning tools and techniques. Massachusetts: Morgan Kaufmann. 2016.
[22] R A S Fisher, J H Bennett. Statistical methods, experimental design and scientific inference. Cambridge: Oxford University Press. 1990.
[23] L Myers, M J Sirois. Spearman correlation coefficients, differences between. In: Encyclopedia of statistical sciences. New York: John Wiley & Sons Inc.. 2006.
[24] H B Barlow. Unsupervised learning. Neural Computation, 1989, 1(3): 295-311.
[25] K A Ciesielski, T Mandat. Unsupervised machine learning in classification of neurobiological data. In: Intelligent methods and big data in industrial applications. Cham: Springer. 2019.
[26] G Erixon. Modular function deployment—a method for product modularization. The Royal Institute of Technology: Stockholm. 1998.
[27] F Börjesson, K Höltä-Otto. A module generation algorithm for product architecture based on component interactions and strategic drivers. Research in Engineering Design, 2014, 25(1): 31-51.
[28] K Wegener, S Andrew, A Raatz, et al. Disassembly of electric vehicle batteries using the example of the audi Q5 hybrid system. Procedia CIRP, 2014, 23: 155-160.
