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A dynamic model for engineering change propagations in multiple product development stages

Published online by Cambridge University Press:  16 February 2022

Yulaing Li Open the ORCID record for Yulaing Li [Opens in a new window] ,
Wei Zhao ,
Wenqi Zhang  and
Meng Chen
Yulaing Li*
Affiliation:
Institute of Artificial Intelligence and Shanghai Engineering Research Center of Industrial Big Data and Intelligent system, Donghua University, No. 2999 North Renmin Road, Songjiang District, Shanghai 201620, China
Wei Zhao
Affiliation:
Zhejiang University of Finance and Economics, 18 Xueyuan Street, Hangzhou, Zhejiang 310018, China
Wenqi Zhang
Affiliation:
Shanghai Aerospace System Engineering Institute, 3888 Yuanjiang Road, Shanghai 201108, China
Meng Chen
Affiliation:
Shanghai Aerospace System Engineering Institute, 3888 Yuanjiang Road, Shanghai 201108, China
*
Author for correspondence: Yuliang Li, E-mail: meyuliang@dhu.edu.cn
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Abstract

To accurately predict propagation dynamics for single or multiple change propagations across different product development stages in a sequential or concurrent way is critical for decision-making of implementing change requests. In this paper, a change propagation dynamic model is built based on the compartmentalization of engineering entities into susceptible engineering entities and affected engineering entities (SA), the ordinary differential equations for describing the rate of affected entities with respect to the total ones and the duration for resolving all the changes for every moment are presented by combining the calculations of change impacts with different split and joint junctions. Considering the difficulty of finding analytical solutions to the differential equations, algorithms for sequential and concurrent simulations of change propagations across different development stages, and random and GA (Genetic Algorithm)-based optimal selections of feasible propagation paths are developed to obtain numerical solutions for single and multiple change requests. Simulation results show that change ripples and blossoms can be observed in both sequential and concurrent change propagations, and these propagation patterns are not sensitive to the initial change effect and the threshold value for propagations, while critical change propagation paths and the number of initiated changes have important effects on both concurrent and sequential change propagation process. It is also demonstrated that concurrent propagation strategy is advantageous for processing single or few of initiated changes since it can shorten product redevelopment time, sequential propagation strategy has an advantage of robustness for handling multiple initiated change requests.

Keywords

Change propagationmultistage developmentpropagation dynamicssimulation

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Type
Research Article
Information
AI EDAM , Volume 36 , 2022 , e13
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Copyright
Copyright © The Author(s), 2022. Published by Cambridge University Press

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References

Ahmad, N, Wynn, D and Clarkson, J (2013) Change impact on a product and its redesign process: a tool for knowledge capture and reuse. Research in Engineering Design 24, 219–244.CrossRefGoogle Scholar
Anderson, R and May, R (1992) Infectious Diseases of Humans: Dynamics and Control. Oxford: Oxford University Press.Google Scholar
Avci, M and Selim, H (2017) A multi-objective simulation-based optimization framework for supply changes with premium freights. Expert Systems with Applications 67, 95–106.CrossRefGoogle Scholar
Barthelemy, M, Barrat, A, Pasto-Satorras, R and Vespignani, A (2005) Dynamical patterns of epidemic outbreaks in complex heterogenous networks. Journal of Theoretical Biology 235, 275–288.CrossRefGoogle Scholar
Cheng, H and Chu, X (2012) A network-based assessment approach for change impacts on complex product. Journal of Intelligent Manufacturing 23, 1419–1431.CrossRefGoogle Scholar
Chua, K and Hossain, M (2012) Predicting change propagation and impact on design schedule due to external changes. IEEE Transactions on Engineering Management 59, 483–493.CrossRefGoogle Scholar
Clarkson, P, Simons, C and Eckert, C (2004) Predicting change propagation in complex design. Transactions of the ASME Journal of Mechanical Design 126, 788–797.CrossRefGoogle Scholar
DeJong, K (2006) Evolutionary Computation: A United Approach. Massachusetts: MIT Press.Google Scholar
Eckert, C, Clarkson, J and Zanker, W (2004) Change and customization in complex engineering domains. Research in Engineering Design 15, 1–21.CrossRefGoogle Scholar
Eckert, C, Isaksson, O and Earl, C (2019) Design margins: a hidden issue in industry. Design Science 5, 1–24.CrossRefGoogle Scholar
Giffin, M, De Weck, O, Bounova, G, Keller, R, Eckert, C and Clarkson, J (2009) Change propagation analysis in complex technical systems. Transactions of the ASME Journal of Mechanical Design 131, 081001.CrossRefGoogle Scholar
Hamraz, B, Caldwell, N and Clarkson, J (2012) A multidomain engineering change propagation model to support uncertainty reduction and risk management in design. Transactions of the ASME Journal of Mechanical Design 134, 100905.CrossRefGoogle Scholar
Hamraz, B, Caldwell, N and Clarkson, J (2013 a) A holistic categorization framework for literature on engineering change management. Systems Engineering 16, 473–505.CrossRefGoogle Scholar
Hamraz, B, Caldwell, N and Clarkson, P (2013 b) A matrix-calculation-based algorithm for numerical change propagation analysis. IEEE Transactions on Engineering Management 60, 186–198.CrossRefGoogle Scholar
Jarratt, T, Eckert, C, Caldwell, N and Clarkson, P (2011) Engineering change: an overview and perspective on the literature. Research in Engineering Design 22, 103–124.CrossRefGoogle Scholar
Kang, C and Hong, YS (2009) Evaluation of acceleration effect of dynamic sequencing of design process in a multiproject environment. Transactions of the ASME Journal of Mechanical Design 131, 021008.CrossRefGoogle Scholar
Karniel, A and Reich, Y (2009) From DSM-based planning to design process simulation: a review of process scheme logic verification issues. IEEE Transactions on Engineering Management 56, 636–649.CrossRefGoogle Scholar
Koh, E, Caldwell, N and Clarkson, P (2012) A method to assess the effects of engineering change propagation. Research in Engineering Design 23, 329–351.CrossRefGoogle Scholar
Lee, S, Ong, K and Khoo, L (2004) Control and monitoring of concurrent design tasks in a dynamic environment. Concurrent Engineering: Research and Applications 12, 59–66.CrossRefGoogle Scholar
Lee, H, Seol, H, Sung, N, Hong, Y and Park, Y (2010) An analytic network process approach to measuring design change impacts in modular products. Journal of Engineering Design 21, 75–91.CrossRefGoogle Scholar
Li, Y and Zhao, W (2014) An integrated change propagation scheduling approach for product design. Concurrent Engineering: Research and Applications 22, 347–360.CrossRefGoogle Scholar
Li, Y, Zhao, W and Shao, X (2012) A process simulation based method for scheduling product design change propagation. Advanced Engineering Informatics 26, 529–538.CrossRefGoogle Scholar
Li, Y, Zhao, W and Ma, Y (2016) A shortest path method for sequential change propagations in complex engineering design processes”. Artificial Intelligence for Engineering Design, Analysis and Manufacturing 30, 107–121.CrossRefGoogle Scholar
Li, Y, Zhao, W and Tong, S (2017) Simulation based scheduling of multiple change propagations in multistage product development processes. Expert Systems with Applications 89, 1–19.CrossRefGoogle Scholar
Long, D and Ferguson, S (2020) Studying dynamic change probabilities and their role in change propagation. Transactions of the ASME Journal of Mechanical Design 142, 101701.CrossRefGoogle Scholar
Maier, J, Eckert, C and Clarkson, PJ (2019) Experimental investigation of the implications of model granularity for design process simulation. Transactions of the ASME Journal of Mechanical Design 141, 0711101.CrossRefGoogle Scholar
Morkos, B, Shankar, P and Summers, J (2012) Predicting requirement change propagation using higher order design structure matrices: an industry case study. Journal of Engineering Design 23, 905–926.CrossRefGoogle Scholar
Nelson, D and Schnieder, G (2000) Applied Manufacturing Process Planning: With Emphasis on Metal Forming and Machining. New Jersey: Prentice Hall.Google Scholar
Pahl, G, Weitz, W, Feldhusen, J and Grote, K (2007) Engineering Design: A Systematic Approach, 3rd Edn. Berlin: Springer.CrossRefGoogle Scholar
Pasqual, M and De Weck, O (2012) Multilayer network model for analysis and management of change propagation. Research in Engineering Design 23, 305–328.CrossRefGoogle Scholar
Siddiqi, A, Nounova, G, De Weck, O, Keller, R and Robinson, B (2011) A posteriori design change analysis for complex engineering projects. Transactions of the ASME Journal of Mechanical Design 133, 101005.CrossRefGoogle Scholar
Suzuki, Y, Yahyaei, M, Jin, Y, Koyama, H and Kang, G (2012) Simulation based process design: modeling and applications. Advanced Engineering Informatics 26, 763–781.CrossRefGoogle Scholar
Taguchi, G, Chowdhury, S and Wu, Y (2004) Taguchi's Quality Engineering Handbook. New Jersey: John Wiley & Sons, Inc.CrossRefGoogle Scholar
Vianelo, G and Kristensen, S (2012) A comparative study of changes across the lifecycle of complex products in a variant and a customized industry. Journal of Engineering Design 23, 99–117.CrossRefGoogle Scholar
Wang, Y, Chen, S and Fu, X (2018) Review and prospect of propagation dynamic models. Communication on Applied Mathematics and Computation 32, 267–294 (in Chinese).Google Scholar
Wynn, D, Caldwell, N and Clarkson, P (2014) Predicting change propagation in complex design workflows. Transactions of the ASME Journal of Mechanical Design 136, 081009.CrossRefGoogle Scholar
Yang, F and Duan, G (2012) Developing a parameter linkage-based method for searching change propagation paths. Research in Engineering Design 23, 353–372.CrossRefGoogle Scholar
Zeigler, P, Muzy, A and Kofman, E (2018) Theory of Modeling and Simulation: Discrete Event & Iterative System Computational Foundations, 3rd Edn. Pittsburgh: Academic Press.Google Scholar