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A Risk Analysis Model in Concurrent Engineering Product Development

Author

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  • Desheng Dash Wu
  • Xie Kefan
  • Chen Gang
  • Gui Ping

Abstract

Concurrent engineering has been widely accepted as a viable strategy for companies to reduce time to market and achieve overall cost savings. This article analyzes various risks and challenges in product development under the concurrent engineering environment. A three‐dimensional early warning approach for product development risk management is proposed by integrating graphical evaluation and review technique (GERT) and failure modes and effects analysis (FMEA). Simulation models are created to solve our proposed concurrent engineering product development risk management model. Solutions lead to identification of key risk controlling points. This article demonstrates the value of our approach to risk analysis as a means to monitor various risks typical in the manufacturing sector. This article has three main contributions. First, we establish a conceptual framework to classify various risks in concurrent engineering (CE) product development (PD). Second, we propose use of existing quantitative approaches for PD risk analysis purposes: GERT, FMEA, and product database management (PDM). Based on quantitative tools, we create our approach for risk management of CE PD and discuss solutions of the models. Third, we demonstrate the value of applying our approach using data from a typical Chinese motor company.

Suggested Citation

  • Desheng Dash Wu & Xie Kefan & Chen Gang & Gui Ping, 2010. "A Risk Analysis Model in Concurrent Engineering Product Development," Risk Analysis, John Wiley & Sons, vol. 30(9), pages 1440-1453, September.
    • Handle: RePEc:wly:riskan:v:30:y:2010:i:9:p:1440-1453
    DOI: 10.1111/j.1539-6924.2010.01432.x
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    References listed on IDEAS

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    Citations

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    Cited by:

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    3. Ali Jamshidi & Shahrzad Faghih‐Roohi & Siamak Hajizadeh & Alfredo Núñez & Robert Babuska & Rolf Dollevoet & Zili Li & Bart De Schutter, 2017. "A Big Data Analysis Approach for Rail Failure Risk Assessment," Risk Analysis, John Wiley & Sons, vol. 37(8), pages 1495-1507, August.
    4. Yongrok Choi & Xiaoxia Ye & Lu Zhao & Amanda Luo, 2016. "Optimizing enterprise risk management: a literature review and critical analysis of the work of Wu and Olson," Annals of Operations Research, Springer, vol. 237(1), pages 281-300, February.
    5. Desheng Wu, 2016. "Risk management and operations research: a review and introduction to the special volume," Annals of Operations Research, Springer, vol. 237(1), pages 1-5, February.
    6. Rutkowski Ireneusz P., 2022. "How to Measure the Success or Failure Rate of a New Product Strategy in Competitive Markets," Marketing of Scientific and Research Organizations, Sciendo, vol. 45(3), pages 27-44, October.
    7. Oliveira, Gilson Adamczuk & Tan, Kim Hua & Guedes, Bruno Turmina, 2018. "Lean and green approach: An evaluation tool for new product development focused on small and medium enterprises," International Journal of Production Economics, Elsevier, vol. 205(C), pages 62-73.
    8. Fei Wang & Jiuchang Wei & Dingtao Zhao, 2014. "A Quantifiable Risky Decision Model: Incorporating Individual Memory into Informational Cascade," Systems Research and Behavioral Science, Wiley Blackwell, vol. 31(4), pages 537-553, July.
    9. Nicholas Shallcross & Gregory S. Parnell & Ed Pohl & Eric Specking, 2020. "Set‐based design: The state‐of‐practice and research opportunities," Systems Engineering, John Wiley & Sons, vol. 23(5), pages 557-578, September.
    10. Ireneusz P. Rutkowski, 2022. "SucTheoretical and empirical research reveals that despite implementing new product development and management best practices, many projects fail with new product strategies. But what if failure rates as high as 90% are true? This would mean that hig," Journal of Marketing and Consumer Behaviour in Emerging Markets, University of Warsaw, Faculty of Management, vol. 1(14), pages 52-61.

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