Risk analysis to assess potential risks before the introduction of a new product design is an important step in the engineering product design process (EPDP); it helps evaluate events that pose adverse effects on the sustainability performance of the product. However, most literature on product design risk assessment focus on the manufactured products rather than conceptual designs, the methods used are unable to capture the interdependencies between risk factors and they do not provide insight into the risks that most influence the performance measures. This paper extends a previously developed Bayesian inference-based approach to evaluate the risk of new product designs by employing backpropagation to calculate the likelihood of parent risks based on the likelihood of the child risk, thus identifying the critical path of risk events. Assessment of the methodology through an industrial case study gives insight into the major risk drivers affecting the achievement of sustainability objectives.
|Number of pages||8|
|State||Published - 2021|
|Event||30th International Conference on Flexible Automation and Intelligent Manufacturing, FAIM 2021 - Athens, Greece|
Duration: Sep 7 2021 → Sep 10 2021
Bibliographical noteFunding Information:
This work was partially supported by funding from the Digital Manufacturing and Design Innovation Institute (DMDII) [grant number 15-05-08].
© 2021 The Authors. Published by Elsevier Ltd.
- Bayesian inference
- Risk assessment
- Sustainable product design
- Total lifecycle
ASJC Scopus subject areas
- Industrial and Manufacturing Engineering
- Artificial Intelligence