Lifecycle Assurance Leadership for Multi-Plant ADAS and Restraint Product Deployment
Main Article Content
Abstract
A number of plants are now simultaneously associated with the manufacture of advanced driver assistance module and airbag restraint controllers by auto parts suppliers. Each plant introduces a different variation in the calibration, testing, supplier management, and change management. To address this issue, this paper suggests the lifecycle assurance leadership as a managerial model, which links safety decisions integral to all stages up to concept, and field feedback. The model has six-dimension Lifecycle Assurance Index and a central assurance office where plant level leads are considered. We experimented the model in a year-long program in five plants which produced forward camera modules, front radar units and restraint controllers. The defect rate in the network decreased from 57.4 percent to 62.0 parts per million. Calibration escapes reduced by 71 percent and the time of audit findings reducing as much as from 41 to 17 days. There was a reduction of defect rate spread in plants by 38 percent. Regression analysis indicates that every one-unit increment in the index decreased the defects by 2.9 parts per million. Ramp up learning went up by a factor from 0.18 to 0.31. These findings show that leadership practice can be used to identify most of the quality difference between the plants.
Article Details

This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.
References
[1] H. Lasi, P. Fettke, H.-G. Kemper, T. Feld, and M. Hoffmann, “Industry 4.0,” Bus. Inf. Syst. Eng., vol. 6, no. 4, pp. 239–242, 2014, doi: 10.1007/s12599-014-0334-4.
[2] M. Hermann, T. Pentek, and B. Otto, “Design Principles for Industrie 4.0 Scenarios,” in 2016 49th Hawaii International Conference on System Sciences (HICSS), 2016, pp. 3928–3937. doi: 10.1109/HICSS.2016.488.
[3] P. Koopman and M. Wagner, “Autonomous Vehicle Safety: An Interdisciplinary Challenge,” IEEE Intell. Transp. Syst. Mag., vol. 9, no. 1, pp. 90–96, 2017, doi: 10.1109/MITS.2016.2583491.
[4] W. Wachenfeld and H. Winner, “The Release of Autonomous Vehicles,” in Autonomous Driving, M. Maurer, J. C. Gerdes, B. Lenz, and H. Winner, Eds., Berlin, Heidelberg: Springer Berlin Heidelberg, 2016, pp. 425–449. doi: 10.1007/978-3-662-48847-8_21.
[5] S. Riedmaier, T. Ponn, D. Ludwig, B. Schick, and F. Diermeyer, “Survey on Scenario-Based Safety Assessment of Automated Vehicles,” IEEE Access, vol. 8, pp. 87456–87477, 2020, doi: 10.1109/ACCESS.2020.2993730.
[6] S. Ulbrich, T. Menzel, A. Reschka, F. Schuldt, and M. Maurer, “Defining and Substantiating the Terms Scene, Situation, and Scenario for Automated Driving,” in 2015 IEEE 18th International Conference on Intelligent Transportation Systems, 2015, pp. 982–988. doi: 10.1109/ITSC.2015.164.
[7] G. Bagschik, T. Menzel, and M. Maurer, “Ontology based Scene Creation for the Development of Automated Vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV), 2018, pp. 1813–1820. doi: 10.1109/IVS.2018.8500632.
[8] S. Feng, Y. Feng, C. Yu, Y. Zhang, and H. X. Liu, “Testing Scenario Library Generation for Connected and Automated Vehicles, Part I: Methodology,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 3, pp. 1573–1582, 2021, doi: 10.1109/TITS.2020.2972211.
[9] T. Menzel, G. Bagschik, and M. Maurer, “Scenarios for Development, Test and Validation of Automated Vehicles,” in 2018 IEEE Intelligent Vehicles Symposium (IV), 2018, pp. 1821–1827. doi: 10.1109/IVS.2018.8500406.
[10] D. Zhao et al., “Accelerated Evaluation of Automated Vehicles Safety in Lane-Change Scenarios Based on Importance Sampling Techniques,” IEEE Trans. Intell. Transp. Syst., vol. 18, no. 3, pp. 595–607, 2017, doi: 10.1109/TITS.2016.2582208.
[11] H. Caesar et al., “nuScenes: A Multimodal Dataset for Autonomous Driving,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 11618–11628. doi: 10.1109/CVPR42600.2020.01164.
[12] Q. Qi and F. Tao, “Digital Twin and Big Data Towards Smart Manufacturing and Industry 4.0: 360 Degree Comparison,” IEEE Access, vol. 6, pp. 3585–3593, 2018, doi: 10.1109/ACCESS.2018.2793265.
[13] C. A. Escobar, M. E. McGovern, and R. Morales-Menendez, “Quality 4.0: a review of big data challenges in manufacturing,” J. Intell. Manuf., vol. 32, no. 8, pp. 2319–2334, 2021, doi: 10.1007/s10845-021-01765-4.
[14] D. Ivanov, “Viable supply chain model: integrating agility, resilience and sustainability perspectives—lessons from and thinking beyond the COVID-19 pandemic,” Ann. Oper. Res., vol. 319, no. 1, pp. 1411–1431, 2022, doi: 10.1007/s10479-020-03640-6.
[15] E. Yurtsever, J. Lambert, A. Carballo, and K. Takeda, “A Survey of Autonomous Driving: Common Practices and Emerging Technologies,” IEEE Access, vol. 8, pp. 58443–58469, 2020, doi: 10.1109/ACCESS.2020.2983149.
[16] S. Kuutti, R. Bowden, Y. Jin, P. Barber, and S. Fallah, “A Survey of Deep Learning Applications to Autonomous Vehicle Control,” IEEE Trans. Intell. Transp. Syst., vol. 22, no. 2, pp. 712–733, 2021, doi: 10.1109/TITS.2019.2962338.
[17] S. Houben et al., “Inspect, Understand, Overcome: A Survey of Practical Methods for AI Safety,” in Deep Neural Networks and Data for Automated Driving, T. Fingscheidt, H. Gottschalk, and S. Houben, Eds., Cham: Springer International Publishing, 2022, pp. 3–78. doi: 10.1007/978-3-031-01233-4_1.
[18] A. Geiger, P. Lenz, and R. Urtasun, “Are we ready for autonomous driving? The KITTI vision benchmark suite,” in 2012 IEEE Conference on Computer Vision and Pattern Recognition, 2012, pp. 3354–3361. doi: 10.1109/CVPR.2012.6248074.
[19] P. Sun et al., “Scalability in Perception for Autonomous Driving: Waymo Open Dataset,” in 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2020, pp. 2443–2451. doi: 10.1109/CVPR42600.2020.00252.
[20] F. Tao, H. Zhang, A. Liu, and A. Y. C. Nee, “Digital Twin in Industry: State-of-the-Art,” IEEE Trans. Ind. Informatics, vol. 15, no. 4, pp. 2405–2415, 2019, doi: 10.1109/TII.2018.2873186.
[21] M. Althoff, M. Koschi, and S. Manzinger, “CommonRoad: Composable benchmarks for motion planning on roads,” in 2017 IEEE Intelligent Vehicles Symposium (IV), 2017, pp. 719–726. doi: 10.1109/IVS.2017.7995802.
[22] F. Tao, J. Cheng, Q. Qi, M. Zhang, H. Zhang, and F. Sui, “Digital twin-driven product design, manufacturing and service with big data,” Int. J. Adv. Manuf. Technol., vol. 94, no. 9, pp. 3563–3576, 2018, doi: 10.1007/s00170-017-0233-1.
[23] E. Sisinni, A. Saifullah, S. Han, U. Jennehag, and M. Gidlund, “Industrial Internet of Things: Challenges, Opportunities, and Directions,” IEEE Trans. Ind. Informatics, vol. 14, no. 11, pp. 4724–4734, 2018, doi: 10.1109/TII.2018.2852491.
[24] L. Xu, W. He, and S. Li, “Internet of Things in Industries: A Survey,” IEEE Trans. Ind. Informatics, vol. 10, no. 4, pp. 2233–2243, Nov. 2014, doi: 10.1109/TII.2014.2300753.
[25] G. Macher, E. Armengaud, E. Brenner, and C. Kreiner, “A Review of Threat Analysis and Risk Assessment Methods in the Automotive Context,” in Computer Safety, Reliability, and Security, A. Skavhaug, J. Guiochet, and F. Bitsch, Eds., Cham: Springer International Publishing, 2016, pp. 130–141. doi: https://doi.org/10.1007/978-3-319-45477-1_11.
[26] C. Chen, A. Seff, A. Kornhauser, and J. Xiao, “Deepdriving: Learning affordance for direct perception in autonomous driving,” in Proceedings of the IEEE international conference on computer vision, 2015, pp. 2722–2730. doi: https://doi.org/10.1109/ICCV.2015.312.