Skip to main content

Identifying risky components of display products for redesign considering user attention and failure causality

Soft Computing volume 27, pages 2921–2942 (2023)Cite this article

Abstract

Identifying risky components is crucial to improving product reliability in the final redesign of products. Design failure mode and effects analysis has become a prevalent application in product redesign as a useful risk assessment method. However, critical data, which contain failure causality relationships (FCRs) between failure modes, correlations among risk factors, and user attention index of the product component, are not considered. This study develops an improved approach for identifying the target risky components considering importance index, user attention, and FCRs based on the design risky component (DRC) and nonlinear optimization model. The DRC, which integrates the customer requirement level, quality test level, and failure risk information of product components, is proposed to represent the risk degree of product components. The nonlinear optimization models are constructed to derive the weights of risk factors and final redesign of product components. Firstly, a two-stage fuzzy quality function deployment is established to map the importance index of customer requirements under a trapezoidal fuzzy number. A local–global normalization measure is implemented to calculate the index of user attention based on quality test data. Secondly, the FCRs of failure modes between or within product components are characterized by a directed network model. In this network, the failure modes are modeled as vertices, and the causality relationships among failure modes are modeled as directed edges. The values of the directed edges are characterized by weighted risk priority numbers, and the weight of risk factors is optimized by a nonlinear optimization model. Then, the FCRs incorporate the internal failure effect and the external failure effect, which are characterized by PROMETHEE II with the net flow. A 0–1 optimization model with the maximum redesign value and resource constraints of product components is constructed to decide on the final redesign of target risky components. Finally, a real-world case of display product is conducted to demonstrate the validity and feasibility of the proposed approach. The results demonstrate that the proposed method is more effective in identifying risk components.

This is a preview of subscription content, access via your institution.

Access options

Buy single article

Instant access to the full article PDF.

USD 39.95

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11

Data availability

The authors confirm that the data supporting the findings of this study are available within the article.

References

  • Aguirre García PA, Pérez-Domínguez L, Luviano-Cruz D et al (2021) PFDA-FMEA, an integrated method improving fmea assessment in product design. Appl Sci 11(4):1406. https://doi.org/10.3390/app11041406

    Article  Google Scholar 

  • Belu N, Anghel DC, Rachieru N (2013) Application of fuzzy logic in design failure mode and effects analysis. Appl Mech Mater 371:832–836

    Google Scholar 

  • Bhattacharjee P, Dey V, Mandal UK (2022) Failure mode and effects analysis (FMEA) using interval number based BWM-MCDM approach: Risk expected value (REV) method. Soft Comput. https://doi.org/10.1007/s00500-022-07264-9

    Article  Google Scholar 

  • Bovea MD, Wang B (2007) Redesign methodology for developing environmentally conscious products. Int J Prod Res 45(18–19):4057–4072

    MATH  Google Scholar 

  • Brenner W, Uebernickel F (2016) Design thinking for innovation. Springer, Research and Practice, pp 85–102

    Google Scholar 

  • Chang KH, Wen TC (2010) A novel efficient approach for DFMEA combining 2-tuple and the OWA operator. Expert Syst Appl 37(3):2362–2370

    Google Scholar 

  • Chen SH (2016) Determining the service demands of an aging population by integrating QFD and FMEA method. Qual Quant 50(1):283–298

    MathSciNet  Google Scholar 

  • Chutia R, Gogoi MK (2018) Fuzzy risk analysis in poultry farming based on a novel similarity measure of fuzzy numbers. Appl Soft Comput 66:60–76

    Google Scholar 

  • De Andrade JMM, de Leite AFCSM, Canciglieri MB et al (2022) A multi-criteria decision tool for FMEA in the context of product development and industry 4.0. Int J Comput Integr Manuf 35(1):36–49

    Google Scholar 

  • Deng X, Yuan Y (2021) A novel fuzzy dominant goal programming for portfolio selection with systematic risk and non-systematic risk. Soft Comput 25(23):14809–14828

    MATH  Google Scholar 

  • Fazeli HR, Peng Q (2021) Integrated approaches of BWM-QFD and FUCOM-QFD for improving weighting solution of design matrix. J Intell Manuf. https://doi.org/10.1007/s10845-021-01832-w

    Article  Google Scholar 

  • Filz MA, Langner JEB, Herrmann C, Thiede S (2021) Data-driven failure mode and effect analysis (FMEA) to enhance maintenance planning. Comput Ind 129:103451. https://doi.org/10.1016/j.compind.2021.103451

    Article  Google Scholar 

  • Gautam N, Singh N (2008) Lean product development: maximizing the customer perceived value through design change (redesign). Int J Prod Econ 114(1):313–332

    Google Scholar 

  • Geyer F, Lehnen J, Herstatt C (2018) Customer need identification methods in new product development: what works “best”? Int J Innov Technol Manag 15(01):1850008. https://doi.org/10.1142/S0219877018500086

    Article  Google Scholar 

  • Hou L, Jiao RJ (2020) Data-informed inverse design by product usage information: a review, framework and outlook. J Intell Manuf 31(3):529–552

    Google Scholar 

  • Huang Q, Wu G, Li ZS (2019) Design for reliability through text mining and optimal product verification and validation planning. IEEE Trans Reliab 70(1):231–247

    Google Scholar 

  • Jiao R, Commuri S, Panchal J, Milisavljevic-Syed J et al (2021) Design engineering in the age of industry 4.0. J Mech Des 143(7):070801. https://doi.org/10.1115/1.4051041

    Article  Google Scholar 

  • Kusiak A (2017) Smart manufacturing must embrace big data. Nat News 544(7648):23–25. https://doi.org/10.1038/544023a

    Article  Google Scholar 

  • Lee J, Ardakani HD, Yang S, Bagheri B (2015) Industrial big data analytics and cyber-physical systems for future maintenance & service innovation. Procedia 38:3–7

    Google Scholar 

  • Li ZS, Kou FH, Cheng XC, Wang T (2006) Model-based product redesign. Int J Comput Sci Netw Secur 6(1A):100–108

    Google Scholar 

  • Liang D, Li F (2021) Risk assessment in failure mode and effect analysis: improved ORESTE method with hesitant pythagorean fuzzy information. IEEE Trans Eng Manag. https://doi.org/10.1109/TEM.2021.3073373

    Article  Google Scholar 

  • Lin H, You J, Zhang X (2021) Supplier selection with different risk preferences and attribute sets: an innovative study based on generalized linguistic term sets. Adv Eng Inform 50:101424. https://doi.org/10.1016/j.aei.2021.101424

    Article  Google Scholar 

  • Liu HC, Chen YZ, You JX, Li H (2016a) Risk evaluation in failure mode and effects analysis using fuzzy digraph and matrix approach. J Intell Manuf 27(4):805–816

    Google Scholar 

  • Liu SF, Cheng JH, Lee YL, Gau FR (2016b) A case study on FMEA-based quality improvement of packaging designs in the TFT-LCD industry. Total Qual Manag Bus Excell 27(3–4):413–431

    Google Scholar 

  • Ma H, Chu X, Xue D, Chen D (2017) A systematic decision making approach for product conceptual design based on fuzzy morphological matrix. Expert Syst Appl 81:444–456

    Google Scholar 

  • Ma H, Chu X, Xue D, Chen D (2019) Identification of to-be-improved components for redesign of complex products and systems based on fuzzy QFD and FMEA. J Intell Manuf 30(2):623–639

    Google Scholar 

  • Molla MU, Giri BC, Biswas P (2021) Extended PROMETHEE method with pythagorean fuzzy sets for medical diagnosis problems. Soft Comput 25(6):4503–4512

    MATH  Google Scholar 

  • Mu R, Zheng Y, Zhang K, Zhang Y (2021) Research on Customer Satisfaction Based on Multidimensional Analysis. Int J Comput Intell Syst 14(1):605–616

    Google Scholar 

  • Provost F, Fawcett T (2013) Data science and its relationship to big data and data-driven decision making. Big Data 1(1):51–59

    Google Scholar 

  • Rivera Torres PJ, Serrano Mercado EI, Anido Rifón L (2018) Probabilistic Boolean network modeling and model checking as an approach for DFMEA for manufacturing systems. J Intell Manuf 29(6):1393–1413

    Google Scholar 

  • Safari H, Faraji Z, Majidian S (2016) Identifying and evaluating enterprise architecture risks using FMEA and fuzzy VIKOR. J Intell Manuf 27(2):475–486

    Google Scholar 

  • Sellappan N, Nagarajan D, Palanikumar K (2015) Evaluation of risk priority number (RPN) in design failure modes and effects analysis (DFMEA) using factor analysis. Int J Appl Eng Res 10(14):34194–34198

    Google Scholar 

  • Selvachandran G, Garg H, Alaroud MH, Salleh AR (2018) Similarity measure of complex vague soft sets and its application to pattern recognition. Int J Fuzzy Syst 20(6):1901–1914

    MathSciNet  Google Scholar 

  • Serrano-Guerrero J, Olivas JA, Romero FP, Herrera-Viedma E (2015) Sentiment analysis: a review and comparative analysis of web services. Inf Sci 311:18–38

    Google Scholar 

  • Shin JH, Kiritsis D, Xirouchakis P (2015) Design modification supporting method based on product usage data in closed-loop PLM. Int J Comput Integr Manuf 28(6):551–568

    Google Scholar 

  • Smith S, Smith G, Shen YT (2012) Redesign for Product Innovation. Des Stud 33(2):160–184

    Google Scholar 

  • Smith S, Smith GC, Chen YR (2013) A KE-LSA approach for user-centered design. J Intell Manuf 24(5):919–933

    Google Scholar 

  • Sun SY, Zhu Hm (2017) PROMETHEE’s parameters setting method based on robustness analysis. Syst Eng Electron 39(1):120–124 ((in Chinese))

    MATH  Google Scholar 

  • Tang L, Meng Y (2021) Data analytics and optimization for smart industry. Front Eng Manag 8(2):157–171

    Google Scholar 

  • Tian ZP, Wang JQ, Zhang HY (2018) An integrated approach for failure mode and effects analysis based on fuzzy best-worst, relative entropy, and VIKOR methods. Appl Soft Comput 72:636–646

    Google Scholar 

  • Wan SP, Dong JY (2015) Interval-valued intuitionistic fuzzy mathematical programming method for hybrid multi-criteria group decision making with interval-valued intuitionistic fuzzy truth degrees. Inf Fus 26:49–65. https://doi.org/10.1016/j.inffus.2015.01.006

    Article  Google Scholar 

  • Van Waarde HJ, Eising J, Trentelman HL, Camlibel MK (2020) Data informativity: a new perspective on data-driven analysis and control. IEEE Trans Autom Control 65(11):4753–4768

    MathSciNet  MATH  Google Scholar 

  • Wang WZ, Liu XW, Liu SL (2019) Failure mode and effect analysis for machine tool risk analysis using extended gained and lost dominance score method. IEEE Trans Reliab 69(3):954–967

    Google Scholar 

  • Wu X, Liao H (2021) Customer-oriented product and service design by a novel quality function deployment framework with complex linguistic evaluations. Inf Process Manag 58(2):102469. https://doi.org/10.1016/j.ipm.2020.102469

    Article  Google Scholar 

  • Xia HC, Li DF, Zhou JY, Wang JM (2006) Fuzzy LINMAP method for multi attribute decision making under fuzzy environments. J Comput Syst Sci 72(4):741–759

    MATH  Google Scholar 

  • Xia Y, Tan D, Wang B (2021) Use of a product service system in a competing remanufacturing market. Omega 102:102387. https://doi.org/10.1016/j.omega.2020.102387

    Article  Google Scholar 

  • Xu Q, Jiao RJ, Yang X, Helander M, Khalid HM, Opperud A (2009) An analytical Kano model for customer need analysis. Des Stud 30(1):87–110

    Google Scholar 

  • Yan HB, Ma T (2015) A group decision-making approach to uncertain quality function deployment based on fuzzy preference relation and fuzzy majority. Eur J Oper Res 241(3):815–829

    MathSciNet  MATH  Google Scholar 

  • Yu JX, Wu SB, Chen HC, Yu Y et al (2021) Risk assessment of submarine pipelines using modified FMEA approach based on cloud model and extended VIKOR method. Process Saf Environ Prot 155:555–574

    Google Scholar 

  • Yucesan M, Gul M (2021) Failure modes and effects analysis based on neutrosophic analytic hierarchy process: method and application. Soft Comput 25:11035–11052

    Google Scholar 

  • Zhang Z, Chu X (2011) Risk prioritization in failure mode and effects analysis under uncertainty. Expert Syst Appl 38(1):206–214

    Google Scholar 

  • Zhang H, Rao H, Feng J (2018) Product innovation based on online review data mining: a case study of Huawei phones. Electron Commer Res 18(1):3–22

    Google Scholar 

  • Zhang L, Chu X, Xue D (2019) Identification of the to-be-improved product features based on online reviews for product redesign. Int J Prod Res 57(8):2464–2479

    Google Scholar 

  • Zhang W, Wang SJ, Hou L et al (2021) Operating data-driven inverse design optimization for product usage personalization with an application to wheel loaders. J Ind Inf Integr 23:100212. https://doi.org/10.1016/j.jii.2021.100212

    Article  Google Scholar 

  • Zheng L, He Z, He S (2021) An integrated probabilistic graphic model and FMEA approach to identify product defects from social media data. Expert Syst Appl 178:115030. https://doi.org/10.1016/j.eswa.2021.115030

    Article  Google Scholar 

  • Zhou F, Ji Y, Jiao RJ (2013) Affective and cognitive design for mass personalization: status and prospect. J Intell Manuf 24(5):1047–1069

    Google Scholar 

  • Zhou J, Liu Y, Xiahou T, Huang T (2021) A novel FMEA-based approach to risk analysis of product design using extended choquet integral. IEEE Trans Reliab. https://doi.org/10.1109/TR.2021.3060029

    Article  Google Scholar 

Download references

Acknowledgements

This research is supported by the National Natural Science Foundation of China (No. 51975495) and the Ministry of Science and Technology of China (No. 2020IM010100).

Funding

This work was supported by the National Natural Science Foundation of China [grant number 51975495] and the Ministry of Science and Technology of China [grant number 2020IM010100].

Author information

Authors and Affiliations

  1. Department of Mechanical and Electrical Engineering, Xiamen University, Xiamen, 361102, China

    Xiaozhen Lian, Liang Hou, Wenbo Zhang & Husehng Yan

  2. Department of Consumer Quality, Tianma Microelectronics Co., Ltd, Xiamen, 361101, China

    Husehng Yan

  3. Institute of Mechanical and Manufacturing Engineering, Cardiff University, Cardiff, CF24 3AA, UK

    Ying Liu

Authors
  1. Xiaozhen Lian
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Liang Hou
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Wenbo Zhang
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Husehng Yan
    View author publications

    You can also search for this author in PubMed Google Scholar

  5. Ying Liu
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

XL: performed the data analyses and wrote the manuscript, the main idea of the article, and the conclusion analysis of the article. LH: was responsible for the overall understanding of the structure of the article. WZ and HY: performed the data analyses. YL: performed revision of the full text.

Corresponding author

Correspondence to Liang Hou.

Ethics declarations

Conflict of interest

No potential conflict of interest was reported by all authors.

Ethical approval

This article does not contain any studies with human participants or animals performed by any of the authors.

Informed consent

Informed consent was obtained from all individual participants included in the study.

Appendix

Related Tables are presented as follows:

See Tables 10, 11, 12, 13, and 14.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Lian, X., Hou, L., Zhang, W. et al. Identifying risky components of display products for redesign considering user attention and failure causality. Soft Comput 27, 2921–2942 (2023). https://doi.org/10.1007/s00500-022-07660-1

Download citation

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00500-022-07660-1

Keywords

  • Design failure mode and effects analysis
  • Fuzzy quality function deployment
  • Failure causality relationships
  • Design risky component
  • Optimization model
  • Display products