Abstract
Product–service system (PSS) conceptual design process plays a critical and strategic rule in offering an optimal set of products and services to customers. Parameter translation driven by customer requirements (CRs) is the mainline in this process. A two-phase parameter translating process is presented based on a three-domain PSS conceptual design framework: translating CRs into design requirements (DRs), and then into module characteristics (MCs) of products and services. DRs and MCs of PSS are both divided into product-related part and service-related part. There exist complex relationships between and within the two parts of parameters in one domain and those between parameters in two adjacent domains. This condition increases the complexity and difficulty to translate CRs into DRs and MCs with definite specifications. The traditional parameter translating methods which depend on the designers’ experiences are insufficient to fulfill the goal of domain mapping. The wealthy conceptual design cases for existing PSS can be reused to generate sufficient knowledge for designers. An apriori-based association rule mining algorithm is proposed to elicit parameter translating rules for aiding the PSS conceptual design. To ensure the availability of the mined rules, rule redundancy and conflicting solutions are taken into account. Considering the new design data are continuously added in, an incremental updating technique is proposed to maintain the association rules without retracing the original database. A weighting strategy is adopted to highlight the novelty the newly added records, and a competitive strategy is employed to avoid ignoring some promising rules. A case study is carried out to demonstrate the effectiveness of the developed approach for aiding PSS conceptual design.
Article PDF
Similar content being viewed by others
Explore related subjects
Discover the latest articles, news and stories from top researchers in related subjects.Avoid common mistakes on your manuscript.
References
Aurich JC, Wolf N, Siener M, Schweitzer E (2009) Configuration of product-service systems. J Manuf Technol Manag 20(5):591–605
Manzini E, Vezzoli C (2003) A strategic design approach to develop sustainable product service systems: examples taken from the“environmentally friendly innovation” Italian prize. J Clean Prod 11(8):851–857
Williams A (2007) Product service systems in the automobile industry: contribution to system innovation? J Clean Prod 15(11–12):1093–1103
Aurich JC, Fuchs C, DeVries MF (2004) An approach to life cycle oriented technical service design. CIRP Ann Manuf Technol 53(1):151–154
Aurich JC, Fuchs C, Wagenknecht C (2006) Life cycle oriented design of technical Product–Service Systems. J Clean Prod 14(17):1480–1494
Sakao T, Shimomura Y (2007) Service engineering: a novel engineering discipline for producers to increase value combining service and product. J Clean Prod 15(6):590–604
Sakao T, Shimomura Y, Sundin E, Comstock M (2009) Modeling design objects in CAD system for service/product engineering. Computer-Aided Des 41(3):197–213
Bitran G, Pedrosa L (1998) A structured product development perspective for service operations. Eur Manag J 16(2):169–189
Suh N (1990) The principles of design. Oxford University Press, USA
Griffin A, Hauser JR (1993) The voice of the customer. Mark Sci 12(1):1–27
Shen XX, Tan KC, Xie M (2001) The implementation of quality function deployment based on linguistic data. J Intell Manuf 12:65–75
Han CH, Kim JK, Choi SH (2004) Prioritizing engineering characteristics in quality function deployment with incomplete information: a linear partial ordering approach. Int J Prod Econ 91:235–249
Fung R, Popplewell K, Xie J (1998) An intelligent hybrid system for customer requirements analysis and product attribute targets determination. Int J Prod Res 36(1):13–34
Harding J, Popplewell K, Fung R, Omar A (2001) An intelligent information framework relating customer requirements and product characteristics. Comput Ind 44(1):51–65
Chen YZ, Tang J, Fung RYK et al (2004) Fuzzy regression-based mathematical programming model for quality function deployment. Int J Prod Res 42(5):1009–1027
Fung RYK, Chen YZ, Tang JF (2006) Estimating the functional relationships for quality function deployment under uncertainties. Fuzzy Set Syst 157:98–120
Kim KJ, Moskowitz H, Dhingra A, Evans G (2000) Fuzzy multicriteria models for quality function deployment. Eur J Oper Res 121:504–518
Zhang XP, Bode J, REn SJ (1996) Neural networks in quality function deployment. Comput Ind Eng 31(3/4):669–673
Jiao J, Zhang Y (2005) Product portfolio identification based on association rule mining. Computer-Aided Des 37(2):149–172
Shao X, Wang Z, Li P, Feng C (2006) Integrating data mining and rough set for customer group-based discovery of product configuration rules. Int J Prod Res 44(14):2789–2812
Romanowski C, Nagi R (2004) A data mining approach to forming generic bills of materials in support of variant design activities. J Comput Inf Sci Eng 4(4):316–328
Sadoyan H, Zakarian A, Mohanty P (2006) Data mining algorithm for manufacturing process control. Int J Adv Manuf Technol 28:342–350
Alisantoso D, Khoo LP, Ivan Lee BH, Fok SC (2005) A rough set approach to design concept analysis in a design chain. Int J Adv Manuf Technol 26:427–435
Yin J, Li D, Peng Y (2006) Knowledge acquisition from metal forming simulation. Int J Adv Manuf Technol 29:279–286
Agrawal R, Srikant R (1994) Fast algorithms for mining association rules in large database. Proceedings of 20th International Conference on Very Large Data Bases, Santiago, Chile, 1–32
Agrawal R, Imieliński T, Swami A (1993) Mining association rules between sets of items in massive databases. Proceedings of ACM/SIGMOD International Conference on Management of Data, 207–216
Cheung D, Ng V, Fu A, Fu Y (1996) Efficient mining of association rules in distributed databases. IEEE Trans Knowl Data Eng 8(6):911–922
Chen Y, Weng C (2008) Mining association rules from imprecise ordinal data. Fuzzy Set Syst 159(4):460–474
Delgado M, Marin N, Sánchez D, Vila M (2003) Fuzzy association rules: general model and applications. IEEE Trans Fuzzy Syst 11(2):214–225
Cheung D, Han J, Ng V, Wong C (1996b) Maintenance of discovered association rules in large databases: An incremental updating technique. Proceeding of the 12th international conference on data engineering, New Orleans, Louisiana, March 1996: 106–114
Cheung DW, Lee SD, Kao B (1997) A general incremental technique for maintaining discovered association rules. Proceeding of the 5th international conference on database systems for advanced applications, MElboume, 1997:185–194
Lee S, Cheung D, Kao B (1998) Is sampling useful in data mining? A case in the maintenance of discovered association rules. Data Min Knowl Disc 2(3):233–262
Dudek D (2009) RMAIN: Association rules maintenance without reruns through data. Inform Sci 179(24):4123–4139
Zhang S, Zhang C, Yan X (2003) Post-mining: maintenance of association rules by weighting. Inf Syst 28(7):691–707
Author information
Authors and Affiliations
Corresponding author
Rights and permissions
About this article
Cite this article
Geng, X., Chu, X. & Zhang, Z. An association rule mining and maintaining approach in dynamic database for aiding product–service system conceptual design. Int J Adv Manuf Technol 62, 1–13 (2012). https://doi.org/10.1007/s00170-011-3787-3
Received:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s00170-011-3787-3