In variant design, the proliferation of bills of materials makes it difficult for designers to find previous designs that would aid in completing a new design task. This research presents a novel, data mining approach to forming generic bills of materials (GBOMs), entities that represent the different variants in a product family and facilitate the search for similar designs and configuration of new variants. The technical difficulties include: (i) developing families or categories for products, assemblies, and component parts; (ii) generalizing purchased parts and quantifying their similarity; (iii) performing tree union; and (iv) establishing design constraints. These challenges are met through data mining methods such as text and tree mining, a new tree union procedure, and embodying the GBOM and design constraints in constrained XML. The paper concludes with a case study, using data from a manufacturer of nurse call devices, and identifies a new research direction for data mining motivated by the domains of engineering design and information.

1.
Prebil
,
I.
,
Zupan
,
S.
, and
Lu
,
P.
,
1995
, “
Adaptive and Variant Design of Rotational Connections
,”
Eng. Comput.
,
11
, pp.
83
93
.
2.
Opitz, H., 1970, A Classification System to Describe Workpieces (translated by A. Taylor), Pergamon Press, New York.
3.
Hegge
,
H. M. H.
, and
Wortmann
,
J. C.
,
1991
, “
Generic Bill-Of-Material: A New Product Model
,”
International Journal of Production Economics
,
23
, pp.
117
128
.
4.
Farrell
,
R.
, and
Simpson
,
T.
,
2003
, “
Product Platform Design to Improve Commonality in Custom Products
,”
Journal of Intelligent Manufacturing
,
14
(
6
), pp.
541
556
.
5.
Simpson
,
T.
,
Umapathy
,
K.
,
Nanda
,
J.
,
Halbe
,
S.
, and
Hodge
,
B.
,
2003
, “
Development of a Framework for Web-Based Product Platform Customization
,”
ASME J. Comput. Inf. Sci. Eng.
,
3
, pp.
119
129
.
6.
Koenig, D. T., 1994, Manufacturing Engineering: Principles for Optimization, Taylor & Francis, Washington, D.C.
7.
McKernan, T. J., and Jayaraman, B., 2000, “CobWeb: A Constraint-Based XML for the Web,” Department of Computer Science, University at Buffalo.
8.
Ham
,
I.
,
Marion
,
D.
, and
Rubinovich
,
J.
,
1986
, “
Developing a Group Technology Coding and Classification Scheme
,”
Industrial Engineering
,
18
(
7
), pp.
90
97
.
9.
Henderson
,
M.
, and
Musti
,
S.
,
1988
, “
Automated Group Technology Part Coding From a Three-Dimensional CAD Database
,”
ASME J. Eng. Ind.
,
110
(
3
), pp.
278
287
.
10.
Harhalakis, G., Kinsey, A., and Minis, I., 1992, “Automated Group Technology Code Generation Using PDES,” in Proc. 3rd Int. Conf. Computer Integrated Manufacturing, Rensselaer Polytechnic Institute, Troy NY.
11.
Ham, I., Hitomi, K., and Yoshida, T., 1985, Group Technology: Applications to Production Management (International Series in Management Science/Operations Research, 9), Kluwer Academic Publishers, Dordrecht.
12.
Shah
,
J.
, and
Bhatnagar
,
A.
,
1989
, “
Group Technology Classification From Feature-Based Geometric Models
,”
Manufacturing Review
,
2
(
3
), pp.
204
213
.
13.
Kao
,
Y.
, and
Moon
,
Y. B.
,
1991
, “
Unified Group Technology Implementation Using the Backpropagation Learning Rule of Neural Networks
,”
Computers & Industrial Engineering
,
20
(
4
), pp.
425
437
.
14.
Iyer
,
S.
, and
Nagi
,
R.
,
1997
, “
Automated Retrieval and Ranking of Similar Parts in Agile Manufacturing
,”
IIE Transactions, Design and Manufacturing, special issue on Agile Manufacturing
,
29
(
10
), pp.
859
876
.
15.
Lee-Post
,
A.
,
2000
, “
Part Family Identification Using a Simple Genetic Algorithm
,”
Int. J. Prod. Res.
,
38
(
4
), pp.
793
810
.
16.
Jiao
,
J.
, and
Tseng
,
M. M.
,
1999
, “
Methodology of Developing Product Family Architecture for Mass Customization
,”
Journal of Intelligent Manufacturing
,
10
(
1
), pp.
3
20
.
17.
Jiao
,
J.
,
Tseng
,
M. M.
,
Ma
,
Q.
, and
Zou
,
Y.
,
2000
, “
Generic Bill-Of-Materials-and-Operations for High-Variety Production Management
,”
Concurrent Engineering-Research & Applications
,
8
(
4
), pp.
297
321
.
18.
Ramabhatta
,
V.
,
Lin
,
L.
, and
Nagi
,
R.
,
1997
, “
Object Hierarchies to aid Representation and Variant Design of Complex Assemblies in an Agile Environment
,”
International Journal of Agile Manufacturing
,
1
(
1
), pp.
77
90
.
19.
Cook
,
D. J.
, and
Holder
,
L. B.
,
2000
, “
Graph-Based Data Mining
,”
IEEE Intell. Syst.
,
15
(
2
), pp.
32
41
.
20.
Romanowski, C. J., and Nagi, R., 2004, “On Comparing Bills Of Materials: A Similarity/Distance Measure for Unordered Trees,” accepted by IEEE Trans. Sys. Man Cybern., Part A (to appear May 2005).
21.
Ng, R., and Han, J., 1994, “Efficient and Effective Clustering Methods for Spatial Data Mining,” Proc. of 20th International Conference on Very Large DataBases, pp. 144–155, Santiago de Chile, Chile.
22.
Romanowski, C. J., and Nagi, R., 2004, “Adaptive Data Mining in a Variant Design Support System,” in Proc. of the 13th Industrial Engineering Research Conference, Houston TX.
23.
Orlicky, J., 1975, Material Requirements Planning, McGraw-Hill Book Company, New York.
24.
Agrawal
,
R.
,
Imielinski
,
T.
, and
Swamı´
,
A.
,
1993
, “
Mining Association Rules Between Sets of Items in Large Databases
,”
SIGMOD Record (ACM Special Interest Group on Management of Data)
,
22
(
2
), pp.
207
216
.
25.
Liu, B., Hsu, W., and Ma, Y., 1998, “Integrating Classification and Association Rule Mining,” Proc. of the Fourth International Conference on Knowledge Discovery and Data Mining (KDD-98, Plenary Presentation), New York, USA.
26.
Zaki
,
M. J.
,
2000
, “
Scalable Algorithms for Association Mining
,”
IEEE Transactions on Knowledge & Data Engineering
,
12
(
3
), pp.
372
390
.
27.
Romanowski, C. J., and Nagi, R., 2001, “A Data Mining-Based Engineering Design Support System: A Research Agenda,” in Data Mining for Design and Manufacturing: Methods and Applications, D. Braha, ed., Kluwer Academic Publishers, The Netherlands, pp. 235–254.
28.
Romanowski, C. J., Nagi, R., and Sudit, M., 2003, “Data Mining in an Engineering Design Environment: OR Applications From Graph Matching,” submitted to Computers and Operations Research, special issue on Data Mining.
You do not currently have access to this content.