Research Papers: Design Automation

Learning From the Past: Uncovering Design Process Models Using an Enriched Process Mining

[+] Author and Article Information
Lijun Lan, Wen Feng Lu

Department of Mechanical Engineering,
National University of Singapore,
Singapore 117576

Ying Liu

Mechanical and Manufacturing Engineering,
School of Engineering Cardiff University,
Cardiff CF24 3AA, UK
e-mail: LiuY81@Cardiff.ac.uk

1Corresponding author.

Contributed by the Design Automation Committee of ASME for publication in the JOURNAL OF MECHANICAL DESIGN. Manuscript received March 29, 2017; final manuscript received November 23, 2017; published online February 27, 2018. Assoc. Editor: Harrison M. Kim.

J. Mech. Des 140(4), 041403 (Feb 27, 2018) (13 pages) Paper No: MD-17-1245; doi: 10.1115/1.4039200 History: Received March 29, 2017; Revised November 23, 2017

Design documents and design project footprints accumulated by corporate information technology systems have increasingly become valuable sources of evidence for design information and knowledge management. Identification and extraction of such embedded information and knowledge into a clear and usable format will greatly accelerate continuous learning from past design efforts for competitive product innovation and efficient design process management in future design projects. Most of the existing design information extraction systems focus on either organizing design documents for efficient retrieval or extracting relevant product information for product optimization. Different from traditional systems, this paper proposes a methodology of learning and extracting useful knowledge using past design project documents from design process perspective based on process mining techniques. Particularly different from conventional techniques that deal with timestamps or event logs only, a new process mining approach that is able to directly process textual data is proposed at the first stage of the proposed methodology. The outcome is a hierarchical process model that reveals the actual design process hidden behind a large amount of design documents and enables the connection of various design information from different perspectives. At the second stage, the discovered process model is analyzed to extract multifaceted knowledge patterns by applying a number of statistical analysis methods. The outcomes range from task dependency study from workflow analysis, identification of irregular task execution from performance analysis, cooperation pattern discovery from social net analysis to evaluation of personal contribution based on role analysis. Relying on the knowledge patterns extracted, lessons and best practices can be uncovered which offer great support to decision makers in managing any future design initiatives. The proposed methodology was tested using an email dataset from a university-hosted multiyear multidisciplinary design project.

Copyright © 2018 by ASME
Topics: Mining , Design , Workflow
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Kotinurmi, P. , Laesvuori, H. , Jokinen, K. , and Soininen, T. , 2004, “ Integrating Design Document Management Systems Using the Rosettanet E-Business Framework,” 6th International Conference on Enterprise Information Systems (ICEIS 2004), Porto, Portugal, Apr. 14–17, pp. 502–509.
Linas, G. , and Romualdas, B. , 2006, “ Electronic Document Management in Building Design,” J. Civ. Eng. Manage., 12(2), pp. 103–108.
Efthymiou, K. , Sipsas, K. , Mourtzis, D. , and Chryssolouris, G. , 2015, “ On Knowledge Reuse for Manufacturing Systems Design and Planning: A Semantic Technology Approach,” CIRP J. Manuf. Sci. Technol., 8, pp. 1–11. [CrossRef]
Baxter, D. , Gao, J. , Case, K. , Harding, J. , Young, B. , Cochrane, S. , and Dani, S. , 2007, “ An Engineering Design Knowledge Reuse Methodology Using Process Modelling,” Res. Eng. Des., 18(1), pp. 37–48. [CrossRef]
van der Aalst, W. M. P. , Weijters, T. , and Maruster, L. , 2004, “ Workflow Mining: Discovering Process Models From Event Logs,” IEEE Trans. Knowl. Data Eng., 16(9), pp. 1128–1142. [CrossRef]
Browning, T. R. , Fricke, E. , and Negele, H. , 2006, “ Key Concepts in Modeling Product Development Processes,” Syst. Eng., 9(2), pp. 104–128. [CrossRef]
Lan, L. , Liu, Y. , and Lu, W. F. , 2016, “ Discovering a Hierarchical Design Process Model Using Text Mining,” ASME Paper No. DETC2016-59829.
Tao, S. , Huang, Z. , Ma, L. , Guo, S. , Wang, S. , and Xie, Y. , 2013, “ Partial Retrieval of CAD Models Based on Local Surface Region Decomposition,” Comput.-Aided Des., 45(11), pp. 1239–1252. [CrossRef]
Tao, S. , Wang, S. , and Chen, A. , 2017, “ 3D CAD Solid Model Retrieval Based on Region Segmentation,” Multimedia Tools Appl., 76(1), pp. 103–121. [CrossRef]
Sivakumar, S. , and Dhanalakshmi, V. , 2013, “ An Approach Towards the Integration of CAD/CAM/CAI Through STEP File Using Feature Extraction for Cylindrical Parts,” Int. J. Comput. Integr. Manuf., 26(6), pp. 561–570. [CrossRef]
Yu, W. D. , and Hsu, J. Y. , 2013, “ Content-Based Text Mining Technique for Retrieval of CAD Documents,” Autom. Constr., 31, pp. 65–74. [CrossRef]
Huang, R. , Zhang, S. , Bai, X. , Xu, C. , and Huang, B. , 2015, “ An Effective Subpart Retrieval Approach of 3D CAD Models for Manufacturing Process Reuse,” Comput. Ind., 67, pp. 38–53. [CrossRef]
Liang, Y. , Liu, Y. , Kwong, C. , and Lee, W. , 2012, “ Learning the ‘Whys’: Discovering Design Rationale Using Text Mining—An Algorithm Perspective,” Comput.-Aided Des., 44(10), pp. 916–930. [CrossRef]
Jin, J. , Ji, P. , and Liu, Y. , 2015, “ Translating Online Customer Opinions Into Engineering Characteristics in QFD: A Probabilistic Language Analysis Approach,” Eng. Appl. Artif. Intell., 41, pp. 115–127. [CrossRef]
Rajpathak, D. G. , 2013, “ An Ontology Based Text Mining System for Knowledge Discovery From the Diagnosis Data in the Automotive Domain,” Comput. Ind., 64(5), pp. 565–580. [CrossRef]
Lan, L. , Liu, Y. , Lu, W. , and Alghamdi, A. , 2015, “ Automatic Discovery of Design Task Structure Using Deep Belief Nets,” ASME Paper No. DETC2015-47369.
Liu, Y. , Liang, Y. , Kwong, C. K. , and Lee, W. B. , 2010, “ A New Design Rationale Representation Model for Rationale Mining,” ASME J. Comput. Inf. Sci. Eng., 10(3), p. 031009. [CrossRef]
Jin, G. , Jeong, Y. , and Yoon, B. , 2015, “ Technology-Driven Roadmaps for Identifying New Product/Market Opportunities: Use of Text Mining and Quality Function Deployment,” Adv. Eng. Inf., 29(1), pp. 126–138. [CrossRef]
Jans, M. , van der Werf, J. M. , Lybaert, N. , and Vanhoof, K. , 2011, “ A Business Process Mining Application for Internal Transaction Fraud Mitigation,” Expert Syst. Appl., 38(10), pp. 13351–13359. [CrossRef]
da Cruz, J. I. B. , and Ruiz, D. D. , 2011, “ Conformance Analysis on Software Development: An Experience With Process Mining,” Int. J. Bus. Process Integr. Manage., 5(2), pp. 109–120. [CrossRef]
Luengo, D. , and Sepúlveda, M. , 2011, “ Applying Clustering in Process Mining to Find Different Versions of a Business Process That Changes Over Time,” International Conference on Business Process Management (BPM), Clermont-Ferrand, France, Aug. 29–Sept. 2, pp. 153–158.
Agrawal, R. , Gunopulos, D. , and Leymann, F. , 1998, “ Mining Process Models From Workflow Logs,” Sixth International Conference on Extending Database Technology: Advances in Database Technology (ETBT), Valencia, Spain, Mar. 23–27, pp. 469–483.
Tiwari, A. , Turner, C. J. , and Majeed, B. , 2008, “ A Review of Business Process Mining: State-of-the-Art and Future Trends,” Bus. Process Manage. J., 14(1), pp. 5–22. [CrossRef]
Li, J. , OuYang, J. , and Feng, M. , 2012, “ A Heuristic Genetic Process Mining Algorithm,” Seventh International Conference on Computational Intelligence and Security (CIS), Hainan, China, Dec. 3–4, pp. 15–19.
Seung-kyung, L. , Bongseok, K. , Minhoe, H. , Sungzoon, C. , Sungkyu, P. , and Daehyung, L. , 2013, “ Mining Transportation Logs for Understanding the After-Assembly Block Manufacturing Process in the Shipbuilding Industry,” Expert Syst. Appl., 40(1), pp. 83–95. [CrossRef]
Caron, F. , Vanthienen, J. , and Baesens, B. , 2013, “ A Comprehensive Investigation of the Applicability of Process Mining Techniques for Enterprise Risk Management,” Comput. Ind., 64(4), pp. 464–475. [CrossRef]
De Weerdt, J. , Schupp, A. , Vanderloock, A. , and Baesens, B. , 2013, “ Process Mining for the Multi-Faceted Analysis of Business Processes—A Case Study in a Financial Services Organization,” Comput. Ind., 64(1), pp. 57–67. [CrossRef]
Rojas, E. , Munoz-Gama, J. , Sepúlveda, M. , and Capurro, D. , 2016, “ Process Mining in Healthcare: A Literature Review,” J. Biomed. Inf., 61, pp. 224–236. [CrossRef]
Gunther, C. W. , and van der Aalst, W. M. P. , 2007, “ Fuzzy Mining—Adaptive Process Simplification Based on Multi-Perspective Metrics,” Fifth International Conference on Business Process Management (BPM 2007), Berlin, Sept. 24–28, pp. 328–343.
Maggi, F. M. , Mooij, A. J. , and Van Der Aalst, W. M. P. , 2011, “ User-Guided Discovery of Declarative Process Models,” IEEE Symposium on Computational Intelligence and Data Mining (CIDM 2011), Paris, France, Apr. 11–15, pp. 192–199.
Maggi, F. M. , Burattin, A. , Cimitile, M. , and Sperduti, A. , 2013, “ Online Process Discovery to Detect Concept Drifts in LTL-Based Declarative Process Models,” OTM Confederated International Conferences on the Move to Meaningful Internet Systems, Graz, Austria, Sept. 9–13, pp. 94–111.
Diamantini, C. , Genga, L. , and Potena, D. , 2016, “ Behavioral Process Mining for Unstructured Processes,” J. Intell. Inf. Syst., 47(1), pp. 5–32. [CrossRef]
Sinha, A. , and Paradkar, A. , 2010, “ Use Cases to Process Specifications in Business Process Modeling Notation,” IEEE International Conference on Web Services (ICWS), Miami, FL, July 5–10, pp. 473–480.
Friedrich, F. , Mendling, J. , and Puhlmann, F. , 2011, Process Model Generation From Natural Language Text, Springer, Berlin. [CrossRef]
Hinton, G. E. , and Salakhutdinov, R. , 2009, “ Replicated Softmax: An Undirected Topic Model,” 23rd Annual Conference on Neural Information Processing Systems (NIPS 2009), Vancouver, BC, Canada, Dec. 7–10, pp. 1607–1614.
Bengio, Y. , 2009, “ Learning Deep Architectures for AI,” Found. Trends Mach. Learn., 2(1), pp. 1–27. [CrossRef]
Manning, C. D. , Surdeanu, M. , Bauer, J. , Finkel, J. R. , Bethard, S. , and McClosky, D. , 2014, “ The Stanford CoreNLP Natural Language Processing Toolkit,” 52nd Annual Meeting of the Association for Computational Linguistics: System Demonstrations (ACL), Baltimore, MD, June 22–27, pp. 55–60.
Lan, L. , Wu, X. , and Liu, Y. , 2015, “ Designing a Fast Adaptive Clustering Approach for Traffic Wave Simulation,” ASME Paper No. DETC2015-47873.


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Fig. 1

The methodology of learning from archival design project documents

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Fig. 3

Example of local context

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Fig. 2

Top-down clustering for constructing hierarchical tree

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Fig. 4

Workflow of design event discovery

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Fig. 5

The three-dimensional model for analyzing the discovered process model

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Fig. 6

Histogram of document length

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Fig. 7

Examples of design event

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Fig. 13

Examples of human resource utilization

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Fig. 8

Comparison between the automated subprocesses and the originating tasks

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Fig. 9

A segment of the hierarchical process model

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Fig. 10

Performance analysis: (a) dotted chart of subprocesses and (b) temporal event number

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Fig. 11

Role analysis via dotted chart

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Fig. 12

Social network analysis



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