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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.

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Copyright © 2018 by ASME
Topics: Mining , Design , Workflow
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Figures

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Fig. 1

The methodology of learning from archival design project documents

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Fig. 2

Top-down clustering for constructing hierarchical tree

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Fig. 3

Example of local context

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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. 12

Social network analysis

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Fig. 11

Role analysis via dotted chart

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Fig. 10

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

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Fig. 9

A segment of the hierarchical process model

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Fig. 8

Comparison between the automated subprocesses and the originating tasks

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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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