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Special Graduate Schools
Graduate School of Science and Technology
Department of Industrial Engineering
3. Theses(Master)
비즈니스 프로세스의 수행시간 변동에 관한 연구
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.advisor | 張重淳 | - |
| dc.contributor.author | 이재훈 | - |
| dc.date.accessioned | 2018-11-08T07:51:48Z | - |
| dc.date.available | 2018-11-08T07:51:48Z | - |
| dc.date.issued | 2011-02 | - |
| dc.identifier.other | 11330 | - |
| dc.identifier.uri | https://dspace.ajou.ac.kr/handle/2018.oak/8237 | - |
| dc.description | 학위논문(박사)--아주대학교 일반대학원 :산업공학과,2011. 2 | - |
| dc.description.tableofcontents | Chapter I. Introduction 1 A. Research Motivation 1 B. Related Works 3 1. Variations in Business Processes 3 2. BPM and Variations 5 C. Research Scope 8 Chapter II. Bursts and Heavy Tails in Business Processes 10 A. Data Acquisition 10 B. Heavy Tails in Execution and Inter-event Times 13 1. Definition and Property 13 2. Test of Heavy Tails in Execution Times 14 3. Test of Heavy Tails in Inter-event Times 17 C. Possible Factors 18 Chapter III. Self-similarity in Business Processes 22 A. Overview 22 B. Test of Self-similarity in Empirical Data 24 C. Self-similarity in Engineering Perspective 29 Chapter IV. Business Process Planning under Heavy Tails using Simulation 31 A. Effects of Heavy Tails on BPM 31 B. Business Process Planning and Time Variations 32 C. Workflow Simulation 36 Chapter V. Agent-based Process Monitoring System 42 A. Motivation and Requirements 42 B. System Architecture 44 C. Implementation 47 Chapter VI. Evaluation of Business Process Capabilities 50 A. Motivation 50 B. Evaluation of Business Processes using PCIs 51 C. Evaluation Procedures 55 Chapter VII. Conclusion 58 Appendix A. Virtual activity type: source code 60 References 61 | - |
| dc.language.iso | eng | - |
| dc.publisher | The Graduate School, Ajou University | - |
| dc.rights | 아주대학교 논문은 저작권에 의해 보호받습니다. | - |
| dc.title | 비즈니스 프로세스의 수행시간 변동에 관한 연구 | - |
| dc.title.alternative | On the Variations of Execution Time in Business Processes | - |
| dc.type | Thesis | - |
| dc.contributor.affiliation | 아주대학교 일반대학원 | - |
| dc.contributor.alternativeName | Lee, Jae-Hoon | - |
| dc.contributor.department | 일반대학원 산업공학과 | - |
| dc.date.awarded | 2011. 2 | - |
| dc.description.degree | Master | - |
| dc.identifier.localId | 569180 | - |
| dc.identifier.url | http://dcoll.ajou.ac.kr:9080/dcollection/jsp/common/DcLoOrgPer.jsp?sItemId=000000011330 | - |
| dc.subject.keyword | Business | - |
| dc.subject.keyword | Process | - |
| dc.description.alternativeAbstract | Although the variation of business processes have been recognized very large, its nature and effects have been rarely investigated due to the difficulties in measurement and analysis. In this thesis, we analyzed the probabilistic behaviors of business process times, and an effective BPM methods which considers the time variations are suggested. To acquire the evidence of business process variations, empirical data of business process execution times from various domains such as finance, service, S/W, and manufacturing industries were collected and analyzed. We found heavy tails which imply that very small parts of business process operation can take extremely long time durations. Based on the literatures which concerned the nature of human actions, possible factors and effects of heavy tails are described. In addition, we found self-similarity which the pattern of processes are similar regardless of observation scale using R/S statistics and Variance-time plots. If actual execution times of business processes and activities are heavy tailed, the traditional BPM methods can be ineffective. We proposed a workflow engine based simulation which enacts a workflow virtually under predefined conditions for business process planning. Because heavy tailed activities raise difficulty in prediction, simulation may be useful with its realistic and flexible features. In operation phase, we developed am agent-based monitoring system for process variations. Customized software agents were used to automate process data gathering, processing, preservation, and diagnosis. Lastly, we evaluated the empirical time data using process capability indices, and the results represent that normal process indices overestimate than those of non-normal process indices because of skewness of time data. | - |
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