{"title":"业务流程基准如何帮助组织提高绩效","authors":"Ünal Aksu, H. Reijers","doi":"10.1109/EDOC49727.2020.00032","DOIUrl":null,"url":null,"abstract":"The recurring but mutually distinct ways of executing a business process are referred to as process variants. There are approaches available in the literature aimed at finding such process variants and determining how they differ from each other. However, organizations are more interested in understanding the effect of these differences in terms of the performance of a business process. In this context, we propose a novel approach to enable organizations to learn from each other through business process benchmarks. To do so, the approach bins organizations based on what extent they achieve their performance targets in relation to their Key Performance Indicators (KPIs). Within each bin, process variants are identified using trace clustering. Then, significant differences among process variants are determined and highlighted. These differences help organizations to improve the performance of their processes. We implemented our approach, evaluated its performance, and applied it in a case study.","PeriodicalId":409420,"journal":{"name":"2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0000,"publicationDate":"2020-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"How Business Process Benchmarks Enable Organizations To Improve Performance\",\"authors\":\"Ünal Aksu, H. Reijers\",\"doi\":\"10.1109/EDOC49727.2020.00032\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"The recurring but mutually distinct ways of executing a business process are referred to as process variants. There are approaches available in the literature aimed at finding such process variants and determining how they differ from each other. However, organizations are more interested in understanding the effect of these differences in terms of the performance of a business process. In this context, we propose a novel approach to enable organizations to learn from each other through business process benchmarks. To do so, the approach bins organizations based on what extent they achieve their performance targets in relation to their Key Performance Indicators (KPIs). Within each bin, process variants are identified using trace clustering. Then, significant differences among process variants are determined and highlighted. These differences help organizations to improve the performance of their processes. We implemented our approach, evaluated its performance, and applied it in a case study.\",\"PeriodicalId\":409420,\"journal\":{\"name\":\"2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC)\",\"volume\":\"19 1\",\"pages\":\"0\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2020-10-01\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC)\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://doi.org/10.1109/EDOC49727.2020.00032\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"2020 IEEE 24th International Enterprise Distributed Object Computing Conference (EDOC)","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1109/EDOC49727.2020.00032","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
How Business Process Benchmarks Enable Organizations To Improve Performance
The recurring but mutually distinct ways of executing a business process are referred to as process variants. There are approaches available in the literature aimed at finding such process variants and determining how they differ from each other. However, organizations are more interested in understanding the effect of these differences in terms of the performance of a business process. In this context, we propose a novel approach to enable organizations to learn from each other through business process benchmarks. To do so, the approach bins organizations based on what extent they achieve their performance targets in relation to their Key Performance Indicators (KPIs). Within each bin, process variants are identified using trace clustering. Then, significant differences among process variants are determined and highlighted. These differences help organizations to improve the performance of their processes. We implemented our approach, evaluated its performance, and applied it in a case study.