遭受 发表于 2025-3-28 16:47:31
Thinking Tri-laterally About Business Processes, Services and Business Models: An Innovation Perspec process) represents a “value proposition” to the service consumer (client) that enables them to “get their job done.” A service is, in effect, a mini-business or “business within a business” and therefore is implicitly governed by a business model of the process/service owner, the “CEO” of that bustolerance 发表于 2025-3-28 19:10:14
Process Innovation with Disruptive Technology in Auto Insurance: Lessons Learned from a Smartphone-Bovation behaviour. This chapter provokes the critical thinking with regard to the exploration and exploitation of disruptive technology into process innovation. Further, the chapter contributes new knowledge to the research of process innovation with disruptive digital technologies.acrimony 发表于 2025-3-28 23:46:29
Extracting Event Data from Databases to Unleash Process Mining(e.g., BPM/WFM systems), most information systems do not record events explicitly. Cases and activities only exist implicitly. However, when creating or using process models “raw data” need to be linked to cases and activities. This paper uses a novel perspective to conceptualize a .. Starting from蹒跚 发表于 2025-3-29 06:19:58
Enabling Process Innovation via Deviance Mining and Predictive Monitoringr process performance. Deviance mining deals with the analysis of process execution logs offline in order to identify typical deviant executions and to characterize deviance that leads to better or to worse performance. Predictive monitoring meanwhile aims at predicting—at runtime—the impact of acticonnoisseur 发表于 2025-3-29 09:22:22
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Sanjaya Mishra,Pradeep K. Misraovation behaviour. This chapter provokes the critical thinking with regard to the exploration and exploitation of disruptive technology into process innovation. Further, the chapter contributes new knowledge to the research of process innovation with disruptive digital technologies.Nostalgia 发表于 2025-3-30 01:11:05
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Carl Van Dyke,Marc Meketon,Bruce W. Pattyr process performance. Deviance mining deals with the analysis of process execution logs offline in order to identify typical deviant executions and to characterize deviance that leads to better or to worse performance. Predictive monitoring meanwhile aims at predicting—at runtime—the impact of acti