Estimation and Characterization of Activity Duration in Business Processes
Process-aware information systems are typically used to log events in a variety of domains (e.g. commercial, logistics, healthcare) describing the execution of business processes. The analysis of such logs can provide meaningful knowledge for organization
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Information Systems Group, Eindhoven University of Technology, De Lismortel 2, 5612AR Eindhoven, The Netherlands {r.m.t.goncalves,rjalmeida,r.m.dijkman}@tue.nl IDMEC, Instituto Superior T´ecnico, Universidade de Lisboa, Lisboa, Portugal [email protected]
Abstract. Process-aware information systems are typically used to log events in a variety of domains (e.g. commercial, logistics, healthcare) describing the execution of business processes. The analysis of such logs can provide meaningful knowledge for organizations to improve the quality of their services as well as their efficiency. The prediction of activity durations, based on historic data from execution logs, allows the creation of feasible plans for business processes. However, a problem arises when there are discrepancies between execution logs and the actual execution. When event logs are partially human-generated there is an underlying uncertainty related to the time at which events (recorded by means of user interaction) are logged. If not taken into account, this uncertainty can lead to wrong predictions of activity durations. In this paper, we focus on creating assumptions to estimate activity durations and analyse their impact in the stochastic characterization. A partially human-generated logistics database is used as example. Keywords: Event logs processes
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Stochastic characterization
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Business
Introduction
As information systems are becoming more intertwined with the operational processes they support, multitudes of events (e.g., transaction logs or audit trails) are recorded. These execution logs can be extracted from almost every process aware information system (PAIS) and provide a chronological record of events referring to the business activities that have been carried out. This gives a detailed overview about the process history [1,2]. However, despite the increasing amount of sensors and respective data, many PAIS still lack of a completely automated log process. Instead, they rely on the interaction of users to log activities that the system can not keep track of. As an example, in transportation processes, drive activities can be easily detected by analysing GPS coordinates but load and unload tasks, which represent the core of logistic processes, are c Springer International Publishing Switzerland 2016 J.P. Carvalho et al. (Eds.): IPMU 2016, Part II, CCIS 611, pp. 729–740, 2016. DOI: 10.1007/978-3-319-40581-0 59
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commonly manually introduced since they are performed outside of the PAIS environment. When event logs are partially human-generated there is an underlying uncertainty related to the time at which events (introduced by means of user interaction) are logged [3]. In other words, users are able to log events before, or after, the execution of such occurrences. This behaviour may lead to wrong estimations while assessing the duration of activities that were manually logged [4]. As a consequence, process planning often leads to violated time windows, unnecessary delays and underuti
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