A Case-Based Approach to Business Process Monitoring

Montani, Stefania; Leonardi, Giorgio

doi:10.1007/978-3-642-15286-3_10

Cited by 10 publications

(7 citation statements)

References 22 publications

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“…Finally, the method we structure, being based both on KPIs controlling and on AI, puts into practice the theories proposed by Calabró et al. (2015) and Montani and Leonardi (2010): indeed, we build an agile holistic framework that relies on predictive business process monitoring to achieve the organization’s or department’s strategic goals. As previously mentioned, this allows the evolution of one of the five main components of the Business Process Management life cycle, i.e.…”

Section: Discussionmentioning

confidence: 99%

Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations

Bruccoleri

Pietrosi

Scaccianoce

2023

BPMJ

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PurposeThe nature and amount of data that public organizations have to monitor to counteract corruption lead to a phenomenon called “KPI overload”, consisting of the business analyst feeling overwhelmed by the amount of information and resulting in the absence of appropriate control. The purpose of this study is to develop a solution based on Artificial Intelligence technology to avoid data overloading and, at the same time, under-controlling in business process monitoring.Design/methodology/approachThe authors adopted a design science research approach. The authors started by observing a specific problem in a real context (a healthcare organization); then conceptualized, designed and implemented a solution to the problem with the goal to develop knowledge that can be used to design solutions for similar problems. The proposed solution for business process monitoring integrates databases and self-service business intelligence for outlier detection and artificial intelligence for classification analysis.FindingsThe authors found the solution powerful to solve problems related to KPI overload in process monitoring. In the specific case study, the authors found that the combination of Business Intelligence and Artificial Intelligence can provide a significant contribution to the detection of fraud, corruption and/or policy misalignment in public organizations.Originality/valueThe authors provide a big-data-based solution to the problem of data overload in business process monitoring that does not sacrifice any monitored Key Performance Indicators and that also reduces the workload of the business analyst. The authors also developed and implemented this automated solution in a context where data sensitivity and privacy are critical issues.

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Section: Discussionmentioning

confidence: 99%

Artificial intelligence to counteract “KPI overload” in business process monitoring: the case of anti-corruption in public organizations

Bruccoleri

Pietrosi

Scaccianoce

2023

BPMJ

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“…Such kind of modelling leads to case representation. Other choices of selecting similarity measure, solution technique and adaptation method depend on the nature of the case [38, 46, 52].…”

Section: Analysis and Discussionmentioning

confidence: 99%

“…CBR has also been successfully applied in other sub‐areas of process and project management like selection of a software development model [49], CBR‐based resource allocation and feasibility analysis [50], tailoring software business process using CBR [51, 52].…”

Section: Applications Of Cbr In Se: Outcomes Of the Systematic Mappmentioning

confidence: 99%

Applications of case‐based reasoning in Software Engineering: a systematic mapping study

Khan

2014

IET softw.

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Domain knowledge for various decision-making activities of Software Engineering (SE) is rarely available in a structured or well-formalised form. Owing to lack of the well-informed knowledge, decision making for different kinds of predictions and estimations in SE domain is a challenge. Maintenance and elicitation of domain knowledge is an overwhelming task and causes the knowledge acquisition bottleneck. Most of the artificial intelligence techniques of prediction and estimation do not work in absence of complete and structured knowledge. Case-based reasoning (CBR) is a lazy learning paradigm of artificial intelligence which takes care of this challenge and helps to reduce the knowledge availability bottleneck. This technique exploits the similar experience of past which may be available in unstructured form, and improves its learning curve with passage of time. In literature, CBR has been successfully applied in various areas of SE, but there is lack of single systematic panoramic picture which might have highlighted the potential research questions in this direction. In this study, the author has presented a comprehensive and panoramic systematic mapping study of various CBR applications in SE domain, and identified some promising future research directions.

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“…Due to its ability of adaptation to changing scenarios using previous results, this technique is widely used to develop support systems for business management (Brown & Gupta, 1994). For example, it has been used successfully in areas such as business process monitoring (Montani, Leonardi, & Bramer, 2010) or business failure detection (Li & Sun, 2011;Lin, Yeh, & Lee, 2011;Yeh, Chi, & Hsu, 2010), where they have been proven to be quite effective.…”

Section: Cbr Applicationsmentioning

confidence: 99%