Narrowing the Business-IT Gap in Process Performance Measurement

Abstract

To determine whether strategic goals are met, organizations must monitor how their business processes perform. Process Performance Indicators (PPIs) are used to specify relevant performance requirements. The formulation of PPIs is typically a managerial concern. Therefore, considerable effort has to be invested to relate PPIs, described by management, to the exact operational and technical characteristics of business processes. This work presents an approach to support this task, which would otherwise be a laborious and time-consuming endeavor. The presented approach can automatically establish links between PPIs, as formulated in natural language, with operational details, as described in process models. To do so, we employ machine learning and natural language processing techniques. A quantitative evaluation on the basis of a collection of 173 real-world PPIs demonstrates that the proposed approach works well.

Comparing and Aligning Process Representations

“…An applicant will be immediately rejected for a loan in case he has a credit score below… q Application is complete q Basic credit check passed q Applicant interviewed q Loan offer sent Loan-assessment process checklist (1) (2) (3) Figure 2.4: Loan application process with fragmented process information tion, and (3) a checklist. This fragmentation has clear advantages because it allows an organization to provide users with the process information that is most relevant to them, in a suitable format.…”

“…A matching task creates an n × n similarity matrix M(C 1 , C 2 ) over C 1 × C 2 . Each M i, j in the matrix represents a degree of similarity, usually a real number in [0,1], between the i-th concept in C 1 and the j-th concept in C 2 . To perform the matching task, matching techniques typically perform several sequential steps, in which different classes of matchers are applied.…”

“…To perform these steps, we employ the ProM6 framework, which provides a vast amount of so-called plug-ins that implement process mining techniques. 1 In the first two steps of this evaluation, we build on existing plug-ins for event-to-activity mapping techniques, as described in [37]. For the third step, we implemented the generation of behavioral spaces and our proposed technique for conformance checking as a plug-in, which is available as part of the BehavioralSpaces package in ProM6.…”

Visual ppinot: A Graphical Notation for Process Performance Indicators

“…An ongoing work on the automation of PPI management is the application of machine learning and natural language processing techniques for the automatic transformation of natural language PPI definitions into VISUAL PPINOT models that are directly amenable to automated computation (van der Aa et al 2016). We are also working on extending VISUAL PPINOT for managing variability on PPIs (Estrada-Torres et al 2016), and, in the near future, we intend to extend VISUAL PPINOT to allow the definition of resource-aware PPIs (del-Río-Ortega et al 2013b).…”

“…On the one hand, it takes time and resources, which increases the cost of deploying a performance management solution in the organisation and limits PPI evolution. On the other hand, PPI evolution may introduce errors because the gap between natural language and implementation languages is significant van der Aa et al 2016). Furthermore, ambiguities introduced by natural language have to be manually detected and removed to automatically compute PPIs.…”