Tesis doctorales: TESEO

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Ministerio de Universidades - Gobierno de España

Título: ENHANCING THE MODELLING PERSPECTIVE OF PROCESS PERFORMANCE MANAGEMENT
Autor: ESTRADA TORRES, IRENE BEDILIA
Universidad: Universidad de Sevilla
Departamento: Lenguajes y sistemas informáticos
Fecha de Lectura: 30/11/2018
Mención Europeo / Mención Internacional: Concedido
Programa de doctorado: Programa de Doctorado en Ingeniería Informática por la Universidad de Sevilla
Dirección:
- del Río Ortega, Adela (DIRECTOR)
- RESINAS ARIAS DE REYNA, MANUEL (DIRECTOR)
- RUÍZ CORTÉS, ANTONIO (TUTOR/PONENTE)
Tribunal:
- Toro Bonilla, Miguel (PRESIDENTE)
- DURÁN TORO, AMADOR (SECRETARIO)
- REIJERS, HAJO ALEXANDER (VOCAL)
- Noguera García, Manuel (VOCAL)
- DUSTDAR, SCHAHRAM (VOCAL)
Descriptores:
- INFORMATICA
Localización: ESCUELA INTERNACIONAL DE DOCTORADO (EIDUS)
Marcador: https://www.educacion.gob.es/teseo/mostrarRef.do?ref=1719054Añadir a favoritos.
Resumen:
Process Performance Management, which comprises the stages of planning, monitoring and controlling of business process performance, is considered an essential part of business process management and provides a detailed understanding of how business processes can be designed and redesigned to improve their performance. The performance of business processes is usually measured by means of process performance indicators (PPIs), that are evaluated and monitored with the aim of identifying whether strategic and operational goals are being achieved. The same as business processes, and actually together with them, these PPIs must be defined and modelled, so in this dissertation, we focus on their Modelling Perspective. This perspective allows us to describe in detail all PPIs associated with a business process, specifying the set of attributes that define them and the information that needs to be obtained from the business process for their computation. Most proposals related to the performance measurement of business processes have focused on measuring the performance of structured, repetitive and highly defined business processes. Changes and new requirements of businesses have given rise to new needs in the management of business processes, requiring more flexibility for instance, to manage collections of business process alternatives and knowledge-intensive and highly dynamic processes. However, current techniques of process performance management have not evolved at the same pace as business processes. Existing proposals cannot be used ¿as is¿ in scenarios with business processes that require more flexibility because those processes have different nature. For example, those proposals are not able to define PPIs on collections of business processes or to define PPIs taking into account process elements that are not usually present in traditional processes, such as decisions, interactions or collaborations between participants. This generates a gap between business processes and the measurement of their performance. In order to face this challenge, this dissertation seeks to extend the current boundaries of process performance measurement. To do so, we based on existing and wellfounded techniques, mainly applicable to structured business processes whose behaviour is mostly known a priory, and extend them to cope with the new identified requirements. Specifically, we propose a set of artefacts that focus on the definition and modelling perspective of performance measures and indicators. First, we propose the extension of the PPINOT metamodel, a metamodel previously used for defining and modelling of process performance indicators, in four directions: the total or partial reuse of PPI definitions; the modelling of PPIs taking into account the variability of business processes and PPIs themselves; the definition of PPIs on knowledge-intensive processes and the relationship of the definition of PPIs to a particular set of business process elements, such as decisions. The extensions of the metamodel are designed to work with relevant proposals for each one of the areas addressed, such as the Knowledge-Intensive Process Ontology (KIPO), an ontology that provides constructs for defining Knowledge-intensive Processes; the Decision Model and Notation (DMN), a standard for defining and modelling of decisions; Provop and C-iEPC, two business process modelling languages associated with variability. To facilitate the representation of PPI definitions and models, we also propose the extension of the two PPINOT notations: Visual PPINOT, a graphical notation and the template based notation that uses linguistic patterns for PPI definitions. In addition to those extensions, we provide two more contributions: a methodology based on the integration of PPINOT and KIPO, and on concepts of lead and lag indicators with the aim of guiding participants in the implementation of PPIs in accordance to business goals, and guidelines in the form of a set of steps that can be used to identify decisions that affect the process performance. Finally, we have also taken the first steps to implement the metamodel extensions to the PPINOT modelling tools. Specifically, we modified the graphical editor to facilitate the modelling of PPIs by reusing PPI definitions. We validated the proposals presented through different scenarios and case studies: by analysing the processes and performance measures of the Supply Chain Operations Reference (SCOR) model, using measures and indicators provided by the Andalusian Health Service (Spain) and by means of a case study in an information and communication technology outsourcing company in Brazil. Our proposal of ¿Enhancing the Modelling Perspective of Process Performance Management¿ allows us to: reuse total or partial PPI definitions in different PPIs or business processes; define PPIs in a collection of business processes (process family) taking into account the variability of processes and PPIs themselves; define PPIs over Knowledge-intensive Processes; use a methodology to guide participants in the implementation of PPIs in accordance to business goals; analyse the impact of decisions related to business processes on PPIs; define decision performance indicators (DPIs) to measure the performance of decisions related to business processes; and use process performance information in the definition of decisions.

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