Process Mining for Six Sigma

Process mining offers a set of techniques for gaining data-based insights into business processes from event logs. The literature acknowledges the potential benefits of using process mining techniques in Six Sigma-based process improvement initiatives. However, a guideline that is explicitly dedicated on how process mining can be systematically used in Six Sigma initiatives is lacking. To address this gap, the Process Mining for Six Sigma (PMSS) guideline has been developed to support organizations in systematically using process mining techniques aligned with the DMAIC (Define-Measure-Analyze-Improve-Control) model of Six Sigma. Following a design science research methodology, PMSS and its tool support have been developed iteratively in close collaboration with experts in Six Sigma and process mining, and evaluated by means of focus groups, demonstrations and interviews with industry experts. The results of the evaluations indicate that PMSS is useful as a guideline to support Six Sigma-based process improvement activities. It offers a structured guideline for practitioners by extending the DMAIC-based standard operating procedure. PMSS can help increasing the efficiency and effectiveness of Six Sigma-based process improving efforts. This work extends the body of knowledge in the fields of process mining and Six Sigma, and helps closing the gap between them. Hence, it contributes to the broad field of quality management

Modelización econométrica de la prensa diaria: Una aproximación desde la metodología VAR

Este trabajo estudia la relación existente entre la promoción de ventas editorial, las cifras de difusión y la cuota de mercado de la prensa diaria. Para la realización de la presente investigación se utilizan series de datos mensuales del diario La Voz de Galicia, para el periodo 1991-2000. El enfoque empírico utilizado está basado en la metodología VAR. En primer lugar, se realizan los siguientes test de raíces unitarias: el test ADF de Dickey-Fuller (1981), el test DF-GLS de Elliot, Rothenberg y Stock (1996), el test KPSS de Kwiatkoski-Phillips-Schmidt- Shin (1992) y el test de raíces unitarias estacionarias de Hylleberg, Engle, Granger y Yoo (1990) o test HEGY, en la versión para datos mensuales propuesta por Beaulieu y Mirón (1993). Posteriormente, se elabora un VAR convencional y se completa el análisis con su interpretación dinámica. Los datos han sido extraídos de la Oficina de la Justificación de la Difusión (OJD) y los programas econométricos que han sido utilizados para la realización de los distintos análisis son: EViews 5.1, RATS 6.02 y CATS 1.04

Discovering Data-Aware Declarative Process Models from Event Logs

Abstract. A wealth of techniques are available to automatically discover business process models from event logs. However, the bulk of these techniques yield procedural process models that may be useful for detailed analysis, but do not necessarily provide a comprehensible picture of the process. Additionally, barring few exceptions, these techniques do not take into account data attributes associated to events in the log, which can otherwise provide valuable insights into the rules that govern the process. This paper contributes to filling these gaps by proposing a technique to automatically discover declarative process models that incorporate both control-flow dependencies and data conditions. The discovered models are conjunctions of first-order temporal logic expressions with an associated graphical representation (Declare notation). Importantly, the proposed technique discovers underspecified models capturing recurrent rules relating pairs of activities, as opposed to full specifications of process behavior – thus providing a summarized view of key rules governing the process. The proposed technique is validated on a real-life log of a cancer treatment process

Measuring patient flow variations : a cross-organisational process mining approach

Variations that exist in the treatment of patients (with similar symptoms) across different hospitals do substantially impact the quality and costs of healthcare. Consequently, it is important to understand the similarities and differences between the practices across different hospitals. This paper presents a case study on the application of process mining techniques to measure and quantify the differences in the treatment of patients presenting with chest pain symptoms across four South Australian hospitals. Our case study focuses on cross-organisational benchmarking of processes and their performance. Techniques such as clustering, process discovery, performance analysis, and scientific workflows were applied to facilitate such comparative analyses. Lessons learned in overcoming unique challenges in cross-organisational process mining, such as ensuring population comparability, data granularity comparability, and experimental repeatability are also presented