Michael Arias

A Framework for Recommending Resource Allocation Based on Process Mining

Dynamically allocating the most appropriate resource to execute the different activities of a business process is an important challenge in business process management. An ineffective allocation may lead to an inadequate resources usage, higher costs, or a poor process performance. Different approaches have been used to solve this challenge: data mining techniques, probabilistic allocation, or even manual allocation. However, there is a need for methods that support resource allocation based on multi-factor criteria. We propose a framework for recommending resource allocation based on Process Mining that does the recommendation at sub-process level, instead of activity-level. We introduce a resource process cube that provides a flexible, extensible and fine-grained mechanism to abstract historical information about past process executions. Then, several metrics are computed considering different criteria to obtain a final recommendation ranking based on the BPA algorithm. The approach is applied to a help desk scenario to demonstrate its usefulness.

Clinical Processes and Its Data, What Can We Do with Them?

Global healthcare services have evolved over time, and nowadays they are expected to follow high-quality optimized standards. Analyzing healthcare processes has become a relevant field of study, and different techniques and tools have been developed to promote improvements in the efficiency and effectiveness of these processes. There is a research field called process mining that can be used to extract knowledge from the event data stored in the hospital information systems. With the help of this, it is possible to discover the real executed process, examine its performance and analyze the resource interaction during its execution. The goal of this article is to provide a bibliographic survey about the use of process mining algorithms, techniques, and tools in the analysis of healthcare processes, providing a general overview about the main approaches previously used and the information required to apply them in the medical field. We provide important insights about data, algorithms, techniques and methodologies that are required to help answer medical expert questions about their processes, motivating and inspiring a broader usage. So, if we have the information and it is possible to analyze and understand the healthcare processes, why are we not doing it?

Discovering role interaction models in the Emergency Room using Process Mining

OBJECTIVES A coordinated collaboration among different healthcare professionals in Emergency Room (ER) processes is critical to promptly care for patients who arrive at the hospital in a delicate health condition, claiming for an immediate attention. The aims of this study are (i) to discover role interaction models in (ER) processes using process mining techniques; (ii) to understand how healthcare professionals are currently collaborating; and (iii) to provide useful knowledge that can help to improve ER processes. METHODS A four step method based on process mining techniques is proposed. An ER process of a university hospital was considered as a case study, using 7160 episodes that contains specific ER episode attributes. RESULTS Insights about how healthcare professionals collaborate in the ER was discovered, including the identification of a prevalent role interaction model along the major triage categories and specific role interaction models for different diagnoses. Also, common and exceptional professional interaction models were discovered at the role level. CONCLUSIONS This study allows the discovery of role interaction models through the use of real-life clinical data and process mining techniques. Results show a useful way of providing relevant insights about how healthcare professionals collaborate, uncovering opportunities for process improvement.

Towards a Taxonomy of Human Resource Allocation Criteria

Allocating the most appropriate resource to execute the activities of a business process is a key aspect within the organizational perspective. An optimal selection of the resources that are in charge of executing the activities may contribute to improve the efficiency and the performance of the business processes. Despite the existence of resource metamodels that seek to provide a better representation of resources, a detailed classification of the allocation criteria that have been used to evaluate resources is missing. In this paper, we provide an initial proposal for a resource allocation criteria taxonomy. This taxonomy is based on an extensive literature review that yielded 2,370 articles regarding the existing resource allocation approaches within the business process management discipline, from which 95 articles were considered for the analysis. The proposed taxonomy points out the most frequently used criteria for assessing the resources from January 2005 to July 2016.

Human resource allocation in business process management and process mining: A systematic mapping study

Purpose Human resource allocation is considered a relevant problem in business process management (BPM). The successful allocation of available resources for the execution of process activities can impact on process performance, reduce costs and obtain a better productivity of the resources. In particular, process mining is an emerging discipline that allows improvement of the resource allocation based on the analysis of historical data. The purpose of this paper is to provide a broad review of primary studies published in the research area of human resource allocation in BPM and process mining. Design/methodology/approach A systematic mapping study (SMS) was conducted in order to classify the proposed approaches to allocate human resources. A total of 2,370 studies published between January 2005 and July 2016 were identified. Through a selection protocol, a group of 95 studies were selected. Findings Human resource allocation is an emerging research area that has been evolving over time, generating new proposals that are increasingly applied to real case studies. The majority of proposed approaches relate to the period 2011-2016. Journals and conference proceedings are the most common venues. Validation research and evaluation research are the most common research types. There are two main evaluation methods: simulation and case studies. Originality/value This study aims to provide an initial assessment of the state of the art in the research area of human resource allocation in BPM and process mining. To the best of the authors’ knowledge, this is the first research that has been conducted to date that generates a SMS in this research area.

Business process analysis in advertising: An extension to a methodology based on process mining projects

Nowadays organizations generate large amount of data. Only a few make a good use to optimize the performance of the business. Process mining appears as a branch of the data science that tries to understand the actual operational processes in the organizations through different algorithms, allowing the discovery of process models to give insight of the processes and understand how they can be improved. In this work different process mining techniques are applied to a company dedicated to the advertisement market, specifically the process of dealing with contract issues with customers. The Process Mining Project Methodology was followed to execute a case study. Additional to the basic methodology, elements from the others areas of studies were added to generate better results and have a better understanding of the problem. The case study includes three scenarios with three different hypotheses that were validated through our method.

A Multi-criteria Approach for Team Recommendation

Team recommendation is a key and little-explored aspect within the area of business process management. The efficiency with which the team is conformed may influence the success of the process execution. The formation of work teams is often done manually, without a comparative analysis based on multiple criteria between the individual performance of the resources and their collective performance in different teams. In this article, we present a multi-criteria framework to allocate work teams dynamically. The framework considers four elements: (i) a resource request characterization, (ii) historical information on the process execution and expertise information, (iii) different metrics which calculate the suitability of the work teams taking into account both individual performance as well as collective performance of the resources, and (iv) a recommender system based on the Best Position Algorithm (BPA2) to obtain a ranking for the recommended work teams. A software development process was used to test the usefulness of our approach.