Workforce Upskilling : A History-Based Approach for Recommending Unfamiliar Process Activities

Human resource allocation decisions have a direct impact on the performance of a business process. Many approaches to optimal resource allocation have been proposed in the Business Process Management community. The majority of these approaches aim to optimise process performance; hence, recommend activities to experienced employees. To remain competitive, modern organisations also need to grow the capabilities of their employees and offer them upskilling opportunities. In this article, we propose an approach for recommending unfamiliar activities to employees by comparing their work histories with work histories of other similar employees. The aim of the proposed approach is to put employees on a gradual path of multi-skilling whereby they are provided with an opportunity to perform unfamiliar process activities and thus gain their experience through learning-by-doing. The approach is based on the analysis of process execution data and has been implemented. In the evaluation, we compared recommendations provided by the approach with actual activity executions of different employees recorded in process data. The evaluation demonstrated the effectiveness of the approach for different publicly available event logs and configuration settings.</p

Augmenting and assisting model elicitation tasks with 3D virtual world context metadata

Accurate process model elicitation continues to be a time consuming task, requiring skill on the part of the interviewer to extract explicit and tacit process information from the interviewee. Many errors occur in this elicitation stage that would be avoided by better activity recall, more consistent specification methods and greater engagement in the elicitation process by interviewees. Theories of situated cognition indicate that interactive 3D representations of real work environments engage and prime the cognitive state of the viewer. In this paper, our major contribution is to augment a previous process elicitation methodology with virtual world context metadata, drawn from a 3D simulation of the workplace. We present a conceptual and formal approach for representing this contextual metadata, integrated into a process similarity measure that provides hints for the business analyst to use in later modelling steps. Finally, we conclude with examples from two use cases to illustrate the potential abilities of this approach

Model as you do: engaging an S-BPM vendor on process modelling in 3D virtual worlds

Accurate process model elicitation continues to be a time consuming task, requiring skill on the part of the interviewer to extract explicit and tacit process information from the interviewee. Many errors occur in this elicitation stage that would be avoided by better activity recall, more consistent specification methods and greater engagement in the elicitation process by interviewees. Metasonic GmbH has developed a process elicitation tool for their process suite. As part of a research engagement with Metasonic, staff from QUT, Australia have developed a 3D virtual world approach to the same problem, viz. eliciting process models from stakeholders in an intuitive manner. This book chapter tells the story of how QUT staff developed a 3D Virtual World tool for process elicitation, took the outcomes of their research project to Metasonic for evaluation, and finally, Metasonic’s response to the initial proof of concept

Designing optimal robotic process automation architectures

The design and implementation of Robotic process automation (RPA) requires an architecture where there is seamless coordination between humans, robotic agents, and intelligent agents automating information acquisition tasks and decision-making tasks. Effective coordination of agents would need to consider the efficiency of different types of resources in completing tasks, the quality when handling complex tasks, and the cost of resources executing the task. In this work, a novel approach for generating an optimal architecture considering distinct types of resources that include human, intelligent and robotic agents is proposed. An optimal architecture is the optimal enactment of process instances executed by a combination of human and automation agents based on their characteristics. The architecture considers resources, resource types, and their characteristics that meet multiple objectives of process execution.</p

Resource-based Adaptive Robotic Process Automation

Robotic process automation is evolving from robots mimicking human workers in automating information acquisition tasks, to robots performing human decision tasks using machine learning algorithms. In either of these situations, robots or automation agents can have distinct characteristics in their performance, much like human agents. Hence, the execution of an automated task may require adaptations with human participants executing the task when robots fail, to taking a supervisory role or having no involvement. In this paper, we consider different levels of automation, and the corresponding coordination required by resources that include human participants and robots. We capture resource characteristics and define business process constraints that support process adaptations with human-automation coordination. We then use a real-world business process and incorporate automation agents, compute resource characteristics, and use resource-aware constraints to illustrate resource-based process adaptations for its automation

A Method to Enable Ability-Based Human Resource Allocation in Business Process Management Systems

Part 1: Business Process ModelingInternational audienceBusiness process management systems are used to orchestrate the activities in an organization. These information systems allocate resources to perform activities based on information that describes those resources and activities. It is widely recognized that resource allocation can be enhanced by considering resource characteristics during selection. However, little guidance is available that shows how such characteristics should be specified. Human ability is one such characteristic, with the advantage that it is well-defined in the Fleishman Taxonomy of Human Abilities. This paper presents a method that leverages the Fleishman taxonomy to specify activities and human resources. Those specifications are then used to allocate resources to activities during process run-time. We show how ability-based resource allocation can be implemented in a business process management system and evaluate the method in a real-world scenario