Workflow management as you like it

Workflow Management Systems (WfMS’s) offer a tremendous potential for organizations. Shorter lead times, less mistakes in work handoffs, and a better insight into process execution are some of the most notable advantages experienced in practice. At the same time, the introduction of these systems on the work floor undoubtedly results in great changes in the way that business professionals coordinate their work. If a WfMS's coordination of work is experienced as too rigid or mechanistic, it may negatively affect employees' motivation, performance, and satisfaction. In this paper, we propose a set of measures to tune functioning workflow systems to minimize such effects. The measures we propose do not require undue cost, time, or organizational changes, as they characteristically lie within the configurable options of a WfMS. We asked an expert panel to select and validate the 6 most promising measures, which we present in this paper. From our evaluation of three commercial WfMS products, we conclude the ease with which the 6 measures can be implemented depends on the specific WfMS product

Cohesion and coupling metrics for workflow process design

Workflow designers experience considerable freedom in designing the smaller steps (or activities) within a process. An operational notion of activity cohesion and coupling may help them to design more well-structured workflow activities. Inspired by resemblances between software programs and workflow processes, this paper gives an overview of software quality metrics and their applicability to workflow process design. New cohesion and coupling metrics – inspired by these software metrics – are introduced, which are integrated in a design heuristic. This heuristic can be used by workflow designers to identify the strongly cohesive and weakly coupled process design among several alternatives. The paper includes an application of this heuristic in a realistic workflow process setting

Developing a cyber-physical system for hybrid manufacturing in an internet-of-things context

\u3cp\u3eThis chapter describes design and development of the HORSE system for processoriented hybrid manufacturing that seamlessly integrates human and robotics actors in vertical manufacturing cells that are horizontally coupled in end-to-end manufacturing processes. The HORSE system supports advanced dynamic actor allocation to work cells, direct robot control and human actor instruction, closedloop local event processing, and near-real-time global event processing. The system handles abstract process definitions and status information on the one hand and directly interfaces to industrial sensors and actuators on the other hand, making it a system with a strong cyber-physical character. The physical side of the system is deployed in an internet-of-things context, where the things are the industrial robots controlled by the HORSE system, the sensors feeding data to the system, and the products being manufactured in the industrial process managed by the system. The system will be deployed in real-world, industrial pilot scenarios in a European Horizon 2020 project.\u3c/p\u3

Towards a solution space for BPM issues based on debiasing techniques

\u3cp\u3eIn previous work, we discussed how cognitive biases may lead to issues in the design phases of the business process management lifecycle, such as the development of suboptimal process architectures and incomplete process models, the identification of irrelevant bottlenecks and weaknesses in a process, and the selection and implementation of confirmatory redesigns. This position paper makes a first step towards solving these issues through the use of debiasing techniques. Such techniques can be used to reduce or avoid the cognitive biases that potentially lead to BPM issues.\u3c/p\u3

When cognitive biases lead to business process management issues

There is a broad consensus that design decision making is important for Business Process Management success. Despite many business process design approaches and practices that are available, the quality of business process analysis and design relies heavily on human factors.\u3cbr/\u3eSome of these factors concern cognitive biases. In this paper, we explore the role of cognitive biases in four key issues regarding the designt ime phases of the business process management lifecycle. We outline some research directions that may help us understand and improve the\u3cbr/\u3eeffects of cognitive biases in the design-related practices of business process management.\u3cbr/\u3

Making decision process knowledge explicit using the decision data model

In this paper we present an approach for mining decisions. We show that through the use of a Decision Data Model (DDM) we can make explicit the knowledge employed in decision making. We use the DDM to provide insights into the data view of a business decision process. To support our claim we introduce our complete, functional decision mining approach. First, a ‘decision-aware system’ introduces the decision maker to a simulated environment containing all data needed for the decision. We log the user’s interaction with the system (focusing on data manipulation and aggregation). The log is mined and a DDM is created. The advantage of our approach is that, when needed to investigate a large number of subjects, it is much faster, less expensive and produces more objective results than classical knowledge acquisition methods such as interviews and questionnaires. The feasibility and usability of our approach is shown by a case study and experiments