Industry 4.0 and healthcare: Context, applications, benefits and challenges

Industry 4.0 refers to the digital transformation in the manufacturing domain through new technology. Currently, it expands well beyond manufacturing, affecting many areas of life and posing implications for all types of business. This paper focuses on the relationships between Industry 4.0 and Healthcare which transitions to increased interconnectivity, automation and smart decision making. The integration context of Industry 4.0 into Healthcare is only partly understood. Little was done until now to consolidate what is known on the integration benefits and the challenges. This article reports results of a systematic mapping study that analysed 69 papers to extract knowledge about the concepts of Industry 4.0 and the emerging Healthcare 4.0., and the relationships between them. We found 10 different perspectives of Healthcare 4.0, ranging from strategic to tactical and operational levels. Next, our results show: (i) nine applications of Industry 4.0 in the Healthcare domain: Augmented Reality and Simulation, Autonomous Robotics, Cybersecurity, Big Data Analytics, Internet of Things, Cloud Computing, Additive Manufacturing and Systems Integration; and (ii) 10 benefits and nine challenges in Healthcare 4.0. The most frequently mentioned benefits are patients' diagnosis, monitoring, treatment, and financial benefits. The most researched challenges are data fragmentation, heterogeneity, complexity, and privacy

Performance Enhancement of Formula One Drivers with the Use of Group Driven Learning

Within motorsports less experienced drivers lack pace and performance compared to their peers. Training these drivers requires time, which, due to the regulations and resources, teams often do not have. Less experienced drivers are expected to perform at the same level as experienced drivers. This paper has the aim of analyzing the abilities and performances of both drivers within a Formula One team to redesign the driver training method. The focus is to provide drivers with real-time insights and feedback on their performance during a simulator training session. By using a combination of the principles of process mining and statistical analysis, data markers are created on the track. Based on the differences in telemetry, visual feedback is provided to the driver. Throughout the research, this manner of training has proven to be promising. Drivers showed an increase in their overall performance and an increase in car control and confidence. Despite these promising results more exper iments need to be done to guarantee a consistent outcome and to prove the effectiveness of this training program. To continue developments, further research can be conducted on the topic of visualization and communication

An Agent-Based Process Mining Architecture for Emergent Behavior Analysis

Information systems leave a traceable digital footprint whenever an action is executed. Business process modelers capture these digital traces to understand the behavior of a system, and to extract actual run-time models of those business processes. Despite the omnipresence of such traces, most organizations face substantial differences between the process specifications and the actual run-time behavior. Analyzing and implementing the results of systems that model business processes tend, however, to be difficult due to the inherent complexity of the models. Moreover, the observed reality in the form of lower-level real-time events, as recorded in event logs, is seldom solely explainable by higher-level process models. In this paper, we propose an architecture to model system-wide behavior by combining process mining with a multi-agent system. Digital traces, in the form of event logs, are used to iteratively mine process models from which agents can learn. The approach is initially applied to a case study of a simplified job-shop factory in which automated guided vehicles (AGVs) carry out transportation tasks. Numerical experiments show that the workflow of a process mining model can be used to enhance the agent-based system, particularly, in analyzing bottlenecks and improving decision-making

A Methodology for Aligning Process Model Abstraction Levels and Stakeholder Needs

Process mining derives knowledge of the execution of processes through analyzing behavior as observed from real-life events. While benefits of process mining are widely acknowledged, finding an adequate level of detail at which a mined process model is suitable for a specific stakeholder is still an ongoing challenge. Process models can be mined at different levels of abstraction, often resulting in either highly complex or highly abstract process models. This may have an important impact on the comprehensibility of the process model, which can also differ from the perspective of a particular stakeholder. To address this problem from a stakeholder-centric perspective, we propose a methodology for determining an appropriate level of process model abstraction. To this end, we use quantitative metrics on process models as well as a qualitative evaluation by using a technology acceptance model (TAM). A logistics case study involving the fuzzy process mining discovery algorithm shows init ial evidence that the use of appropriate abstraction levels is key when considering the needs of various stakeholders

Automatic Q.A-Pair Generation for Incident Tickets Handling: An Application of NLP

Chatbots answer customer questions by mostly manually crafted Question Answer (Q.A.)-pairs. If organizations process vast numbers of questions, manual Q.A. pair generation and maintenance become very ex-pensive and complicated. To reduce cost and increase efficiency, in this study, we propose a low threshold QA-pair generation system that can automatically identify unique problems and their solutions from a large incident ticket dataset of an I.T. Shared Service Center. The system has four components: categorical clustering for structuring the semantic meaning of ticket information, intent identification, action recommendation, and reinforcement learning. For categorical clustering, we use a Latent Semantic Indexing (LSI) algorithm, and for the intent identification, we apply the Latent Dirichlet Allocation (LDA), both Natural Language Processing techniques. The actions are cleaned and clustered and resulting Q.A. pairs are stored in a knowledge base with reinforcement learning capabilities. The system can produce Q.A. pairs from which about 55% are useful and correct. This percentage is likely to in-crease significantly with feedback in its usage stage. By this study, we contribute to a further understanding of the development of automatic service processes