- Prof. Dr. Brigitte Werners, Ruhr-Universität Bochum
- Prof. Dr. Jens Brunner, Universität Augsburg
- Prof. Dr. Hansjörg Fromm, Karlsruhe Institute of Technology
- Prof. Dr. Herbert Meyr, Universität Hohenheim
- Dr. Dorothea Wiesmann, IBM Research
Due to an increasing number of available data and the process complexity in the healthcare sector the application of both analytics and optimization methods becomes more and more important. Applications not only arise in internal logistics of hospitals but also in doctor’s practices, home healthcare and healthcare provision. However, the unique feature in this field of application is to not only put emphasis upon the economic efficiency but also to take into account the quality of care and patient satisfaction. Accordingly, the medical competence is never interfered in.
Regarding hospitals, scheduling problems and internal logistics are playing a crucial role. With the help of medical and technical devices patients are examined, treated and cured if possible. Hospital logistics are all technical and organizational measures that are needed to transfer patients from an initial state (“ill”) into a final state (in the best case “healthy”) while also regarding the corresponding goods and information. Usually, hospital processes are grown historically (“We have always done it this way.”). Consequently, data and processes have not been analyzed critically until reforms of the health system have put increasing pressure on hospitals. Nowadays, hospitals are looking for possibilities to improve their processes. Therefore, the success of logistics concepts in hospitals lies in resource conservation for non-value-adding activities (not directly relevant for the healing process, e.g., administrative work) and high resource utilization for value-adding activities (e.g., surgery). In the healthcare part of this session the focus is on physician scheduling in hospitals.
Analytics and Optimization are becoming more and more accepted and important for all industries. Initiatives like Industry 4.0 and Big Data aim to combine knowledge from research and industry to tackle new business applications and generate competitive advantages. Typical application areas for Service Analytics and Optimization are machine diagnostics, predictive maintenance, spare parts planning, technician scheduling, call center dispatching and risk analysis for full service contracts. Many existing disciplines such as operations research, data mining and machine learning, and economic decision theory merge into a new discipline called “service science”.
In this context we offer two lectures - Analytics in IT Services and Service Analytics and Optimization in Industry. The two lectures around this exciting topic will cover several aspects of Service Analytics and Optimization. On the one hand, an overview of existing methods and software packages will be given that enable optimal planning and scheduling, so-called APS Systems (Advanced Planning and Scheduling) which typically link to operational ERP systems. On the other hand, industrial examples will be presented where Service Analytics have already been integrated into the daily business. An overview of several analytics models and techniques will be given.