Extracting service process models from location data

Abstract

Services are today over 70% of the Gross National Product in most developed countries. The productivity improvement of services is increasingly important and it relies heavily on a deep understanding of the service processes. However, how to collect data from services has been a problem and service data is largely missing in national statistics, which brings challenges to service process modelling. This work aims to simplify the procedure of automated process modelling, and focuses on modelling generic service processes that are location-aware. An approach based on wireless indoor positioning is developed to acquire the minimum amount of location-based process data that can be used to automatically extract the process models. The extracted models can be further used to analyse the possible improvements of the service processes. This approach has been tested and used in dental care clinics. Besides, the automated modelling approach can be used to greatly improve the traditional process modelling in various other service industries.