Fleet Performance Monitoring System Using The Internet of Things (Business Model) - Alsobhi, Maryam (Université d'Ottawa / University of Ottawa, 2018)
Fleet management is one of the most challenging tasks faced by fleets managers. These companies need to shed light on several aspects that have direct and indirect impacts on their businesses. For instance, monitoring driver performance and behaviour can be used as a mean to improve their operational efficiency and effectiveness, which may lead to increase customer satisfaction. Technological advances have made it possible for fleet companies to monitor fleet drivers. The Internet of things (IoT), is one of these technologies, which is a network of connected devices embedded with sensors through the internet, allowing them to communicate with one another and exchange information. Due to increase in societies needs, fleet companies need to maximize their operations. Thus, they encounter high operational costs and productivity issues that cost them a lot economically and environmentally. There are many suggested solutions on how to reduce costs such as reduce the number of operations. In this thesis we are suggesting using IoT to monitor fleet performance in order to cut unnecessary expenditure and improve efficiency, particularly by managing driver as a mean to overcome the problem. IoT solution works by implementing number of sensors to do specific function (for example: detecting risky event and warn fleet's driver) and generate data. Data are valuable for businesses in order to make better decisions that may lead to increase revenues and reduce costs. The simulation of our suggested solution shows improvement on fleet performance compare to non-IoT solutions. Then, we build an innovative sustainable business model to illustrate the benefits that fleet businesses can get by implementing such a solution predominantly because IoT is an economic and multi-purpose solution that can be used to solve a number of crossover problems.
Keywords: Internet of Things; IoT; Business Model; Fleet management; Transportation