The importance of cost optimization in fleet management for public institutions

Fleet managers are continuously challenged with determining the optimal equipment replacement time based on increasing operation costs and decreasing economic value. In addition, they are challenged with determining if a lease or purchase provides the greatest value. New technologies (e.g., electric vehicles) and governmental regulations can substantially alter and muddle the best course of action. Compounding upon these challenges, new equipment purchases and leases can create a negative perception among the general public. Therefore, there is a need for a literature review to analyze what information has previously been gathered to best understand whether a piece of equipment should be leased or purchased provided these regulations and new technologies. One important component of fleet management when looking at public institutions is the idea of cost optimization. Cost optimization allows for a continuous analysis of how public funds are being properly allocated and, in turn, where they can be saved. Cost optimization considers many factors, including public perception, where government agencies need the backing of the population that pays taxes to support their operations. The goal of this work is to research key factors and variables that influence the dynamics of cost optimization within the context of public sector fleet management. Some of these variables may include cost of ownership (economic value, salvage value, etc.) and operations (fuel, lubrication oil, tire selection and repair, etc.), technology integration, and stakeholder involvement. To begin, the researchers will collect data from the literature to determine the state of the practice around quantifiable approaches for optimal replacement time along with publicly available models, and various approaches to the lease-versus-buy decision. The literature review will further capture how public perception has historically been managed when new equipment purchase is necessary. By employing this conceptual model, the research aims to provide a comprehensive understanding of cost optimization in fleet management for public institutions. This model will help to create a methodical approach to investigating the different variables and provide recommendations to enhance cost-effective decisions in the management of fleets in public institutions while making sure the service to the general public is high-quality. Three core tasks will have been performed as part of this research (found in Figure 1). From the first task (literature review), the researchers expect to identify common impacts from the key factors (ownership and maintenance costs) and how they affect the overall budget, the growing impact of technology and savings provided from real-time data, any possible positive environmental impacts by reduced fuel consumption and emissions. From the second (survey) and third (interviews) tasks, it can be anticipated that information will be collected from entities indicating best practices and to allow for in-depth examination of historical data on their equipment fleets to develop the future tool. The discussions are likely to further increase the validity of the data collected, and ensure all topics were captured throughout the data collection process. The literature review is presented as part of this creative component and will examine current life-cycle analysis and equipment management practices. This literature review will capture the robust state of the practice with regard to the optimal equipment replacement time in a dynamic environment. This literature review examines the state of the practice for leased vehicle costs/valuation and evaluate models that consider changes in cost in order to provide a simple summary to LRRB members who have to make a lease-versus-buy decision. The survey and interview results are presented as part of this research, and include findings around topics such as driving factors and challenges in determining optimal replacement time, in actually making the replacement, and in determining between leasing and buying. Interviews will then be used to create a greater understanding of the data and to discuss various maintenance approaches and challenges. In addition, nonfinancial decision parameters will be discussed and lessons learned captured and presented

Gamification of telematics data to enhance operators’ behaviour for improvement of machine productivity in loading cycles

Construction industry is suffering from low productivity rate in various projects such as excavation. Although this issue is discussed in literature and several approaches are proposed to address it, productivity rate is still low in construction industry compared to other domains like manufacturing. Three core components directly affect the overall productivity in construction sector, i.e. labour productivity, raw material productivity, and machine or equipment productivity. With a focus on construction machinery, three factors influence productivity at excavation sites; i.e. 1) machine-based productivity and its configuration, 2) site layout and environmental conditions, and 3) operators’ behaviour. Operators’ competence and motivation represent two key parameters that affect their behaviour. On one side, gamification has attracted a growing area of interest both in literature and practice, seeking to place a layer of entertainment and pleasure to the top of serious activities (with a focus on improving the applicant’s motivation and behaviour). On the other side, telematics systems are utilized to collect operational data of the machine, and calculate its productivity rate. Telematics data are presented to operators (via a built-in screen available in the cabin of the machine) to provide real-time feedback about machine performance. In addition, these data can support machine owners to perceive operators’ behaviour on a real-time basis. To conclude, telematics systems are providing real-time data which can be a great input into gamification. A guideline is proposed in this dissertation that helps gamification designers to develop more transparent gamification models. This guideline is utilized to introduce a gamification model that gamifies telematics data with a focus on enhancing operators’ behaviour (machine productivity) in loading and transferring activities. The model was implemented at two sites(one recycling and one mining site) and could encourage operators (who were operating wheel-loaders and dump-trucks) to prevent redundant activities like texting, phoning, and even eating while operating the machine. Subsequently, it enhanced overall machine productivity up to 37% during the site observation. To summarize, a gamified platform in which different operators from different organizations can share their achievements, or can get scored and ranked in a leader-board will potentially lead to a more proper operators’ behaviour at work and subsequently can improve overall productivity rate at construction sites

Gamification of telematics data to enhance operators’ behaviour for improvement of machine productivity in loading cycles

Construction industry is suffering from low productivity rate in various projects such as excavation. Although this issue is discussed in literature and several approaches are proposed to address it, productivity rate is still low in construction industry compared to other domains like manufacturing. A gamified platform in which different operators from different organizations can share their achievements, or can get scored and ranked in a leader-board will potentially address this issue