A Decision Support System for Equipment Replacement

The construction industry relies on heavy equipment to carry out many of its operations. The quality of fleet management within an organization, therefore, can greatly affect an organization's long-term profitability. Making cost-effective decisions about replacing or maintaining equipment requires practitioners to understand and forecast the lifecycle cost of their equipment. However, the equipment management decision-making process can be challenging because of a lack of data-driven cost analysis models. Indeed, previous research studies have identified a “sweet point” in life cycle of equipment where the total cost per hour of owning and operating equipment reaches a minimum amount. Notably, after this point, the cost-effectiveness of owning equipment without taking further action reduces significantly. However, the equipment management decision-making process, including the identification of the sweet point, is challenging due to the absence of predictive models that rely on historical data. To address this limitation, this research is proposing a novel cost analysis framework that supports equipment replacement decisions by forecasting equipment maintenance costs and trading prices through an equipment's lifecycle or useful life. A net present value analysis trial is used to objectively identify and evaluate the outcome of four different scenarios for the owned equipment: (1) keeping it as-is, (2) keeping and undergoing refurbishment, (3) replacing with a used model, and (4) replacing with a new model. The expected cash flow for each scenario is developed in this analysis, and the results are compared. The results can be used (1) on their own to support decision-making about the current fleet or (2) to identify more optimal strategies for future equipment replacement. The model is developed and implemented on an actual case study to verify the concepts and test the feasibility of the proposed framework.