Downtime model development for construction equipment management

Abstract

Downtime (DT) caused by non‐availability of equipment and equipment breakdown has non‐trivial impact on the performance of construction projects. Earlier research has often addressed this fact, but it has rarely explained the causes and consequences of DT – especially in the context of developing countries. This paper presents a DT model to address this issue. Using this model, the generic factors and processes related to DT are identified, and the impact of DT is quantified. By applying the model framework to nine road projects in Nepal, the impact of DT is explored in terms of its duration and cost. The research findings highlight how various factors and processes interact with each other to create DT, and mitigate or exacerbate its impact on project performance. It is suggested that construction companies need to adopt proactive equipment management and maintenance programs to minimize the impact of DT.

Using Machine Learning Algorithms on data residing in SAP ERP Application to predict equipment failures

“…When unforeseen equipment breakdown occurs, it creates unplanned maintenance costs, hinders operation and impacts productivity. Both time and money loss are experienced when an unplanned equipment downtime occurs, the example of the adverse effects can be seen in construction industry [15]. Usually, the reactive maintenance costs that are a result of unplanned equipment failure come out to be six to eight times costlier than preventive or predictive maintenance [16].…”