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Downtime resulting from equipment failure is a major problem consistently faced in highway construction. Since managing construction equipment is tightly connected to various activities and parties inside as well as outside of the firm, failure to account for this fact invariably causes downtime to be even more severe. Variation in equipment management practices is thus, indeed, a root cause of the dynamics of machine downtime. This study is intended to address key dynamic features of heavy equipment management practices and downtime in small to medium highway contracting firms and propose policies for equipment performance improvement.

Face‐to‐face interviews with equipment managers from five different small to medium highway construction companies in Thailand were conducted. Data were analysed using a system dynamics (SD) simulation approach.

To overcome downtime problems, contractors need to understand the dynamics of downtime as well as its influential factors, and thus manage their equipment as a dynamic process rather than one that is static. Based on the simulation, various policies are proposed to improve the performance of heavy equipment for small to medium highway contractors.

The research is of value in facilitating better understanding on the dynamics of equipment management practices and downtime as well as their interdependency.

This study is intended to investigate the current practices and problems in heavy equipment management as well as to identify practices capable of alleviating equipment management problems for highway contractors in Thailand.

Equipment management practices were identified and analysed by SPSS using a questionnaire survey. ANOVA test was used to reveal significant differences in equipment management practices among different contractor sizes. Relationships between equipment management practices and problems were also revealed.

The equipment management practices vary, to some extent, among different contractor sizes. While practices of medium and small contractors tend to be similar, practices of large contractors are different from those of smaller contractors. Large contractors often put more emphasis on outsourcing strategy for equipment management. Moreover, large contractors frequently dispose of or replace equipment as soon as the equipment becomes inefficient before incurring high repair costs. Conversely, smaller contractors tend to mainly emphasise on the company finance and the budget availability as they often rely on purchasing strategy, especially buying used machines. Overall, equipment practices of large contractors were found to be more successful than smaller contractors in minimising equipment management problems, including long downtime duration and cost.

This research is of value for better understanding practices and problems relating to heavy equipment management among different contractor sizes. The study also highlights practices that are capable of reducing problems relating to heavy equipment management for highway contractors.

The purpose of this paper is to propose a system dynamic simulated process model for maintenance work management incorporating the Fourth Industrial Revolution (4IR) technologies.

The extant literature in physical assets maintenance depicts that poor maintenance management is predominantly because of a lack of a clearly defined maintenance work management process model, resulting in poor management of maintenance work. This paper solves this complex phenomenon using a combination of conceptual process modeling and system dynamics simulation incorporating 4IR technologies. A process for maintenance work management and its control actions on scheduled maintenance tasks versus unscheduled maintenance tasks is modeled, replicating real-world scenarios with a digital lens (4IR technologies) for predictive maintenance strategy.

A process for maintenance work management is thus modeled and simulated as a dynamic system. Post-model validation, this study reveals that the real-world maintenance work management process can be replicated using system dynamics modeling. The impact analysis of 4IR technologies on maintenance work management systems reveals that the implementation of 4IR technologies intensifies asset performance with an overall gain of 27.46%, yielding the best maintenance index. This study further reveals that the benefits of 4IR technologies positively impact equipment defect predictability before failure, thereby yielding a predictive maintenance strategy.

The study focused on maintenance work management system without the consideration of other subsystems such as cost of maintenance, production dynamics, and supply chain management.

The maintenance real-world quantitative data is retrieved from two maintenance departments from company A, for a period of 24 months, representing years 2017 and 2018. The maintenance quantitative data retrieved represent six various types of equipment used at underground Mines. The maintenance management qualitative data (Organizational documents) in maintenance management are retrieved from company A and company B. Company A is a global mining industry, and company B is a global manufacturing industry. The reliability of the data used in the model validation have practical implications on how maintenance work management system behaves with the benefit of 4IR technologies' implementation.

This research study yields an overall benefit in asset management, thereby intensifying asset performance. The expected learnings are intended to benefit future research in the physical asset management field of study and most important to the industry practitioners in physical asset management.

This paper provides for a model in which maintenance work and its dynamics is systematically managed. Uncontrollable corrective maintenance work increases the complexity of the overall maintenance work management. The use of a system dynamic model and simulation incorporating 4IR technologies adds value on the maintenance work management effectiveness.