Modeling the Dynamics of Heavy Equipment Management Practices and Downtime in Large Highway Contractors

Thanapun Prasertrungruang1 and B. H. W. Hadikusumo2

Introduction
In the construction industry, the tangible benefits of using machinery are obvious as greater productivity, performance, cost reductions, and improved competitiveness for contractors can be obtained. This is particularly so in highway construction organizations where a variety of construction equipment has been heavily deployed as a major resource in generating work production. However, managing construction equipment effectively is not an easy task since the contractor is required to dynamically interact with various parties and activities. Highway contractors are thus invariably plagued by a number of equipment management problems. Downtime resulting from machine breakdown during operations is of prime concern in views of contractors (Prasertrungruang and Hadikusumo 2007). Indeed, equipment practices and policies are some of the most important factors that affect machine downtime significantly (Elazouni and Basha 1996). Variation in practices regarding the flow of factors (e.g., spare parts, operators, equipment, mechanics, and information) over time is claimed as a major cause of the dynamics of downtime (Nepal and Park 2004). Nevertheless, to date, little efforts have been made to study the effect of less tangible factors (e.g., equipment management practices) on downtime, which control the dynamic behavior of the system, particularly in the construction context (Edwards et al. 2002). Hence, this research attempts to address this issue by exploring and highlighting key dynamic structures of equipment management practices and downtime inherent in each particular stage of machine lifecycle and then uses them as a framework in building a system dynamics (SD) simulation model. Scope of this study covers merely on large highway contractors with five types of heavy equipment for highway construction (see Table 1) as machine weight is one of the major indicators of downtime and maintenance cost (Edwards et al. 2002). It is noted that weight interval for each equipment type is also assigned in order to allow for machine generalization.

Applications of SD in Construction Decision-Making

By nature, construction project management is considered as a complex system (Richardson and Pugh 1981). Several researchers have adopted a SD methodology to model construction project.
For instance, Richardson and Pugh (1981) introduced a SD model for project management. This model concentrates on schedule overrun controlled by the magnitude of the workforce and rework. Subsequently, large-scale projects using fast-track procurement were modeled using the SD approach (Huot and Sylvestre 1985). The results reveal that the major problems in project failure are problems of quality, productivity, and worker morale. The SD was also used to model rework in construction (Love et al. 1999). Results show that rework is predominantly attributable to designer’s errors, design changes and construction errors. To solve this problem, teamwork between design and construction people, training, and skill development must be emphasized.

In the context of construction equipment management, the use of SD in modeling the dynamics of downtime is highly promising (Nepal and Park 2004). It was proposed that downtime and its consequences on construction equipment are significantly influenced by many factors: equipment-related factors, site-related factors, project-related factors, company’s policies, crew-level factors, site management actions, and force majeure.

Equipment Management Practices and Downtime
As the challenge of selecting, managing, and maintaining the equipment asset becomes more complex and costly every day, effective management of these assets directly fuels the success for business by significantly minimizing direct and indirect costs of equipment while still concurrently ensuring high availability of equipment productivity. Realizing the right practices on equipment management is dependent on where the machines are in their lifecycle. Indeed, equipment management practices can be categorized into four groups: machine acquisition, operations, maintenance, and disposal. Key practices in each particular stage of machine lifecycle include, for example, procurement decision approach (equipment acquisition stage), safety and training programs (equipment operational stage), schedule PM inspection and standby repair-maintenance facilities (equipment maintenance stage), equipment economic life and replacement decisions (equipment disposal stage) (Prasertrungruang and Hadikusumo
2006).

When the machine fails during operations, it is said to be “down or unavailable” which means that it is waiting for repair and thus incurring downtime (Nagi 1987). Typically, downtime duration consists of three major components, including (1) administrative time: time required for communication flow from user to manufacturer, time required for commercial formalities, and hours necessary to report a machine failure and give work directions for maintenance; (2) supply time: time when repair is delayed due to non-availability of spare parts and materials necessary to perform maintenance; and (3) active repair: time when technicians are working on the equipment to actually commission it including both preventive and corrective maintenance (Komatsu 1986). To minimize the consequential impact of downtime, contractors may opt to seek for substitute equipment, wait until the repair finished, accelerate work pace, modify work schedule, or transfer crews to other works (Nepal and Park 2004).

The research methodology was divided into two parts: data collection and data analysis. For the Data Collection, the research uses data collected from face-to-face interviews with five large highway contractors located in Bangkok and the surrounding provinces in Thailand. An equipment manager with at least 10 years work experience was selected as the interviewee for each of the participated contractors. A convenience sampling technique was used in identifying not only the sample contractors but also the interviewees. The interview checklist is in a semi-structured format in order to cover both open and closed-end dialogs. During the interviews, causal relationships between each pair of variables were disclosed and confirmed by the interviewees. For the data analysis, data collected from all five large contractor cases was administered using within-case as well as cross-case analysis approaches (Eisenhardt 1989). First, within-case analysis was employed to reveal the data characteristics for each particular contractor case. Then, attempt was made to draw the integrated picture among all contractor cases regarding the generic feedback structures of equipment management practices and downtime using cross-case analysis approach. The generic feedback structures were rechecked again with experts for validation until they are satisfactorily valid. Next, the generic feedback structures were used as a foundation in constructing the generic SD simulation model, using Powersim software. During this step, a number of stock and flow diagrams, which are all connected together in the generic SD model, have been identified. “Stock” represents accumulated quantities that change over time, while “flow” controls the changing rate of quantity going into or out of the stock (Sterman 2000). After data from each of the five contractor cases was input separately into the generic SD model, five applied SD models could be launched. Each of the applied SD models was then subjected to a number of validation tests to ensure that the model is structurally and behaviorally valid. Upon passing all validation tests, the generic SD model is deemed valid in representing the equipment management system as related to downtime of large contractors.

Conclusions
The aim of this paper is to give an insight into the dynamics of equipment management practices and downtime in large highway contractors. The dynamics of equipment management practices and downtime are presented through five generic feedback structures: machine acquisition, operations, maintenance, disposal, and downtime. Each of the feedback structures is interrelated and used as a framework in constructing the generic SD simulation model. A number of validation tests were used to ensure that the model is structurally and behaviorally valid.

To be successful in managing downtime, equipment management practices must be perceived as a combination of multiple feedback processes, which are interrelated to machine downtime. Indeed, downtime is interdependent and stimulated by three reinforcing cycles: schedule disruption and acceleration, operator schedule pressure creep, and mechanics’ schedule pressure creep. Even though downtime can be tackled through adoption of three balancing cycles (i.e., repair outsourced adjustment, operator skill adjustment, and mechanics’ skill adjustment), their expected benefits are always delayed, which retard or sometimes deteriorate the scenarios if contractors opt to stop the improvement processes. In addition, downtime is partly minimized through the reduction of disruption of work sequences by activating another two balancing cycles (i.e., rental machine adjustment and subcontractor adjustment). With high downtime, PM efforts are eroded, which in turn even worsen the scenarios as the reinforcing cycles of operator schedule pressure creep and mechanics’ schedule pressure creep have now been activated. However, contractors can mitigate this problem through adoption of balancing cycle of dealer maintenance services adjustment and the reinforcing cycle of management commitment in proactive maintenance.

Future work could be directed toward studying the interactions among equipment policies that have been addressed in the study. This would be useful especially when there are multiple performance tradeoffs involved among the stated policies (e.g., adopting participatory multi-skilled training policy may cause more fatigue to equipment operators and thus reduce the operator’s effort in performing the autonomous maintenance policy). Additional case studies are also needed to validate the effectiveness and practicability of the proposed system and make further adjustments for a more reliable system.

This paper is part of the Journal of Construction Engineering and Management, Vol. 135, No. 10, October 1, 2009. Full paper is available upon request.

Abstract is copied and posted.

Abstract: Machine downtime is invariably perceived as one of the most critical problems faced by highway contractors. Attempts to reduce downtime often result in failure due to the dynamic behaviors between equipment management practices and downtime. This paper is thus intended to highlight the dynamics of heavy equipment management practices and downtime in large highway contractors and utilizes them as a framework in constructing a simulation model using a system dynamics approach. Face-to-face interviews were conducted with equipment managers from five different large highway contractors in Thailand. The finding reveals that, to be successful in alleviating downtime, contractors must view their practices on equipment management as an integration of multiple feedback processes, which are interrelated and interdependent with downtime. Based on various validation tests, the simulation model is deemed appropriate in representing the equipment management system as related to downtime of large 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.

DOI: 10.1061/_ASCE_CO.1943-7862.0000076

CE Database subject headings: Maintenance; Dynamic models; Construction equipment; Contractors; Systems management; Construction industry; Thailand; Contractors; Highway and road construction.


1Researcher, Construction Engineering and Infrastructure Management,
School of Engineering and Technology, Asian Institute of Technology,
Pathumthani 12120, Thailand (corresponding author). E-mail:
st101533@ait.ac.th

2Associate Professor, Construction Engineering and Infrastructure
Management, School of Engineering and Technology, Asian Institute of
Technology, Pathumthani 12120, Thailand.