Empirical Application of GPS Fleet Tracking Technology to a Soil Excavation Process Kang, J. Texas A&M University (email: juliankang@tamu.edu) Ahn, S.M. Samsung C&T Corporation (email: smahn1@samsung.com) Abstract The unpredictable traffic condition around the construction site and individual operator s uneven productivity often make it difficult to identify an optimum number of hauling units. for a soil excavation process at once. The balance between hauling units for a soil excavation process, for example, has to be updated repetitively until it reaches an optimum stage. Speeding up this process is therefore expected to increase productivity especially at the beginning of the soil excavation process. One of the challenges in figuring out the optimum balance between hauling units, however, is to monitor the hauling units operation accurately. This paper presents our investigation to figure out whether the real-time fleet tracking technology integrated with a stochastic construction simulation could facilitate the process of identifying the optimum number of hauling units for a soil excavation process. Keywords: soil excavation, hauling unit, fleet tracking technology 76
1. Introduction Developing the optimum combination of hauling units for a new construction project has never been easy because the unpredictable traffic condition around the job site and uneven individual operator s productivity make it difficult to reasonably presume the productivity of a certain combination of hauling units. 1.1 Difficulty in predicting the time for the hauling unit s round trip The time required for a hauling unit to make a round trip is critical information in fleet planning for removing soils excavated from the job site. However, the speed of a hauling unit such as dump truck can be easily changed by the traffic condition on the route, and it has never been easy to predict how long it would take for a hauling unit to make a round trip. Developing a fleet combination for the soilremoving project in a foreign country is even harder because of more uncertain situation one has to bring in to consideration. For example, more international projects demand to use the local labours and it is not easy to figure out the productivity of the local operators at the early stage of project planning. 1.2 Difficulty in following up the hauling unit s operation in real time Identification of the actual productivity of the fleet assigned to the project is critical for evaluating the goodness of the current fleet combination and making an adjustment. However, in many cases it has not been easy to figure out the hauling unit s operational pattern in real time and discover what is decreasing the productivity of the fleet. 1.3 Difficulty in planning an optimized hauling unit It has never been clearly determined at the early stage of project planning how a certain construction fleet combination would produce in a foreign country. Construction professionals often have to increase the number of vehicles in a fleet by about 30% to manage unpredictable risk, which obviously would end up increasing the project cost. 2. Problem solving opportunity using Kaizen theory Kaizen refers to a Japanese management strategy that demands constant and repetitive improvement in a process. It is a daily activity whose purpose goes beyond simple productivity improvement. It suggests professionals how to perform experiments to detect and eliminate waste in business processes. A PDCA (Plan-Do-Check-Act) cycle is one of the tools suggested to apply Kaizen to actual business processes. It is an iterative problem-solving process typically used in the auto industry. Activities implemented at each step are: 77
PLAN: Establish the objectives and processes necessary to deliver results in accordance with the specifications. DO: Implement the processes. CHECK: Monitor and evaluate the processes and results against objectives and specifications and report the outcome. ACT: Apply actions to the outcome for necessary improvement. This means reviewing all steps (Plan, Do, Check, Act) and modifying the process to improve it before its next implementation. Gray Construction in the U.S. demonstrated that the Kaizen strategy could also be applied to construction management (ENR March 5 th, 2007). The Gray Construction s revenue was ranked 294 th in the U.S. when the Kaizen strategy was applied to its management for the first time in 1986. Since then, Gray Construction has been growing to 126 th among the U.S. construction firms with the revenue of $350 million in 20 years to some extent due to its utilization of the Kaizen strategy. Jim Gray, CEO of Gray Construction, emphasized that the construction process is somewhat similar to the automobile assembly process and therefore the application of the Kaizen strategy could increase the productivity in the construction industry as it had done in the auto industry. It is known that the Kaizen is more effective 1) when it is applied to a new project that no one has ever experienced, and 2) when there are repetitive components in the process. The process of removing soils from the excavation job site in a foreign country has these conditions and therefore it is reasonable to speculate that the Kaizen strategy might be working effectively for an overseas excavation project. 3. Tools for implementing the Kaizen strategy in construction 3.1 Stochastic simulation for optimizing the construction fleet combination Stochastic simulation is a technology utilized to predict the productivity of a network of activities while handling uncertain conditions reasonably. The Construction Industry Institute (CII), a research consortium funded by major construction companies in the U.S., expected that 3D construction simulation technology based on stochastic probability would enable to develop a reasonable construction plan that minimizes the impact of unforeseen variables (CII 2001). FIATECH, another research consortium in the U.S. leading the efforts to best utilize emerging technologies for improving construction quality, presented lately that the construction simulation is one of the top 10 future technologies sought by many construction companies (Wood and Alvarez 2005). Texas A&M 78
University and other universities in the U.S. have reported the advantages of the stochastic simulation in construction planning (Kang, Ahn, and Nam 2007, Kang, Chae, and Park 2007). 3.2 GPS-based Real-Time fleet location identification technology The city of New York expected that they might have to use about 250 dump trucks to remove the debris of the World Trade Centre attack. The city manager also expected that the removal of the debris from the WTC site would take well over a year, and cost nearly $2 billion. At the early stage of the debris removal project, the city of New York figured out that the project was not moving as fast as expected because of the traffic jam in Manhattan. The project manager then suggested getting the GPS-based fleet tracking device mounted on trucks and monitoring their operation in real time in order to avoid the traffic jams and make sure the truck drivers were running on the right track. The outcome of this suggestion was exceeding their expectation (Becker 2007): The frequency of the truck s round trip increased from 5 to 10 times per shift. The total number of trucks used was reduced from 250 down to 50. The project was finished earlier than scheduled. They saved 4 month of time. The project cost was reduced from $2 billion down to $750 million. 4. Objective The objective of the paper is to investigate whether the Kaizen strategy could facilitate construction professionals to better plan and control the soil excavation project in a foreign country. 5. Methods 5.1 Transportation network of soil excavation process The first step was to develop a transportation network of the on-going soil excavation process. The research team decided to apply the Kaizen Theory to the excavation project that went on for the Sejong Special Autonomous City construction project in South Korea. The name of the city is a tribute to Sejong the Great, who commissioned Korean scholars to create the Korean alphabet (Hangul) that is used today. This city is located 160 km south of Seoul, the capital of South Korea. The research team visited the job site and collected basic information such as traffic condition of the designated routes, capacity of various hauling units and excavators, variation of elapsed time required for the hauling unit s round trip, and so on. Figure 1 shows a typical job site configuration. 79
Figure 1: A typical job site configuration With the data collected, the research team developed the transportation network shown in Figure 2. Figure 2: Transportation Network of Soil Excavation Process 5.2 Stochastic simulation model The research team then developed a stochastic simulation model of the transportation network, which is presented in Figure 3. Arena Simulator, which is an off-the-shelf computer application specialized in stochastic simulation modelling, was used for this process. By implementing a series of sensitivity 80
Q uicktim e and a decom pr essor ar e needed t o see t his pict ur e. Q uicktim e and a decom pr essor ar e needed t o see t his pict ur e. Q uicktim e and a decom pr essor ar e needed t o see t his pict ur e. Q uicktim e and a decom pr essor ar e needed t o see t his pict ur e. analysis, the research team identified the optimum balance between different hauling units, loaders, and excavators. Figure 3: Snapshot of an ARENA simulation model 5.3 GPS fleet tracking system As a next step, the research team developed a GPS fleet tracking system that can track down the locations of hauling units and display them on the Web browser in real time. The figure 4 shows the concept of our GPS fleet tracking system. The system is designed to identify the location of a truck using GPS, and transmit it to the Web-server over the wireless telephone communication. Figure 5 shows the snapshot of the Web-based Fleet Tracking system. TruckÕs location presented on a Webpage TruckÕs location identified in realtime by GPS TruckÕs location sent to a Web server by a cellular network Figure 4: Concept of the fleet tracking system 81
Figure 4: Web-based fleet tracking system showing the location of dump trucks 5.4 Application of Kaizen theory The research team monitored the location of the hauling units in real time using the GPS fleet tracking system. The data collected here were compared with the outcome of the stochastic simulation model. The difference between the simulation model and actual hauling units operation was used to come up with modified parameters for the stochastic simulation model. By running the updated simulation model, the research team determined a new combination of hauling units. This process iterated until the gap between the simulation model and fleet s actual performance was minimized. 6. Conclusion The GPS location identification system presented in this paper demonstrated its potential in speeding up the process of getting the optimum balance between excavation equipments and hauling units identified. However, some equipment operators refused to get this system installed on their dump trucks because of the privacy issue. They did not want to get their location identified automatically in real time. Unless we demonstrate a tangible benefit of using the GPS location tracking system, it would not be easy to apply this technology to actual practices. The project contract type was an anther challenge. Since the earth excavation and transportation work was outsourced to the subcontractors, the project team failed to find the dramatic benefit to the general contractors for applying the GPS system to their project. The research team learned that our system would work more effectively if it were applied to an overseas project, for which the general contractors need to purchase and operate construction equipments by themselves. This is an on-going project, and project participants are currently interviewed to figure out pros and cons of the GPS fleet tracking system. 82
Acknowledgement This research was funded by the Samsung Construction Research Foundation. References Becker P (2007) Digital Identity Saves Billions in 9/11 Cleanup, Digital Identity World. CII (2001) Electronic Simulation in Construction, CII Report RR154-11. Kang J, Ahn S, Nam J (2007) Configuration of Rock Transportation System using Visual Simulation. The 4th Civil Engineering Conference in the Asian Region, Taipei, Taiwan. Kang J, Chae J W, and Park W (2007) Estimation of Cargo Handling Capacity of a Floating Container Terminal using 3D Simulation. 7th World Congress on Structural and Multidisciplinary Optimization, Seoul, Korea. Wood C, and Alvarez M (2005) Emerging Construction Technologies, FIATECH Catalogue. 83