Simulation-Based Procedure for Bottleneck Identification
This study presents a simulation-based procedure to identify bottleneck station(s) related to Theory of Constraints (TOC). Bottleneck identification starts by running simulation model of existing system to collect data of the utilization of each machine/p
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Abstract. This study presents a simulation-based procedure to identify bottleneck station(s) related to Theory of Constraints (TOC). Bottleneck identification starts by running simulation model of existing system to collect data of the utilization of each machine/process and the time between arrivals and departures of each machine/process. Three factors, the machine/process utilization, the process utilization factor (ρ) and the product bottleneck rate (Rb) are used to identify potential bottleneck candidates. The real bottleneck must come from the process that has high value of two factors; the utilization of machine/process and the utilization factor and low value for the bottleneck rate. The simulation is used again to evaluate the solution by increasing capacity of a bottleneck candidate. If no improvement in throughput is observed, then the station is not a bottleneck and the procedure is run again using the other bottleneck candidate. Numerical examples are given to illustrate the proposed procedure in both case of single bottleneck and multiple bottlenecks. Keywords: Theory of Constraints (TOC), Simulation, Bottleneck Identification, Single Bottleneck, Multiple Bottlenecks.
1 Introduction This paper presents a simulation-based procedure to identify bottleneck in the context of the Theory of Constraints (TOC) concept. In this study, the simulation-based tool for TOC implementation proposed in [1] is used (See also [2]). By applying this simulation tool, the data such as machines, jobs, and processes are read into the physical flow model to identify bottleneck in the first step in TOC implementation. Data such as machine utilization and the time between arrivals and departures of parts are collected for used as indicators to the true bottleneck. The performance of the system can be measured by using throughput data obtained from simulation tool and compared with the target demand by using the mean confident interval comparison provided by ARENA. The procedure stops when the target demand is met. Finally, the real bottleneck can be identified. J.-W. Park, T.-G. Kim, and Y.-B. Kim (Eds.): AsiaSim 2007, CCIS 5, pp. 46–55, 2007. © Springer-Verlag Berlin Heidelberg 2007
Simulation-Based Procedure for Bottleneck Identification
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The practices of Theory of Constraints (TOC) have been extensively developed and provide a total solution to managing a factory to optimize on time delivery, inventory and operation cost [3]. In fact, every process is a chain of operation, and the strength of the chain is the same as its weakest link, called “Constraint”. The key TOC concept is “The system output rate is limited by the slowest rate of any machine” [4]. In TOC, there are only two types of machines; a bottleneck machine or CCR (Capacity constraint resource) and a non-bottleneck machine or nonCCR. Bottleneck Resource or CCR is the resource with capacity equal or less than the demand. There are fewer resources of this type in the factory. The other type is the non-bottleneck machine or non-CCR. It is the resource with capa
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