• Journal of Biosystems Engineering
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• v.38 no.1
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• pp.33-40
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• 2013

Purpose: The goal of this study was to build an accurate digital factory that evaluates the performance of a factory using computer simulation. To achieve this goal, we evaluated the effect of worker-related variables on production in a simulation model using comparative analysis of two cases. Methods: The overall work process and worker-related variables were determined and used to build a simulation model. Siemens PLM Software's Plant Simulation was used to build a simulation model. Also, two simulation models were built, where the only difference was the use of the worker-related variable, and the total daily production analyzed and compared in terms of the individual process. Additionally, worker efficiency was evaluated based on worker analysis. Results: When the daily production of the two models were compared, a 0.16% error rate was observed for the model where the worker-related variables were applied and error rate was approximately 5.35% for the model where the worker-related variables were not applied. In addition, the production in the individual processes showed lower error rate in the model that included the worker-related variables than the model where the worker-related variables were not used. Also, among the total of 22 workers, only three workers satisfied the IFRS (International Financial Reporting Standards) suggested worker capacity rate (90%). Conclusions: In the daily total production and individual process production, the model that included the worker-related variables produced results that were closer to the real production values. This result indicates the importance of worker elements as input variables, in regards to building accurate simulation models. Also, as suggested in this study, the model that included the worker-related variables can be utilized to analyze in more detail actual production. The results from this study are expected to be utilized to improve the work process and worker efficiency.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.42 no.4
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• pp.145-152
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• 2019

All machines deteriorate in performance over time. The phenomenon that causes such performance degradation is called deterioration. Due to the deterioration, the process mean of the machine shifts, process variance increases due to the expansion of separate interval, and the failure rate of the machine increases. The maintenance model is a matter of determining the timing of preventive maintenance that minimizes the total cost per wear between the relation to the increasing production cost and the decreasing maintenance cost. The essential requirement of this model is that the preventive maintenance cost is less than the failure maintenance cost. In the process mean shift model, determining the resetting timing due to increasing production costs is the same as the maintenance model. In determining the timing of machine adjustments, there are two differences between the models. First, the process mean shift model excludes failure from the model. This model is limited to the period during the operation of the machine. Second, in the maintenance model, the production cost is set as a general function of the operating time. But in the process mean shift model, the production cost is set as a probability functions associated with the product. In the production system, the maintenance cost of the equipment and the production cost due to the non-confirming items and the quality loss cost are always occurring simultaneously. So it is reasonable that the failure and process mean shift should be dealt with at the same time in determining the maintenance time. This study proposes a model that integrates both of them. In order to reflect the actual production system more accurately, this integrated model includes the items of process variance function and the loss function according to wear level.

• Korean Management Science Review
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• v.13 no.3
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• pp.37-48
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• 1996

This paper presents an Economic Lot Scheduling Problem in which controllable production rates are considered. We also take into account the controllable range of production rate (i.e., maximum and minimum production rate) of each product and the mold cost which varies to the production rate. A mathematical model is developed and an iterative solution procedure is suggested. The objective of this problem is to minimize production related cost and the decision variables are common production cycle time and production rate of each product. As a case study, we adapted this model to the press machine of a company.

• Proceedings of the Korean Institute of Building Construction Conference
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• 2019.11a
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• pp.42-43
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• 2019

In-situ production of PC (precast concrete) members can reduce costs by about 14.5% -21.6% compared to in-plant production due to the reduction of transportation costs, factory profits and overhead costs. However, in-situ production of PC members presents a variety of risks, including member production and yard area securing, and lead time for production within the installation period. To solve this, it is necessary be able to analyze and control and monitor the risk factors that influence in-situ production for PC member. The purpose of this study is to develop a dynamic simulation model for in-situ production and erection integrated management for PC members. For this study, risk factor identification, causal loop diagram, and dynamic simulation model construction were performed sequentially. The results of this study will be used as a basis for developing a risk management model for PC in-situ production.

• Agribusiness and Information Management
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• v.6 no.1
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• pp.1-11
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• 2014

The purpose of this study is to develop the growth prediction model that can predict growth and development information influencing on the production of citrus fruits. The growth model was developed to predict the floral leaf ratio, number of fruit sets, fruit width, and overweight fruits depending on the main period of growth and development by considering the weather factors because the fruit production is influenced by weather depending on the growth and development period. To predict the outdoor-grown citrus fruit production, the investigation result for the standard farms is used as the basic data; in this study, we also understood that the influence of weather factors on the citrus fruit production based on the data from 2004 to 2013 of the outdoor-grown citrus fruit observation report in which the standard farms were targeted by the Agricultural Research Service and suggested the growth and development information prediction model with the weather information as an independent variable to build the observation model. The growth and development model for outdoor-grown citrus fruits was assumed by using the Ordinary Least Square method (OLS), and the developed growth prediction model can make a prediction in advance with the weather factors prior to the observation investigation for the citrus fruit production. To predict the growth and development information of the production of citrus fruits having a great ripple effect as a representative crop in Jeju agriculture, the prediction result regarding the production applying the weather factors depending on growth and development period could be applied usefully.

• Journal of the Korean Data and Information Science Society
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• v.17 no.1
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• pp.63-76
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• 2006

Marine production forecasting in fisheries is a crucial factor for managing and maintaining fishery resources. Thus this paper aims to generate a forecasting model of total marine production. The most generally method of time series model is to generate the most optimal single forecasting model. But the method could induce a different forecasting results when it does not properly infer a model To overcome the defect, I am trying to propose a single forecasting through multiple time series model. In other word, by comparing and integrating the output resulted from ARIMA and VAR model (which are typical method in a forecasting methodology), I tried to draw a forecasting. It is expected to produce more stable and delicate forecasting prospect than a single model. Through this, I generated 3 models on a yearly and monthly data basis and then here I present a forecasting from 2006 to 2010 through comparing and integrating 3 models. In conclusion, marine production is expected to show a decreasing tendency for the coming years.

• Transactions of the Korean Society of Mechanical Engineers B
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• v.20 no.1
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• pp.304-320
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• 1996

A multiple production .epsilon. equation model was developed in the low Reynolds number $\kappa$-$\varepsilon$ model with the aids of DNS data. We derived the model theoretically and avoided the use of empirical correlations as much as possible in order for the model to have generality in the prediction of complex turbulent flow. Unavoidable model constants were, however, optimized with the aids of DNS data. All the production and dissipation models in the $\varepsilon$ equation were modified with damping functions to satisfy the wall limiting behavior. A new $f_{\mu}$ function, turbulent diffusion and pressure diffusion model for the k and .epsilon. equations were also proposed to satisfy the wall limiting behavior. By, computational investigation on the plane channel flows, we found that the multiple production model for .epsilon. equation could improve the near wall turbulence behavior compared with the standard production model without the complicated empirical modification. Satisfication of the wall limiting conditions for each turbulence model term was found to be most important for the accurate prediction of near wall turbulence behaviors.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.375-380
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• 2003

This study is concerned with the machine part grouping m cellular manufacturing. To group machines into the set of machine cells and parts into the set of part families, new p-median model considering the production data such as the operation sequences and production volumes for parts is proposed. Unlike existing p-median models relying on the classical binary part-machine incidence matrix which does not reflect the real production factors which seriously impact on machine-part grouping, the proposed p-median model reflects the production factors by adopting the new similarity coefficient based on the production data-based part-machine incidence matrix of which each non-binary entry indicates actual intra-cell or inter-cell flows to or from machines by parts. Computation test compares the proposed p median model favorably.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2003.05a
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• pp.1-7
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• 2003

This paper considers an Integrated one-vendor multi-buyer production-inventory model where the vendor manufactures multiple products In lot at Her associated finite production rates In the model. It is allowed for earth product to be shipped In lot to the buyers before the whole product production is not completed yet. Each product lot is dispatched to the associated buyer In a number or shipments. The buyers consume their products at fixed rates. The objective is to the production and shipment schedules in the Integrated system. which minimizes the mean total annual cost per unit time. The mean total annual cost consists or production setup cost inventory holding cost and shipment cost. For the model, an Iterative optimal solution procedure with shipment consolidation policy incorporated is derived. It is then tested through numerical experiments to show how efficient and effective He shipment consolidation policy is.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.8
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• pp.1205-1215
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• 2002

The purpose of this study was to develop a production line using simulation method, which could improve work allocation, labor utility and productivity. Using simulation software AIM, a simulation model of functional sports wear assembly line was developed. A functional sports wear production factory were analysed to gather data necessary for this research. Factory layouts, production facilities, work time of each unit jobs were investigated. The data obtained were used as to build a base simulation model. Then, the base simulation model was verified using the obtained data, such as daily productivity. Using simulation method, low alternative production plans were suggested, which were to enhance productivity, and work efficiency and to reduce queue length and throughput time.