• Journal of the Korea Institute of Military Science and Technology
• /
• v.13 no.4
• /
• pp.601-611
• /
• 2010

Currently, a life cycle cost estimates(LCCE) is the most important factor in weapon system acquisition process. However, operation and maintenance(O&M) cost related studies are insufficient from the previous literature survey. O&M cost consists of various cost factors such a man power, maintenance and direct & indirect support costs. We have known that spare-parts cost is a key factor in the O&M cost. In this paper, we developed the spare-parts cost estimating relationships(CERs) of fixed-wing aircraft and armored vehicle weapon systems which include 4 historical cost drivers ; system acquisition cost, deterioration rate, localization rate, mission characteristic. Furthermore, we proposed the application methodologies that O&M cost estimating, total life cycle cost estimating and determination of the economic life using the spare-parts CERs.

• Journal of the Korea Safety Management & Science
• /
• v.9 no.4
• /
• pp.121-127
• /
• 2007

CSP(Concurrent Spare Parts) is supplied with the procurement of new equipment or weapon system and is used to sustain the equipment without resupply during the initial coverage period. This study is concerned with a problem of determining the near optimal inventory level of the spare parts, especially Concurrent Spare Parts. For this, we utilize the mixed periodic and continuous review polices considering the CSP and (r,Q) Policies concurrently in a two-echelon distribution system. We propose the mathematical model to minimize the total cost which is composed with ordering cost, purchasing cost, holding cost, and stickout cost. If the mixed policy is compared to other policies(CSP, (r,Q)), the proposed methodology performs well and is best policy in the equipment maintenance expenses.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 2005.05a
• /
• pp.938-946
• /
• 2005

We study the firm's strategy to price its products and plan the spare parts manufacturing so as to maximize its profit and at the same time to fulfill its commitment to providing the customers with the key parts continuously over the relevant decision time horizon, i.e., the production plus warrantee period. To examine the research question, we developed and solved a two-stage optimal control theory model. Our analysis suggests that if the cost to produce the spare part during the warrantee period is more expensive than that during the production period, the firm should increase its sales price gradually throughout the production period to control its sales. In addition, during the production period it is optimal for the firm to produce the spare parts more than needed so that the overproduced spare parts can be used to partially meet the demand during the warrantee period. We conducted numerical analysis to investigate the sensitivity dynamics among key variables and parameters such as inventory holding cost, unit spare part production costs, part failure rate, and parameters in the demand function.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.27 no.3
• /
• pp.406-415
• /
• 2024

As the equipment of the military has become more advanced and expensive, the cost of securing spare parts is also constantly increasing along with the increase in equipment assets. In particular, forecasting demand for spare parts one of the important management tasks in the military, and the accuracy of these predictions is directly related to military operations and cost management. However, because the demand for spare parts is intermittent and irregular, it is often difficult to make accurate predictions using traditional statistical methods or a single statistical or machine learning model. In this paper, we propose a model that can increase the accuracy of demand forecasting for irregular patterns of spare parts demanding by using a combination of statistical and machine learning algorithm, and through experiments on Cheonma spare parts demanding data.

• Journal of the Korea Institute of Military Science and Technology
• /
• v.21 no.4
• /
• pp.529-540
• /
• 2018

As the equipment of the military has recently become more sophisticated and expensive, the cost of purchasing spare parts is also steadily increasing. Therefore, demand forecast accuracy is also becoming an issue for the effective execution of the spare parts budget. This study predicts the demand by using the data of spare parts consumption of the KF-16C fighter which is being operated in the Republic of Korea Air Force. In this paper, SARIMA(Seasonal Autoregressive Integrated Moving Average) is applied to seasonal data after dividing the spare parts consumptions into seasonal data and non-seasonal data. Proposing new methods, Majority Voting and Hybrid Method, to the non-seasonal data which consists of spare parts of low consumption with unclear pattern, We want to prove that the demand forecast accuracy of spare parts improves.

• Journal of the military operations research society of Korea
• /
• v.24 no.2
• /
• pp.146-161
• /
• 1998

This article develops model of the nonstationary state behavior of the multiechelon spare parts provisioning systems. This study is concerned with a problem of determining the near optimal requirements level of the spare parts, especially Concurrent Spare Parts(CSP). CSP is supplied with the procurement of new equipment system, and is used to sustain the equipment without resupply during the initial coverage period. We consider this situation as a multiechelon inventory model with several bases and one depot. And we assume an equipment system which consists of many types of parts would grounded if one of the parts fail. Also this multiechelon CSP problem is considering the nonstationary poisson failure process and nonstationary exponential repair process in a dynamic environment. We develop an efficient computational procedure to find the near optimal number of spare parts minimizing the total expected cost, while achieving the required system availability. Finally we present a simple example of suggested method.

• Proceedings of the Society of Korea Industrial and System Engineering Conference
• /
• 2002.05a
• /
• pp.189-194
• /
• 2002

As a environment of SCM is issued recently, many enterprises are concerned about inventory control for an efficiency of operation and reliablity. The goal of them is to match up to improving of facilities and redoing of inventory cost. To put an efficiency in the operation rate of facilites, A moderate provision of spare parts for that would come into a matter. This study designs two-echelon spare parts distribution system and sees over the relationship between operating rate and inventories of spare parts. and then it determines fitting inventories and investment cost considering an operating rate.

• Korean Management Science Review
• /
• v.26 no.1
• /
• pp.209-223
• /
• 2009

In this paper, an analysis problem of repair levels and spare part allocation for MIME(Multi indenture multi echelon) systems is studied using simulation and meta-heuristics. We suggest a method to determine simultaneously repair levels and spare parts allocation to minimize the life cycle cost of MIME system under availability constraint. A simulated annealing method is used to analyze the repair levels and genetic algorithm is used to obtain the optimal allocation of spare parts. We also develop a simulation system to calculate the life cycle cost and system availability. Some numerical examples are also studied.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.21 no.47
• /
• pp.87-99
• /
• 1998

This study is concerned with a problem of determining the optimal requirements level of the spare parts, especially Concurrent Spare Parts(CSP). CSP is supplied with the procurement of new equipment system, and is used to sustain the equipment without resupply during the initial coverage period. We consider this situation as a multiechelon inventory model with several bases and one depot. And we assume a equipment system which consists of many types of parts would grounded if one of the parts fail. Also this multiechelon CSP problem is considering a time-varing (dynamic) environment. We develop a computational procedure to find the optimal number of spare parts minimizing the total expected cost, while achieving the required system availability. Finally we present a simple example of suggested method.

• Journal of Korean Society of Industrial and Systems Engineering
• /
• v.40 no.4
• /
• pp.253-259
• /
• 2017

Recent development in science and technology has modernized the weapon system of ROKN (Republic Of Korea Navy). Although the cost of purchasing, operating and maintaining the cutting-edge weapon systems has been increased significantly, the national defense expenditure is under a tight budget constraint. In order to maintain the availability of ships with low cost, we need accurate demand forecasts for spare parts. We attempted to find consumption pattern using data mining techniques. First we gathered a large amount of component consumption data through the DELIIS (Defense Logistics Intergrated Information System). Through data collection, we obtained 42 variables such as annual consumption quantity, ASL selection quantity, order-relase ratio. The objective variable is the quantity of spare parts purchased in f-year and MSE (Mean squared error) is used as the predictive power measure. To construct an optimal demand forecasting model, regression tree model, randomforest model, neural network model, and linear regression model were used as data mining techniques. The open software R was used for model construction. The results show that randomforest model is the best value of MSE. The important variables utilized in all models are consumption quantity, ASL selection quantity and order-release rate. The data related to the demand forecast of spare parts in the DELIIS was collected and the demand for the spare parts was estimated by using the data mining technique. Our approach shows improved performance in demand forecasting with higher accuracy then previous work. Also data mining can be used to identify variables that are related to demand forecasting.