• IE interfaces
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• v.14 no.3
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• pp.286-295
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• 2001

This paper addresses the problem of determining the proper level of concurrent spare parts(CSP) for a system consisting of multi-item parts under an available budget constraint. Initial provisioning of spare parts plays a major role in the acquisition of a new equipment system. Therefore, the proper level of spare parts should be on hand to maintain the availability of the system. This paper proposes a new CSP model and solution procedure that determines the proper level of spare parts satisfying the item priority and simultaneously available budget constraint.

• Journal of the military operations research society of Korea
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• v.16 no.2
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• pp.75-82
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• 1990

When the new equipment or weapon system is introduced to the operating unit its repair facilities and spare parts supply channels are not regularly generated. therefore, its required spare parts are initially supplied at the same time when the main equipment is deployed. For these circumstances, we are faced with the following problems : the first one is to decide the budget amounts to acquire the spare parts, the second is to choose the amounts of initial provisioning spare parts, and the third one is how to allocate the given budget for the initial provisioning spare parts. This paper emphasizes on determining the budget amounts, but also suggests the spare parts allocating methods.

• Journal of the Korea Institute of Military Science and Technology
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• v.21 no.4
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• pp.529-540
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• 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 Korean Society of Industrial and Systems Engineering
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• v.35 no.2
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• pp.163-172
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• 2012

In this study, It is assumed that customers are both usage unit of spare parts and stores of displaying and selling the goods that are installation unit of having the spare parts. The demand pattern through the effective order of spare parts and issue list in installation unit is investigated based on the assumption. Current ASL (Authorized Stockage List) selection of the army has been conducted in the way of using the analysis result of real usage experiences on spare parts used during the Korea War. For this study, ASL selection criteria and procedures based on army regulations and field manuals are specified. Since the traditional method does not presents the association analysis on spare parts used for the current equipment operating and does not have the clear criterion and analysis system about the ASL selection, in order to solve these problems, it was carried out that the association rule is employed for analyzing relationship between the effective order and issue list of the spare parts in point of the spare parts between usage unit and occurring month about purchase spare parts based on the star-schema table. Finally the new ASL selection way using the analysis result is proposed.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.05a
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• pp.938-946
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• 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 military operations research society of Korea
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• v.24 no.2
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• pp.146-161
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• 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.

• Journal of Korean Society for Quality Management
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• v.52 no.2
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• pp.275-286
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• 2024

Purpose: The spare part cost is one of the most important cost factors with which construct Life Cycle Cost. The LCSP(Life Cycle Sustainment Plan) Guidebook issued by Korea Ministry of Defense, however, suggests a simple equation to estimate the spare part cost using maintenance task frequencies and each part cost. Therefore, following the cost estimation method in the LCSP Guidebook may lead to an improper cost estimation result since both the hierarchical structure of the weapon system and the part discard rate are not considered. The purpose of this study is to develop a new life cycle cost estimation method for spare parts of weapon system during its life cycle. Methods: In this study, the detailed cost structure of spare parts is provided. Also a new spare part cost estimation methods for the each cost element are proposed, considering the hierarchical structure of weapon system and the part discard rate. And the proposed spare cost estimation methods are applied to K system for a case study. Results: Based on the case study of K system, the spare part cost estimation method, proposed by this study, shows that it can complement the estimation method suggested by the LCSP Guidebook. It also shows that it is applicable to the weapon systems for Korea armed forces. Conclusion: The proposed life cycle cost estimation method for spare parts has an advantage of estimating the spare part cost more accurately. It is expected to be useful in analyzing the procurement alternatives objectively and making up the Korea armed forces budget effectively.

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

• Journal of the Korea Institute of Military Science and Technology
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• v.13 no.4
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• pp.601-611
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• 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 Institute of Military Science and Technology
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• v.27 no.3
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• pp.406-415
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• 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.