• The Journal of Information Systems
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• v.30 no.1
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• pp.151-177
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• 2021

Purpose This paper aims to improve management performance by effectively responding to production needs and reducing inventory through synchronizing production planning and procurement in the aviation industry. In this study, the differences in production planning and execution were first analyzed in terms of demand, supply, inventory, and process using the big data collected from a domestic aircraft manufacturers. This paper analyzed the problems in procurement and inventory management using legacy big data from ERP system in the company. Based on the analysis, we performed a simulation to derive an efficient procurement and inventory management plan. Through analysis and simulation of operational data, we were able to discover procurement and inventory policies to effectively respond to production needs. Design/methodology/approach This is an empirical study to analyze the cause of decrease in inventory turnover and increase in inventory cost due to dis-synchronize between production requirements and procurement. The actual operation data, a total of 21,306,611 transaction data which are 18 months data from January 2019 to June 2020, were extracted from the ERP system. All them are such as basic information on materials, material consumption and movement history, inventory/receipt/shipment status, and production orders. To perform data analysis, it went through three steps. At first, we identified the current states and problems of production process to grasp the situation of what happened, and secondly, analyzed the data to identify expected problems through cross-link analysis between transactions, and finally, defined what to do. Many analysis techniques such as correlation analysis, moving average analysis, and linear regression analysis were applied to predict the status of inventory. A simulation was performed to analyze the appropriate inventory level according to the control of fluctuations in the production planing. In the simulation, we tested four alternatives how to coordinate the synchronization between the procurement plan and the production plan. All the alternatives give us more plausible results than actual operation in the past. Findings Based on the big data extracted from the ERP system, the relationship between the level of delivery and the distribution of fluctuations was analyzed in terms of demand, supply, inventory, and process. As a result of analyzing the inventory turnover rate, the root cause of the inventory increase were identified. In addition, based on the data on delivery and receipt performance, it was possible to accurately analyze how much gap occurs between supply and demand, and to figure out how much this affects the inventory level. Moreover, we were able to obtain the more predictable and insightful results through simulation that organizational performance such as inventory cost and lead time can be improved by synchronizing the production planning and purchase procurement with supply and demand information. The results of big data analysis and simulation gave us more insights in production planning, procurement, and inventory management for smart manufacturing and performance improvement.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.03a
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• pp.858-862
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• 2008

KICT has been carrying out inventory research on the cut slopes of national roads. Inventory research results are basic data used in cut slope management system. Inventory data are classified by general status, cut slope characteristics and inspector opinion. Cut slope inventory data are utilized to figure out dangerous slopes and decide survey ranking of detailed safety diagnostication. This paper drew the distribution of dangerous cut slopes using inventory data in Chungcheongdo, then verified an efficiency on distribution of dangerous cut slopes by comparing occurrence frequency of real collapsed cut slopes.

• Proceedings of the Korean Geotechical Society Conference
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• 2008.10a
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• pp.980-983
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• 2008

KICT has been carrying out inventory research on the cut slopes of national roads. Inventory research results are basic data used in cut slope management system. Inventory data are classified by general status, cut slope characteristics and inspector opinion. Cut slope inventory data are utilized to figure out dangerous slopes and decide survey ranking of detailed safety diagnostication. This paper will draw dangerous cut slope and more important inventory data in Chungcheongdo via correlation analysis.

• 한국방재학회:학술대회논문집
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• 2008.02a
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• pp.585-588
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• 2008

KICT has been carried out inventory research on the cut slopes of national roads. Inventory research results are basic data using cut slope management system. Inventory data are classified by general status, cut slope characteristics and inspector opinion. Inventory data are utilized to figure out dangerous slopes and decide survey ranking of detailed safety diagnostication. This paper drew the distribution area of dangerous cut slopes using inventory data in Chungcheongdo, and verified efficiency on distribution area of dangerous cut slopes by comparing occurrence frequency of real collapsed cut slopes.

• Korean Management Science Review
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• v.30 no.3
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• pp.17-31
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• 2013

The purpose of this paper is to illustrate the importance of identifying and considering 'purchase dependence' when purchase of an item is dependent on availability of other items demanded in the same order. This paper is the first study to develop an inventory model for purchase-dependent inventory systems. Through simulation experiments, we demonstrate that the developed inventory model incurs less inventory operations cost than other inventory models that ignore purchase dependence. For empirical validation of the developed inventory model, the actual inventory data at the Hyundai Engine Europe Service Center is used. We explain the process of identifying purchase dependencies among items through a data mining technique. The empirical study results in similar results to the simulation experiment, demonstrating that the developed inventory model is applicable to real situations.

• Journal of Korean Society for Atmospheric Environment
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• v.30 no.1
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• pp.26-36
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• 2014

The uncertainty assessment is important to improve the reliability of emission inventory data. The DARS (Data Attribute Rating System) have recommended as the uncertainty assessment technic of emission inventory by U.S. EPA (Environmental Protection Agency) EIIP (Emission Inventory Improvement Program). The DARS score is based on the perceived quality of the emission factor and activity data. Scores are assigned to four attributes; measurement/method, source specificity, spatial congruity and temporal congruity. The resulting emission factor and activity rate scores are combined to arrive at an overall confidence rating for the inventory. So DARS is believed to be a useful tool and may provide more information about inventories than the usual qualitative grading procedures (e.g. A through E). In this study, the uncertainty assessment for 2009 CAPSS (Clean Air Policy Support System) emission inventory is conducted by DARS. According to the result of this uncertainty assessment, the uncertainty for fugitive dust emission data is higher than other sources, the uncertainty of emission factor for surface coating is the highest value, and the uncertainty of activity data for motor cycle is the highest value. Also it is analysed that the improvement of uncertainty for activity data is as much important as the improvement for emission factor to upgrade the reliability of CAPSS emission inventory.

• Journal of the Korean Data and Information Science Society
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• v.19 no.4
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• pp.1345-1352
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• 2008

An inventory with constant demand is studied. We adopt a renewal argument to obtain the transient and stationary distribution of the level of the inventory. We show that the stationary distribution can be also derived by making use of either the level crossing technique or the renewal reward theorem. After assigning several managing costs to the inventory, we calculate the long-run average cost per unit time. A numerical example is illustrated to show how we optimize the inventory.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.18 no.5
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• pp.237-244
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• 2018

The Smart Factory level of manufacturing factories of SMEs now lacks a system for grasping the accurate inventory amount associated with inventory movements in managing warehouses at the basic level. Also, it is difficult to manage accurate materials for loss of data due to worker manual work and production method due to experience. In order to solve this problem, in this paper, automatic acquisition of inventory to minimize manual work to grasp workers' Inventory and improve automation is done. In the smart warehouse management system using the IoT-based autonomous mobile module, the autonomous mobile module acquires the data of the inventory storage while moving through the line. In order to grasp the material of the Inventory storage, The Camera module recognizes the name of the inventory storage. And Then, If output matches, the data measured by the sensor is transferred to the server. This data can be processed, saved in a database, and real-time inventory quantity and location can be grasped in a web-based monitoring environment for administrators. The Real-time Automatic Inventory (RAIC) systems is reduce manual tasks and expect the effects of automated inventory management systems.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.43 no.3
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• pp.61-67
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• 2020

The fourth industrial revolution encourages manufacturing industry to pursue a new paradigm shift to meet customers' diverse demands by managing the production process efficiently. However, it is not easy to manage efficiently a variety of tasks of all the processes including materials management, production management, process control, sales management, and inventory management. Especially, to set up an efficient production schedule and maintain appropriate inventory is crucial for tailored response to customers' needs. This paper deals with the optimized inventory policy in a steel company that produces granule products under supply contracts of three targeted on-time delivery rates. For efficient inventory management, products are classified into three groups A, B and C, and three differentiated production cycles and safety factors are assumed for the targeted on-time delivery rates of the groups. To derive the optimized inventory policy, we experimented eight cases of combined safety stock and data analysis methods in terms of key performance metrics such as mean inventory level and sold-out rate. Through simulation experiments based on real data we find that the proposed optimized inventory policy reduces inventory level by about 9%, and increases surplus production capacity rate, which is usually used for the production of products in Group C, from 43.4% to 46.3%, compared with the existing inventory policy.

• Journal of the Korean Geotechnical Society
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• v.25 no.12
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• pp.27-35
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• 2009

KICT (Korea Institute of Construction and Technology) and KISTEC (Korea Infrastructure Safety and Technology Corporation) have been carrying out inventory survey on cut slopes along national roads since 2006. Unlike precision safety check, cut slope inventory survey is a simple check about cut slope's characteristics with the naked eye to collect the data base of slope maintenance. Inventory survey is classified into general status, cut slope characteristics and inspector opinions. The inventory data are analyzed to identify dangerous slopes and decide a safety ranking. In this paper, we performed a correlation analysis using SPSS (ver.15) about the 10,461 cut slope inventory data which are collected in Kangwondo and Chungcheongdo from 2006 to 2008. We calculated the correlation coefficient between cut slope inventory data and the danger score derived from the data. And we evaluated cut slope inventory data which have the more influence on the danger degree of cut slope. According to results of correlation analysis, we found that inventory data influencing cut slope danger degree are stuck and fallen rock, orientation of discontinuity and angle of upper slope. And these data are slightly different by regionally. Later on, if inventory research is finished, we will understand regional characteristics of cut slopes.