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Ȩ Ȩ > ¿¬±¸¹®Çå > ±¹³» ³í¹®Áö > Çѱ¹Á¤º¸°úÇÐȸ ³í¹®Áö > Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Á¤º¸°úÇÐȸ ÄÄÇ»ÆÃÀÇ ½ÇÁ¦ ³í¹®Áö (KIISE Transactions on Computing Practices)

Current Result Document : 1 / 35   ´ÙÀ½°Ç ´ÙÀ½°Ç

ÇѱÛÁ¦¸ñ(Korean Title) û±¸½ÇÀû°ú ¼Ò¸ð½ÇÀû Á¶ÇÕ±â¹Ý ¼ö¸®ºÎ¼Ó ¼ö¿ä¿¹Ãø ¸ðµ¨ °³¹ß ¿¬±¸
¿µ¹®Á¦¸ñ(English Title) Development of Demand Forecasting Model for Spare Parts To Be Repaired Based on Combination of Claim Performance and Consumption Performance
ÀúÀÚ(Author) Á¤µ¿¿À   ¹Ú¸í±¹   ·ù±ÙÈ£   Dong Oh Jeong   Myung Kook Park   Keun Ho Ryu  
¿ø¹®¼ö·Ïó(Citation) VOL 26 NO. 04 PP. 0179 ~ 0187 (2020. 04)
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(Korean Abstract)
º» ¿¬±¸ÀÇ ¸ñÀûÀº ¼ö¿ä¿¹ÃøÀ» À§ÇÑ ´Ù¾çÇÑ ¼ö¿äÁý°è ¹æ¹ý Áß ÃÖÀûÀÇ ¹æ¹ýÀ» ã¾Æ³»´Â °ÍÀÌ´Ù. ¼ö¿äÁý°è ºñ±³´ë»ó ¹æ¹ýÀº û±¸½ÇÀû±â¹Ý ¼ö¿äÁý°è, ¼Ò¸ð½ÇÀû±â¹Ý ¼ö¿äÁý°è, û±¸½ÇÀû°ú ¼Ò¸ð½ÇÀûÀ» Á¶ÇÕÇÑ ¼ö¿äÁý°è ¹æ¹ýÀ¸·Î ±¸¼ºÇϸç, Åë°èÀûÀÎ ±â¹ýÀ» È°¿ëÇÏ¿© ¼ö¿ä¸¦ ¿¹ÃøÇÑ´Ù. ¶ÇÇÑ Åë°èÀûÀÎ ±â¹ýÀº »ê¼úÆò±Õ, À̵¿Æò±Õ, ÃÖ¼ÒÀÚ½ÂÀ¸·Î ±¸ºÐÇÏ¿© ¿¹ÃøµÈ °á°ú¸¦ ºÐ¼®ÇÑ´Ù. ¼ö¿äÁý°è¸¦ À§ÇÑ ÀڷᱸÃàÀº À°±º¿¡¼­ »ç¿ëÇÏ°í ÀÖ´Â Àü Ç°¸ñÀ» ´ë»óÀ¸·Î 5°³³âÀÇ ½ÇÁ¦ µ¥ÀÌÅ͸¦ Åä´ë·Î ÇÏ¿´´Ù. ¼ö¿ä¿¹Ãø °á°ú¿¡ ´ëÇÑ Æò°¡´Â ¿¹ÃøÄ¡¿Í ½ÇÁ¦Ä¡ »çÀÌÀÇ ¿ÀÂ÷¸¦ ÃøÁ¤ÇÏ´Â MSD, MSE, MAPE¸¦ ÀÌ¿ëÇϸç, Ç°¸ñ±âÁØ ¼ö¿ä¿¹Ãø Á¤È®µµ¿Í ±Ý¾×±âÁØ ¼ö¿ä¿¹ÃøÁ¤È®µµ¸¦ Æò°¡ÇÑ´Ù. ¼ö¿ä¿¹ÃøÇÑ Àü Ç°¸ñ Áß¿¡¼­ 00³â¿¡ Á¶´ÞÇÑ Ç°¸ñÀ» ´ë»óÀ¸·Î ¿ÀÂ÷¸¦ ÃøÁ¤ÇÑ °á°ú û±¸½ÇÀû¿¡ ÀÇÇÑ ¼ö¿äÁý°è º¸´Ù û±¸½ÇÀû°ú ¼Ò¸ð½ÇÀûÀ» Á¶ÇÕÇÑ ¼ö¿äÁý°è¹æ¹ýÀÌ ¿ì¼öÇÏ°Ô ³ªÅ¸³µÀ¸¸ç, Ç°¸ñ±âÁØ ¼ö¿ä¿¹Ãø Á¤È®µµ¿Í ±Ý¾×±âÁØ ¼ö¿ä¿¹Ãø Á¤È®µµ ¶ÇÇÑ ¿ì¼öÇÑ °á°ú¸¦ ³ªÅ¸³Â´Ù. º» ¿¬±¸ °á°ú´Â ¼ö¿ä¿¹ÃøÀ» À§ÇÑ ÀÔ·ÂÀÚ·á¿¡ ´ëÇÑ ½Å·Ú¼º °ËÁõÀÌ ¼±ÇàµÇ¾î¾ß µÈ´Ù´Â °³¼± ¹æ¾È¿¡ ´ëÇÑ ½Ã»çÁ¡À» µµÃâÇÏ¿´´Ù.
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(English Abstract)
The purpose of this study is to find the optimal method among various demand aggregation methods for demand forecast. The comparison method of demand aggregation claim performace-based demand aggregation, consumption performance-based demand aggregation, a demand aggregation method that ombines claim performance and consumption performance. use statistical techniques to predict demand. The statistical techniques divided into arithmetic mean, moving average, and least squares. ata collection for demand aggregation was based on actual data for five years for all items used by the Army. The evaluation of the demand forecast results uses the MSD, MSE, and MAPE, which measure the error between the forecast and the actual value to evaluate the accuracy the item-base demand forecast and the amount-based demand forecast accuracy. As a result of measuring the errors of items procured in 00 among all the demand forecastitems, the demand aggregation method combining claim performance and consuming performance was superior to methd aggregation by claim demand. Item-based demand forecast accuracy and amount-based demand forecast accuracy also showed excellent results. The result of this study suggest the improvement method that reliability verification of input data for demand prediction should precede.
Å°¿öµå(Keyword) ¼ö¿ä¿¹Ãø   ¼ö¿äÁý°è   û±¸½ÇÀû   ¼Ò¸ð½ÇÀû   û±¸½ÇÀû°ú ¼Ò¸ð½ÇÀû Á¶ÇÕ   demand forecast   demand aggregation   claim performance   consumption performance   combination of claim performance   consumption performance  
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