| Budget Amount *help |
¥3,600,000 (Direct Cost: ¥3,600,000)
Fiscal Year 2001: ¥800,000 (Direct Cost: ¥800,000)
Fiscal Year 2000: ¥1,100,000 (Direct Cost: ¥1,100,000)
Fiscal Year 1999: ¥1,700,000 (Direct Cost: ¥1,700,000)
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| Research Abstract |
The purpose of this research is to analyze a strategic supply chain management system (SCS). A focus was put specially in the demand forecasting, inventory control and delivery scheduling. Short lifecycle product was the target. In chapter 2, products were classified into functional and innovative type, and a quantitative model of responsive SCM (QM-RSCM) was proposed For the innovative product, the QM-RSCM reduced average shortages and inventory simultaneously compare to the traditional efficient SCM model. In chapter 3, an efficient utilization method of the forecasting error for safety stock was proposed. A single stage reorder point system was analyzed and moving average method used for demand forecasting. The proposed method reduced average stock by 50 % in comparison with other methods when demand pattern changes over the time. In chapter 4, 5, 6, centralized optimal inventory policies in an arborescent inventory system consists of one central warehouse (CW) and m branch warehous
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e (BW) were analyzed. In chapter 4, an optimal ship-to-to-level (Y), optional quantity of retaining stock in CW (Cs) were determined analytically, while optimal allocation time of the Cs was obtained by simulation under the condition of given Y and Cs. A dynamic determination method for the allocation time was proposed in chapter 5. It was shown that average shortages were reduced more than 35 % by using dynamic allocation time. In chapter 6, profit was made an evaluation index, and the most suitable experiments of chapter 4 were done again. The result was alike fundamentally with the results obtained in chapter 4, while an optimal allocation time could be first period without setting upper limits of Y and Cs. If consider thansportation cost from CW to BW and transshipment cost between BW's, optimal allocation time shifted too latter half periods in a cycle. In chapter 7, we and proposed a time discount model based on that a client's allowable time limit of delivery might be able to be delayed by discount, and calculated an optimal discount instant for the model. It was shown that profits could be increased while shortages could be restrained to be zero. In chapter 8, we derived out an analytical calculation function of decoupling stock. Through numerical examination, we fond that the decoupling stock could be reduced by sharing information of downstream without integrating decision makings. If information sharing not available, it was shown that a reduction of lot-size so as to be equivalent demand quantity during the leadtime of neighboring upstream is essential to reduce decoupling stock. Finally, in the chapter 9, a practical approach of IRP model in which a delivery distance was estimated by the client distribution density was proposed eliminating complicated calculation of VRP. One big step was taken forward toward the utilizing IRP to real world. Less
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