PERAMALAN JUMLAH PERMINTAAN SPARE PART LCV BUSHING STRUTHBAR DENGAN MENGGUNAKAN METODE CROSTON DAN METODE SYNTETOS BOYLAN APPROXIMATION

• Main Author: Yesi Kurnia Simamora, - (Author)
• Format: Book
• Published: 2018-08-29.
• Subjects: Thesis; NonPeerReviewed
• Online Access: Link Metadata

Summary

Meningkatnya permintaan kendaraan bermotor akan mempengaruhi jumlah permintaan akan spare parts karena kegiatan pemeliharaan mesin kendaraan bermotor bergantung pada ketersediaan spare parts. Permasalahan persediaan kerap kali dihadapi oleh para pengambil keputusan khususnya dalam hal manajemen persediaan. Apabila hal ini tidak dikelola dengan baik, sistem persediaan pada perusahaan dapat menjadi tidak efektif dan efisien. Pada data permintaan spare part LCV BUSHING STRUTHBAR diketahui bahwa tidak selalu terjadi permintaan setiap bulannya sehingga membentuk pola data intermittent. Intermittent demand adalah permintaan yang memiliki nilai zero dan non-zero. Metode peramalan yang cocok untuk pola data intermittent adalah metode Croston dan Syntetos Boylan Approximation (SBA). Dengan bantuan software R perhitungan peramalan dilakukan pada metode Croston dan metode Syntetos Boylan Approximation (SBA) serta nilai ukuran kesalahan peramalan berdasarkan Mean Absolute Percentage Error (MAPE) untuk memilih metode mana yang terbaik. Peramalan metode Croston dan metode SBA menunjukkan hasil yang bias. Oleh karena itu, perlu dilihat mana metode yang memiliki varians minimum. Berdasarkan analisis yang telah dilakukan ternyata metode SBA adalah metode yang memiliki varians lebih kecil dibandingkan metode Croston, dengan nilai varians sebesar 0,906.;---Increasing demand for motorized vehicles will affect the number of requests for spare parts because the motor vehicle engine maintenance activities depend on the availability of spare parts. Inventory problems are often faced by decision makers, especially in terms of inventory management. If this is not managed properly, the inventory system in the company can be ineffective and inefficient. On demand data for LCV BUSHING STRUTBAR spare part, it is known that there is not always a demand every month so that it forms an intermittent data pattern. Intermittent demand is a demand that has zero and non-zero values. Forecasting methods suitable for intermittent data patterns are Croston and Syntetos Boylan Approximation (SBA) methods. With assisted of software R, forecasting calculations were performed on Croston method and Syntetos Boylan Approximation (SBA) method and forecasting error size values based on Mean Absolute Percentage Error (MAPE) to choose the best method. Forecasting of Croston method and SBA method shows biased results. Therefore, it is necessary to see which method has minimum variance. Based on the analysis that has been done, it turns out that the SBA method is a method that has a smaller variance than the Croston method, with a variance value of 0.906.

Item Description

http://repository.upi.edu/45242/1/S_MAT_1301234_Title.pdf
http://repository.upi.edu/45242/2/S_MAT_1301234_Abstract.pdf
http://repository.upi.edu/45242/3/S_MAT_1301234_Table_of_Contents.pdf
http://repository.upi.edu/45242/4/S_MAT_1301234_Chapter1.pdf
http://repository.upi.edu/45242/5/S_MAT_1301234_Chapter2.pdf
http://repository.upi.edu/45242/6/S_MAT_1301234_Chapter3.pdf
http://repository.upi.edu/45242/7/S_MAT_1301234_Chapter4.pdf
http://repository.upi.edu/45242/8/S_MAT_1301234_Chapter5.pdf
http://repository.upi.edu/45242/9/S_MAT_1301234_Bibliography.pdf
http://repository.upi.edu/45242/10/S_MAT_1301234_Appendix.pdf