Inventory management improvement suggestion through time-siries forecasting for financial service company
Management is consistently facing fast-flowing and lots of changes in business, including in the inventory management. Especially for fast-moving inventories, the correct stocking, controlling, checking and safety stock calculation is highly needed to have an exquisite inventory management and to...
Saved in:
| Main Authors: | , , |
|---|---|
| 格式: | Article |
| 語言: | English |
| 出版: |
Penerbit Akademia Baru
2018
|
| 主題: | |
| 在線閱讀: | http://eprints.utm.my/id/eprint/82010/1/RaditiantoAdipriyana2018_InventoryManagementImprovementSuggestionthroughTime.pdf http://eprints.utm.my/id/eprint/82010/ http://www.akademiabaru.com/doc/ARDV49_N1_P7_14.pdf |
| 標簽: |
添加標簽
沒有標簽, 成為第一個標記此記錄!
|
| 總結: | Management is consistently facing fast-flowing and lots of changes in business, including in the inventory management. Especially for fast-moving inventories, the correct stocking, controlling, checking and safety stock calculation is highly needed to have an exquisite inventory management and to reduce the possibility of running out of inventory which leads to unavailability to meet the demand. One of the ways to overcome this is by doing an excellent and appropriate forecasting. Therefore, the objective of this concept paper is to analyse and recommend tools to improve inventory management using the appropriate time-series forecasting method. The firm studied in this study is serving its employees as customers that demand the routine items including stationeries and other routine products to support their job as auditors and consultants for its client. However, there are occasions when there is out-of-stock situation for fast-moving items, especially in the peak season period. Furthermore, the firm is only applying replenishment based on the used inventories from the previous month. Therefore, this study suggests to eliminate out-of-stock items situation by applying precaution initiatives such as time-series forecasting. This study is planned to employ 10 time-series forecasting methods such as moving average, exponential smoothing, regression analysis, Holt-Winters analysis, Seasonal analysis and Autoregressive Integrated Moving Average (ARIMA) using Risk Simulator Software. By simulating those methods, the most appropriate method is selected based on the forecasting accuracy measurement. |
|---|