INVENTORY MANAGEMENT ANALYSIS BY OPTIMIZING THE FORCASTING METHODS (CASE STUDY AT PT XYZ INDONESIA)

Authors

  • Rosalendro Eddy Nugroho Mercu Buana University, Jakarta
  • Maryana Resodiharjo Mercu Buana University, Jakarta

DOI:

https://doi.org/10.31933/dijms.v2i3.705

Keywords:

Inventory, simple moving average forecasting, exponential smoothing forecasting, linear regression forecasting

Abstract

This research aims to analyze inventory management by optimizing the forecasting method at PT XYZ Indonesia. The research approach that  used was descriptive research with the aim of explaining and describing the object of research by qualitative and quantitative data. Qualitative data defined a description from the activities of a company which was carried out by managing inventory. Quantitative data in form of sales data, inventory data, quantity data of the arrival of goods in the company. The research was conducted in the Storage System department using data for 30 months from January 2014 to June 2016. From the ABC analysis data conducted by the company, A category was taken as many as 10 items (stock keeping units). The results of research using the three types of forecasting methods, it is known that on the article level the smallest MAD value was for D product with linear regression forecasting method. The smallest MAPE value was for C product with simple moving average forecasting method. On the level of aggregate products, the smallest MAD and MAPE values were exponential smoothing forecasting, thus the exponential smoothing forecasting method is a method to predicts the smallest and most accurate aggregate for product deviation.

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Published

2021-01-23

How to Cite

Eddy Nugroho, R., & Resodiharjo, M. . (2021). INVENTORY MANAGEMENT ANALYSIS BY OPTIMIZING THE FORCASTING METHODS (CASE STUDY AT PT XYZ INDONESIA). Dinasti International Journal of Management Science, 2(3), 435–455. https://doi.org/10.31933/dijms.v2i3.705

Issue

Vol. 2 No. 3 (2021): Dinasti International Journal of Management Science (January - February 2021)

Section

Articles

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