| dc.description.abstract | The primary goal of this thesis is to formulate effective methodologies for forecasting
intermittent demand in the raw material sector of an outdoor company. In the pursuit of
identifying the most efficient approach, a thorough examination of three distinct strategies
was undertaken, ranging from fundamental techniques to models specifically designed for
predicting intermittent demand items. SES, a strategy for short-term forecasting, operates
under the assumption of a relatively stable mean in the data without any discernible trend.
The Moving Average (MA) method aims to predict demand by calculating an average
quantity of actual demand from previous periods. Notable for its adaptability and
versatility in predictive analytics, linear regression emerged as a valuable technique. The
selection of the optimal method was based on its capacity to significantly reduce errors
compared to actual demand. Throughout the research, there was a focus on precision and
accuracy, aiming to improve the predictability of spare parts within the outdoor furniture
industry. To determine the optimal value, a conventional optimization strategy was
employed, considering the lowest Mean Absolute Percentage Error (MAPE), Mean
Absolute Error (MAE), and Root Mean Square Error (RMSE). Furthermore, for cost
optimization, this study incorporates inventory management techniques such as ABC
analysis, Reorder Point (ROP), Economic Order Quantity (EOQ), and Safety Stock into
the forecasting approach. | en_US |
