Open AccessForecasting Techniques for Sales of Spare Parts
Author(s) -
Stevani Deska Syriac BR M,
A. P.,
A. P.,
M.S. Narassima
Publication year - 2019
Publication title -
international journal of recent technology and engineering
Language(s) - English
Resource type - Journals
ISSN - 2277-3878
DOI - 10.35940/ijrte.c3863.098319
Subject(s) - spare part , autoregressive integrated moving average , inflow , sales forecasting , procurement , order (exchange) , operations research , computer science , economic order quantity , operations management , econometrics , business , engineering , economics , marketing , time series , finance , supply chain , geography , meteorology , machine learning
Forecasting plays a significant role in planning the future activities of an organization. An effective trade-off is achieved between inventory management and catering demands through an accurate forecast. A detailed study on procurement and planning processes has been conducted in this study. The need for a decision making statistical tool to forecast sales data of spare parts is the main area of focus. Spare part sales pattern remains to be undetermined as it does not follow a specific trend or seasonality. Statistical programming has been performed using ‘R Studio’ to analyse the monthly sales performance. The process is found to improve when a weekly order inflow is considered. ARIMA model is found to improve the accuracy of forecast by 40 percent. Also, accuracy of forecasting performed considering weekly order inflow was higher than that obtained by considering monthly inflow.