Forecasting Techniques for Sales of Spare Parts
A M Saravanan1, S P Anbuudayasankar2, P Arul William David3, Narassima M S4
1Saravanan A M, Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
2Anbuudayasankar S P*, Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
3Arul William David P, Vice-President, ELGI Equipment Ltd., Coimbatore.
4Narassima M S, Department of Mechanical Engineering, Amrita School of Engineering, Coimbatore, Amrita Vishwa Vidyapeetham, India.
Manuscript received on 15 September 2022. | Revised Manuscript received on 15 September 2022. | Manuscript published on 30 September 2022. | PP: 27-30 | Volume-8 Issue-3 September 2019 | Retrieval Number: C3863098319/19©BEIESP | DOI: 10.35940/ijrte.C3863.098319
Open Access | Ethics and Policies | Cite | Mendeley | Indexing and Abstracting
© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: 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.
Keywords: Time Series Forecasting, Spare Part Sales, ARIMA Forecasting.
Scope of the Article: Spare Part Sales