Demand Forecasting of Anti-Aircraft Missile Spare Parts Using Neural Network

One of the major hurdles today is maintaining the right amount of stock keeping units since it may lead to under utilization or over utilization. Spare parts play a very vital role in any inventory or industrial companies. The importance of spare parts can be identified by their sizeable amount and their impact on business operations. One of the major industries today is aircraft industry which includes some of the most fundamental factors like increasing terrorist activities across the world, rising request for technologically robust anti-aircraft missiles and the growing defense funds of emerging countries. Inventory needs to keep an eye on these activities so as to estimate the future consumption. In this study, the dataset used is that of Vietnam War Bombing Operations .The proposed modeling involved- the aircraft name along with its unit of issue, how many spare parts are exhausted and how many are remaining. This helps in finding out the quantity required for demanding the exhausted spare parts by maintaining the budget .The trial about Multi-layer Perceptron (MLP) and XGBoost demonstrates effectiveness in terms of time and memory which is the ultimate aim to improve the precision in terms of demand accuracy.