Artificial neural network: An answer to right order quantity

Abstract

In recent years, the concept of artificial intelligence (AI) is being used in various functions across the globe. This is particularly because of the versatile range of utility of artificial intelligence. Artificial intelligence is also being used very much in logistics industry. As we all know, the six basic functional areas of logistics are as follows: (1) inventory planning and management, (2) warehousing, (3) procurement of goods and services, (4) packaging and storage, (5) transportation, and (6) customer service. Along with the six functional areas, there are also six key performance indicators. The following are the key performance indicators of logistics operations as mentioned and collected from various literature: (1) arrival precision, (2) pick up discrepancy alert, (3) number of incidents, (4) late delivery alert, (5) filling rate in transport equipment, (6) stock accuracy. The aim of this paper is to find the variables which play a vital role in the decision of the right quantity to be ordered and simultaneously to propose a model which can integrate inputs from all those variables and use the inputs through artificial neural network and finally propose a suitable model.