Safety stock calculation for jewelry industries based on consumption forecast by artificial neural network

Abstract

To present the safety stock calculation based on consumption of components in the jewelry business was investigated using the forecasting capability of an Artificial Neural Network (ANN). Generally, this business also has links with fashion; therefore, rapid change in the fashion industry makes the forecasting situation more complicated. The demand fluctuates with customer requirements and high competition in the marketplace. Factors such as late delivery and bad component quality can cause shortages of components. To prevent shortages, provide support to supply management and enhance customer satisfaction, an ANN is utilized for consumption forecast. Safety stock is the way to protect against shortages. Safety stock is created when a company either orders before an order is needed or orders more than the expected demand. In the contrast, keeping less safety stock is important for decreasing cost and increasing interests for a firm. In practice, business has calculated safety stock based on their experiences, non theoretical support, and leads for retaining component in the warehouse. Hence, safety stock is essential to protect against the fluctuation of unexpected demand and supply. The central focus of this paper is to enhance the forecast on safety stock accuracy in the jewelry business. The artificial neural network is employed as a tool for the prediction. It is assumed that the accurate consumption leads to the accuracy in safety stock calculation.