Analysis of modified time series prediction techniques: A case study with bakery industry

Abstract

Abstract In the current scenario, it is a very difficult task to predict the demand for a particular product in the market. It is very challenging work for the industries to manufacture the product without being acquainted with the actual sale. To meet the demand of customers and to make the supply chain process optimize various strategies are being adopted at each node of the supply chain. Manufacturing the product by the market consumption significantly affects the revenue generation of small and medium scale enterprises especially of the FMCG industry. In this paper, a case study of a medium-scale industry named “Gopal Ji Food Products.” Kosi Kalan (U.P.) has been performed, Highlights of this case study have been kept to know the demand for the number of boxes of biscuits which are to be distributed in the market. The primary work is done by collecting historical data of the requirement of boxes for the last 104 weeks (April 2017 to March 2018). With the help of this data, the demand pattern is known. After knowing the demand pattern, an attempt has been made to develop a prediction model using time series forecasting to acquaint with the number of boxes to be manufactured in advance. The result shows that the data obtained through Non-linear Autoregressive time series forecasting can be used for better planning and fulfilling market demand on time and making the whole supply chain more optimize, effective, and cost-efficient.