Optimization of Demand Forecasting in Fast Moving Consumer Goods Supply Chain: A Case Study

Abstract

Fast moving consumer goods companies are expanding their operations since the past few decades. Profit growth coupled with an effective strategy has become the primary need of supply chain strategy. The issues faced by the company are sales loss due to lack of raw materials as well as profit loss due to inventory, which companies cannot afford to lose if they want to stay competitive. In order to reduce excessive inventory or stock out at each stage of the production, it is necessary to know the demand for the next stage to do the effective production planning. The process of sales forecasting is undertaken to predict sales at different stages. Since it is a complex managerial function and hence needed to be undertaken by a scientific way. This study attempts to examine some forecasting techniques such as moving average, weighted moving average, simple exponential smoothing, double exponential smoothing, triple exponential smoothing and regression analysis. To do the analysis sales data of previous year from 2013–2014 to 2015–2016 were collected from a FMCG manufacturing unit and the forecasting has been done for the year 2016–2017 by each technique. Later the forecasted data has been compared with the actual sales data of the year 2016–2017 for each technique and forecasting errors were calculated. Triple exponential and regression analysis gives nearly close to the actual sales data but triple exponential gives negative error whereas the regression analysis gives a positive value.