Exponential Smoothing Case Study

Abstract

In this paper, an attempt has been made to study the application of forecasting technique (i.e) Exponential smoothing model in Supply Chain Management of Flower Marketing. Periodical data on air lifting of Flowers (particularly Jasmine) were analyzed in Exponential Forecasting model using SPSS software package. Seasonal decomposition was done by Exponential smoothening model using Moving average method using STATISTICA software package. The data was analyzed to estimate accurate forecasting of supply of flowers to match increasing consumer demand and to supply flowers at the right place at the right time. Export of of flowers was forecasted by exponential method. The forecasting has paved the way for supply management in advance to meet out the demand. Further the seasonal index results showed In January, February, November and December months seasonal indices were below average

For example, for the first model with α = 1.0, δ = 0.135, and γ = 0.013, the MAPE value is 0.20418. This means that on average the smoothed (predicted) values computed by this model only deviated 0.20 % from the actual observed values.

Table 4: Seasonal Factor Table

The results showed that average supply was represented by 100. (e.g) In January seasonal index was 30.79 which indicted the supply was 70% less than average supply. Hence exporter was suggested by reducing orders and supply to the regular customers in this month. Naturally the price will be high in January due to shortage of supply of flowers to market. In January, February, November and December months seasonal indices were below average supply hence exporters were suggested to minimize accepting their