Demand forecasting of tea by seasonal ARIMA model
by E.V. Gijo
International Journal of Business Excellence (IJBEX), Vol. 4, No. 1, 2011

Abstract: A tea packaging company in India was implementing supply chain planning process to improve its delivery performance. For this purpose the company was interested in forecasting the monthly demand for tea from its depots across the country. Time series data on demand of tea for 57 months were available. This series was modelled by Box-Jenkins seasonal auto regressive integrated moving average (ARIMA) model. Adequacy of the fitted model has been tested using Ljung-Box test criteria followed by residual analysis. Thus, the most appropriate model was used to forecast the monthly demand of tea. This model has helped the organisation to plan the production activities more efficiently so that shortages or excess production can be avoided.

Online publication date: Sat, 27-Sep-2014

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