 Demand forecasting of tea by seasonal ARIMA modelE.V. Gijo International Journal of Business Excellence, 2011, vol. 4, issue 1, 111-124 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. Keywords: demand forecasting; Box-Jenkins model; seasonal ARIMA models; autoregressive integrated moving average; auto correlation function; ACF; autocorrelation; partial auto correlation function; PACF; Ljung-Box test; statistical testing; forecast accuracy; Akaike's information criterion; AIC; augmented Dickey–Fuller test; ADF; tea packaging; India; SCM; supply chain management; delivery performance; monthly demand; supply depots; time series data; residual analysis; production planning; shortages; excess production; business excellence. (search for similar items in EconPapers) Downloads: (external link) Related works: Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text Persistent link: https://EconPapers.repec.org/RePEc:ids:ijbexc:v:4:y:2011:i:1:p:111-124 Access Statistics for this article More articles in International Journal of Business Excellence from Inderscience Enterprises Ltd |
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