The Jia Wu Expansion
Sales forecasting is needed for planning. This case describes a situation in which monthly sales forecasts can be produced using very elementary methods. The case may be used as an introduction to this topic. Ideally, it should be followed by other cases in which more advanced methods should be employed.
The objective of the case is to illustrate a simple forecasting method for monthly sales data. The data set, supplied in an Excel file (jiawu-aus-data.xls), contains the total sales of household goods in Australia, covering the period from January 1995 to December 2009. Students are expected to produce a monthly forecast for 2010, based on a trend and a collection of seasonal effects. Basic knowledge of Excel or an analogous open source spreadsheet application is required. It is assumed that the calculations will be performed using standard spreadsheet functions.
Collection: IESE (España)
Ref: ADFC-3-E
Format: PDF
Number of pages: 2
Publication Date: Sep 10, 2013
Language: English
What material is included in this case:
Description
Sales forecasting is needed for planning. This case describes a situation in which monthly sales forecasts can be produced using very elementary methods. The case may be used as an introduction to this topic. Ideally, it should be followed by other cases in which more advanced methods should be employed.
The objective of the case is to illustrate a simple forecasting method for monthly sales data. The data set, supplied in an Excel file (jiawu-aus-data.xls), contains the total sales of household goods in Australia, covering the period from January 1995 to December 2009. Students are expected to produce a monthly forecast for 2010, based on a trend and a collection of seasonal effects. Basic knowledge of Excel or an analogous open source spreadsheet application is required. It is assumed that the calculations will be performed using standard spreadsheet functions.
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Year: 1995-2009
Geographic Setting: Australia, Taiwan
Industry Setting: Retail
Learning Objective
This case illustrates a simple forecasting method for monthly sales data, based on a linear trend and a set of seasonal factors. Students are not expected to have any previous experience with time series analysis.
The main points for the class discussion are: (a) how to fit a linear trend to time series data, (b) how to calculate the seasonal factors, (c) how to forecast the future values of the series using the trend and the seasonal factors, and (d) how to calculate confidence limits for the forecasts, based on the distribution of the prediction error (optional).
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