In reviewing the PLE data, Elizabeth Burke noticed that defects received from su

Question

In reviewing the PLE data, Elizabeth Burke noticed that defects received from suppliers have decreased (worksheet Defects After Delivery ). Upon investigation, she learned that in 2010, PLE experienced some quality problems due to an increasing number of defects in materials received from suppliers. The company instituted an initiative in August 2011 to work with suppliers to reduce these defects, to more closely coordinate deliveries, and to improve materials quality through re-engineering supplier production policies. Elizabeth noted that the program appeared to reverse an increasing trend in defects; she would like to predict what might have happened had the supplier initiative not been implemented and how the number of defects might further be reduced in the near future.

What would be the best analysis to use to forecast where the company would have been if they did not implement a change assuming the use of XLMiner? We have the data available for the first 20 months prior to implementation. Can the same analysis be used to forecast where the company will be in the next few months?

Explanation / Answer

Without the data , the question becomes very open ended. However , please keep in mind that it is not the tool that is imporatant but the method used to build the forecast

There are several ways which we can use to solve a forecasting problem based on the historical data

Conduct a time series analysis : That is plot a ARIMA model based on time and see how well this model fits the actual observed data , once this is done yuo can use a forecast window to forecast the values for the next few months. There are certain methods to account for seasonaliy , trend and events , assuming that the data in available

Another possibility is to fit a linear regression model based on the data and then use the linear equation to make predictions.

So if you have data for the first 20 months , simpy fit a arima model in R/SAS/matlab etc and then see how the forecast values compare with the actual values. This should give you some idea on how the company would have performed if there was no change

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