TECHNOLOGY OVERVIEW

About Change Analysis

Dr Juergen Ude

Change Analysis is used to detect changes in chronological ordered data. Using machine power algorithms, combined with statistical significance testing, changes in the process average, standard deviation and slopes can be rapidly detected.

The technology was first applied in the cigarette industry to identify reasons for changes in cigarette firmness by comparing changes between cigarette firmness with changes in other variables. Such change comparisons are called fingerprint analysis. The fingerprint analysis showed that changes in moisture content corresponded to changes in cigarette firmness. The fact that moisture lowers cigarette firmness was expected, however cigarette firmness results were corrected for moisture and there should thus not have been a correlation. The initial reason for the correlation was thought to be due to inadequate moisture correction but extensive testing showed that the correction formulae used were correct. Further fingerprint analysis showed that there was a relationship between moisture contents and two volatile materials used in the tobacco expansion process. Because moisture content was measured by the oven volatile method the measured moisture content was inflated causing overcorrection for moisture and hence the change relationship.

Change Analysis is a better alternative to Shewhart Control charts as a Statistical Process Control tool to improve quality. Shewhart control charts without additional tests only show when individual points fall outside two limits determined statistically. If a point falls outside control limits the process is deemed to be out-of-control. This is inefficient and provides little information for the analyst. Change Analysis is more effective as it is more sensitive to picking small changes and shows the magnitude, onset and duration of a change.

Change analysis has many applications. Outside manufacturing it can be used to quantify the effect of medication on blood pressure, help manage diabetes, reduce obesity, detect water leaks, identify cannibalism by competing brands, show changes in power consumption, detect changes in sales and relate these to promotions and competitor behavior, plus virtually unlimited other applications such as global warming.

The algorithms are complex relying on machine power to search for changes in the process mean, standard deviation and slopes. Changes once found are statistically validated. This itself is a complicated process because changes in standard deviation effect significance testing for means and slopes. These in turn affect standard deviation.

There are currently five major types of change analysis that can be performed. These are: