Despite its simplicity, Pareto analysis is one of the most powerful of the problem-solving tools for system improvement. Getting the most from Pareto analysis includes making subdivisions, multi-perspective analyses, and repeat analyses.
Subdivisions are useful when data has been first recorded at a very general level, but problem solving needs to occur at a more specific level. A retail chain manager might create a Pareto diagram for all the customer returns of furniture by store in his district. Once he or she has identified the store which contributes most returns to the total, the next step might be to analyze that store's returns by furniture type. If "chairs" turned up as the biggest category of furniture returns for the store in question, yet another Pareto of chair returns might help to discover whether dining room chairs, occasional chairs, wooden chairs, or upholstered chairs were being returned more frequently. Because the Pareto principle holds for subgroupings of data, such successive analyses can be performed to help teams target small elements of a large problem.
Multi-perspective analyses are useful when data can be stratified or subdivided in several different ways. The retail manager might study customer returns of furniture by number of units and again by cost. A store might discover that chairs have accounted for the majority of items returned over a period of time, but that fine dining sets accounted for the majority of cost. Depending on priority, the problem could be attacked to reduce either the highest frequency or the highest cost item. The district retail manager might study his or her district-wide furniture returns by store, by lot number, by furniture type, by cause for return, by frequency, by cost, by salesperson, by delivery carrier, or by any other set of categories he or she thinks may reveal opportunities for improvement. Multi-perspective Pareto analysis helps assure that a set of data is reviewed from all angles and that many explanations for variability are considered.
Repeat analyses are useful when improvement activity is underway and performance data is changing over time. If the retail manager worked with the store's delivery staff to reduce the number of fine dining sets being damaged and subsequently returned, it would be useful to repeat an earlier Pareto analysis using more recent data to see if the target category has shrunk. Depending on the cycle of data collection—hourly, daily, weekly, monthly, quarterly, or other—repeated Pareto analyses help to monitor the improvements made to the system producing the data.
Caution is in order for users of Pareto analysis who have not monitored the systems they are studying for stability. A wildly fluctuating system will produce inconsistent Pareto rankings that can lead to misjudgments. If, for example, the retail manager failed to note that customer furniture returns varied greatly from month to month, the ranking of categories may be entirely different in a month with high returns from those of a month in which returns were unusually low. Repeated Pareto analyses can help to confirm rankings, but the most effective protection against being misled is to first use a control chart to tell if the system is stable and predictable.
