A pareto chart is a graphical tool that allows you to identify the most significant problems across the shop floor using quantifiable data. The chart takes its name from Vilfredo Pareto, originator of the 80/20 rule, which postulates that 80 percent of the losses come from 20 percent of the causes.

Using a pareto chart to visualize e.g. production downtime will provide you with an immediate overview of your production efficiency. By ordering the bars from largest to smallest, a pareto chart will help you visualize the 20 percent most critical stop causes in your production in one single chart.


Target your improvements

A pareto chart is ideal when you want to target your improvement efforts. By visualizing the 20 percent most critical factors for downtime in your production, you will be able direct your efforts where they will make the most impact. When you have data on unplanned downtime available, a pareto chart will easily reveal where you need to take action to get the most out of your efforts. 



The Pareto chart identifies the top few issues that causes 80% of the pain

Visualize unplanned downtime

With DIAPs OEE software, you will gain access to a number of charts and graphs visualizing different aspects of your production – one of them being a pareto chart. In order to get the most valuable chart, operators need to enter the reason for each single production stop during or shortly after the stop. Subsequently, the DIAP stop cause pareto will give you a complete overview of why and for how long your equipment has been standing still. You will then be able to use the chart actively as a quality tool to help you analyze stop causes and prioritize your efforts to resolve the most critical issues.


Gain immediate overview of all stop reasons


Use the pareto as a basis for discussions

Thanks to the graphical overview, it is easy to use a pareto chart such as the DIAP stop cause pareto as a basis for discussions about improvement initiatives both at operator and management level. Often, the actual reasons for downtime do not correspond to the reasons predicted by both operators and management. By using a DIAP stop cause pareto, you can eliminate predictions and guesswork and start basing your improvement efforts on actual data from your own production.