Why data is important in Quality Management.

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iso9001:2015 Quality
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At the center of all quality management systems is the concept of continuous improvement. This implies that you have the ability to prove that there has been some kind of improvement. It may sound straightforward, but it isn’t always that easy to do. Many times there is limited data or even no data to even come up any results. In order to prove progress, it is important to have the consistent data to back it up.

Collecting Data

Data is a main ingredient to show how the company improves on certain KPI’s. However, collecting the data is more often that not a very labor intensive task, mainly done by the quality department. In order to make it easy for the organization to share the required data, it should be fairly easy for employees to do this. A mobile app helps in this process. Having all the important forms at your fingertips makes it so much easier to share this information, which allows the quality department to focus on analyzing the data instead of pushing people to provide it. Also having an open IT infrastructure which allows for sharing data between systems is key in order to pull the data from different sources.

Analysing Data

When the data is gathered it should be analyzed properly. This is not just drawing graphs but also interpreting the changes of the data over time. Different time frames could show interesting effects. Furthermore, mapping out different root causes will give more insight on how to improve the company. It is important to play around with the data and not only focus on the predefined KPI’s. Playing around and plotting different variables against each other can give completely new insights. A great technique to check out where this goes wrong is by using the “Pareto” plot of the data.

 Showing Results

Maybe the most important aspect of using data to improve quality management is communicating the results. Not only to top management, but to the employees within the company. Every employee helps by providing the data. When you involve them in the results of the analysis, they see the impact they had on the company instantly. Which then makes them more willing to share again in the future.

 Pitfalls

The biggest pitfall with analysis data is the inconsistency in the data. It is very important the data is clean and usable. This can be easily checked simply by plotting the data and looking for weird spikes. Also during the setup, it is required to predefine certain choices. When you give employees the option such as “I don’t know” or “general”, they will most likely choose that. This will lead to a lot of data with that option, which completely ruins the possibility to analyze it, so try to prevent these options.

 

Qooling makes collecting information and analyzing it a lot easier by a simple to use mobile app and the straightforward interface of its platform.

1 comment

  1. Pingback: How to Perform a Failure Mode Effects Analysis (FMEA) in 10 Easy Steps - Qooling

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