RCA Step 2: Current Situation/Data Analysis
The RCA process is a data driven process. We try to avoid making assumptiopns about what the problem might be - rather we work towards identifying the data that tells us what the problem is. In simplest terms the process is to write a data based problem statement; look at the data to see how to refine the prblem statement to a more definitive statement; write the new problem statment and repeat.
In team based large problem solving sessions the first iteration through the data analysis step includes the added task of identifying all the data available to the team. This initial data identification is referred to as current situation and includes a review of previous efforts at resoling the problem if any.
After all available data is identified, the problem statement is reviewed and the appropriate data is analysed. In some cases no data exists in an area the team fells is important. In that case data must be generated.
Here is an example of how this works: A team that was solving a problem of packages arriving damaged at the customer's dock in Japan. Previous troublshooting efforts had focussed on damage caused by the shipping companies as it was assumed that no damage could have occured in the production facility. For three years the facility had worked with the shipping company but had been unable to resolve the problem with damage in the shipping process. The customer had put a deadline on resolving the issue or their orders would be cancelled. The first thing that the new troubleshooting team noticed was that although there was excellent data by the customer quantifying the level of damage at the dock - all other troubleshooting had been based solely on conjecture as to what might be happeneing.
Their first step was to develop some data - specifically they unloaded several newly loaded containers and counted the number of damaged packages. It was found that the percentage of damaged packages in the newly loaded containers was exactly the same percentage of damage that the customer was receiving. Therefore it could be readily shown that the damage was indeed occuring at the production facility and not in transport. Armed with that data the team was able to track the damage to its sources in the facility and resolve the problem.
The above example highlights the problem with inaccurate problem statements and illustrates the use of data to build and refine a problem statment.
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