Milling News
DMAIC: Analysis
Gustavo Sosa
The Six Sigma methodologies are strong because they are based in evidence and data, and not in hunches. Instead of relying on luck, or just strength of will, you take decisions based in tangible information.
What is causing the problem? The Analysis Phase is often not given much attention, as the team assumes having already discovered the essential patterns and relationships during the previous phases and doesn’t take the time to examine those impressions. This behavior leads to implementing solutions that don’t solve anything. Even more, it causes wasted time, wasted resources, more variation and new problems. The statistical analysis faces the practical problem that was defined earlier, looking at the families of variation to find which are the causes, which simple correlations, and which are effects. The analysis begins with a hypothesis and follows through the information trying to “fail to reject” or “reject” the hypothesis. The ideal is for teams to brainstorm potential causes (not solutions), develop hypotheses about how problems develop, and then try to prove or disprove them. The first tool to use is the Root Cause Analysis, that we have already seen in a previous article, but we will see it again.
Steps in an RCA
After doing a data analysis and a process analysis, we follow with:
• Making a list of all the possible causes of variation • Taking apart the cause within the causes of variation (using the five ‘why’s) • Making a short list of the most important causes of variation • Ascertaining and measuring the causes of variation. Tools used in an RCA: • Cause and effect diagram, also known as Ishikawa diagram • Process map analysis, which is a great visualisation tool, helping see the workflow and the process steps taken • Regression analysis, which helps estimate the impact of specific variables on the final product.
Families of variation
An FOV diagram is a tool commonly used in the Measure phase, but it is of use here too. This chart can be used to determine where the source of the most variation is originating. Many times, each one of the family’s sources contributes to the overall process variation.
How do we apply all this into the grain industry?
Identify the problem. For example, you are a pet feed manufacturer, and batches are coming out with an insufficient protein content Classify the data. Use a FOV diagram to separate the different shifts, operators, machines, raw materials, etc. This diagram can lead to identify the night shift as the only one where the problem
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30 | December 2019 - Milling and Grain
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