Root cause of increase in defects - analysisPerform classification on market basket analysisMarket Basket...
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Root cause of increase in defects - analysis
Perform classification on market basket analysisMarket Basket Analysis - Data ModellingCreate data visualization for unstructured data - Basket Market Analysismarket basket analysis - Hierarchical association analysisChecking My Data Analysis Workdata analysis EDA issues, indent or typeExploratory Data AnalysisGame Data Analysis (Stats)100 items 100 baskets divisor association analysis problemAnalysis of Time Series data
$begingroup$
I have a question regarding conducting an analysis on defects:
Say I have a list of sellers that ship items to my company, and I have data on all items coming in. Some of these items have defects (eg. it's missing a label or it came in a different color than ordered) and I've collected data on the nr of items that belong to each defect category, per seller. I compare data for Jan and Feb, and see that the share of missing label defects out of the total received items has increased drastically in Feb. I want to do some analysis to figure out what the reason for this is. In particular, I want to see whether this is due to some sort of mix effect. Could it be that the sellers that shipped a lot of items with missing label defects in Jan simply make up a larger share of the total volume coming in Feb, and therefore the missing label is not an increasing problem per se, but can be attributed to an unfortunate change in volume mix?
How would you go about calculating the mix effect in such a scenario? And in general, how would you approach such a problem when you want to find the root cause for the increase in the share of missing label defect items?
data-analysis market-basket-analysis
asked 18 hours ago
Kara WKara W
1
New contributor
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
add a comment |
$begingroup$
I have a question regarding conducting an analysis on defects:
Say I have a list of sellers that ship items to my company, and I have data on all items coming in. Some of these items have defects (eg. it's missing a label or it came in a different color than ordered) and I've collected data on the nr of items that belong to each defect category, per seller. I compare data for Jan and Feb, and see that the share of missing label defects out of the total received items has increased drastically in Feb. I want to do some analysis to figure out what the reason for this is. In particular, I want to see whether this is due to some sort of mix effect. Could it be that the sellers that shipped a lot of items with missing label defects in Jan simply make up a larger share of the total volume coming in Feb, and therefore the missing label is not an increasing problem per se, but can be attributed to an unfortunate change in volume mix?
How would you go about calculating the mix effect in such a scenario? And in general, how would you approach such a problem when you want to find the root cause for the increase in the share of missing label defect items?
data-analysis market-basket-analysis
asked 18 hours ago
Kara WKara W
1
New contributor
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
$begingroup$
It's a very open end question. There could be a lot of reasons for the defects. It could be possible there is a change in the manufacturing process or quality or poor shipping or changed inspection rules/laws. So you have to gather all those data before think of any modelling. In general cases. I would start it from the top. Like which company has more defective products. If the increase is same across all companies then it couldn't be a manufacturing problem. if certain companies have increased defect rate check if they made any change in the manuf/shipping or any other factor think of
$endgroup$
– No_Body
8 hours ago
$begingroup$
I don't think you can use any state of the art method to trim it down to the exact factor.
$endgroup$
– No_Body
8 hours ago
add a comment |
$begingroup$
I have a question regarding conducting an analysis on defects:
Say I have a list of sellers that ship items to my company, and I have data on all items coming in. Some of these items have defects (eg. it's missing a label or it came in a different color than ordered) and I've collected data on the nr of items that belong to each defect category, per seller. I compare data for Jan and Feb, and see that the share of missing label defects out of the total received items has increased drastically in Feb. I want to do some analysis to figure out what the reason for this is. In particular, I want to see whether this is due to some sort of mix effect. Could it be that the sellers that shipped a lot of items with missing label defects in Jan simply make up a larger share of the total volume coming in Feb, and therefore the missing label is not an increasing problem per se, but can be attributed to an unfortunate change in volume mix?
How would you go about calculating the mix effect in such a scenario? And in general, how would you approach such a problem when you want to find the root cause for the increase in the share of missing label defect items?
data-analysis market-basket-analysis
asked 18 hours ago
Kara WKara W
1
New contributor
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$endgroup$
I have a question regarding conducting an analysis on defects:
Say I have a list of sellers that ship items to my company, and I have data on all items coming in. Some of these items have defects (eg. it's missing a label or it came in a different color than ordered) and I've collected data on the nr of items that belong to each defect category, per seller. I compare data for Jan and Feb, and see that the share of missing label defects out of the total received items has increased drastically in Feb. I want to do some analysis to figure out what the reason for this is. In particular, I want to see whether this is due to some sort of mix effect. Could it be that the sellers that shipped a lot of items with missing label defects in Jan simply make up a larger share of the total volume coming in Feb, and therefore the missing label is not an increasing problem per se, but can be attributed to an unfortunate change in volume mix?
How would you go about calculating the mix effect in such a scenario? And in general, how would you approach such a problem when you want to find the root cause for the increase in the share of missing label defect items?
data-analysis market-basket-analysis
data-analysis market-basket-analysis
asked 18 hours ago
Kara WKara W
1
New contributor
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 hours ago
Kara WKara W
1
New contributor
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 hours ago
Kara WKara W
1
New contributor
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
asked 18 hours ago
Kara WKara W
1
asked 18 hours ago
Kara WKara W
1
1
New contributor
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
New contributor
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
Kara W is a new contributor to this site. Take care in asking for clarification, commenting, and answering.
Check out our Code of Conduct.
$begingroup$
It's a very open end question. There could be a lot of reasons for the defects. It could be possible there is a change in the manufacturing process or quality or poor shipping or changed inspection rules/laws. So you have to gather all those data before think of any modelling. In general cases. I would start it from the top. Like which company has more defective products. If the increase is same across all companies then it couldn't be a manufacturing problem. if certain companies have increased defect rate check if they made any change in the manuf/shipping or any other factor think of
$endgroup$
– No_Body
8 hours ago
$begingroup$
I don't think you can use any state of the art method to trim it down to the exact factor.
$endgroup$
– No_Body
8 hours ago
add a comment |
$begingroup$
It's a very open end question. There could be a lot of reasons for the defects. It could be possible there is a change in the manufacturing process or quality or poor shipping or changed inspection rules/laws. So you have to gather all those data before think of any modelling. In general cases. I would start it from the top. Like which company has more defective products. If the increase is same across all companies then it couldn't be a manufacturing problem. if certain companies have increased defect rate check if they made any change in the manuf/shipping or any other factor think of
$endgroup$
– No_Body
8 hours ago
$begingroup$
I don't think you can use any state of the art method to trim it down to the exact factor.
$endgroup$
– No_Body
8 hours ago
$begingroup$
It's a very open end question. There could be a lot of reasons for the defects. It could be possible there is a change in the manufacturing process or quality or poor shipping or changed inspection rules/laws. So you have to gather all those data before think of any modelling. In general cases. I would start it from the top. Like which company has more defective products. If the increase is same across all companies then it couldn't be a manufacturing problem. if certain companies have increased defect rate check if they made any change in the manuf/shipping or any other factor think of
$endgroup$
– No_Body
8 hours ago
$begingroup$
It's a very open end question. There could be a lot of reasons for the defects. It could be possible there is a change in the manufacturing process or quality or poor shipping or changed inspection rules/laws. So you have to gather all those data before think of any modelling. In general cases. I would start it from the top. Like which company has more defective products. If the increase is same across all companies then it couldn't be a manufacturing problem. if certain companies have increased defect rate check if they made any change in the manuf/shipping or any other factor think of
$endgroup$
– No_Body
8 hours ago
$begingroup$
I don't think you can use any state of the art method to trim it down to the exact factor.
$endgroup$
– No_Body
8 hours ago
$begingroup$
I don't think you can use any state of the art method to trim it down to the exact factor.
$endgroup$
– No_Body
8 hours ago
add a comment |
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$begingroup$
It's a very open end question. There could be a lot of reasons for the defects. It could be possible there is a change in the manufacturing process or quality or poor shipping or changed inspection rules/laws. So you have to gather all those data before think of any modelling. In general cases. I would start it from the top. Like which company has more defective products. If the increase is same across all companies then it couldn't be a manufacturing problem. if certain companies have increased defect rate check if they made any change in the manuf/shipping or any other factor think of
$endgroup$
– No_Body
8 hours ago
$begingroup$
I don't think you can use any state of the art method to trim it down to the exact factor.
$endgroup$
– No_Body
8 hours ago