Simple statistics questions on labor data

#1
We've been collecting labor data in production with the intent of setting standards for planning purposes for 10 years and we are finally ready to put this data to use to answer these types of questions.
  • When should we start processing a job given historical labor data?
  • Do we have too much or too little capacity/resources in production?
  • What are our labor costs for product x?
Some open questions I've been looking for answers to are below. I'd rather learn to fish than someone give me a fish. That is if there is a book or a resource I should reference I'd very much appreciate your suggestions.

Removing Outliers

In reviewing the labor data dataset I see data points that I intuitively know aren't possible. As an example, an operation that takes 3-4 minutes, being reported as taking 10 seconds on hundreds of products for a job or it taking 20 minutes per product on hundreds of products... Given humans have to manually start and stop an operation and report how many products were processed the data is subject to human error. Based on this I feel justified to throw out any data points where an operation takes less than 10 seconds or more than 10 minutes on 10 or more products. The resulting dataset still has data points I know can't be valid so I would like to throw out the data points that vary from the mean by x standard deviations. In looking at a couple sample operations I think I need to use 2 standards deviations or less, is there a better way to choose how many standard deviations to use(zed score)? The idea is once we've thrown out our outliers the mean will become our standard for the given operation.

How much data to include

I have about 10 years of labor data, but I know our products and processes have changed over the years so I'm currently looking at the last 2 years of data. On one hand I feel like that makes the datapoints more likely to be accurate for today's processes and products, but I am torn as I believe the general rule of thumb is the more data points the better. Any suggestions on how much data to include?
 
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#3
It sounds like you have a lot of data. There is an emerging field of study for Big Data, which goes by different names, Data Science, Machine Learning, Business Intelligence. I subscribe to the website DataScienceCentral, which has articles and blogs for data practitioners. Here is a list of articles on dealing with Outliers, The US Dept of Commerce maintains the NIST Engineering Statistics Handbook, and the page on Detection of Outliers explains why outliers is a challenge. The blog at Statistics by Jim, I thought was most readable. Jim's advice is to graph your data and use common sense and your knowledge of what is valid what what is not, to sort out outliers.

I couldn't resist responding when you mentioned learning to fish. Check out my tag line.
 
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