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MGSC 395 Week 2 Notes

by: Rachel Whitbeck

MGSC 395 Week 2 Notes MGSC 395

Rachel Whitbeck
GPA 4.0

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These notes cover what we went over in class from Monday, January 25 to Wednesday, January 27. There is a powerpoint that may be posted but we didn't use the PP in class-- notes were just written o...
Operations Management
Pearse Gaffney
Class Notes
Management Science
25 ?




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This 4 page Class Notes was uploaded by Rachel Whitbeck on Monday February 1, 2016. The Class Notes belongs to MGSC 395 at University of South Carolina taught by Pearse Gaffney in Spring 2016. Since its upload, it has received 30 views. For similar materials see Operations Management in Business, management at University of South Carolina.

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Date Created: 02/01/16
MGSC 395 5:30 MW - Week 2 Notes Monday, January 25 , 2016  Process analysis o Process improvement terms- small, gradual incremental improvement (continual, represented as a cycle) o Process reengineering- “rip up the paper and start fresh”  Qualitative tools (quality judgment- no math, no quantifying) o Process flow chart (aka flowchart, process flowmap, process map)  Detailed steps are in the PowerPoint on blackboard  {Start/End} --> [step 1] --> [step 2] --> [step 3]  Just have arrows connecting rectangles. Boom there you go.  Use diamond instead to signify questions, and put multiple arrows off the edges to answer yes or no   3 versions o What you think it is o What is really is o What you want it to be o Swim lane diagram  Shows who is responsible for what steps  Looks exactly like the diagram above except there are vertical lines separating the boxes according to what sector is responsible o Process chart Step Descriptio Time(min Activity n s) Operati Transpor Inspectio Dela Stor on t n y e 1 blah 4 2 blah 6 Total 8 o Service blueprint* (don’t need to know)  Data analysis tools o Pareto chart 1  Simple visual tool  50-20 rule  M&M example in class  Make a histogram of the color and number of m&m’s in a mini bag o 5 Why  Literally as the question “why” five times to solve a problem, like an annoying little kid. Don’t go too far so as to get off subject though  The Washington monument is disintegrating. Why?  Because harsh chemicals are being used on the pigeon poop. Why?  Pigeons like spiders and there are lots of spiders. Why?  Spiders like gnats and there are lots of gnats.  So get rid of the gnats and there’s your solution. o Cause and effect diagram (fishbone) (“6M”)  Structured brainstorming o 5W2H  Who, what, when, where, why?  How, and how much? th Wednesday, January 27 , 2016  Frequency histogram (using the sum of two dice) o Takes the shape of a bell curve o Good to show center, spread, and shape o Bad flashback to MGSC 291  Distance from the mean is a standard deviation  Within three standard deviations is most of your data (99.7%) o “Expected,” or common cause variation o “Unexpected,” or special cause variation- aka assignable cause  Area to the right of the expected limit (when you unexpectedly add a third die)  When it happens, it’s an “alarm”  Sampling o Example: take a sample of 5 coke bottles every 2 hours to weigh them (take data 4 times) o Range- distance between the highest and lowest sample amount o Estimation: 3standard deviations = A R 2  R bar is the average of the samples’ ranges  A 2ou find on a chart  Sample size (n) means the number of data points you get each time you go observe the process  For this coke example, the sample size is 5 for the number of coke bottles, not the number of times you collect the data (4)  Grand average= x (x double bar) = average of the averages o Averages chart- you don’t care about the original data anymore. You now only care about the averages.  Should be centralized around the grand average- if not, it’s an alarm  Upper control limit (for averages)  UCL =xx + A R 2  Lower control limit (for averages)  LCL x x – A R2 o Ranges chart  Upper control limit- UCL = R R 4  Lower control limit- LCL =RD R 3


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