MOD-5 Quality Simulation
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University Of Connecticut *
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5110
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Industrial Engineering
Date
Jan 9, 2024
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Uploaded by PresidentSkunkMaster751
Memo
To: Prof. Pete Schommer
From: Gowtham Ravi
Date: 10/18/2023
Subject: Quality Simulation
Challenge 1: Control Limits for Mean and Range Charts
My control limits for the Mean and Range charts differed from those of my classmates in
the simulation for Challenge 1. This discrepancy was caused by the sample data utilized
in the computations. The mean and range values from each student's unique data set are
then used to generate the Upper and Lower Control Limits (UCL, LCL). As a result,
control limitations may differ from one student to the next.
Furthermore, the sample size and statistical methods used to calculate the control limits
also contribute to variance. Different ways to estimate the process standard deviation
might result in extra control limits, adding another layer of complication to the student
comparison.
Challenge 2: Achieving the Lowest Total Cost
I attained the lowest overall cost in Challenge 2 by concentrating on two critical areas:
machine calibrations and labor replacement. Regular machine calibrations resulted in a
considerable decrease in fault rate, lowering waste and rework costs. Furthermore, labor
substitution aided in optimizing production, particularly during peak hours, resulting in
decreased labor expenses.
It is worth mentioning that machine calibration improved product quality and enhanced
machine uptime, contributing to overall efficiency. On the other hand, labor substitution
enabled greater flexibility in workforce cost management, making it a win-win approach
for productivity and cost efficiency.
Challenge 3: Process Capability
Reviewing the charts in Challenge 3 revealed that specific processes were competent
while others were not. Control limits for capable processes were within specification
limits. In statistical words, minimizing process variability would be the most suitable
remedy for unable processes. This might be accomplished by identifying and removing
unique sources of variance.
Corrective steps for ineffective processes may include altering equipment settings or
retraining workers. Statistically, our goal is to return the process to a condition of
statistical control in which all deviations are attributed to familiar sources, hence
rehabilitating the process.
What is Hospital Laundry's process capability index? Ans:7.53
What is Tea Shop's process capability index? Ans: 3.08
What is Filled Donuts's process capability index? Ans: 0.8
What is Tire Pressure's process capability index? Ans:0.44
The capability index values are less than 1, which suggests that while the process has the
potential to be capable, it is not well-centered within the specification limits. This could
result in a higher likelihood of producing out-of-specification results.
4. Best Total Cost of Quality
Strict adherence to Statistical Process Control (SPC) criteria resulted in the lowest overall
cost of quality. I minimized faults and obtained a reduced total cost of quality by
regularly monitoring the p-charts and taking rapid remedial steps if a data point strayed
outside the control boundaries.
The lowest overall cost of quality was $13k. This significantly improved over the initial.
The cost savings were realized by implementing timely and effective remedial actions
based on data analytics. These measures resulted in a more efficient process with fewer
faults, directly contributing to a lower overall cost of quality.
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