ISOM 2700
Operations Management
L1-L2, Spring 2015
Study Guide
Module
Conceptual Questions
Quantitative Problems
Exponential Smoothing with
Trend Problem (3)
Tracking Signal (2)
EOQ Inventory Model with the
Probability Approach (3)
Inventory Problem with
ISOM2700 Operations Management
Spring 2015
Department of Information Systems, Business Statistics and
Operations Management
COURSE:
ISOM2700 Operations Management (3-0-0:3)
Production and service operations viewed from the strategic, tactical
and operatio
UST-TBC-2014
RONALD LAU
JOSEPH FERNANDEZ
Fat Angelos Italian Restaurants:
Managing the Customer Waiting Experience
Were a family-style Italian restaurant known for our value-for-money tasty
food, friendly service and lively atmosphere1
Fat Angelos was a w
ISOM2500
Tutor: Carey MA
Exercise02 Solution
Short Question
1.
(a)
(b)
(c)
(d)
(e)
2.
(a)
All computer chips produce
1,000 computer chips
The defective rate of all the computer chips (claims that less than 10%)
The sample defective rate from the 1,000 chi
Business Statistics
PAN Baoqian, Kris
ISOM 2500
Topic 3: Estimation
Part b Confidence Interval
What we have done
Descriptive
Statistics
Probability
Random
variables
Discrete
Random
Variable
Continuous
Random
Variable
Sampling
distribution
Simple
Linear
re
ISOM2500
Tutor: Carey MA
ISOM2500 Exercise05
Short Question
1.
A box contains three chips. One chip is red on both sides. One chip is blue on both sides. One
chip is red on one side, and blue on the other. Suppose the box is shaken, a chip drawn and
place
Business Statistics
PAN Baoqian, Kris
ISOM 2500
Topic 2: Probability Part b
What we have done
Descriptive
Statistics
Proba bility
Random
variables
Sampling
distribution
Simple
Linear
regression
Hypothesis
testing
Confidence
intervals
2
Goals for this topi
ISOM2500
Tutor: Carey MA
ISOM2500 Exercise04
MC Question
1.
There are 20 cards numbered from 1 to 20 inclusive. A card is randomly drawn from them. Find
the probability that the number on the card drawn is less than 8.
A.
B.
C.
D.
2.
A fair dice is thrown
ISOM2500 Business Statistics
Tutor: Carey MA
Assignment01 Solution
1. a.
Chart of Risk
212
200
Count
150
100
91
50
13
0
Average
High
Low
Risk
Pie Chart of Risk
Category
Average
High
Low
Average
28.8%
Low
67.1%
High
4.1%
From either the bar chart or the pi
ISOM2500 L1 Business Statistics
Course Outline
LECTURE
Instructor: Dr. Baoqian PAN, Kris
Room: 5041 (LSK Business building); Email: ismtpbq@ust.hk
Office Hours: Wed 4:20pm-5:20pm; or send an email to make an appointment.
Instructional Assistant: Miss. Car
ISOM2500 L01 Business Statistics
Assignment02
Due Date: 24th Oct, 2016 5:00pm
Please submit your assignment to the collection box on LSK 4/F next to LSK Lift 3&4.
If you are work in group, please find one representative to submit the assignment.
Remind th
Business Statistics
PAN Baoqian, Kris
ISOM 2500
Topic 3: Estimation
Part a
What we have done
Descriptive
Statistics
Probability
Random
variables
Discrete
Random
Variable
Continuous
Random
Variable
Sampling
distribution
Simple
Linear
regression
Hypothesis
How FedEx Works:
Inside the Memphis
Super Hub
FedEx Case Study
Read the case text
Read the questions
Watch video
Try to answer all the questions
Discuss your answers with your group and decide on one
answer.
Nominate the speaker from your group to s
Observations from Sample Distribution
UCL
LCL
1
2
3
Sample number
4
Mean and Range Charts
UCL
Detects shift
x-Chart
LCL
UCL
Does not
detect shift
R-chart
LCL
Mean and Range Charts
UCL
LCL
UCL
Does not
detect shift
x-Chart
LCL
UCL
R-chart
Detects shift
LCL
ISOM 3100
1. How difficult was the task Immelt faced when he assumed the CEO role in 2001? Any
imperative changes? Any incentives to maintain the past operation?
Considering that less than a week after taking the over the role of CEO of General Electric,
As a typical P2P platform, Peerby is doing distinguishingly from others. It requires
neither initial installment for membership nor fees for offering lending service. It
deliberately creates a free exchanging community, connecting people with each
other a
ISOM 2500
1.
(1) What is the population of interest?
(1) What is the sample?
(1) What is the parameter?
(1) What is the statistic?
(1) Does the value 10% refer to the parameter or to the statistic?
(1) Is the value 7.5% a parameter or a statistic?
(2) Exp
ISOM 2500
1.
K.H. Chen
(12 points) Shoppers can pay for their purchases with cash, a credit card, or a
debit card. Suppose that the proprietor of a shop determines that 60% of her
customers use a credit card, 30% pay with cash, and the rest use a debit ca
ISOM 2500
1.
Solutions to Problem Set 1
K.H. Chen
(8 points) A manufacturer of computer chips claims that less than 10% of its
products are defective. When 1,000 chips were drawn from a large production,
7.5% were found to be defective.
a.
(1) What is the
ISOM 2500
1.
Solutions to Problem Set 4
K.H. Chen
(42 points) The human resource manager of a telemarketing firm is concerned
about the rapid turnover of the firms telemarketers. It appears that many
telemarketers do not work very long before quitting. Th
ISOM 2500
1.
Solutions to Problem Set 3
K.H. Chen
(8 points) A statistics practitioner took a random sample of 50 observations from a
population with a standard deviation of 25 and computed the sample mean to be 100.
a.
(2) Compute a 90% confidence interv
ISOM 2500
1.
Solutions to Problem Set 2
K.H. Chen
(12 points) Shoppers can pay for their purchases with cash, a credit card, or a
debit card. Suppose that the proprietor of a shop determines that 60% of her
customers use a credit card, 30% pay with cash,
ISOM 2500
1.
K.H. Chen
(42 points) The human resource manager of a telemarketing firm is concerned
about the rapid turnover of the firms telemarketers. It appears that many
telemarketers do not work very long before quitting. There may be a number
of reas
ISOM 2500
1.
c.
d.
(2) Compute a 95% confidence interval estimate of the population mean.
(2) Repeat part (a) if the sample size is 100 instead of 25.
(2) Repeat part (a) if the sample size is 400 instead of 25.
(2) Describe what happens to the confidence
Statistical Process
Control
Purposes
To monitor variation in quality of an ongoing
production or service process
To detect, identify and resolve assignable (special)
causes for quality problems (acknowledges common
causes (noises)
Assignable and Common
Ca
A10
APPENDIX E
"\l
POISSON PROBABILITIES
Appendix E
Entiies in the table give the probability of x occurrences for a Poisson process with a
mean 11. For example, when 11 = 2.5, the probability of four occurrences is 0.1336.
p
x 0.1 0.2 0.3 0.4 0.5 0.0
Samsung Electronics
Solution to Samsung Electronics: Analyzing
Qualitative Complaint Data case
1
Q1 - Create an affinity diagram from the complaints (Pass #1).
First pass at grouping the data
Results are clear that the majority of complaints are with th
Process Capability Analysis
1
Process Capability Analysis
Specification limits
Limits that define the conformance
boundaries for an individual unit of a
manufacturing or service operation
(ANSI/ASQC A1)
Usually refer to physical requirements: length,
thic
Chapter 13
Principles of Six Sigma
1
Key Idea
Although we view quality improvement
tools and techniques from the
perspective of Six Sigma, it is important
to understand that they are simply a
collection of methods that have been
used successfully in all t