Call Centres
Customer Service
Your background is in manufacturing. How do you
view customer service and call centers?
Customer support is a giant process. The call center
is like a call factory. The
Telephone Call Centers: Tutorial, Review, and Research Prospects
Noah Gans
Ger Koole
Avishai Mandelbaum
The Wharton School, University of Pennsylvania,Philadelphia, PA 19104, U.S.A.
[email protected]
IBM Business Consulting Services
Back-office and customer care centers
in emerging economies
A human capital perspective
An IBM Institute for Business Value executive brief
ibm.com/bcs
The IBM Institu
Flight for Survival
content business models
A New Business
Model for the
Airline Industry
To pare down their colossal operating
costs, giant U.S. and European carriers
must restructure the hub-and-spo
Yield Management
Jishnu Hazra, IIMB
What is Revenue Management?
Demand Management practices that aim to
maximize the revenue of available supply
Revenue Management:
Price Controls: Markdown, Rebate
7/26/2013
Procurement Pricing
Types of Products
Direct Manufacturing Material
Commodity-steel, chemicals, customized and
standard components
Indirect Material
MRO, Office supplies
1
7/26/2013
Pric
Managing Inventories
1
Newsboy Model
Retailer orders
Q units (decision variable)
Wholesale price: W; Selling Price: R
Demand: D (random variable)
Salvage value: S (S < W)
How much should the reta
Network structure
Direct Connection: Point-to-Point
Traveling Salesman
Hub and Spoke
Jishnu Hazra IIMB
Hub and Spoke Model
Rationale behind H&S
Passenger Airline Services
Impact on Aircraft Opera
Supply Chain Management
Lecture material on Moodle
Enrollment Key:
Jishnu Hazra
IIMB
Supply Chain Issues that we will cover
Inventory Location in a Supply Chain; Study of
Responsive Supply Chains; Al
Probability Introduction
Why is probability important?
Used
implicitly whenever handling uncertainty
explicitly in many areas like risk and
finance
Provides
definite quantitative answers and decisi
Q1. An insurance salesman meets with 8 prospective customers each week. From
historical data, the proportion of these customers who take out a policy is 18%.
(a) What is the probability that the sales
126 CHAPTER 7. NORMAL PROBABILITY APPROXIMATIONS
Therefore, by the CLT,
X = n? 2 nN(cx/n, tic/rig) = Ncfw_a,o:).
Intuitively, the requirement that o: is large is necessary because one needs to represe
Decision making from Data
Bayes Method:
Decision making from Data Bayes
Method:
A way of organising probability calculations
for problems where you want to update your
state of knowledge about an unce
Q1. Emergency patients arrive at a large hospital at the rate of 0.33 per minute. On
average, 22% of emergency patients are triaged into the most serious category.
(a)
What is the probability of 6 or
Some Problems on Binomial and
Poisson Distribution
An insurance salesman meets with 8 prospective customers each week. From
historical data, the proportion of these customers who take out a policy is
QM-1
Session-1
Pulak Ghosh
Professor, QM & IS
Statistical Method
(Well start here)
Formulate
problem
Do some
statistical
calculations
Get some
data
Visualize the
data
Interpret
results
Data Driven vie
5-1
Using Statistics
Statistical Inference:
Predict and forecast values of
population parameters.
Test hypotheses about values
of population parameters.
Make decisions.
Make
Make
generalizationsabo
Q1. Suppose that 18% of employees of a given corporation engage in physical exercise
during the lunch hour. Moreover, assume that 57% of all employees are male, and 12%
of all employees are males who
Quantitative Methods by SM
Continuous Probability Models
Sujay K Mukhoti
12/16/16
Quantitative Methods by SM
Outilne:
1. Motivation
2. Definition and characteristics: monotone
etc of CDF
3. Pdf & exam
Quantitative Methods by SM
Joint Distribution
Sujay
K Mukhoti
Sujay
Mukhoti
12/16/16
Quantitative Methods by SM
Joint Probability Distributions
Motivation:
Peoples belief: high value orders are serve
Quantitative Methods by SM
Normal Distribution
Sujay K Mukhoti
12/16/16
Quantitative Methods by SM
Normal Approximation to Bin(n,p)
Xi = 1, if price of a stock goes up
0, otherwise
Sn = number of up
Quantitative Methods by SM
Discrete Probability Models: Binomial
Sujay Mukhoti
12/16/16
Quantitative Methods by SM
Probability mass function (pmf)
If X is a discrete random variable then a function f(
Quantitative Methods by SM
Random Variable and Probability Distribution
Sujay K Mukhoti
12/16/16
Quantitative Methods by SM
Agenda
1. Motivation
2. Random variable: discrete and continuous
3. Probabil
Quantitative Methods by SM
Joint Distribution
Sujay
K Mukhoti
Sujay
Mukhoti
12/16/16
Quantitative Methods by SM
Joint Probability Distributions
Motivation:
Peoples belief: high value orders are serve
Quantitative Methods by SM
Discrete Probability Models
Sujay K Mukhoti
12/16/16
Quantitative Methods by SM
Poisson Distribution
Sujay Mukhoti
12/16/16
Quantitative Methods by SM
Motivation 1:
Probabil
Sampling Distributions and Point Estimation - I
IIM Udaipur
July 21, 2014
In this session you will learn about
A Motivating Example
An Introduction to Sampling
Some basic concepts
Advantages
When to d
Sampling Distributions and Point Estimation - II
IIM Udaipur
July 28, 2014
In this session you will learn about
Sampling Distributions
An example
Sampling distribution of X
Central Limit Theorem
Sampl
Notes on Exponential Distribution
Exponential distribution is used to model certain types of data. It is a continuous distribution,
that is, an exponential random variable can take any value within it
Binomial Model: discussion and another example
We have seen in class, how to apply the binomial model to a practical situation. Recall the
example discussed in class: When a machine functions properly