lOMoAR cPSD| 6861666
1
MGT 8803 Week 10 Supply Chain - Bullwhip Effect &
Newsvendor Model Transcript with Complete
Solutions 100% Correct | New 2025-2026 Update
MGT 8803 SUPPLY CHAIN MODULE
Week 10 TRANSCRIPTS
1. Bullwhip Effect
>> Welcome to this lesson on something called the Bullwhip Effect. And the Bullwhip Effect is
probably one of the most famous supply chain concepts. And so for here and in this lesson, our
objectives are going to be to discuss what this thing is called Bullwhip Effect. Then go into what
causes the Bullwhip Effect to occur.
And what are some ways that companies try to mitigate or deal with this phenomenon called the
Bullwhip Effect. A professor by the name of Professor Hau Lee is credited with identifying this
Bullwhip Effect or what's known as the Bullwhip Effect, after observing a multi-echelon supply
chain. And by multi-echelon, what I mean is much what we saw on some earlier lessons, where
maybe you have a supplier here, it's a given company, this supplies to the say a wholesaler and
that wholesaler maybe supplies to a retailer, who supplies on to customers.
So when I say multi-echelon, I'm saying multiple levels in a supply chain. And he was studying,
by the way and first documented this problem with Pampers. And what he was saying
specifically was, there would be small changes in demand at the retailer but he was noticing the
wholesaler was ordering more and less frequently large orders.
And is if you went back to the supplier, we're seeing even larger fluctuations in changes in order.
And by the way, this phenomenon was found to occur in other industries, other products such as
HP Printers. So to look at this in a more graphical way, to illustrate it, right, the notion.
And what he saw was that, the magnification of orders as you moved upstream. And so we when
we say upstream, we start with the customer. And if you go back, the supply chain toward the
manufacturer that's considered upstream. He was seeing the magnification of orders as one
moves up.
, lOMoAR cPSD| 6861666
2
So for example, if the demand say again, for Pampers was forecasted to be 10, the retailer would
order 15, in order to have some safety stock. And when they would place that order for 15 to the
distributor, the distributor says, okay, you just order 50, well, I'm gonna go back and order 20
from the manufacturer.
And then the manufacturer in turn would say, 20, well I'm gonna order 40 from maybe whoever
my, right, we'll call it raw materials suppliers, which isn't on here. And so you notice there's this
amplification, right? Of quantity, amplification of order size, as you move up the supply chain.
So what causes, right? So the really kind of the key question then is okay, if there's this increase
in order quantities as you move upstream and a supply chain, what is causing this behavior to
happen again in a multi-echelon supply chain? And there's four big causes to this.
The first is, when there are fluctuations in prices at the retail level, meaning items are placed on
sale. You know when the price is reduced, when it's placed on sale, the price is reduced and the
demand typically goes up. But that demand is not going up because people want more of it at the
retail level, it was going up because the price has been lowered.
And so we're calling that, creating artificial demand, okay? But the problem is, that the retailer if
they put that item on sale, and it increases demand, the wholesaler doesn't know that the retailer
did that. And the manufacturer doesn't know that the retailer did that, right? So the upstream
levels in the supply chain look at this bumping demand, and think it's not artificial demand but
actual demand.
And so, they ramp up in response. So price fluctuations can cause this, right? Another thing that
can cause these fluctuations is just order batching, right? Where if a company is ordering
infrequently in large amounts, those upstream in the supply chain can't distinguish that change in
batch size from a change in demand.
You've changed your inventory policy at the retail level. You wanna order more, less frequently,
so you place in your first large order. That wholesaler doesn't know why you've done that. They
assume again actual demand is up. Not that you were changing your ordering size. A third cause
is called here shortage gaming.
, lOMoAR cPSD| 6861666
3
And this is where a company orders more than they think they're gonna need just to be safe.
That's this one right here. A buyer orders way more than they need. And then if demand starts to
come in, as being less, they start canceling orders. Well, the problem is that upstream of them has
already begun to work to fulfill the order that has now been canceled.
And then of course the forecast here is forecasting inaccuracies. If there's not good predictions of
what demand's gonna be, companies will tend to over, can rather tend to over order. So what can
companies do to try to mitigate or to try to lower this amplification of order sizes as you move
upstream the supply chain?
Clear first an easy thing is you need to do a better job of giving that information that's occurring
at the actual customer level, which will call point of sale POS, point of sale data, so that the
actual sales data at the retailer take that information and give that information to the wholesaler.
Give that information to the manufacturer and so on, so share that point of sale data backup
supply chain. Other ways to mitigate, if order and costs are problem, reduce the desire, reduce
the incentive for a person downstream of you to want to order larger batches, right? And then if
discounts, promotion, sales, create artificial demand, then eliminate them.
Don't use them, try to discourage them. So that that artificial demand isn't occurring as often. So
it's interesting to kind of think about how having a better information, in terms of point of sale
data, having better information can lead to mitigating and reducing some of these issues
associated with what's called the Bullwhip Effect.
It's also kind of interesting, and I would encourage you to kinda think about how can analytics
and having more data, like that point of sale data. And doing more research and study and
analyzing of that data, how can analytics help lead to even further reducing the Bullwhip Effect.
And making for better, more efficient supply chains.
2. Newsvendor Model
>> Another very famous and well-known concept in supply chain is something called the
newsvendor model. So in this lesson I want us to go through and understand exactly what this
newsvendor model is and how it's used. So while it's not seen as much these days really if at all,
, lOMoAR cPSD| 6861666
4
newspaper or news boys, right, were a very common sight in city street corners for many years,
right?
And the key question in this picture that this newspaper boy had, was how many newspapers
should they buy? Should he, rather, buy from the New York Times every morning, or whatever
local newspaper it was, right? Cuz see, the challenge that he faced, and I'm making up some
numbers here, but the challenge he faced is, he can go buy a paper from the New York Times for
$0.80.
And then he can take it down to the street corner and he sells it for $1.00, right? And so there's a
dilemma that he faces. If he doesn't buy enough papers, too few papers, then he'll run out. And
there will be customers who will show up to get a paper and they won't be able to buy it and
there'll be profit that is lost there, or a sale that's lost.
On the other hand, if he buys too many papers, right, then he's gonna have to eat the price of the
papers that he doesn't sell, right? Which will lower his profit. So this leads us to a situation of
what, kind of, is the framework around when this newsvendor model would be used or where it
would be applicable.
In a supply chain context, right, what you're looking for is a situation where the company has one
chance to decide on a stocking quantity, right, for a product you're selling. And the demand for
that product is not known. It's uncertain, it's unknown, but there is some sort of probability
distribution.
So you know with a certain probability, a range of demands that may occur, right? And in our
case here today, we'll look deeper at normal distribution, right? The known profit for each unit
sold and the known loss for a unit that's not sold are known. We'll call it marginal profit,
marginal loss.
And overall the goal here in this context is to maximize expected profit. I'm using that word
expected here so you should be thinking, okay, when I see expected something, I'm probably
gonna have probabilities in here. So probability times a value, and that is indeed the case here.
Actually turns out that there is a lot of areas where this can be used, far beyond just newspapers.
1
MGT 8803 Week 10 Supply Chain - Bullwhip Effect &
Newsvendor Model Transcript with Complete
Solutions 100% Correct | New 2025-2026 Update
MGT 8803 SUPPLY CHAIN MODULE
Week 10 TRANSCRIPTS
1. Bullwhip Effect
>> Welcome to this lesson on something called the Bullwhip Effect. And the Bullwhip Effect is
probably one of the most famous supply chain concepts. And so for here and in this lesson, our
objectives are going to be to discuss what this thing is called Bullwhip Effect. Then go into what
causes the Bullwhip Effect to occur.
And what are some ways that companies try to mitigate or deal with this phenomenon called the
Bullwhip Effect. A professor by the name of Professor Hau Lee is credited with identifying this
Bullwhip Effect or what's known as the Bullwhip Effect, after observing a multi-echelon supply
chain. And by multi-echelon, what I mean is much what we saw on some earlier lessons, where
maybe you have a supplier here, it's a given company, this supplies to the say a wholesaler and
that wholesaler maybe supplies to a retailer, who supplies on to customers.
So when I say multi-echelon, I'm saying multiple levels in a supply chain. And he was studying,
by the way and first documented this problem with Pampers. And what he was saying
specifically was, there would be small changes in demand at the retailer but he was noticing the
wholesaler was ordering more and less frequently large orders.
And is if you went back to the supplier, we're seeing even larger fluctuations in changes in order.
And by the way, this phenomenon was found to occur in other industries, other products such as
HP Printers. So to look at this in a more graphical way, to illustrate it, right, the notion.
And what he saw was that, the magnification of orders as you moved upstream. And so we when
we say upstream, we start with the customer. And if you go back, the supply chain toward the
manufacturer that's considered upstream. He was seeing the magnification of orders as one
moves up.
, lOMoAR cPSD| 6861666
2
So for example, if the demand say again, for Pampers was forecasted to be 10, the retailer would
order 15, in order to have some safety stock. And when they would place that order for 15 to the
distributor, the distributor says, okay, you just order 50, well, I'm gonna go back and order 20
from the manufacturer.
And then the manufacturer in turn would say, 20, well I'm gonna order 40 from maybe whoever
my, right, we'll call it raw materials suppliers, which isn't on here. And so you notice there's this
amplification, right? Of quantity, amplification of order size, as you move up the supply chain.
So what causes, right? So the really kind of the key question then is okay, if there's this increase
in order quantities as you move upstream and a supply chain, what is causing this behavior to
happen again in a multi-echelon supply chain? And there's four big causes to this.
The first is, when there are fluctuations in prices at the retail level, meaning items are placed on
sale. You know when the price is reduced, when it's placed on sale, the price is reduced and the
demand typically goes up. But that demand is not going up because people want more of it at the
retail level, it was going up because the price has been lowered.
And so we're calling that, creating artificial demand, okay? But the problem is, that the retailer if
they put that item on sale, and it increases demand, the wholesaler doesn't know that the retailer
did that. And the manufacturer doesn't know that the retailer did that, right? So the upstream
levels in the supply chain look at this bumping demand, and think it's not artificial demand but
actual demand.
And so, they ramp up in response. So price fluctuations can cause this, right? Another thing that
can cause these fluctuations is just order batching, right? Where if a company is ordering
infrequently in large amounts, those upstream in the supply chain can't distinguish that change in
batch size from a change in demand.
You've changed your inventory policy at the retail level. You wanna order more, less frequently,
so you place in your first large order. That wholesaler doesn't know why you've done that. They
assume again actual demand is up. Not that you were changing your ordering size. A third cause
is called here shortage gaming.
, lOMoAR cPSD| 6861666
3
And this is where a company orders more than they think they're gonna need just to be safe.
That's this one right here. A buyer orders way more than they need. And then if demand starts to
come in, as being less, they start canceling orders. Well, the problem is that upstream of them has
already begun to work to fulfill the order that has now been canceled.
And then of course the forecast here is forecasting inaccuracies. If there's not good predictions of
what demand's gonna be, companies will tend to over, can rather tend to over order. So what can
companies do to try to mitigate or to try to lower this amplification of order sizes as you move
upstream the supply chain?
Clear first an easy thing is you need to do a better job of giving that information that's occurring
at the actual customer level, which will call point of sale POS, point of sale data, so that the
actual sales data at the retailer take that information and give that information to the wholesaler.
Give that information to the manufacturer and so on, so share that point of sale data backup
supply chain. Other ways to mitigate, if order and costs are problem, reduce the desire, reduce
the incentive for a person downstream of you to want to order larger batches, right? And then if
discounts, promotion, sales, create artificial demand, then eliminate them.
Don't use them, try to discourage them. So that that artificial demand isn't occurring as often. So
it's interesting to kind of think about how having a better information, in terms of point of sale
data, having better information can lead to mitigating and reducing some of these issues
associated with what's called the Bullwhip Effect.
It's also kind of interesting, and I would encourage you to kinda think about how can analytics
and having more data, like that point of sale data. And doing more research and study and
analyzing of that data, how can analytics help lead to even further reducing the Bullwhip Effect.
And making for better, more efficient supply chains.
2. Newsvendor Model
>> Another very famous and well-known concept in supply chain is something called the
newsvendor model. So in this lesson I want us to go through and understand exactly what this
newsvendor model is and how it's used. So while it's not seen as much these days really if at all,
, lOMoAR cPSD| 6861666
4
newspaper or news boys, right, were a very common sight in city street corners for many years,
right?
And the key question in this picture that this newspaper boy had, was how many newspapers
should they buy? Should he, rather, buy from the New York Times every morning, or whatever
local newspaper it was, right? Cuz see, the challenge that he faced, and I'm making up some
numbers here, but the challenge he faced is, he can go buy a paper from the New York Times for
$0.80.
And then he can take it down to the street corner and he sells it for $1.00, right? And so there's a
dilemma that he faces. If he doesn't buy enough papers, too few papers, then he'll run out. And
there will be customers who will show up to get a paper and they won't be able to buy it and
there'll be profit that is lost there, or a sale that's lost.
On the other hand, if he buys too many papers, right, then he's gonna have to eat the price of the
papers that he doesn't sell, right? Which will lower his profit. So this leads us to a situation of
what, kind of, is the framework around when this newsvendor model would be used or where it
would be applicable.
In a supply chain context, right, what you're looking for is a situation where the company has one
chance to decide on a stocking quantity, right, for a product you're selling. And the demand for
that product is not known. It's uncertain, it's unknown, but there is some sort of probability
distribution.
So you know with a certain probability, a range of demands that may occur, right? And in our
case here today, we'll look deeper at normal distribution, right? The known profit for each unit
sold and the known loss for a unit that's not sold are known. We'll call it marginal profit,
marginal loss.
And overall the goal here in this context is to maximize expected profit. I'm using that word
expected here so you should be thinking, okay, when I see expected something, I'm probably
gonna have probabilities in here. So probability times a value, and that is indeed the case here.
Actually turns out that there is a lot of areas where this can be used, far beyond just newspapers.
