Week 5An Introduction to Systems AnalysisComplex Systems.docx

Week 5
An Introduction to Systems Analysis
Complex Systems
We all come from and live in complex systems – cultures,
economies, political organizations, families, and so on – but one
of the constant themes coming out of research in the social
sciences is that the level of complexity in our world as a whole
is increasing at an exponential rate.
When we talk about a complex system, we are talking about a
network of diverse, connected, interdependent, adaptive parts
(Mitchell, 2009). We can contrast these characteristics with
those of a complicated system that may have diverse parts
working together, but they cannot to change.
For example, imagine a watch: it has many diverse parts,
connected to each other, and operating in tandem to keep time,
but you cannot remove any of its parts without causing it to
cease to operate.
On the other hand, imagine a lake: it also have many diverse
parts, also connected to each other, and also operating in
tandem as part of an ecological environment, but some of its
parts can be removed without shutting down its functions, even
if its ecology changes.
A watch is a complicated system, while a lake is a complex
system. Our discussion here is far from a purely academic one.
Complexity matters for understanding complex systems because
they require particular problem-solving approaches. Our
understanding of problem-solving approaches to complex
systems might benefit from first examining the characteristics
of more traditional problem sets and problem-solving
approaches to them.

Problem solving is all about optimization, by which we mean
finding the optimum solution to a problem. We might
understand problems in the first type of problem set by thinking
about them as “Mt. Fuji” problems. Mt. Fuji problems are those
for which we can find one optimum solution. Take, for example,
the problem of figuring out how many rounds of bullets we
should issue police academy cadets to teach them how to shoot
properly.
We might do this by issuing them a certain number of rounds
for practice and then seeing how many can pass the requisite
qualification test. Lets say we start with 50 rounds for practice
and we find that 25% of the cadets pass the test. Then we give a
new group of cadets 75 rounds and we find that 50% pass. 75%
pass at 100 rounds, 85% pass at 125 rounds, and then 95% pass
at 150 rounds. At 175 rounds we find again that only 85% are
passing, and when we increase it further to 200 rounds the
passage rate falls back down to 75%. Clearly we would want to
do some more investigation into the reasons behind the decline,
but suffice it to say that we reached our peak passage rate at
150 rounds. The point here is that in our example, we have one
optimum solution to our problem.
Other problems look less like a simple line moving up towards a
point and then declining and instead look more like a series of
several periods of optimization surrounded by periods of lower
optimization, which we might refer to as “Appalachian
Mountains” problems. In such rugged problem landscapes, we
might find that peaks in optimization are more localized than
universal, and so to venture from one peak to another may
require moving through a drop in optimization.
Take for example deciding which software program to use for
geospatial analysis – something with which we should all be
relatively familiar at this point. We might start out using

Google Earth because it is a very common program and uses an
intuitive user interface. It works great for some tasks, but over
time we realize we need to be able to access the program even
when we have no active internet connection and the type of
analysis we are conducting requires more security than that
available on the free version. So, we convince our boss to
purchase a Google Earth enterprise license, and have now
invested a little more to achieve a more optimum solution to the
problem set of conducting geospatial analysis.
After a while, though, we find that Google Earth as a platform
simply does not support the kinds of robust geospatial analysis
we need. In order to conduct more advance analysis, we have to
abandon Google Earth for ArcGIS, a substantially more
expensive piece of software. In the short term we are actually
electing to decrease the return on our investment for the
promise of reaching higher optimization for our solution. But
we (and our boss) decide it is worth the loss of the Google Earth
Enterprise license we paid for (not to mention the loss in time
required to learn a new program with a less user friendly
interface) in order to acquire the advantages of ArcGIS. Later
we find that with ArcGIS we need to purchase an extension that
allows us to conduct network analysis – more investment, but
with a higher return.
Over time, however, we find once again that we need an even
more robust geospatial analysis program, like ERDAS Imagine
that allows us to produce 3D cross-sectional renderings from
large imagery datasets. So once again we have to assess whether
it is worth it to accept a short-term loss in exchange for higher
optimization.
While these problems may complicate our analysis, requiring us
to increase our computations of payoffs and probabilities,
traditional rational analysis still applies because the landscape
itself is relatively stable. The rational analysis we traditionally
use is often called decision theory, in which we determine our

options, we identify the payoffs of each option, we compute the
probabilities of success for each option, and then we make a
rational choice to select the option with the highest payoff and
chances of success at the lowest cost.
Meaning, once we have figured out the texture of the problem,
we can determine what moves make sense even though they may
require a temporary allocation of resources away from
exploiting peaks in optimization and exploring the details of the
landscape. This tension represents the tension present in any
organization attempting to balance research and development
and profitability derived from execution.
Complex problems, however, are a different beast entirely. They
are neither Mt. Fuji nor Appalachian Mountains problem
landscapes because the interdependence of their diverse,
connected parts means that when one actor behaves in a certain
way, the entire landscape shifts, re-distributing the presence of
peaks and troughs in adaptive ways that cannot be determined
beforehand.
We refer to the landscape of complex problems as ‘dancing
landscapes’ because the connectedness and interdependence of
their parts mean they are susceptible to the large-scale events
that produce bottom-up emergent phenomena. Lets use al-Qaeda
as an example of a complex problem. Members of al-Qaeda
control almost nothing within the organization in themselves,
even at the leadership levels: Lines of direct communication are
diffused into layers of interpretive pathways.
Groups are often geographically isolated and more responsive to
local conditions that the conditions of the organization as a
whole. And yet, a single cell’s impetuousness, poor timing, or
lack of caution can re-write the security environment in which
the rest of the organization must operate. It is true that
behaviors within local cells of al-Qaeda produce macro-level

patterns that we might be able to identify as they emerge.
Because al-Qaeda is a complex social network, however, these
larger patterns cause the local cells in turn to adapt, creating
new patterns and undermining our ability to establish static
representations of the organization. This characteristic of small
movements in the system building toward massive shifts in the
entire landscape leads us to one of the key problems with
analyzing complex problems: they are non-linear.
Related to the concept of emergence, the second aspect we need
to address in complex systems is that of their unpredictability.
Unpredictability is merely the product of emergence, which
simply occurs because a complex system is more than the sum
of its parts. We can understand the behavior of emergence in a
simplified way by thinking of three shapes: a triangle made of
nine smaller triangles, a triangle made of nine smaller circles,
and a circle made of nine smaller circles.
The triangularity and the circularity of the larger shapes are not
dependent upon the presence of triangularity or circularity of
the smaller shapes that compose them. Emergence produces the
large-scale events that take place when elements in a system
interact toward a tipping point where small adjustments build
toward an overwhelming, unanticipated effect.
We might understand tipping points as the place in the system
where one or more of its key characteristics (diversity,
connectedness, interdependence, or adaptation) have either
increased too high or decreased too low. The adaptive
characteristic of complex systems notoriously masks signs of a
major problem until it is too late. All seems well until suddenly
and all at once, the system reaches a tipping point where
everything exponentially changes in a way that could not have
been predicted.

Given the aspects of nonlinearity and unpredictability that mean
al-Qaeda does not operate through static optimization and
produces large-scale, emergent events, what must our analysis
of it as an organization include?
First, we must develop methods for weighing diversity within
al-Qaeda as a system. Al-Qaeda sought to promote diversity
within its organization, but with too much diversity it risked
loosing its coherence (Abuza, 2003). To survive, al-Qaeda had
to seek a balance between ineffective simplicity and ineffective
chaos. We should come to recognize when processes are
mobilized to establish that balance or when the organization
suffers from the decay of groupthink.
Second, we must monitor what phenomenon incentives in the
system actually produce. The adaptive characteristic of complex
systems cause them to experiment mindlessly with every
possible combination and promote the selection of those that
actually work, regardless of how well they work according to an
organization’s dominant logic. Al-Qaeda invested heavily in
Iraq with the hope that successes there might promote its claim
as the defender of the Islamic world against Western
imperialism (Nasir, 2007). That investment, however, resulted
in the emergence of Abu Musa’b al-Zarqawi whose
uncontainable anti-Shi’a position forever concluded al-Qaeda’s
ability to partner with non-Sunni sects of Islam.
Finally, we must identify opportunities for al-Qaeda’s
improving its operations, but not in the way one might think.
Destructive cascade effects can occur if al-Qaeda attempts to
over-optimize its performance and lose the robustness provided
by self-organized adaptability. Because its organizational
mechanisms will respond reflexively to those threats, we may be
able to determine internal threats before its members do
themselves. While other approaches also merit exploring, these
serve as a strong start down a long road of recovering our

analytical capacity regarding the organization we have labeled
our chief enemy for more than a decade.
Al-Qaeda, like other complex problems, is not a simple system
that we can understand without accounting for key elements of
its complexity and their interactions, although from the bulk of
analysis about the organization, one would never know it.
Though we continually act as though we can gain enough
information about al-Qaeda to control it, we might be better off
turning our resources towards attempting to harness it.
The rational decision-making model does not account for the
behavior of other actors in the system - even though game
theory has attempted to address this shortcoming. Classic
decision theory translates complexity into uncertainty, orients
action around capitalizing on knowledge rather than increasing
it, and focuses on discrete outcomes.
Complex systems do not behave in ways that make these
approaches useful. To understand al-Qaeda, let alone to act
against it effectively, we must engage new mechanisms of
analysis that account for the reality of al-Qaeda rather than the
version of it that we would be most comfortable understanding.
Plenty of systems exhibit the characteristics of nonlinearity or
unpredictability without being qualified by the kind of
complexity we find when the two appear together. We may
know that the weight of a rock is its radius cubed, for instance,
so that if we double its size, its weight increases eight-fold,
demonstrating nonlinear predictability. On the other hand, the
quality of certain wines has often been found to have a linear
relationship with the average temperature of certain months in
the regions where the grapes for the wines are grown.
Since we cannot tell in advance exactly what the average
temperatures will be, we have an example of linearity without

predictability. As Heuer and Pherson discuss in Chapter 11
(2011), the intelligence community has generally taken four
approaches to complex systems.
The first manages future unpredictability by establishing
scenarios and indicators that enable analysts to track which of
several possibilities appear to be emerging over the horizon.
The second involves the use of computer models and
simulations. This approach used to be prohibitively costly in the
past because it required the development of proprietary software
or robust defense contracts to create the baseline models. With
advances in the computer sciences, software programs like
Netlogo provide analysts with substantially better access to the
advantages of computer-assisted data analysis of complex
systems.
Third, intelligence analysts attempt to predict the future based
on their best guesses, not always adequately informed by expert
judgment. Obviously this approach greatly increases the chances
of an intelligence failure due to complex systems’ nonlinearity
and unpredictability. Lastly, Heuer and Pherson introduce us to
the Complexity Manager as a structured analytic technique.
Social Network Analysis
Social networks are one of the types of complex systems with
which we are most familiar from our everyday lives, although
we probably rarely think about them consciously. Social
network analysis is a technique for understanding the influences
of relationships between actors on the movement of resources
within them. Social network analysis emerged from the fields of
sociology and cultural anthropology (Prell, 2012), and has been
heavily adapted for the field of intelligence analysis.
Its emphasis on the movement of resources is a key advantage
in its use as a model for intelligence analysis because it can be
used to track the movement of tangible goods and services, as
well as intangible information and ideas. By understanding how

the actors in a network interact, we can identify who controls
the flow of resources, who is central to the network’s ability to
operate, who links different parts of the network, and so on.
Your textbook reading for this week provides a fairly good
introduction to social network analysis. This portion of the
lecture supplements that information with some of the more
technical aspects. In the Lessons menu for this week you will
find an introductory text to social network methods by
Hanneman and Riddle (2005) that you may find useful in your
own work.
Behind a social network lies a binary matrix indicating the
presence connections between actors. If one actor knows
another actor, then a ‘1’ appears in the cell where they meet in
the matrix, otherwise a ‘0’ appears. In Table 1 we see two types
of matrices showing two types of relationship matrices.
Asymmetric matrices show the direction of relationships when
they are uneven. They might be used, for example, to show that
one actor contacts another but the ability to contact does not go
both ways. You can see in Table 1 that Marc has a relationship
with Xu because there is a ‘1’ where he meets Xu in the matrix.
But Xu does not have a relationship (of the same type being
recorded, in any case) with Marc because there is a ‘0’ where he
meets Marc in the matrix. Symmetric matrices are used to show
raw connections without showing the direction of that
relationship, either because a one-way relationship does not
exist in the network or because the social network analysis was
not constructed do conduct that kind of analysis.
The matrices used in social network analysis are often
translated into visual graphs using two primary elements: nodes
and ties.

In Figure 4 we can see a social network diagram of the
relationships between a people in a group produced by a
program called Netdraw, which is packaged with another
common program used for social network analysis in the social
sciences called UCINet (version 6.1 in this case). Each node is
labeled – with the name of each person, in this case – and has
lines connecting it to other nodes in the network. When we look
at the lines themselves we see that they are directional, meaning
that behind this diagram is an asymmetric matrix.
This graph also uses the symbology of the nodes to increase the
amount of information communicated by varying the nodes’
shape, size, and color according to attributes we stored behind
the data. Shape is used to show a person’s role in the
organization, whereby a circle represents an instructor and a
square represents a student. Color is used to represent gender,
whereby red represents a woman and blue represents a man.
Finally, size represents a measure of power in the network
called betweenness, which we will discuss in detail next.
Measures of Centrality
As we mentioned before, we use social networks to track how
resources move between nodes across the ties between nodes.
One of the ways we understand this resource management is
through measures of centrality, which offer insight into the
relative power each node has within the network. Measures of
centrality are quantitative values, meaning that they depend on
algorithms to count the relationships between nodes represented
by the ties – or, if we think in terms of the underlying matrices,
they use matrix algebra to weight the ‘1’s against the ‘0’s. We
can think of each tie as a pathway through which resources
travel, constrained or propelled by the structure of the network.
How we process those pathways depends on how we define
power, and we are usually interested in defining power several

different ways when we conduct our analysis. Though we have
many ways to think about power, we are going to cover three
definitions in this lecture: degree, betweenness, and
eigenvector.
Degree is probably the easiest for us to understand. The level of
degree someone has in a network determines how many options
they have. In visual terms, think of degree as being number of
ties extending from a node to other nodes. In mathematical
terms, degree is calculated by counting each node’s ties (or the
‘1’s in a matrix) and by ranking the values for each node. Under
this idea of power, someone with high degree has the ability to
access more people in the network than others, hence the
number of options they have for accessing resources.
This may (and we say ‘may’ because it really depends on what
we are looking for in a network) mean that a person has more
autonomy in the network and is less dependent on others,
making them more powerful than others with fewer options,
a.k.a., lower degree. When using directional data we can
distinguish between in degree and out degree. A high level of in
degree indicates a higher power to receive resources, while a
higher level of out degree indicates a higher power to
disseminate resources.
Betweenness is a measure of how many times a node must be
crossed when resources move from one part of the network to
another. Many networks contain clusters of relationships, which
we call cliques, where several nodes have many connections to
each other. Between clusters we have what we call gatekeepers,
which connect one cluster to another.
Gatekeepers have high betweenness because we keep running
into them when we count all the different ways we can move
from each node to every other node. In fact, running these
permutations is exactly what social networking software does

for us simply because in a very large network it is usually
impossible or infeasible for use to calculate betweenness by
hand. Based on their ability to control the communication or
transmission of resources between clusters, gatekeepers may be
considered powerful members of the network.
The last measure of centrality that we will discuss here is called
eigenvector. With degree, we might think of power as the
number of people a person knows. But with eigenvector, we
might think of power as the number of people another people
knows who knows other people. For example, lets say we have
are examining Person A and Person B. Person A is directly tied
to 100 people, and each of those people are tied to 1 other
person. Person B is directly tied to 10 people, but each of those
people are tied to 100 other people. Person A has the highest
degree because she can access 100 people, whereas Person B
has the lowest degree because he can only access 10 people.
However, Person B has the highest eigenvector because he can
access 1,000 people within only one step, while Person A has
the lowest eigenvector because she can only access 200 people
in one step. We might say that Person A “knows people”, while
Person B “knows people who know people”.
Does that mean Person B is more powerful than Person A? Well,
that depends on what we are looking for in the network and how
we define power for the purposes of our analysis. In this
example we calculated eigenvector by only considered one step,
but the measure of eigenvector is actually calculated by looking
at all steps within the network. For this reason, eigenvector is
one of the hardest measures of centrality for us to identify
purely by visually inspecting the network. Large networks
demand we use social network analysis software to the ‘heavy
lifting’ for us. Similar to degree, we can use directional data to
track out eigenvector and in eigenvector.
Figure 5 shows us the calculations for in degree, out degree, in

eigenvector, out eigenvector, and betweenness for the network
shown in Figure 4. If we wanted to analyze relative power
within this network, out degree and out eigenvector would not
be particularly helpful because everyone has the same measure
(0.176 and 4.243, respectively). Based on in degree, Pam and
Steve have the lot of power. Based on in eigenvector, Pam has a
relatively high measure of power. And based on betweenness,
Holly has a high measure of power. Who is the most powerful
person in the network? Again, it depends on what we are
looking for.
That being said, if we were considering power within the
network as a whole based on just this information, then we
would probably put our money on Pam because she shows up
twice (and once in an extremely high position). Of course, there
are many other measures of centrality that we may want to
calculate and examine before we labeled any one person as the
most powerful, and even then we would not be able to establish
that person’s criticality within the network without including
functionality in our analysis.
Functionality and Criticality
If centrality serves as the quantitative measure of power in a
network, then functionality serves as the qualitative measure of
power. In fact, calling it a ‘measure’ is already misleading
because there are no calculations for functionality that we can
call upon to automate this aspect of our analysis. Functionality
is heavily dictated by the data we are using to draw ties between
nodes, and it often requires a certain level of cultural expertise
to interpret.
For example, lets say in a network we have a man connected to
a woman who is his wife, and connected to another man who is
his brother. In our culture, a tie between a husband and wife
means something specific about their relationship and sharing

of resources: she is by default his next of kin, his legal medical
proxy, the inheritor of his wealth after his death, and the
custodian of their children.
In some cultures, that relationship exists not between a husband
and wife, but between a man and his brother, and especially so
if his brother is his older brother. Ties between nodes alone do
not represent this aspect of social network analysis, but we must
account for it if we are to determine a person’s criticality within
the network.
When we combine centrality and functionality, we can
determine centrality. Centrality refers to a person’s overall
importance, both in terms of different quantitative definitions of
power and different qualitative assessments as to the nature of
relationships within the network. By understanding a person’s
criticality, we can begin to draw some predictions about how
the network will behave if a person is removed or if a person’s
status changes.
Without criticality, we have few means to prioritizing our
targeting in the event that we want to destroy, disrupt, or
perhaps even coopt (remember our discussion about complex
systems and the advantages of harnessing them over attempting
to control them) the network.
References
Abuza, Z. (2003). Militant Islam in southeast Asia: Crucible of
terror. Boulder, CO: Lynne Rienner Publishers.
Hanneman, R.A., Riddle, M. (2005). Introduction to social
network methods. Riverside, CA: University of California-
Riverside.
Heuer, R.J., & Pherson, R.H. (2011). Structured analytic

techniques for intelligence analysis. Washington, DC: CQ Press.
Mitchell, M. (2009). Complexity: A guided tour. New York,
NY: Oxford University Press.
Nasr, V. (2007). The Shia revival: How conflicts within Islam
will shape the future. New York, NY: W.W. Norton &
Company.
Prell, C. (2012). Social network analysis: History, theory &
methodology. Thousand Oaks, CA: Sage Press.
All the Key Players
On Day 7 of last week you presented your argument to the
Tucson JTTF explaining your progress in your case against El
Movi and the process you used to achieve your results. You had
a chance to review their positions, as well, and after some
conferencing mediated by SA Franks, the SAC of the JTTF has
directed the DEA and the FBI to refrain from interfering with
your operation with the promise that when the case reaches a
point where the TPD are ready to bring down El Movi, you will
have the JTTF’s full support. Not a bad deal, all things
considered, but your work is not over, yet.
Situation
Encouraged by your success, Chief Heaton decides to confide in
you that during Week 3 when Daisy made her connection to El
Movi known, the Chief, in coordination with the Tucson District
Attorney’s office, secured wire taps on the telephones of the
brothel managers who had been contacting Daisy for the their
weekly grocery deliveries. The work you had done was
sufficient to acquire the wire tapping warrant, and now he
would like you to conduct your ‘magic’ on the resulting SIGINT
information. You have been able to process the SIGINT into a

representation of El Movi’s communications network that you
will use to interpret measures of centrality of the network’s
members. You have also been able to review the transcripts of
the conversations in order to glean some information about the
functionality of those relationships, which will help you
identify critical nodes in the network.
From what you have discerned, the network is composed of 17
active brothels, although the number was 18 until Julia and
Marco’s brothel was shut down in Week 1 (closely resembling
the information you originally received from Daisy). A man
referred to only as Angél remains the leader of El Movi. He
actively directs the network’s activities but stays away from the
actual brothels themselves. He appears to use the brothels as his
own source of income, with no identifiable connection to other
organizations. If anything, Angél might be accused of micro
managing, but he does not speak to the brothel managers
aggressively, indicating a high level of cooperation within the
network.
Each brothel has one man and one woman who manage it. Angél
prefers having a man and woman working together because the
men manage the brothels’ security and the women manage the
brothels’ victims. Only the men seem to use their ‘work phones’
to communicate with other members of the network, perhaps as
a security measure. When the men managers talk about the
women managers, the women are always referred to as “busy”,
but you cannot tell what that means since they all seem to
understand its meaning implicitly. The men brothel managers
are in constant communication with each other in order to
maximize their ability to transmit information between them
efficiently.
Based on their conversations, you gather that within half an
hour of the police showing up at Maria and Marco’s brothel
back in Week 1, every other member of the organization had

been informed about it. For his part, Angél seems to use their
relationships to keep tabs on his ‘employees’, with the idea
probably being that if one tries to cheat the organization or
become a police informant, the organization will find out about
it sooner rather than later, at least this is how the brother
managers interpret it.
Six of the brothel managers serve as Angél’s deputies. Four of
the managers – referred to only as Adam, Garrett, Sid, and
Chris – travel around to all the brothels once a month to collect
money, all in cash, and deliver it to Angél himself. Two of the
managers – referred to only as Jesse and Mike – are in charge of
meeting the trucks used to transport victims to the brothels. You
suspect these two are in contact with their Mexican counterparts
to arrange for the “deliveries”, but the warrant for wire-tapping
does not appear to cover whatever means of communication they
are using.
While none of the brothel managers seem to be of low
intelligence, Angél’s deputies seem particularly articulate. You
wonder if maybe Angél recruits his managers specifically
because they are not the type of people you would suspect might
be running a human trafficking ring if they were your
neighbors.
To validate whether what you are seeing in the functionality of
these relationships translates into these individuals criticality
within the network, you will need to include in your analysis as
assessment of their centrality. The communication network as a
whole consists of 293 nodes – way too many for you to analyze
without narrowing them down. Most of the nodes seem to
represent casual connections not directly involved in El Movi
but bumping into their communications network by coincidence:
plumbers, telemarketers, local pharmacies – all the common
phone calls you would expect to see but that have nothing to do
with your investigation.

After dropping all the outlying nodes, you have substantially
fewer nodes to analyze. Further examination indicates most of
these are clients of the brothels or other facilitators of the
business. While of interest, these may not be as useful for
bringing the network down as a whole, and the District Attorney
has already made it quite clear to you that she has no intention
of pursuing prosecution against all those people. So, you have
to prioritize where you want to focus your efforts. Because
directionality is not important at this stage, you only focus on
degree, eigenvector, and betweenness. And this is what you
have found:
case studies/2.1.pdf
Management Information Systems 13e
KENNETH C. LAUDON AND JANE P. LAUDON
continued
Systems
CHAPTER 2 GLOBAL E-BUSINESS: HOW BUSINESSES
USE INFORMATION SYSTEMS
CASE 1 Walmart’s Retail Link Supply Chain
SUMMARY An introduction to Walmart’s Retail Link system,
one of the largest B2B supply-chain systems
in the world. Retail Link connects consumer purchase data to
the Walmart purchasing
system and to vendor supply systems. Retail Link plays a key
role in Walmart’s corporate

strategy to become the dominant low-cost provider of retail
goods. L=7:13.
URL http://www.youtube.com/watch?v=SUe-tSabKag
CASE Walmart is a well-known leader in the application of
network technology to coordinate
its supply chain. Walmart’s supply chain is the secret sauce
behind its claim of offering
the lowest prices everyday. It’s able to make this promise
because it has possibly the most
efficient B2B supply chain in the world. It doesn’t hurt to also
be the largest purchaser of
consumer goods in the world. With sales of more than $443
billion for the fiscal year ending
January 31, 2012, Walmart has been able to use information
technology to achieve a decisive
cost advantage over competitors. As you might imagine, the
world’s largest retailer also has
the world’s largest supply chain, with more than 60,000
suppliers worldwide. In the next
five years, the company plans to expand from around 5,000
retail stores in the United States
(including Sam’s Clubs) to over 5,500 and increase its selection
of goods. Internationally,
Walmart has over 5,200 additional stores in 26 countries outside
the United States, giving it
http://www.youtube.com/watch?v=SUe-tSabKag
Chapter 2, Case 1 Walmart’s retail link supply Chain 2
continued
a total of over 10,000 retail units. The rapid expansion in

Walmart’s international operations
will require an even more capable private industrial network
than what is now in place.
In the late 1980s, Walmart developed the beginnings of
collaborative commerce using an
Electronic Data Interchange (EDI)-based supply chain
management system that required its
large suppliers to use Walmart’s proprietary EDI network to
respond to orders from Walmart
purchasing managers. In 1991, Walmart expanded the
capabilities of its EDI-based network
by introducing Retail Link. This system connected Walmart’s
largest suppliers to Walmart’s
own inventory management system, and it required large
suppliers to track actual sales
by stores and to replenish supplies as dictated by demand and
following rules imposed by
Walmart. Walmart also introduced financial payment systems
that ensure that Walmart does
not own the goods until they arrive and are shelved.
In 1997, Walmart moved Retail Link to an extranet that allowed
suppliers to directly link
over the Internet into Walmart’s inventory management system.
In 2000, Walmart hired an
outside firm to upgrade Retail Link from being a supply chain
management tool toward a
more collaborative forecasting, planning, and replenishment
system. Using demand aggre-
gation software provided by Atlas Metaprise Software, Walmart
purchasing agents can now
aggregate demand from Walmart’s 5,000 separate stores in the
United States into a single
RFQ from suppliers. This gives Walmart tremendous clout with
even the largest suppliers.

In addition, suppliers can now immediately access information
on inventories, purchase
orders, invoice status, and sales forecasts, based on 104 weeks
of online, real-time, item-
level data. The system does not require smaller supplier firms to
adopt expensive EDI soft-
ware solutions. Instead, they can use standard browsers and PCs
loaded with free software
from Walmart. There are now over 20,000 suppliers—small and
large—participating in
Walmart’s Retail Link network.
By 2012, Walmart’s B2B supply chain management system had
mastered on a global scale
the following capabilities: cross docking, demand planning,
forecasting, inventory manage-
ment, strategic sourcing, and distribution management. The
future of Walmart’s SCM lies
in business analytics—working smarter—rather than simply
making the movement and
tracking of goods more efficient. For instance, in 2012 Walmart
purhased Quintiq Inc., a
supply chain management tool for improving load assignment
and dispatch of trucks for
large retailers. Quintiq’s software will enable Walmart’s
managers to optimize the loading of
its trucks and to reduce the time required to supply its retail
stores.
Despite the economic slowdown in 2011–2012, Walmart’s sales
grew. In 2011, Walmart’s reve-
nues of $443 billion were up 6.4 percent from 2010, and its net
income was $15.77 billion, up
from $15.36 billion. In the first half of 2012, sales continued to
grow by over 4 percent.

Chapter 2, Case 1 Walmart’s retail link supply Chain 3
1. Where does Walmart’s supply chain start? What triggers
Walmart’s Retail Link system to
ship goods to local Walmart Stores?
2. Why is a detailed knowledge of consumer purchases at each
store important to
Walmart’s success?
3. Why can’t other large retailers easily duplicate Walmart’s
Retail Link?
4. Why does Walmart encourage its vendors to learn how to use
Retail Link?
VIDEO CASE
Q U E S T I O N S
COPYRIGHT NOTICE
Copyright © 2013 Kenneth Laudon.
This work is protected by United States copyright laws and is
provided solely for the use of instructors
in teaching their courses and assessing student learning.
Dissemination or sale of any part of this work
(including on the World Wide Web) will destroy the integrity of
the work and is not permitted. The work
and materials from this site should not be made available to
students except by instructors using the
accompanying text in their classes. All recipients of this work
are expected to abide by these restrictions and
to honor the intended pedagogical purposes and the needs of
other instructors who rely on these materials.

case studies/3.pdf
continued
Systems
CHAPTER 3 INFORMATION SYSTEMS, ORGANIZATIONS,
AND STRATEGY
CASE 1 NBA: Competing on Global Delivery With Akamai OS
Streaming
SUMMARY The NBA uses Akamai’s global streaming video
service to reach customers and strategic
partners in Asia, Europe, the Middle East, Africa and North
America with high quality video
streams of NBA rich media content and programs.
URL
http://www.akamai.com/html/customers/testimonials/nba.html
NOTE The Akamai video is a high-quality video that requires a
broadband connection of greater
than 5 Mbps. The video plays best at connection speeds of
greater than 15 Mbps (cable
or FIOS ISP speeds). If you have trouble playing it on a Mozilla
browser (Firefox), switch to
Internet Explorer. Also, if you let it play through once, the
second playback will be smoother
because some of the content is cached on local servers and your

computer. Alternatively,
find a campus or corporate network which has the requisite
bandwidth.
CASE The National Basketball Association (NBA) is the
leading professional basketball league
in the United States and Canada with 30 teams. The NBA is one
of four North American
professional sports leagues. The other leagues are the Major
League Baseball, the National
Football League, and the National Hockey League. While
focused on the North America, the
NBA has a large international following and is televised in 212
countries and 42 languages
around the world.
http://www.akamai.com/html/customers/testimonials/nba.html
Chapter 3, Case 1 NBa: CompetiNg oN gloBal Delivery With
akamai os streamiNg 2
continued
Increasingly, fans want and expect high quality game videos,
RSS feeds, widgets, and
Fantasy leagues. NBA.com has an inventory of over 400,000
digital assets, including 15,000
videos. Last year, there were over 850 unique visits to
NBA.com from 20 countries.
Akamai Technologies, Inc. (NASDAQ: AKAM) is a company
that provides a distributed
computing platform for global Internet content and application
delivery. Akamai is head-
quartered in Cambridge, Massachusetts. The company was

founded in 1998 by MIT gradu-
ate student Daniel Lewin, along with MIT Applied Mathematics
professor Tom Leighton and
MIT Sloan School of Management students Jonathan Seelig and
Preetish Nijhawan. Leighton
still serves as Akamai’s Chief Scientist, while Lewin was killed
aboard American Airlines
flight 11 which was crashed in the September 11 attacks of
2001. Akamai is a Hawaiian word
meaning smart or intelligent.
Akamai’s primary service is provided by its proprietary
EdgeNetwork. Akamai transparently
mirrors content —sometimes all content, including HTML and
CSS, and sometimes just
media objects such as audio, graphics, animation, and video—
from customer servers. Large
firms deliver their content to over 95,000 Akamai servers in 70
countries. These local Akamai
servers cache (store) this content awaiting local demand.
Akamai’s network is intelligent
enough not to distribute content to a local server until and
unless there is local demand.
AKAMAI’S GLOBAL CONTENT DISTRIBUTION SYSTEM
continued
When you click on an online video at NBA.com, the domain
name is the same, but the IP
address points to an Akamai server rather than the NBA server.

The Akamai server is auto-
matically picked depending on the type of content and the
user’s network location.
Akamai’s EdgePlatform is one of the world’s largest distributed
computing platforms. The
benefit is that users can receive content from whichever Akamai
server is closest to them or
has a good connection, leading to faster download times and
less vulnerability to network
congestion or outages. The Internet was never designed to
handle large volumes of video
simultaneously streaming from a single corporate server to all
Internet devices. However,
this content can be sent to the “edge” of the network where
Akamai servers are located,
and on a local or regional basis, stream this content on demand
from local servers. Akamai’s
continued
40,000 distributed servers allow it to monitor global Internet
traffic patterns, attacks on the
Internet, and latency (delays caused by excessive Internet
traffic).
In addition to image caching, Akamai provides services which
accelerate dynamic and
personalized content and streaming media. Akamai’s
personalization product is called
EdgeScape, a geolocation service. Much Web content delivered

by Akamai is personalized
to the user’s location and Internet service types. This allows
Akamai’s customers to gain
insight into where end users are coming from and what kind of
Internet service they are
using. Armed with this knowledge they can customize Web
content for individual end users
through a wide range of criteria, making their site more relevant
and compelling to every-
one who visits.
For instance, Akamai knows your:
Internet service provider: Verizon_Trademark_Services_LLC
Country Code: US
Region Code: NY
City: NEWYORK
Area code: 212
Latitude: 40.7128
Longitude: 74.0092
County: NEWYORK
Time zone: EST
Network: verizon
Throughput: vhigh
Akamai Stream OS is another service that runs on Akamai’s
EdgePlatform. It enables the NBA
to get more from its media by providing a simple, automated
solution for managing more
than 45,000 media assets, assigning business policies, and
publishing content to multiple
distribution channels.
● Since implementing Akamai Stream OS, NBA.com’s traffic
has increased exponen-
tially, with over 35M unique users in 222 countries accessing

NBA Web content each
mont.
● Akamai’s suite of products has helped the NBA reach record
traffic levels, with over
35M unique global users per month, while effectively
maintaining employment and
infrastructure costs.
● The reach and stability of Akamai’s network have allowed
the NBA to grow advertis-
ing revenues by 500 percent since 2001.
Resources: NBA.com; Akamai.com.
1. Using Porter’s competitive forces model, analyze the NBA’s
market situation. How does
the use of Akamai help the NBA compete in this market?
2. Using Porter’s generic strategies model, what do you think is
the NBA’s overall strategy or
strategies?
3. Why is it important that all fans in the world have the same
experience?
4. Why is it important that individual franchise owners can
build, manage, and distribute on
the NBA platform their own content?
5. The word “partnership” appears several times in the video.

Who are the NBA’s partners?
How does the concept of a strategic ecosystem apply to the
NBA’s partnership strate-
gies?
VIDEO CASE
Q U E S T I O N S
COPYRIGHT NOTICE
case studies/4.pdf
continued
Systems
CHAPTER 4 ETHICAL, SOCIAL, AND POLITICAL ISSUES
IN E-COMMERCE

CASE 3 Data Mining for Terrorists and Innocents
SUMMARY A look at how monitoring patterns of behavior
online can be construed as subversive
behavior, and how errors in interpreting the results can lead the
innocent to jail. L=5:10.
URL http://www.youtube.com/watch?v=4lKpD7MC22I
CASE Anti-terrorism agencies around the world have made
effective use of new surveillance tech-
nologies that offer unprecedented abilities to identify and
apprehend potential terrorists.
Today’s terrorists are by nature difficult to track, as
disconnected groups of individuals can
use the Internet to communicate their plans with lower chance
of detection. Anti-terrorist
technology has evolved to better handle this new type of threat.
But there are drawbacks to these new strategies. Often, innocent
people may find their
privacy compromised or completely eliminated as a result of
inaccurate information.
Surveillance technologies are constantly improving. While this
makes it more difficult for
terrorists and other criminals to exchange information, it also
jeopardizes our privacy, on the
Internet and elsewhere, going forward. Is this reason for worry?
Are comparisons to Orwell’s
1984 appropriate or overblown?
This video displays both the positive and negative results of
new advances in technology.
The first segment describes a program called the Dark Web
Project being developed by a

team at the University of Tucson that combs the Internet in
search of militant leaders and
their followers. However, the difficulty with this is that most
communication is done via
pseudonyms or completely anonymously. The program creates
author profiles based on
http://www.youtube.com/watch?v=4lKpD7MC22I
Chapter 4, Case 3 Data Mining for terrorists anD innoCents 2
continued
word length, punctuation, syntax, and content, and displays
information about the person-
ality type of an individual graphically.
The plotting of information on the graph represents whether the
user is violent or militant,
inexperienced and seeking advice, or an opinion leader holding
sway over many more
people. The project is of great interest to intelligence agencies
worldwide, who would like
incorporate it into their arsenal of terrorist surveillance
technologies.
It’s unclear if this project infringes on freedom of speech and
individual privacy. On the one
hand, detection of a potential terrorist is an important method of
deterring future terror-
ist attacks. On the other hand, individuals who haven’t done or
said anything wrong may
be profiled and have their private conversations exposed. An
additional concern is how to
distinguish what kinds of speech are grounds for surveillance.

The second segment of the video describes the plight of a
German sociology professor,
Andrej Holm, subjected to jail time and 24-hour surveillance
thanks to his supposed associa-
tion with a terror cell. Holm has written extensively on
gentrification, or the gap between
the rich and the poor. A radical group repeated some of his
themes in a letter claiming
responsibility for terror attacks arson of police vehicles. Police
also found that Holm had
spoken to one of the terrorists twice before. Local law
enforcement jailed him for three
weeks and subjected him to constant surveillance afterwards.
But Holm claims that he is a victim of unfortunate
circumstances, and the courts agreed,
ruling that his imprisonment was illegal. Holm’s phones were
tapped and his Internet usage
recorded, and while he’s been acquitted, he has no assurance
that the surveillance has
stopped.
Chapter 4, Case 3 Data Mining for terrorists anD innoCents 3
1. Does the Tucson data-mining project inappropriately violate
the privacy of Internet
users, or is it an acceptable tradeoff to more intelligently
combat terrorism? Explain your
answer.
2. Were the local police justified in their handling of Holm?
Why or why not? For whichever
view you take, briefly describe the opposing viewpoint.

3. What other issues dealing with data and privacy have you
encountered on the Web?
4. Review the chapter-ending case in Chapter 6 on the FBI
terror watch list. What themes
do the two cases have in common? How are they different?
5. What is meant by the “Dark Web”?
VIDEO CASE
Q U E S T I O N S
COPYRIGHT NOTICE
case studies/5.1.docx
aaaaaaaaaaaaaaaaaaaaaaaaaaaaaaa
aaaaaaaaaaaaaaaaa
1. What business and social problems does data center power

consumption cause?
2. What solutions are available for these problems? Are they
management, organizational, or technology solutions? Explain
your answer.
3. What are the business benefits and costs of these solutions?
4. Should all firms move toward green computing? Why or why
not?
1. What business benefits do cloud computing services provide?
What problems do they solve?
2. What are the disadvantages of cloud computing?
3. How do the concepts of capacity planning, scalability, and
TCO apply to this case? Apply these concepts both to Amazon
and to subscribers of its services.
4. What kinds of businesses are most likely to benefit from
using cloud computing? Why?
case studies/5.pdf

continued
Systems
CHAPTER 5 IT INFRASTRUCTURE AND EMERGING
TECHNOLOGIES
CASE 2 Salesforce.com: SFA on the iPhone and iPod Touch
SUMMARY Salesforce.com develops a mobile sales force
management application for the iPhone
using Apple’s iPhone software development platform (SDK). A
striking, contemporary
example of the emerging digital platform where most computing
will take place by 2015.
L= 4:27.
URL http://www.youtube.com/watch?v=fwo2VbDA1Io
CASE Salesforce.com is a leading provider of software-as-a-
service applications delivered over the
Internet. The SaaS model is a departure from traditional
software bought and installed on
machines locally. Salesforce.com has been very successful and
has shaken up the software
industry with its innovative business model. More information
on Salesforce.com is available in
Chapter 5 of the text.
Apple is another company known for creative, disruptive new
technologies and business models.
One of its most recent products, the iPhone, represents this kind

of technology. Apple opened
up its iPhone platform to third-party developers shortly after
releasing it. They provide what’s
known as the iPhone SDK (software development kit) to
developers free of charge.
This iPhone is very different from other smartphones because of
its third-party development
environment. Third-party applications make the iPhone unique,
and the iPhone is an example
of an open development platform being beneficial to the parent
company. Salesforce.com is
just one of many companies hoping to take advantage of the
iPhone platform to broaden the
market for its applications.
http://www.youtube.com/watch?v=fwo2VbDA1Io
Chapter 5, Case 2 salesforCe.Com: sfa on the iphone and ipod
touCh 2
This video is a demonstration of a Salesforce SFA application,
just one of Salesforce’s 63,000
platform applications. It allows users to manager contracts,
accounts, and leads: “what they’re
selling and who they’re selling it to.” The sophistication of the
iPhone SDK is evident through-
out the video, as the demonstration displays the use of the touch
screen and other advanced
functionality.
Other unique features of the iPhone SDK described in the video
are the capacity to store data
on the iPhone itself, so that connectivity to the Internet isn’t
required to use the application;

the ability to sort deals based on a variety of different metrics,
including progress, dollar
amount, and name; and the ability of the SDK to “speak to the
force.com API,” or, in other
words, to interface with applications via the Internet to provide
real-time updates (such as
incoming deals).
For Apple, Salesforce.com’s partnership means that
Salesforce.com account holders will be
more likely to buy an iPhone to use their applications; for
Salesforce, the iPhone represents a
large, lucrative market to which they can market their
applications. Salesforce itself uses an open
development platform to generate new applications, and other
companies are following suit.
1. What are some examples of “disruptive” products created by
Apple? How disruptive of a
product is the iPhone and why?
2. Describe some of the unique ways Salesforce’s SFA
application uses the iPhone’s features,
including at least one not mentioned above.
3. What other companies or Web sites that you know of have
open development platforms?
4. What advantages does the SFA application have for
salespeople? If you were a salesperson,
how would you use it?
5. Who benefits more from the partnership between the two
companies, Salesforce.com or
Apple? Explain your answer.

VIDEO CASE
Q U E S T I O N S
COPYRIGHT NOTICE
Read the interactive session and then answer the questions
below. A link to this interactive session in the etext is available
in Assignment Resources on the right.
1. What is the value of the CPSC database to consumers,
businesses, and the U.S. government?
2. What problems are raised by this database? Why is it so
controversial? Why is data quality an issue?
3. Name two entities in the CPSC database and describe some of
their attributes.

4. When buying a crib, or other consumer product for your
family, would you use this database? Why or why not?
Read the interactive session and then answer the questions
below. A link to this interactive session in the etext is available
in Assignment Resources on the right.
1. Describe the kinds of big data collected by the organizations
described in this case.
2. List and describe the business intelligence technologies
described in this case.
3. Why did the companies described in this case need to
maintain and analyze big data? What business benefits did they
obtain?
4. Identify three decisions that were improved by using big
data.
5. What kinds of organizations are most likely to need big data
management and analytical tools? Why?

Read the case study and then answer the questions below. A link
to this case study in your etext is available in Assignment
Resources on the right.
1. Why would a customer database be so useful for the
companies described in this case? What would happen if these
companies had not kept their customer data in databases?
2. How did better data management and analytics improve each
company’s business performance? Give examples of two
decisions that were improved by mining these customer
databases.
3. Are there any ethical issues raised by mining customer
databases? Explain your answer.
case studies/6.pdf
continued
Systems
CHAPTER 6 FOUNDATIONS OF BUSINESS
INTELLIGENCE: DATABASES AND INFORMATION
MANAGEMENT
CASE 2 Data Warehousing at REI: Understanding the Customer
SUMMARY REI uses IBM data warehousing technology to
achieve its vision of understanding what its
customers want, and how they want to interact with the

company. L= 4:39.
URL http://www.youtube.com/watch?v=4KEkA3O784s
CASE REI (Recreational Equipment Inc.) is an American
consumers’ cooperative that sells outdoor
recreation gear and sporting goods via the Internet, catalogs,
and over 120 stores in 29
states. It opens six to eight new stores each year. REI’s sales
exceeded $1.8 billion in 2011. Its
major competitors in the U.S. include many other sporting
goods retailers. REI is the largest
consumer co-op in the United States.
A consumer’s cooperative is a business owned by its customers
for their mutual benefit.
The goal of a co-op is to sell quality goods and services at the
lowest cost to its consumers,
as opposed to the traditional model of selling goods and
services at the highest cost that
consumers are willing to pay. However, a co-op is no different
from traditional firms in the
following regard: databases and information management still
play key roles in efficiency
and strong customer service and are critical to the company’s
bottom line.
There is a one-time fee of $20 for lifetime membership to REI.
The company normally pays
an annual dividend check to its members equal to 10% of what
they spent at REI on regular-
priced merchandise in the prior year, although this is not
guaranteed. The refund, which
expires on December 31 two years from the date of issue, can be
used as credit for further

http://www.youtube.com/watch?v=4KEkA3O784s
Chapter 6, Case 2 Data Warehousing at rei: unDerstanDing the
Customer 2
purchases or taken as cash or check between July 1 and
December 31 of the year that the
dividend is valid.
In order to better understand its three million active co-op
members and customers, REI
launched an initiative to build a data warehouse containing
many different types of informa-
tion about its customers. To better serve its customers, REI
needs to know precisely what
they do in the outdoors. With the help of IBM, REI hopes its
data warehouse will allow them
to identify and organize all of the ways that each customer
interacts with the company,
including what they bought online or retail outlets, whether they
attended special training
sessions for equipment and outdoor activities, or items they
returned.
REI also deployed IBM’s DB2 9 Viper technology to run REI’s
marketing campaigns. REI’s site
will recognize its members when they log in, allowing them to
serve up content customized
towards individual users.
1. What is a data warehouse and why is REI building one?
2. What are some of the disadvantages of consumer cooperatives
compared to ‘traditional’

firms?
3. Describe some of the marketing strategies that REI’s data
warehouse will allow it to use.
Would these have been possible before the data warehouse was
built?
4. What are some of the risks or concerns surrounding the
creation of a data warehouse?
5. Why do you think REI chose to work with IBM’s data
warehouse technology?
VIDEO CASE
Q U E S T I O N S
COPYRIGHT NOTICE

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