Running Head: W3 Case Studies W3 Case Studies2 <> University of the Cumberland’s ITS-531-09 Business Intelligence Professor: <> 8th Nov 2019 Table of Contents Introduction3 Application case3 1. What do you think about data mining and its implications concerning privacy? What is the threshold between knowledge discovery and privacy infringement?3 2. Did Target go too far? Did they do anything illegal? What do you think they should have done? What do you think they should do now (quit these types of practices)?5 Coors Brewers’ Case Study5 Application case for End of the chapters.5 1. Why is beer flavor important to Coors' profitability?5 2. What is the objective of the neural network used at Coors?6 3. Why were the Results of Coors’ Neural Network Initially Poor, And What Was Done to Improve the Results?6 4. What benefits might Coors derive if this project is successful?6 5. What Modifications Would You Make to Improve the Results of Beer Flavor Prediction?7 Outline of Modification8 Target Case StudyIntroduction Target’s case study aims at analyzing the concept of data collection, mining and its use to predict a customer’s buying behavior and patterns. The firm obtains crucial customer data by assigning each customer a unique guest identification number that is used to track her buying patterns. This case study brings out an important issue; the threshold between data mining and privacy infringement as clearly depicted in the teenager’s case.Application case1. What do you think about data mining and its implications concerning privacy? What is the threshold between knowledge discovery and privacy infringement? As highlighted in Target’s case study, privacy is a significant concern in data mining for business purposes. Apart from the data collection technique employed by Target, there exist other data mining applications such as social media and mobile services accessed via the internet that are substantially adopted by individuals in their daily life. The lack of proper privacy protection plan in the process poses a severe threat to individuals. In most instances, the storage and processing of such mined data are usually outsourced to third-party data centers based on the cloud. The privacy concern presents a tremendous obstacle to the full exploitation of the benefits of huge data assets (Analysis and Design of Secured Privacy Data Mining Environment, 2015). Therefore, there is a need to investigate privacy issues in data mining to minimize cases of customer rights infringement. Privacy is essential to everyone. It allows individuals to decide whether to share any information in question or not. It implicates the supreme sanctity of individual autonomy, and it’s an essential value in any society as it allows people to be individuals. Loss of privacy can be equated with a loss of some traits of humanity. Therefore, the breach of confidentiality by data mining results in a feeling of embarrassment by the offended party. In the .

1. Running Head: W3 Case Studies
W3 Case Studies2
<>
University of the Cumberland’s
ITS-531-09 Business Intelligence
Professor: <>
8th Nov 2019
Table of Contents
Introduction3
Application case3
1. What do you think about data mining and its implications
concerning privacy? What is the threshold between knowledge
discovery and privacy infringement?3
2. Did Target go too far? Did they do anything illegal? What do
you think they should have done? What do you think they
should do now (quit these types of practices)?5
Coors Brewers’ Case Study5
Application case for End of the chapters.5
1. Why is beer flavor important to Coors' profitability?5
2. What is the objective of the neural network used at Coors?6
3. Why were the Results of Coors’ Neural Network Initially
Poor, And What Was Done to Improve the Results?6
4. What benefits might Coors derive if this project is
successful?6

2. 5. What Modifications Would You Make to Improve the Results
of Beer Flavor Prediction?7
Outline of Modification8
Target Case StudyIntroduction
Target’s case study aims at analyzing the concept of data
collection, mining and its use to predict a customer’s buying
behavior and patterns. The firm obtains crucial customer data
by assigning each customer a unique guest identification
number that is used to track her buying patterns. This case study
brings out an important issue; the threshold between data
mining and privacy infringement as clearly depicted in the
teenager’s case.Application case1. What do you think about data
mining and its implications concerning privacy? What is the
threshold between knowledge discovery and privacy
infringement?
As highlighted in Target’s case study, privacy is a significant
concern in data mining for business purposes. Apart from the
data collection technique employed by Target, there exist other
data mining applications such as social media and mobile
services accessed via the internet that are substantially adopted
by individuals in their daily life. The lack of proper privacy
protection plan in the process poses a severe threat to
individuals. In most instances, the storage and processing of
such mined data are usually outsourced to third-party data
centers based on the cloud. The privacy concern presents a
tremendous obstacle to the full exploitation of the benefits of
huge data assets (Analysis and Design of Secured Privacy Data
Mining Environment, 2015). Therefore, there is a need to
investigate privacy issues in data mining to minimize cases of
customer rights infringement.
Privacy is essential to everyone. It allows individuals to decide
whether to share any information in question or not. It
implicates the supreme sanctity of individual autonomy, and it’s

3. an essential value in any society as it allows people to be
individuals. Loss of privacy can be equated with a loss of some
traits of humanity. Therefore, the breach of confidentiality by
data mining results in a feeling of embarrassment by the
offended party. In the case study, the teenage girl is most likely
to feel embarrassed because she intended to keep the pregnancy
to herself for some time before letting her father know.
Generally, privacy invasion makes one vulnerable to all manner
of attacks. To maintain the individual’s autonomy and promote
a cohesive existence in the society, a boundary between data
mining and privacy must be struck.
The threshold between Knowledge Discovery and Privacy
Infringement
The data mining for knowledge discovery purposes should only
be limited to general information. When sensitive personal
information is involved, consent from the individual whose
privacy is to be invaded should be sought. Also, assurance of
privacy preservation and protection should be guaranteed. Such
personal information includes; identification, demographic,
financial, and health record data. To obtain such information,
the conditions stipulated above should be met and adhered to by
the data-mining firm.
The mining of general data such as purchase history (without
necessarily noting the client’s name), preference of a particular
brand, and general views on the product quality and opinion can
be undertaken without many conditions because they don’t
affect an individual’s privacy significantly. In our case with
Target, the matter of privacy infringement is not coming out
clearly because the firm relied on general purchase statistics as
a source of their data. What they should have done is to obtain
the teenager’s consent on email messaging.
In instances where consent is granted, privacy preservation
should be a matter of great interest. The firms can preserve the
privacy of sensitive data by using techniques such as;
randomization where noise is added to cover the sensitive data
records, k-anonymity model, and I-diversity. The two

4. techniques maintain the privacy of the individuals while serving
the same purpose of delivering the required results (Analysis
and Design of Secured Privacy Data Mining Environment,
2015).
2. Did Target go too far? Did they do anything illegal? What do
you think they should have done? What do you think they
should do now (quit these types of practices)?
As discussed above, the matter of privacy infringement by
Target is not coming out clearly. One may argue that they used
general purchase history, but on the other hand, it is
questionable whether they obtained the teenager's consent
before sending her emails. In my opinion, they did nothing
illegal; they did a good thing by following up on the lady's
pregnancy while advising her on the required purchases. It’s
only questionable if the lady had consented to such
communication.
They need to conduct their follow-ups differently. They should
confirm the customer’s willingness to share sensitive
communication with them and through which channels. The
follow-ups are helpful to the clients, especially in cases of first-
time pregnancies where the ladies don’t know what to purchase
and when. Coors Brewers’ Case Study
Coors brewers, a British brewing giant, delved into a study to
understand beer flavors based on their chemical composition.
Such information is vital to Coors in coming up with better
flavour’s that suit the customers’ expectations.Application case
for End of the chapters.1. Why is beer flavor important to
Coors' profitability?
The beer industry solely depends on the customer’s tastes and
preferences. These (tastes and preferences) are mainly based on
the beer flavor. Therefore, coming up with a wide range of
flavors assure Coors of an increased market share by giving
customers a wide variety to choose from. The consumer
preference decisions on beer are guided by the sweetness and
dislike for sourness. This illustrates the fundamental role played
by flavor on the beer consumption and justifies Coor’s need for

5. creativity to come up with a variety of brands ("The Use of
Flavors in Beer and Malt Beverages: A Brief Introduction",
2019). 2. What is the objective of the neural network used at
Coors?
The neural network employed was mainly used to obtain the
link between inputs and outputs. This was achieved through the
exhibition of several blends of required input/output recipes
(Sharda, Delen & Turban, n.d.). 3. Why were the Results of
Coors’ Neural Network Initially Poor, And What Was Done to
Improve the Results?
The first neural network yielded substandard results due to two
factors. First, it was unable to extract vital relationships from
the data due to the concentration on one product’s quality. This
led to a small discrepancy in the data. Second, the functioning
of the neural network was influenced by the “noise” generated
by inputs that did not affect the flavor at all. This is because
only a particular subgroup of the provided inputs influenced the
designated beer flavor (Sharda, Delen & Turban, n.d.).
A genetic algorithm was introduced to resolve the challenge.
The algorithm could control the various input modifications in
reaction to the inaccurate term from the neural network. The
algorithm minimized the error term, and later, the switch
configuration would recognize the analytical inputs that were
probable to forecast the required flavor (Sharda, Delen &
Turban, n.d.).4. What benefits might Coors derive if this project
is successful?
If it becomes effective, Coors will enjoy an increased market
share derived from the portfolio diversification. With beer
flavors that fit any occasion, venue, and mood, their beers will
be the most demanded brands. Ultimately, this translates into an
increase in turnover.
5. What Modifications Would You Make to Improve the Results
of Beer Flavor Prediction?
Although the analytical method proved successful, modification
on the technique by increasing the number of flavor-causal

6. analytics deliberated in the study, in addition to that, flavor
active materials and substantial contributors should be taken
into account in the complete sensory outline (Sharda, Delen &
Turban, n.d.). Besides, the development of sensors that can
figure out how to implement diverse fermentation scenarios and
using AI solutions to measure the aromas and flavors created by
the ingredients will substantially improve the beer quality. Such
AI solutions include machine algorithms. The above
modifications can be later fine-tuned to come up with a robust
software that efficiently predicts and differentiates between
different flavors (Laursen & Thorlund, 2017). The schematic
diagram below outlines the modification process;
Outline of Modification
Figure 1: Outline of modification
References
Analysis and Design of Secured Privacy Data Mining

7. Environment. (2015). International Journal of Science and
Research (IJSR), 4(11), pp.149-152.
Laursen, G., & Thorlund, J. (2017). Business analytics for
managers. Hoboken, New Jersey: John Wiley & Sons, Inc.
Sharda, R., Delen, D., & Turban, E. Business intelligence,
analytics, and data science.
The Use of Flavors in Beer and Malt Beverages: A Brief
Introduction. (2019). Technical Quarterly. doi: 10.1094/tq-56-3-
0811-01
Vázquez-Araújo, L., Parker, D. and Woods, E. (2013).
Comparison of Temporal-Sensory Methods for Beer Flavor
Evaluation. Journal of Sensory Studies, 28(5), pp.387-395.
Increase flavor-causal analytes(Analytical method)
Modification of currennt analytical technique
Introduce AI solutions(Machine Algorithms) that measure
flavors and aroma
Introducing advanced AI soltions will greatly improve the
current progress. Microsoft AI can be utilised.
Fine tune and develop software that differentiates flavors
instantly.
This will amplify the current progress with surprising levels of
efficiency.

8. Note: - Must require--------
APA format (Times New Roman, size 12 and 2 space)
MS Visio diagram OR MS Word Smart Art
Minimum 3 or more References including Sharda(Below)
W5: Case Studies
Graded Assignment: Case Studies - (Follow all steps below)
Carefully review and read both case studies found in your
textbook from Pages 433 and 465-467
Sharda, R., Delen, D., & Turban, E. (2015) Business
intelligence and analytics: Systems for decision support (10th
ed.). Boston: Pearson.
Digital: ISBN-13: 978-0-13-340193-6 or Print: ISBN-13: 978-0-
13-305090-5
When concluding the paper, expand your analytical and critical
thinking skills to develop ideas as a process or operation of
steps visually represented in a flow diagram or any other type of
created illustration to support your idea which can be used as a
proposal to the entity or organization in the cases to correct or
improve any case related issues addressed. This is required for
both cases.
When developing illustrations to support a process or operation
of steps, Microsoft Word has a tool known as “Smart Art”
which is ideal for the development of these types of illustrations
or diagrams. To get acquainted with this tool, everyone can
visit www.youtube.com using a keyword search “Microsoft
Word Smart Art Tutorials” to find many video demonstrations
in using this tool.
QUESTIONS FOR THE END-OF-CHAPTER from Page# 433
APPLICATION CASE

9. 1. What were the main challenges encountered by CARE
International before they created their warehouse prepositioning
model?
2. How does the objective function relate to the organization's
need to improve relief services to affected areas?
3. Conduct online research and suggest at least three other
applications or types of software that could handle the
magnitude of variable and constraints CARE International used
in their MIP model.
4. Elaborate on some benefits CARE International stands to gain
from implementing their pre-positioning model on a large scale
in future.
Screen Shot
QUESTIONS FOR THE END-OF-CHAPTER (Page NO#465-
467)
APPLICATION CASE
1. Describe the problem that a large company such as HP might
face in offering many product lines and options.
2. Why is there a possible conflict between marketing and
operations?
3. Summarize your understanding of the models and the
algorithms.
4. Perform an online search to find more details of the
algorithms.
5. Why would there be a need for such a system in an
organization?
6. What benefits did HP derive from implementation of the
models?

10. Conclusion
Reference info. Minimum 3 or more.
End-of-Chapter Application Case
Pre-Positioning of Emergency Items for CARE International
Problem
CARE International is a humanitarian organization that provides
relief aid to areas that are affected by natural disasters such as
earthquakes and hurricanes. The organization has relief
programs in over 65 countries worldwide. Just like other

11. humanitarian organizations, CARE International faces
challenges in offering the needed help to affected areas in the
event of natural disasters. In the event of a disaster, CARE
International identifies suppliers that could provide the needed
relief items. Arrangements are then made regarding the
acquisition of warehouses to transport the items. With respect to
the transportation of the items, a third-party company transports
the items by air to the affected country from where they are
further transported by road to CARE International’s warehouse
and distribution center. This mode of response to disasters
could be slow, not to mention the unreliability of the
transportation network used. Hitherto, CARE has preferred
purchasing relief items from local suppliers since they are
closer to the disaster areas and, also, it helps reinvigorate the
local economy after a disaster. However, in the wake of a
disaster, there are always issues with availability, price, and
quality of needed items.
Specifically, CARE International’s challenges are twofold as
identified by the authors of the research. First, the organization
wanted the ability to gather supplies and relief items from both
local and international suppliers in an agile manner so they
could better serve people affected by disasters. Second, once the
supplies are mobilized, they wanted to be able to effectively
distribute them in the most timely and cost-efficient manner to
affected regions.
Methodology/
Solution
In collaboration with Georgia Institute of Technology, CARE
developed a model in which relief items were placed in a pre-

12. positioned network to serve as a complement to the existing
mode of supplying relief items to disaster areas. Using a mixed-
integer programming (MIP) inventory-location model, a pre-
positioning network was designed based on two main factors.
The first factor was up-front investment related to initial
stocking of inventory and warehouse setup. The second factor
was related to the average response time it takes to get relief
items to affected regions. Basically, the main concern was to
determine a configuration that would allow for the least
response time given an up-front investment value. Demand data
for the model was based on historical records of previous
operations. Supply data was estimated hypothetically since
historical data was not present. It was assumed that any supplier
would be able to ship relief items within 2 weeks. The model
for warehouse establishment was built based on 12 locations
CARE considered as low or no-cost, as well as seven relief
items necessary for most disaster relief operations. The object
function was to reduce the total response time in moving items
to affected areas. The capacity constraints employed were the
number of warehouses to maintain and the amount of items to
keep in them. The MIP model consisted of 470,000 variables
and 56,000 constraints. It took the ILOG OPL Studio with
CPLEX solver application about 4 hours to produce an
optimal solution.
Results/Benefits

13. The main purpose of the model was to increase the capacity and
swiftness to respond to sudden natural disasters like
earthquakes, as opposed to other slow-occurring ones like
famine. Based on up-front cost, the model is able to provide the
best optimized configuration of where to locate a warehouse and
how much inventory should be kept. It is able to provide an
optimization result based on estimates of frequency, location,
and level of potential demand that is generated by the model.
Based on this model, CARE has established three warehouses in
the warehouse pre-positioning system in Dubai, Panama, and
Cambodia. In fact, during the Haiti earthquake crises in 2010,
water purification kits were supplied to the victims from the
Panama warehouse. In the future, the pre-positioning network is
expected to be expanded.
Questions for the End-of-Chapter Application Case
1. What were the main challenges encountered by CARE
International before they created their warehouse pre-
positioning model?
2. How does the objective function relate to the organization’s
need to improve relief services to affected areas?
3. Conduct online research and suggest at least three other
applications or types of software that could handle the
magnitude of variable and constraints CARE International used
in their MIP model.
4. Elaborate on some benefits CARE International stands to gain

14. from implementing their pre-positioning model on a large scale
in future.

15. End-of-Chapter Application Case
HP Applies Management Science Modeling to Optimize Its
Supply Chain and Wins a Major Award
HP’s groundbreaking use of operations research not only
enabled the high-tech giant to successfully transform its product
portfolio program and return $500 million to the bottom line
over a 3-year period, but it also earned HP the coveted 2009
Edelman Award from INFORMS for outstanding achievement in
operations research. “This is not the success of just one person
or one team,” said Kathy Chou, vice president of Worldwide
Commercial Sales at HP, in accepting the award on behalf of the
winning team. “It’s the success of many people across HP who
made this a reality, beginning several years ago with
mathematics and imagination and what it might do for HP.”
To put HP’s product portfolio problem into perspective,
consider these numbers: HP generates more than $135 billion
annually from customers in 170 countries by offering tens of
thousands of products supported by the largest supply chain in
the industry. You want variety? How about 2,000 laser printers
and more than 20,000 enterprise servers and storage products?
Want more? HP offers more than 8 million configure-to-order
combinations in its notebook and desktop product line alone.
The something-for-everyone approach drives sales, but at what
cost? At what point does the price of designing, manufacturing,
and introducing yet another new product, feature, or option

16. exceed the additional revenue it is likely to generate? Just as
important, what are the costs associated with too much or too
little inventory for such a product, not to mention additional
supply chain complexity, and how does all of that impact
customer satisfaction? According to Chou, HP didn’t have good
answers to any of those questions before the Edelman award–
winning work.
“While revenue grew year over year, our profits were eroded
due to unplanned operational costs,” Chou said in HP’s formal
Edelman presentation. “As product variety grew, our forecasting
accuracy suffered, and we ended up with excesses of some
products and shortages of others. Our suppliers suffered due to
our inventory issues and product design changes. I can
personally testify to the pain our customers experienced because
of these availability challenges.” Chou would know. In her role
as VP of Worldwide Commercial Sales, she’s “responsible and
on the hook” for driving sales, margins, and operational
efficiency.
Constantly growing product variety to meet increasing customer
needs was the HP way—after all, the company is nothing if not
innovative—but the rising costs and inefficiency associated
with managing millions of products and configurations “took
their toll,” Chou said, “and we had no idea how to solve it.”
Compounding the problem, Chou added, was HP’s
“organizational divide.” Marketing and sales always wanted

17. more—more SKUs, more features, more configurations—and for
good reason. Providing every possible product choice was
considered an obvious way to satisfy more customers and
generate more sales.
Supply chain managers, however, always wanted less. Less to
forecast, less inventory, and less complexity to manage. “The
drivers (on the supply chain side) were cost control,” Chou said.
“Supply chain wanted fast and predictable order cycle times.
With no fact-based, data-driven tools, decision making between
different parts of the organization was time-consuming and
complex due to these differing goals and objectives.”
By 2004, HP’s average order cycle times in North America were
nearly twice that of its competition, making it tough for the
company to be competitive despite its large variety of products.
Extensive variety, once considered a plus, had become a
liability.
It was then that the Edelman prize–winning team—drawn from
various quarters both within the organization (HP Business
Groups, HP Labs, and HP Strategic Planning and Modeling) and
out (individuals from a handful of consultancies and
universities) and armed with operations research thinking and
methodology—went to work on the problem. Over the next few
years, the team: (1) produced an analytically driven process for
evaluating new products for introduction, (2) created a tool for
prioritizing existing products in a portfolio, and (3) developed

18. an algorithm that solves the problem many times faster than
previous technologies, thereby advancing the theory and
practice of network optimization.
The team tackled the product variety problem from two angles:
prelaunch and postlaunch. “Before we bring a new product,
feature, or option to market, we want to evaluate return on
investment in order to drive the right investment decisions and
maximize profits,” Chou said. To do that, HP’s Strategic
Planning and Modeling Team (SPaM) developed “complexity
return on investment screening calculators” that took into
account downstream impacts across the HP product line and
supply chain that were never properly accounted for before.
Once a product is launched, variety product management shifts
from screening to managing a product portfolio as sales data
become available. To do that, the Edelman award–winning team
developed a tool called revenue coverage optimization (RCO) to
analyze more systematically the importance of each new feature
or option in the context of the overall portfolio.
The RCO algorithm and the complexity ROI calculators helped
HP improve its operational focus on key products, while
simultaneously reducing the complexity of its product offerings
for customers. For example, HP implemented the RCO
algorithm to rank its Personal Systems Group offerings based on
the interrelationship between products and orders. It then
identified the “core offering,” which is composed of the most

19. critical products in each region. This core offering represented
about 30 percent of the ranked product portfolio. All other
products were classified as HP’s “extended offering.”
Based on these findings, HP adjusted its service level for each
class of products. Core offering products are now stocked in
higher inventory levels and are made available with shorter lead
times, and extended offering products are offered with longer
lead times and are either stocked at lower levels or not at all.
The net result: lower costs, higher margins, and improved
customer service.
The RCO software algorithm was developed as part of HP Labs’
“analytics” theme, which applies mathematics and scientific
methodologies to help decision making and create better-run
businesses. Analytics is one of eight major research themes of
HP Labs, which last year refocused its efforts to address the
most complex challenges facing technology customers in the
next decade.
“Smart application of analytics is becoming increasingly
important to businesses, especially in the areas of operational
efficiency, risk management, and resource planning,” says Jaap
Suermondt, director, Business Optimization Lab, HP Labs. “The
RCO algorithm is a fantastic example of an innovation that
helps drive efficiency with our businesses and our customers.”
In accepting the Edelman Award, Chou emphasized not only the
company-wide effort in developing elegant technical solutions

20. to incredibly complex problems, but also the buy-in and
cooperation of managers and C-level executives and the wisdom
and insight of the award-winning team to engage and share their
vision with those managers and executives. “For some of you
who have not been a part of a very large organization like HP,
this might sound strange, but it required tenacity and skill to
bring about major changes in the processes of a company of
HP’s size,” Chou said. “In many of our business [units], project
managers took the tools and turned them into new processes and
programs that fundamentally changed the way HP manages its
product portfolios and bridged the organizational divide.”
Questions for the End-of-Chapter Application Case
1. Describe the problem that a large company such as HP might
face in offering many product lines and options.
2. Why is there a possible conflict between marketing and
operations?
3. Summarize your understanding of the models and the
algorithms.
4. Perform an online search to find more details of the
algorithms.
5. Why would there be a need for such a system in an
organization?
6. What benefits did HP derive from implementation of the
models?