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Chapter 1
Introduction
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The Nature of Management Science
• Management science is a discipline that attempts to aid
managerial decision making by applying a scientific
approach to managerial problems that involve quantitative
factors.
• The traditional name given to the discipline (and the one
that still is widely used today outside of business schools)
is operations research. This name was applied because
the teams of scientists in World War II were doing
research on how to manage military operations.
• Management science aids managerial decision making.
Business analysts employing management science don’t
make managerial decisions. Managers do.
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What is Business Analytics?
• Business analytics can be defined as the art and the
science of transforming data into insight for making better
business decisions.
• Business analytics has grown in prominence over the past
couple decades largely because we have entered the era
of big data where massive amounts of data (accompanied
by massive amounts of computational power) are now
commonly available help guide managerial decision
making.
• A primary focus of business analytics is on how to make
the most effective use of all these data.
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Three Categories of Analytics
1
Descriptive Analytics.
• Uses innovative techniques (including algorithms) to explore
the data, locate and extract the data that are relevant, and
then identify the interesting patterns and summary data.
• A key tool of descriptive analytics is data visualization.
After exploring the data to identify the insights, the goal of
data visualization is then to communicate those insights
through the careful selection of the most effective visual
graphics.
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Three Categories of Analytics
2
Predictive Analytics.
• Involves applying statistical models to predict future events
or trends.
• Includes classification and prediction models (Chapter 3),
forecasting models (Chapter 4), and computer simulation
models (Chapters 14 and 15).
Prescriptive Analytics.
• Involves applying decision models to the data to prescribe
what should be done in the future.
• The powerful optimization models and techniques of
managements science are commonly used here.
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The Role of Data Science
• Data science is an interdisciplinary field that uses
scientific methods, processes, algorithms, and systems to
extract knowledge or insights from even massive amounts
of data in various forms.
• Although business analytics also is interdisciplinary and
uses scientific methods, the important differences in these
definitions are that data science is more interdisciplinary,
more based on scientific methods, and more applicable to
various areas in beyond business.
• Highly trained practitioners of data science frequently are
given the title of data scientists.
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The Role of Machine Learning
The goal of machine learning is to allow computers to learn
automatically from historical relationships and trends in the
data in order to do such things as making data-driven
predictions.
A few of the applications of machine learning include:
• self-driving cars.
• practical speech recognition.
• effective web search.
• image recognition.
• medical diagnosis.
• a vastly improved understanding of the human genome.
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The Role of Artificial Intelligence
• The goal of artificial intelligence (AI) is to build intelligent
computer programs and machines that can simulate
human thinking capability and behavior.
• Machine learning (ML) and artificial intelligence (AI) are
closely related technologies. The terms often are used
interchangeably, but ML actually is just one important part
of AI.
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The Relationship Between Management
Science and Business Analytics
• The most important relationship between management science
and business analytics is that they complement each other
extremely well.
• Someone who once was a management science specialist now
needs to be well trained in business analytics as well, and vice
versa.
• Management science became an established discipline in the
middle of the 20th century and has been active ever since with
further developing and extending its techniques. Business
analytics only became well established starting in the early
years of the 21st century.
• Business schools now need to train future business analysts
who have a solid foundation in the traditional disciplines of both
management science and business analytics.
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Impact Outside of the Business World
1
Sports fans have learned that sports analytics have
substantially changed the strategies used throughout the
sports world, including baseball, American football, and
basketball.
Analytics also has been making important contributions in
such areas as.
• political campaigns.
• healthcare.
• combating crime.
• personal financial analysis.
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Impact Outside of the Business World
2
Management Science has made important contributions in
such areas as.
• Healthcare.
• combating climate change.
• increasing the productivity of crops.
• developing military strategy.
• contributions to criminology.
• many more.
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A Case Study: VRX Company Advertising
Budget
• VRX is a small tech company that specializes in virtual
reality headsets. Its most popular model is the VRX2000.
• VRX relies heavily on advertising to continually drive the
sales of its products.
• The VRX2000 has had a variety of different advertising
budgets in previous quarters. Not surprisingly, management
has noted a strong relationship between advertising and
sales—when they advertise more, they sell more.
• Question: At what point does increasing the advertising
budget no longer pay for itself?
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Some Data for the VRX2000
1
Revenue.
• The VRX is sold directly to consumers for $400.
Fixed Cost.
• Fixed costs are incurred regardless of how many units are
produced.
• These might include a prorated share of the salaries for
upper management, capital equipment, property taxes,
and more.
• VRX estimates these fixed costs to be $100,000 per
quarter.
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Some Data for the VRX2000
2
Variable Cost.
• Variable costs include all of the costs that are proportional
to the number of units produced.
• These would include the cost of the raw materials and
labor required to assemble each unit, and any other costs
that are incurred for each additional unit. VRX has
estimated the mean variable cost of producing each VR
X2000 to be $295.
Marketing Cost.
• VRX can choose whatever advertising budget for the VR
X2000 they would like. It is neither a fixed cost nor a
variable cost, but rather a decision to be made.
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Historical Advertising and Sales Data for
the VRX2000
Advertising Budget
Sales
Q4
$60,000
2,118
Q1
$120,000
2,998
Q2
$20,000
1,376
Q3
$80,000
2,296
Q4
$200,000
3,454
Q1
$100,000
2,622
Q2
$0
399
Q3
$40,000
1,808
Q4
$160,000
3,206
• The data seems to suggest that quarters with higher advertising budgets also
had higher sales.
• But how large is the effect, and what is the exact nature of the relationship
between advertising and sales?
• A bunch of numbers in a table can only reveal so much.
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Performing Descriptive Analytics to
Explore the Data
• Descriptive analytics needs to be performed to explore the
data to better understand the relationship between
advertising and sales.
• One main technique of descriptive analytics is called data
visualization. The goal of data visualization is to improve
the communication of numerical information.
• A relatively straightforward visual graphic called a scatter
plot is all that is needed for this study.
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A Scatter Plot (Sales versus Advertising)
Key insights.
• It is now very obvious that higher advertising budgets have led to higher sales.
• The rate of the increase in sales seems to tail off somewhat for higher
advertising budgets (there appear to be diminishing returns from advertising).
Access the text alternative for slide images.
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Performing Predictive Analytics to Predict
the Impact of Advertising
• VRX will need to be able to predict how sales will vary as a
function of the advertising level.
• The key technique for doing this is causal forecasting
with regression.
• The with regression part of the name of the technique
refers to the generation of a trendline in the scatter plot,
where each point on the trendline shows the predicted
sales for the corresponding value of the advertising
budget.
• The trendline can be either a straight line (for linear
regression) or a smooth curve (for nonlinear
regression).
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Linear Regression
• Linear regression in this case involves approximating the
relationship between the sales (the dependent variable) and the
advertising budget (the independent variable) by a straight
trendline.
• In general, the equation for the linear regression trendline has
the form
y = ax + b.
Where
y = Estimated value of the dependent variable.
a = Slope of the linear regression trendline.
x = Value of the independent variable.
b = Intercept of the linear regression trendline with the y-axis.
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Linear Trendline
Sales ≈ 0.0139 (Advertising Budget) + 1,046.3
Access the text alternative for slide images.
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Polynomial Trendline
Adding an x 2 term helps capture the diminishing returns.
Access the text alternative for slide images.
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Sixth-Order Polynomial Trendline
• A sixth-order polynomial fits the data nearly perfectly.
• Is that good?
Access the text alternative for slide images.
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Overfitting the Data
• It may appear that the sixth-order polynomial equation does a
nearly perfect job. The trendline nearly “perfectly fits the data.”
• We don’t want a trendline that is so tied to the data that it shows
a strange shape that contradicts what we know about the effect
on sales of increasing the advertising level (always increasing
but with diminishing returns from advertising).
• What we have here is an extreme example of overfitting the
data (making a predictive model less accurate when using new
data sets by having the model align too closely to the given data
instead of taking into account the inherent idiosyncrasies in the
given data due to randomness).
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The Square-Root Effect
• Through years of experience at VRX, the marketing
manager has developed a rule of thumb for the
relationship between sales and advertising. In particular,
there tends to be a square-root effect.
• Sales do not increase proportionally with the level of
advertising, but rather with the square root of advertising.
• The square-root effect would suggest using nonlinear
regression to look for a curving trendline of the form
Sales  a Advertising  b.
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Sales versus the Square Root of
Advertising Trendline
Sales  7.01 Advertising  400
Access the text alternative for slide images.
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A Comparison of the Various Trendlines
Access the text alternative for slide images.
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Performing Prescriptive Analytics to
Determine the Best Advertising Level
• With a prediction model now developed to predict sales for
any given advertising level (the square root equation), the
analytics study team now is ready to turn to prescriptive
analytics to determine the best amount to spend on
advertising the VRX2000 during the next quarter.
• A spreadsheet model is formulated to estimate the profit
for any chosen advertising budget.
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VRX 2000 Spreadsheet Model
Access the text alternative for slide images.
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Profit vs. Advertising Level (Trial and
Error)
Advertising Budget
Predicted Sales
Total Profit
400
−$57,967
$25,000
1,508
$33,391
$50,000
1,968
$56,588
$75,000
2,320
$68,571
$100,000
2,617
$74,749
$125,000
2,878
$77,217
$150,000
3,115
$77,051
$175,000
3,332
$74,887
$200,000
3,535
$71,143
$0
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Excel’s Solver Dialog Box
• Excel’s Solver will find the
value of decision variable
cells (in this case Advertising
Budget, or C9) that will
maximize the value of an
objective cell (in this case
Total Profit, or C22).
• This is an example of what is
called optimization (finding
the best solution for a
decision model).
Access the text alternative for slide images.
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The Optimal Solution After Running Solver
Access the text alternative for slide images.
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The Impact of Management Science and
Business Analytics
1
Organization
Area of Application
Section
Annual Savings
IBM
Reduce downtime for over 840.000 servers
1.2
$1 billion
Intel
Optimization of product design and supply chains
1.3
$5 billion more profit
General Motors
Numerous applications
1.5
Not estimated
Vungle
Apply predictive analytics to mobile advertising
2.5
$12 million more revenue
Continental and
United
Reassign crews to flights when schedule disruptions
occur
2.6
$40 million
Ingram Micro
Data-driven marketing campaigns
3.1
$350 million more revenue
CSAV
Various forecasting methods 4.6 $81 million
Swift & Company
Improve sales and manufacturing performance
5.1
$12 million
Samsung
Electronics
Reduce manufacturing times and inventory levels
5.7
$200 million more revenue
INDEVAL
Settle all securities transactions in Mexico
6.2
$150 million
Chevron
Optimize refinery operations
6.4
Nearly $1 billion
Taylor
Communications
Assign print jobs to printers
6.6
$10 million
Welch's
Optimize use and movement of raw materials
7.3
$150,000
Hewlett-Packard
Product portfolio management
9.1
$180 million
CSX Transportation
Allocate empty railcars to customers
9.1
$51 million
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The Impact of Management Science and
Business Analytics
2
Organization
Area of Application
Section
Annual Savings
Norwegian companies
Maximize flow of natural gas through offshore pipeline
network
9.3
$140 million
Swedish Forest
Industry
Optimize the routes for transport services
9.4
$40-120 million
Waste Management
Develop a route-management system for trash
collection and disposal
10.2
$100 million
MISO
Administer the transmission of electricity in 13 states
10.3
$700 million
Netherlands Railways
Optimize operation of a railway network
10.5
$105 million
Vattenfall
Optimize the design of offshore wind farms
10.6
$175 million more NPV
Bank Hapoalim Group
Develop a decision-support system for investment
advisors
11.3
$31 million mote revenue
DHL
Optimize the use of marketing resources
11.5
$260 million
CDC
Eradicate polio
12.7
$1.5 billion
General Motors
Improve the throughput of its production lines
13.5
$150 million
Syngenta
Increase the productivity of crops
14.1
$57 million
FAA
Manage air traffic flows in severe weather
14.2
$200 million
Sasol
Improve the efficiency of its production processes
14.3
$23 million
Merrill Lynch
Pricing analysis for providing financial services
15.4
$50 million more revenue
Kroger
Pharmacy inventory management
15.8
$10 million
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Examples in the Area of Operations
Management
1
Location
Type of Application
Sec. 5.1 onward
A case study: What is the most profitable mix of products?
Case 5-1
Which mix of car models should be produced?
Case 5-2
Which mix of ingredients should go into the casserole in a university cafeteria?
Case 5-3
Which mix of customer-service agents should be hired to staff a call center?
Sec. 6.3
Personnel scheduling of customer-service agents
Sec. 6.5
Minimize the cost of shipping a product from factories to customers
Sec. 6.6
Optimize the assignment of personnel to tasks
Case 6-1
How should a product be shipped to market?
Case 6-3
Which mix of women's clothing should be produced for next season?
Cases 6-5, 8-4. 10-3
Develop a plan for assigning students to schools so as to minimize busing costs
Case 6-6
Which mixes of solid waste materials should be amalgamated into different grades of a salable
product?
Case 6-7
How should qualified managers be assigned to new R&D projects?
Case 8-2
Develop and analyze a steel company's plan for pollution abatement
Case 8-3
Plan the mix of livestock and crops on a farm with unpredictable weather
Sec. 9.1
Minimize the cost of operating a distribution network
Sees. 9-2, 9-3
A case study: Maximize the flow of goods through a distribution network
Sec. 9.4
Find the shortest path from an origin to a destination
Case 9-1
Logistical planning for a military campaign
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Examples in the Area of Operations
Management
2
Location
Type of Application
Case 9-3
Develop the most profitable flight schedules for an airline
Cases 9-4, 10-4
Operate and expand a private computer network
Sec. 10.3
Choose the best combination of R&D projects to pursue
Sec. 10.4
Select the best sites for emergency services facilities
Sec. 10.5
Airline crew scheduling
Sec. 10.6
Production planning when setup costs are involved
Case 10-2
Make inventory decisions for a retailer's warehouse
Sec. 11.4
Production planning when overtime is needed
Sec. 11.6
Find the shortest route to visit all the American League ballparks
Sec. 13.2
Many examples of commercial service systems, internal service systems, and transportation
service systems that can be analyzed with queueing models
Sec. 13.4 onward
A case study: An analysis of competing proposals for more quickly providing maintenance services
to customers
Cases 13-2.14-2
Analysis of proposals for reducing in-process inventory
Sec. 14.1
Comparison of whether corrective maintenance or preventive maintenance is better
Sees. 14.2t 14.3
A case study: Would it be profitable for the owner of a small business to add an associate?
Case 14-1
Analysis of proposals for relieving a production bottleneck
Sec. 15.1
A case study: How much of a perishable product should be added to a retailer’s inventory?
Sec. 15.3
Plan a complex project to ensure a strong likelihood of meeting the project deadline
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Examples in the Area of Finance
1
Location
Type of Application
Supplement to Chap 1
Break-even analysis
Case 1-1
Break-even analysis and what-if analysis
Sec. 2.1 onward
A case study: A bank evaluates applications for unsecured loans
Sec. 6-2
An airline choosing which airplanes to purchase
Sec. 6.2
Capital budgeting of real-estate development projects
Case 6-2
Develop a schedule for investing in a company's computer equipment
Sec. 7.1 onward
A case study: Develop a financial plan for meeting future cash flow needs
Case 7-1
Develop an investment and cash flow plan for a pension fund
Sec. 9.4
Minimize the cost of car ownership
Case 9-2
Find the most cost-effective method of converting various foreign currencies into
dollars
Sec. 10.2
A case study: Determine the most profitable combination of investments
Case 10-1
Develop an investment plan for purchasing art
Sec. 11.3
Portfolio selection that balances expected return and risk
Sec. 11.6
Select a portfolio to beat the market as frequently as possible
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Examples in the Area of Finance
2
Location
Type of Application
Case 11-2
Determine an optimal investment portfolio of stocks
Case 11-3
Develop a long-range plan to purchase and sell international bonds
Sec. 12.1 onward
A case study: Choose whether to drill for oil or sell the land instead
Case 12-1
Choose a strategy for the game show, "Who Wants to Be a Millionaire?"
Sec. 14.1
Analysis of a new gambling game
Sec. 15.2
Choose the bid to submit in a competitive bidding process
Sec. 15.4
Develop a financial plan when future cash flows are somewhat unpredictable
Sec. 15.5
Risk analysis when assessing financial investments
Sec. 15.6
How much overbooking should be done in the travel industry?
Case 15-1
Analysis of how a company's cash flows might evolve over the next year
Case 1 5-2
Calculate the value of a European call option
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Examples in the Area of Marketing
Location
Type of Application
Sec. 1.4
A case study: Optimize the amount of advertising for products
Sec. 3.1 onward
A case study: Predicting the future behavior of prospective customers
Sec. 3.6
Affinity analysis and recommendation systems
Case 4-1
Improve forecasts of demand for a call center
Sec. 4.2 onward
A case study: Manage a call center for marketing goods over the telephone
Sees. 5.7, 6.3
Determine the best mix of advertising media
Case 5-1
Evaluate whether an advertising campaign would be worthwhile
Sees. 6.1,6.4
A case study: Which advertising plan best achieves managerial goals?
Case 6-4
Develop a representative marketing survey
Case 8-1
Analysis of the trade-off between advertising costs and the resulting increase in sales of several products
Sec. 9.4
Balance the speed of bringing a new product to market and the associated costs
Sec. 11.4
Deal with nonlinear marketing costs
Case 11-1
Refine the advertising plan developed in the case study presented in Sections 6.1 and 6.4
Case 12-2
Should a company immediately launch a new product or test-market it first?
Case 12-3
Should a company buy additional marketing research before deciding whether to launch a new product?
Case 12-4
Plan a sequence of decisions for a possible new product
Case 13-1
Estimate customer waiting times for calling into a call center
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Because learning changes everything.
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© McGraw Hill LLC. All rights reserved. No reproduction or distribution without the prior written consent of McGraw Hill LLC.