# Stakeholder Presentation Final Project The stakeholder presentation serves as the bridge between analytics and business decision making. The presentation p

Stakeholder Presentation Final Project The stakeholder presentation serves as the bridge between analytics and business decision making. The presentation provides you with the framework to integrate the course content through a real life example.

Choose an organization that you are passionate about. Consider how this organization is performing. Are there areas for improvement? Even the most well-run organizationsexperience problems.

Now imagine for a moment that your team has been recently hired as analytic consultants for this organization. Brainstorm and come up with 2-3 ideas that that organization may be facing currently. It is your team’s job to investigate why things are happening. For example: Why are sales declining or why do our products sell more during poor weather?

Choose the most appealing idea, and use the template below to answer that question for your organization.

Using the provided template, analyze the problem and present your findings. You are expected to integrate relevant models and concepts from assigned readings in your analysis, along with using logic and insights/skills from previous classes and personal experiences. The presentation should be at least 15 minutes and should not exceed 2 minutes. Your team should use PowerPoint slides to support your presentation.

You should provide sufficient information to capture all the components of the Business Analytics Process. A presentation must include a statement of and background description of the problem; identification of data sources; synopsis of data preparation methodologies used and detailed discussion of the data preparation steps taken; description of and reasoning for the modeling techniques used in analysis; appropriate and detailed visualization of results; explanation of conclusions, recommendations, and predictions drawn from each of the three types of business analytics: descriptive, predictive and prescriptive. (Business) Analytics is the use of:

data,

information technology,

statistical analysis,

quantitative methods, and

mathematical or computer-based models

to help managers gain improved insight about

their business operations and make better, factbased decisions.

Pricing

◦ setting prices for consumer and industrial goods, government

contracts, and maintenance contracts

Customer segmentation

◦ identifying and targeting key customer groups in retail, insurance,

and credit card industries

Merchandising

◦ determining brands to buy, quantities, and allocations

Location

◦ finding the best location for bank branches and ATMs, or where to

service industrial equipment

Social Media

◦ understand trends and customer perceptions; assist marketing

managers and product designers

Business intelligence

Information Systems

Statistics

Operations research/Management science

Decision support systems

Benefits

◦ …reduced costs, better risk management, faster

decisions, better productivity and enhanced bottom-line

performance such as profitability and customer

satisfaction.

Challenges

◦ …lack of understanding of how to use analytics,

competing business priorities, insufficient analytical skills,

difficulty in getting good data and sharing information,

and not understanding the benefits versus perceived

costs of analytics studies.

Descriptive analytics: the use of data to

understand past and current business

performance and make informed decisions

Predictive analytics: predict the future by

examining historical data, detecting patterns or

relationships in these data, and then extrapolating

these relationships forward in time.

Prescriptive analytics: identify the best

alternatives to minimize or maximize some

objective

Database queries and analysis

Dashboards to report key performance measures

Data visualization

Statistical methods

Spreadsheets and predictive models

Scenario and “what-if” analyses

Simulation

Forecasting

Data and text mining

Optimization

Social media, web, and text analytics

Most department stores clear seasonal inventory

by reducing prices.

Key question: When to reduce the price and by

how much to maximize revenue?

Potential applications of analytics:

Descriptive analytics: examine historical data for similar

products (prices, units sold, advertising, …)

Predictive analytics: predict sales based on price

Prescriptive analytics: find the best sets of pricing and

advertising to maximize sales revenue

IBM Cognos Express

◦ An integrated business intelligence and planning solution

designed to meet the needs of midsize companies,

provides reporting, analysis, dashboard, scorecard,

planning, budgeting and forecasting capabilities.

SAS Analytics

◦ Predictive modeling and data mining, visualization,

forecasting, optimization and model management,

statistical analysis, text analytics, and more.

Tableau Software

◦ Simple drag and drop tools for visualizing data from

spreadsheets and other databases.

Data: numerical or textual facts and figures that

are collected through some type of measurement

process.

Information: result of analyzing data; that is,

extracting meaning from data to support

evaluation and decision making.

Annual reports

Accounting audits

Financial profitability analysis

Economic trends

Marketing research

Operations management performance

Human resource measurements

Web behavior

page views, visitor’s country, time of view, length of time, origin

and destination paths, products they searched for and viewed,

products purchased, what reviews they read, and many others.

Data set – a collection of data.

◦ Examples: Marketing survey responses, a table of

historical stock prices, and a collection of measurements

of dimensions of a manufactured item.

Database – a collection of related files containing

records on people, places, or things.

◦ A database file is usually organized in a two-dimensional

table, where the columns correspond to each individual

element of data (called fields, or attributes), and the rows

represent records of related data elements.

Records

Entities

Fields or Attributes

Big data to refer to massive amounts of business data

from a wide variety of sources, much of which is

available in real time, and much of which is uncertain or

unpredictable. IBM calls these characteristics volume,

variety, velocity, and veracity.

“The effective use of big data has the potential to

transform economies, delivering a new wave of

productivity growth and consumer surplus. Using big

data will become a key basis of competition for existing

companies, and will create new competitors who are

able to attract employees that have the critical skills for a

big data world.” – McKinsey Global Institute, 2011

Metric – a unit of measurement that provides a

way to objectively quantify performance.

Measurement – the act of obtaining data

associated with a metric.

Measures – numerical values associated with a

metric.

Discrete metric – one that is derived from

counting something.

◦ For example, a delivery is either on time or not; an order

is complete or incomplete; or an invoice can have one,

two, three, or any number of errors. Some discrete

metrics would be the proportion of on-time deliveries; the

number of incomplete orders each day, and the number

of errors per invoice.

Continuous metrics are based on a continuous

scale of measurement.

◦ Any metrics involving dollars, length, time, volume, or

weight, for example, are continuous.

Categorical (nominal) data – sorted into

categories according to specified characteristics.

Ordinal data – can be ordered or ranked

according to some relationship to one another.

Interval data – ordinal but have constant

differences between observations and have

arbitrary zero points.

Ratio data – continuous and have a natural zero.

Reliability – data are accurate and consistent.

Validity – data correctly measures what it is supposed to measure.

Examples:

◦ A tire pressure gage that consistently reads several pounds of pressure

below the true value is not reliable, although it is valid because it does

measure tire pressure.

◦ The number of calls to a customer service desk might be counted

correctly each day (and thus is a reliable measure) but not valid if it is

used to assess customer dissatisfaction, as many calls may be simple

queries.

◦ A survey question that asks a customer to rate the quality of the food in a

restaurant may be neither reliable (because different customers may

have conflicting perceptions) nor valid (if the intent is to measure

customer satisfaction, as satisfaction generally includes other elements

of service besides food).

Model – an abstraction or representation of a real

system, idea, or object.

Captures the most important features

Can be a written or verbal description, a visual

representation, a mathematical formula, or a

spreadsheet.

The sales of a new product, such as a first-generation iPad or

3D television, often follow a common pattern.

1. Verbal description: The rate of sales starts small as early

adopters begin to evaluate a new product and then begins

to grow at an increasing rate over time as positive

customer feedback spreads. Eventually, the market

begins to become saturated and the rate of sales begins

to decrease.

2. Visual model: A sketch of sales as an S-shaped curve

over time

3. Mathematical model: S = aebect

where S is sales, t is time, e is the base of natural

logarithms, and a, b and c are constants.

Influence diagram – a visual representation of a

descriptive model that shows how the elements of

the model influence, or relate to, others.

An influence diagram is a useful approach for

conceptualizing the structure of a model and can assist in

building a mathematical or spreadsheet model.

Basic

Expanded

total cost = fixed cost + variable cost

variable cost = unit variable cost × quantity produced

(1.2)

total cost = fixed cost + variable cost

= fixed cost + unit variable cost × quantity produced

(1.3)

(1.1)

Mathematical model:

TC = Total Cost

F = Fixed cost

V = Variable unit cost

Q = Quantity produced

TC = F +VQ

(1.4)

Decision model – a logical or mathematical

representation of a problem or business situation that

can be used to understand, analyze, or facilitate making

a decision.

Inputs:

◦ Data, which are assumed to be constant for purposes of the

model.

◦ Uncontrollable variables, which are quantities that can change but

cannot be directly controlled by the decision maker.

◦ Decision variables, which are controllable and can be selected at

the discretion of the decision maker.

TC(manufacturing) = $50,000 + $125*Q

TC(outsourcing) = $175*Q

Breakeven Point: TC(manufacturing) = TC(outsourcing)

$50,000 + $125 × Q = $175 × Q

$50,000 = 50 × Q

Q = 1,000

General Formula

F + VQ = CQ

Q = F/(C – V)

(1.5)

In the grocery industry, managers typically need to know

how best to use pricing, coupons and advertising

strategies to influence sales. Grocers often study the

relationship of sales volume to these strategies by

conducting controlled experiments to identify the

relationship between them and sales volumes. That is,

they implement different combinations of pricing, coupons,

and advertising, observe the sales that result, and use

analytics to develop a predictive model of sales as a

function of these decision strategies.

Sales = 500 – 0.05(price) + 30(coupons) + 0.08(advertising) +

0.25(price)(advertising)

If the price is $6.99, no coupons are offered, and no advertising is done

(the experiment corresponding to week 1), the model estimates sales as

Sales = 500 – 0.05 × $6.99 + 30 × 0 + 0.08 × 0 + 0.25 × $6.99 × 0 =

500 units

Assumptions are made to

◦ simplify a model and make it more tractable; that is, able to be

easily analyzed or solved.

◦ better characterize historical data or past observations.

The task of the modeler is to select or build an

appropriate model that best represents the behavior of

the real situation.

Example: economic theory tells us that demand for a

product is negatively related to its price. Thus, as prices

increase, demand falls, and vice versa (modeled by

price elasticity — the ratio of the percentage change in

demand to the percentage change in price).

As price increases, demand falls.

Assumes price elasticity is constant (constant ratio of

% change in demand to % change in price)

Uncertainty is imperfect knowledge of what will happen

in the future.

Risk is associated with the consequences of what

actually happens.

“To try to eliminate risk in business enterprise is futile.

Risk is inherent in the commitment of present resources

to future expectations. Indeed, economic progress can

be defined as the ability to take greater risks. The

attempt to eliminate risks, even the attempt to minimize

them, can only make them irrational and unbearable. It

can only result in the greatest risk of all: rigidity.”

– Peter Drucker

Prescriptive decision models help decision

makers identify the best solution.

Optimization – finding values of decision variables

that minimize (or maximize) something such as

cost (or profit).

Objective function – the equation that minimizes (or

maximizes) the quantity of interest.

Constraints – limitations or restrictions.

Optimal solution – values of the decision variables at the

minimum (or maximum) point.

A firm wishes to determine the best pricing for one

of its products in order to maximize revenue.

Analysts determined the following model:

Sales = -2.9485(price) + 3240.9

Total revenue = (price)(sales)

= price × (-2.9485 × price + 3240.9)

= 22.9485 × price2 + 3240.9 × price

Identify the price that maximizes total revenue,

subject to any constraints that might exist.

Deterministic model – all model input information

is known with certainty.

Stochastic model – some model input

information is uncertain.

◦ For instance, suppose that customer demand is an

important element of some model. We can make the

assumption that the demand is known with certainty; say,

5,000 units per month (deterministic). On the other hand,

suppose we have evidence to indicate that demand is

uncertain, with an average value of 5,000 units per

month, but which typically varies between 3,200 and

6,800 units (stochastic).

1. Recognizing a problem

2. Defining the problem

3. Structuring the problem

4. Analyzing the problem

5. Interpreting results and making a decision

6. Implementing the solution

Problems exist when there is a gap between what is

happening and what we think should be happening.

For example, costs are too high compared with

competitors.

Clearly defining the problem is not a trivial task.

Complexity increases when the following occur:

– large number of courses of action

– the problem belongs to a group and not an

individual

– competing objectives

– external groups are affected

– problem owner and problem solver are not the

same person

– time limitations exist

Stating goals and objectives

Characterizing the possible decisions

Identifying any constraints or restrictions

Analytics plays a major role.

Analysis involves some sort of experimentation or

solution process, such as evaluating different

scenarios, analyzing risks associated with various

decision alternatives, finding a solution that meets

certain goals, or determining an optimal solution.

Models cannot capture every detail of the real

problem

Managers must understand the limitations of

models and their underlying assumptions and

often incorporate judgment into making a decision.

Translate the results of the model back to the real

world.

Requires providing adequate resources,

motivating employees, eliminating resistance to

change, modifying organizational policies, and

developing trust.

www.puzzlOR.com

Maintained by an analytics manager at ARAMARK.

Each month a new puzzle is posted.

Many puzzles can be solved using techniques you

will learn in this book.

The puzzles are fun challenges.

A good one to start with is SurvivOR (June 2010).

Have fun!

Many commercial software packages can be used

for Business Analytics.

Spreadsheet software, such as Microsoft Excel, is

widely available and used across all areas of

business.

Spreadsheets provide a flexible modeling

environment for manipulating data and

developing and solving models.

Mac versions of Excel do not have the full

functionality that Windows versions have –

particularly statistical features which are important

to this book.

The Excel add-in that we use in later chapters,

Analytic Solver Platform, only runs on Windows.

Thus, if you use a Mac, you should either run

Bootcamp with Windows or use a third-party

software product such as Parallels or VMWare.

Opening, saving, and printing files

Using workbooks and worksheets

Moving around a spreadsheet

Selecting cells and ranges

Inserting/deleting rows and columns

Entering and editing text, data, and formulas

Formatting data (number, currency, decimal)

Working with text strings

Formatting data and text

Modifying the appearance of a spreadsheet

Tabs – Home, Insert, Page Layout, Formulas, …

Groups – Font, Alignment, Number, Styles, …

Buttons and Menus

– Buttons appear as small icons.

– Menus of additional choices are indicated by

small triangles.

Common mathematical operators are used.

For example:

a−

bP5

c

+ would be entered into Excel as:

d

=a− b*P^5 + c/d

Cell references can be relative or absolute. Using a dollar sign

before a row and/or column label creates an absolute reference.

◦ Relative references: A2, C5, D10

◦ Absolute references: $A$2, $C5, D$10

Using a $ sign before a row label (for example, B$4) keeps the

reference fixed to row 4 but allows the column reference to

change if the formula is copied to another cell.

Using a $ sign before a column label (for example, $B4) keeps

the reference to column B fixed but allows the row reference to

change.

Using a $ sign before both the row and column labels (for

example, $B$4) keeps the reference to cell B4 fixed no matter

where the formula is copied.

Two models for predicting demand as a function of price

Linear

D = a – bP

Formula in cell B8:

=$B$4-$B$5*$A8

Nonlinear

D = cP-d

Formula in cell E8:

=$E$4*D8^-$E$5

Note how the absolute addresses are used so that as these formulas

are copied down, the demand is computed correctly.

Formulas in cells can be copied in many ways.

Use the Copy button in the Home tab, then use

the Paste button

Use Ctrl-C, then Ctrl-V

Drag the bottom right corner of a cell (the fill

handle) across a row or column

Split Screen

Paste Special

Column and Row Widths

Displaying Formulas in Worksheets

Displaying Grid Lines and Column Headers for

Printing

Filling a Range with a Series of Numbers

=MIN(range)

=MAX(range)

=SUM(range)

=AVERAGE(range)

=COUNT(range)

=COUNTIF(range,criteria)

◦ Excel has other useful COUNT-type functions: COUNTA counts

the number of nonblank cells in a range, and COUNTBLANK

counts the number of blank cells in a range. In addition,

COUNTIFS(range1, criterion1, range2, criterion2,… range_n,

criterion_n) finds the number of cells within multiple ranges that

meet specific criteria for each range.

=MIN(F4:F97)

=MAX(F4:F97)

=SUM(G4:G97)

=AVERAGE(H4:H97)

=COUNT(B4:B97)

=COUNTIF(D4:D97,”=O-Ring”)

=COUNTIF(H4:H97,” greater than

>= greater than or equal to

< less than
=10000, “Large”, “Small”)
Suppose that large orders with a total cost of at
least $25,000 are considered critical.
◦ Cell L4: =IF(AND(K4=“Large”, G4>=25000),“Critical”,“”)

These functions are…

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