[MPKD1] Introduction to business analytics and simulation

INTRODUCTION TO BUSINESS
ANALYTICS & SIMULATION

The rise in demand for
Analytics and Data Science
talent
Source: LinkedIn’s data indicates

Google trends graph of searches on the term Analytics
Source: http://www.google.com.vn/trends/explore#q=analytics

Source: Rainer et al. (2014)
Information technology inside the organization
Business Analytics

Source: http://www.gartner.com/technology/cio/

PwC 18th Annual Global CEO Survey (2015)
How strategically important are the following
categories of digital technologies for your
organisation?
The strategic importance
of key technologies
Business Analytics

Source: www.pwc.com/ceosurvey
The strategic importance
of key technologies

Evolution of Business Analytics
Source: Delen (2015)

The Institute for Operations
Research and the
Management Sciences
(INFORMS) is the largest
society in the world for
professionals in the field of
operations research (O.R.),
management science,
and analytics.
https://www.informs.org

(Business) Analytics:
• Scientific process of transforming data into
insight for making better decisions.
• Used for fact-driven decision making, which is
often seen as more objective than other
alternatives for decision making.
Camm et al. (2015)
WHAT IS BUSINESS ANALYTICS?

Analytics vs Analysis
• Analysis refers to the process of separating a
whole problem into its parts so that the parts
can be critically examined at the granular level.
• Analytics is a variety of methods, technologies,
and associated tools for creating new
knowledge/insight to solve complex problems
and make better and faster decisions.
Delen (2015)

• Analytics = The science of analysis  Data Science
• People who conduct analyses and develop analytic
applications = data analysts  data scientists (have a
deeper knowledge of algorithms)
• Analytics includes any type of computer-supported
analysis used to support fact-based decisions.
• Analytics may be input for human decisions or drive
fully automated decisions.
Power (2013)

TYPES OF BUSINESS ANALYTICS
• Decision making: A process of choosing among two or more
alternative courses of action for the purpose of attaining a
goal(s)
• Managerial decision making is synonymous with the entire
management process (Simon, 1977)
• All management is prediction (Deming, 1993)
• Humans consciously or subconsciously follow a systematic
decision-making process: (Simon, 1977)
1. Intelligence
2. Design
3. Choice
4. Implementation
Decisionmaking
Problemsolving
Descriptive analytics
Prescriptive analytics
Predictive analytics
BA process

 Descriptive analytics
(reporting analytics, business intelligence)
 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
(decision analytics, normative analytics)
 identify the best alternatives to minimize or maximize some
objective
Advancedanalytics
Proposed by INFORMS (the Institute of Operations
Research and Management Sciences)

Example: Retail Markdown Decisions
Most department stores clear seasonal inventory by
reducing prices.
The question is: When to reduce the price and by how
much?
 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

Source: Gartner (2013)

Categories of business analytics modeling techniques
Ragsdale (2015)

The Spectrum of Business Analytics

Data vs Information
 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.
DATA FOR BUSINESS ANALYTICS

• 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.
• Data warehouse - a collection of databases used for
reporting and data analysis.
• Data mart: A departmental data warehouse that stores only
relevant data.

Four Types Data Based on Measurement Scale:
 Nominal data
 Ordinal data
 Interval data
 Ratio data
Categorical data
Scale/numerical/quantitative dada
Data
Categorical Numerical
Nominal Ordinal Interval Ratio
Structured
Unstructured or
Semi-Structured
MultimediaTextual HTML/XML

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 display, a
mathematical formula, or a spreadsheet representation
Example: The sales of a new product, such as a first-
generation iPad or 3D television, often follow a common
pattern.
MODELS IN BUSINESS ANALYTICS
S = aebect

 A decision model is a logical or mathematical
representation of a problem or business situation that
can be used to understand, analyze, or facilitate making
a decision.
 Building decision models is more of an art than a
science. Creating good decision models requires:
 solid understanding of business functional areas
 knowledge of business practice and research
 logical skills
 It is best to start simple and enrich models as necessary.

• Three types of input:
• Data (or parameters), 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 (or controllable variables), which are
controllable and can be selected at the discretion of the decision
maker.
Nature of Decision Models
dependent variables
independent variables
f(X1, X2,…, Xn) = Y

Strategies for Modeling
• Logic-Driven Models: based on experience, knowledge,
and logical relationships of variables and constants
connected to the desired business performance outcome
situation
• Data-Driven Models: use data collected from many
sources to quantitatively establish model relationships
Influence Diagrams visually show how various model
elements relate to one another

Example: A Profit Model
• Develop a decision model for predicting profit in face of
uncertain demand.
MODELS IN BA: LOGIC-DRIVEN MODELS
P = profit
R = revenue
C = cost
p = unit price
c = unit cost
F = fixed cost
S = quantity sold
D = demand
Q = quantity
produced
Influence Diagram

Example: A Profit Model
• Cost = fixed cost + variable cost
C = F + c*Q
• Revenue = price * quantity sold
R = p*S
• Quantity sold =
Minimum{demand, quantity produced}
S = min{D, Q}
• Profit = Revenue − Cost
P = p*min{D, Q} − (F + cQ)
MODELS IN BA: LOGIC-DRIVEN MODELS

Example: A Sales-Promotion Model
In the grocery industry, managers typically need to know
how best to use pricing, coupons and advertising
strategies to influence sales.
Using Business Analytics, a grocer can develop a model
that predicts sales using price, coupons and advertising.
MODELS IN BA: DATA-DRIVEN MODELS
Price
Coupons
Advertising
Sales
Influence Diagram

Example: A Sales-Promotion Model
Sales = 500 – 0.05(price) + 30(coupons)
+0.08(advertising) + 0.25(price)(advertising)

Example: Predicting Crude Oil Prices
• Line chart of historical crude oil prices

Logarithmic y = 13 ln(x) + 39 R2 = 0.382
Power y = 45.96x0.0169 R2 = 0.397
Exponential y = 50.5e0.021x R2 = 0.664
Polynomial 2° y = 0.13x2 − 2.4x + 68 R2 = 0.905
Polynomial 3° y = 0.005x3 − 0.111x2
+ 0.648x + 59.5 R2 = 0.928
R-squared values closer
to 1 indicate better fit of
the trendline to the data.

• Third Order Polynomial Trendline fit to the data

MONTE CARLO SIMULATION
• Monte Carlo simulation
is the process of
generating random values
for uncertain inputs in a
model, computing the
output variables of
interest, and repeating
this process for many
trials to understand the
distribution of the output
results.

SYSTEM SIMULATION
• Discrete-Event System Simulation
• Continuous Simulation (System Dynamics)

WINTERSIM.ORG
• A very useful resource is the Winter Simulation
Conference (wintersim.org) to track the
developments in the field of simulation.
• The Winter Simulation Conference provides the
central meeting place for simulation
practitioners, researchers, and vendors working
in all disciplines and in the industrial,
governmental, military, and academic sectors.

[MPKD1] Introduction to business analytics and simulation

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