Business Analytics and Optimization Introduction (part 2)

Business Analytics and Optimization: A Technical Introduction (Part 2)
Oleksandr Romanko, Ph.D.
Senior Research Analyst, Risk Analytics – Business Analytics, IBM
Adjunct Professor, University of Toronto
Toronto SMAC Meetup
October 9, 2014

© 2014 IBM Corporation
 Business Analytics

Predictive Analytics
What will happen?
Descriptive Analytics
What has happened?
Prescriptive Analytics
What should we do?
What is analytics?
Data
Insight
Action
Decide
Analyze
Business Value
3
Analytics is the scientific process of deriving insights from data in order to make decisions

 Business Analytics Education

IBM Academic Initiative program
Cognos SPSS ILOG

Business Analytics programs – curriculum
Applied Statistics and Probability
Fundamentals of Computational Mathematics
Data Mining and Knowledge Discovery
Simulation Modelling
Optimization
Financial Decision Making
Computational Methods for Business Data Analysis
Computational Finance and Risk Management
Visual Analytics and Knowledge Representation
Mathematical Modelling for Business
Machine Learning, Cognitive Computing and Artificial Intelligence
Marketing Analytics
Strategies for Managing Innovations
Analytics of Web, Social Networks and Business News

 Applied Statistics

What kind of data are we dealing with?
Types of data
•Quantitative
•Categorical (ordered, unordered)
Data collection
•Independent observations (one observation per subject)
•Dependent observations (repeated observation of the same subject, relationships within groups, relationships over time or space)
Type of data drives the direction of your analysis
•How to plot
•How to summarize
•How to draw inferences and conclusions
•How to issue predictions
8

Quantitative data
Examples: temperature, age, income
Quick check: “Does it makes sense to calculate an average?”
Appropriate summary statistics:
–Mean and Median
–Standard Deviation
–Percentiles
More advanced predictive methods: Regression, Time Series Analysis, …
Plot your data!
9

Summarizing quantitative data
One-number summaries
–Mean Average, obtained by summing all observations and dividing by the number of obs.
–Median The center value, below and above which you will find 50% of the observations.
Summarizing your data with one number may not tell the whole story:
10
Median = 19.8
Median = 19.8
Median = 10.5

“Most observations fall within ±2 standard deviations of the mean.”
Standard deviation

11
If the data is normally distributed
95 % of observations
Standard Deviation = 4.2
~95% of observations between 11.4 and 28.2

Distributions: Normal distribution
12

Distributions
13

14
Distributions
Estimate of the probability distribution of global mean temperature resulting from a doubling of CO2 relative to its pre-industrial value, made from 100000 simulations

 Modeling

16
Models

17
Models
Simplified representation or abstraction of reality
Capture essence of system without unnecessary details
Models tailored for specific types of problems
Models help us understand the world
– Prediction (What if?)
– Optimization (What’s best?)
Often models much easier, faster, and cheaper to experiment with than the real system

18
Models and reality
Problem
Decisions
Model
Interpretation
Calculations
From Monahan, G., “Management Decision Making”, Cambridge University Press, 2000
“Real” World
Analysts World
Simplified abstraction of reality
Capture essence of problem

19
Environmental risk management

20
Predictive maintenance
Wind turbines are big and expensive machines, so keeping them running smoothly helps keeping their operational cost down. The sensor data generated by the turbine can help achieving this – by analysing it, you can spot potential failures earlier. The longer the warning period before a part fails, the better you can prepare for it.
To do that, you need to be able to anticipate failures in heavy and expensive parts like the gearbox, generator and main shaft.
Preventive maintenance saves money:
Shorter downtime and less lost production
Better planning of people and materials
Cheaper repairs
Source: Algoritmica, http://www.algoritmica.nl

21
Predictive maintenance – how it works
Wind turbines have an array of sensors that measure temperatures, pressures, voltages, currents, and blade angles. This data is available for analysis, typically as 10-minute averages of the sensor values.
The computer that controls the turbine uses these measurements for its operations. This includes error thresholds like ‘the gearbox oil temperature should be below 120 degrees Celsius’. However, by the time the threshold is exceeded it is usually too late: the damage has already been done. To catch failures earlier we should look for anomalies, e.g. measurements that are unexpected and therefore might indicate a problem – but are not yet so severe that they exceed a threshold.

22
Predictive maintenance – anomaly detection
Anomaly detection begins by defining what measurement values are expected and then calculating the difference with the actual situation. Since sensor data is delivered as a time series, we create a model that predicts the next value of a specific sensor given its previous values as well as the previous values of any other sensors that may be relevant. Based on these multiple inputs, the model then calculates its predicted value and compares it with the actual sensor reading. The difference (or residual) is now a measure of how much the turbine is deviating from its expected performance. If it is persistent or grows too large (i.e. becomes an anomaly), an analyst can investigate the cause and decide on a course of action together with the operations staff at the wind farm.

23
Predictive maintenance – machine learning model
To create such a sensor model we apply machine learning or data mining, i.e. one or more algorithms that use a set of examples (the ‘training set’) to learn a predictive model.
For a wind turbine, it is a natural fit to use a year of sensor data as the training set so that all seasonal variations are included.

24
Predictive maintenance – driven by data
This is a data-driven approach: the model learns the relationship between the various sensor readings purely based on the training data. This is in contrast to a so-called physical model that explicitly describes the turbine design using detailed knowledge of its physical characteristics.
The main advantage of a data-driven approach is that the model can be trained by a non- turbine expert and matches the actual situation by definition, whereas a physical model has to be carefully calibrated by an expert.

 Simulation – Business Case Study

26
Study environmental impact of restaurant operations
 Restaurant
 order types and probabilities
 processing times (fixed portion and variable portion)
 design alternatives
 Drive Through
 number of service windows
 queuing capacity
 Parking Lot
 parking capacity
 customer prioritization
 Goals:
 maximize customer satisfaction (high customer service level)
 minimize environmental impact (quantity of emissions)
Case study – optimal store design

Problem description

Restaurant operations

30
Most of the variable portion of the emissions are generated at the drive through lane
Customers should be encouraged to park their cars and enter the restaurant
Drive through customers should be served as fast as possible
Problems with the standard design

less than 12 minutes
waiting for more than 1 minute to enter
Results – key indicatotrs

33 © 2014 IBM Corporation
Emissions vs. Customer Satisfaction
Data Points
96
97
99
100
Data Points and Efficient Frontier
94
95
98
35 45 55 65 75 85 95
Emissions (kg/week)
Customer satisfaction (%)
Customer Prioritization
95
96
97
98
99
100
Customer satisfaction (%)
Outside
Equal
Inside
 Comparing 72 alternatives:
– Limiting drive through to coffee/bakery orders
– Pull-off space for large drive through orders
– 2 or 3 service windows in drive through
– Customer prioritization: inside, outside or equal
– Varying queuing/parking capacity
Drive Through 2- and 3-Window Design
3-Window Design
2-Window Design
Pull-Off Space
65 75 85 95
(kg/week)
Disabled
Enabled
Parking Capacity
Capacity #1
Capacity #2
Capacity #3
Capacity #4
Drive Through Food Variety
45 85 95
Drive through limited to
cof fee/baked goods
Drive through serving everything
yes
no
3
outside
layout #4
(6/19)
Results - alternatives

34
Additional extensions and policies
Make orders more expensive for the drive through customers
–equivalent of introducing the emission sales tax and can be justified from the environmental point of view
Provide customers with the information about expected waiting times and greenhouse gas emissions per vehicle for the drive through lane and for using the parking lot
–this information can be displayed on the illuminated indicator board (lighting panel) outside the restaurant
The “green” policy of the restaurant:
make drive through more efficient or
encourage customers to use parking lot instead

35
Recommendations
We recommend implementing the following design:
Drive through limited to coffee and baked goods
No pull-off space
Separate pay and pickup windows at the drive through (3 service windows)
Priority given to drive through customers (or equal priority if any difficulties are expected with prioritizing the outside customers)
Any reasonable parking lot/drive through design would work (it depend more on the physical restrictions on the available space for the newly planned locations than on the other factors)
Implement our additional recommendations about the staffing patterns and waiting area size as well as “green” policies

 Data Mining

Data mining
37
Data mining application classes of problems
–Classification
–Clustering
–Regression
–Forecasting
–Others
Hypothesis or discovery driven
Iterative
Scalable

What is the difference between descriptive (BI) and predictive
analytics?
38
John
Lives in Seattle, zip: 98109
21 years old
iPhone 5
Plan: $98 a month
Talk: 400 minutes
Data: 1.9Gb
SMS: 370
Complaints: 0
Customer care calls: 1
Dropped calls: low
Mike
Lives in Atlanta, zip: 30308
38 years old
Samsung Galaxy S3
Plan: $78 a month
Talk: 1200 minutes
Data: 0.2 Gb of data
SMS: 8
Customer care calls: 6
Dropped calls: high
Low churn
risk
High churn
risk
Descriptive Predictive

Classification
Classification is a supervised learning technique, which maps data into predefined classes or groups
Training set contains a set of records, where one of the records indicates class
Modeling objective is to assign a class variable to all of the records, using attributes of other variables to predict a class
Data is divided into test / train, where “train” is used to build the model and “test” is used to validate the accuracy of classification
Typical techniques: Decision Trees, Neural Networks
39
Gender
Age
Lipstick
Female
21
Yes
Male
30
No
Female
14
No
Female
35
Yes
Male
17
No
Female
16
Yes
Customers
Female
Male
>=15 years
<15 years
Yes
No
No

Classification: Creating Model
40
Gender
Age
Lipstick
Female
21
Yes
Male
30
No
Female
14
No
Female
35
Yes
Male
17
No
Female
16
Yes
Classification Algorithms
Training Data
Trained Classifier
Purchased lipstick if Gender = Female and Age >= 15
Works with both interval and categorical variables

Classification: Applying Rules
41
Gender
Age
Lipstick
Female
27
?
Male
55
?
Female
47
?
Male
39
?
Female
27
?
Male
19
?
Gender
Age
Lipstick
Female
27
P Yes
Male
55
P No
Female
47
P Yes
Male
39
P No
Female
27
P Yes
Male
19
P No
Apply Scoring
If Gender = Female and Age >= 15 then Purchase lipstick = YES

Decision (classification) Trees
A tree can be "learned" by splitting the source set into subsets based on an attribute value test
Tree partitions samples into mutually exclusive groups by selecting the best splitting attribute, one group for each terminal node
The process is repeated recursively for each derived subset, until the stopping criteria is reached
Works with both interval and categorical variables
No need to normalize the data
Intuitive if-then rules are easy to extract and apply
Best applied to binary outcomes
Decision trees can be used to support multiple modeling objectives
oCustomer segmentation
oInvestment / portfolio decisions
oIssuing a credit card or loan
oMedical patient / disease classification
Customers
Female
Male
>=15 years
<15 years
Yes
No
No

Cluster Analysis (segmentation)
Unsupervised learning algorithm
oUnlabeled data and no “target” variable
Frequently used for segmentation (to identify natural groupings of customers)
oMarket segmentation, customer segmentation
Most cluster analysis methods involve the use of a distance measure to calculate the closeness between pairs of items
oData points in one cluster are more similar to one another
oData points in separate clusters are less similar to one another
43
Spend
Income
Cluster #1
Cluster #3
Cluster #2

K-means clustering
44

K-means clustering
45

K-means clustering
46

Clustering: LinkedIn
47

48
Clustering: LinkedIn

 Optimization

50
Optimization
Optimization problem
Examples:
– Minimize cost
– Maximize profit

Shortest path or most beautiful path?
7

53
53
.85
1
.80
1.05
200
M1
100
M2
500
M3
600
M4
Cash, USD
Debt, USD
Cash, EUR
Debt, EUR
200 +
Collateral optimization – problem setup
x8
x1
200
R1
550
R2
300
R3
Only cash
Any
Only EUR

54
54
Collateral optimization – problem setup
.85
1
.80
1.05
200
M1
100
M2
500
M3
600
M4
Cash, USD
Debt, USD
Cash, EUR
Debt, EUR
200 +
x8
x1
200
R1
550
R2
300
R3
Only cash
Any
Only EUR

55
55
200
M1
100
M2
500
M3
200
R1
550
R2
600
M4
300
R3
.85
1
.80
1.05
200 +
Cash, USD
Debt, USD
Cash, EUR
Debt, EUR
100
Collateral optimization – optimal cost = 985
0
Only cash
Any
Only EUR
0
100
415
600

56
56
.85
1
.80
1.05
200
M1
100
M2
500
M3
600
M4
Cash, USD
Debt, USD
Cash, EUR
Debt, EUR
200 +
Collateral optimization – concentration constraints
x8
x1
200
R1
550
R2
300
R3
Only cash
Any
Only EUR
At most 50% EUR in total

57
Multi-objective optimization
Multi-objective optimization: simultaneously optimizing two or more conflicting objectives subject to certain constraints
 Examples:
Finance: Minimize risk & Maximize return
Business: Minimize cost & Minimize environmental impact
Health care: Maximize X-ray dose to tumor & Minimize X-ray dose to healthy tissues
Units of the objectives are typically not the same:
dollars, probability, units of time, …

58
Multi-objective optimization
Solving multi-objective optimization problems:

 Visual Analytics

60
Visual analytics
Visual statistics of the Napoleon Campaign: the Minard Map

61
Visual analytics

62
Visual analytics – portfolio

63
Historical visualization
Activity Histogram
Heat Map
Track Summary
Distribution of events over time
How long objects spent in different places
Show tracks of all objects returned from search

64
Visual analytics

65
Visual analytics
http://www.nytimes.com/2011/11/06/opinion/sunday/population-control-marauder-style.html
•cause (vertical location)
•historical time (horizontal location)
•duration (equator)
•number of deaths (circle size)
•continent (color)
•rank, cause, number of deaths (text)

66
Visualization types

67
Visualization formatting

68
Watson Analytics
Natural language dialogue
Cloud-based agility
Data discovery
Quick start intuitive interface
Mobile- ready

69
Watson Analytics
Unified analytics experience
Visual storytelling
Intelligent automation
Data access and refinement
Report and dashboard creation
Integrated social business
Guided analytic discovery

70
Watson Analytics

71
Watson Analytics

 Analytics Software

73
Software for analytics

74
Lavastorm survey of analytics tools
Source: R. Muenchen "The Popularity of Data Analysis Software", http://r4stats.com/articles/popularity/

75
Gartner “Magic Quadrant” plot of companies that sell advanced analtyics software (2014)
Source: R. Muenchen "The Popularity of Data Analysis Software", http://r4stats.com/articles/popularity/

77
Questions?

Business Analytics and Optimization Introduction (part 2)

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