Who Uses Analytics - Ohio University College of Business

Report
Analytics in Strategic Decision Making
Brazil Executive Seminar, April 2014
PRESENTED BY
DR. FAIZUL HUQ
OHIO UNIVERSITY
Strategic Business Leadership
Executive Education Seminar
AGENDA
• Introduction to Business Analytics.
• Example of Analytical Tool Implementation for
Supply Chain Sustainability
• How to make Analytics work for Strategic
Success
• Practice Quiz
2
Introduction to Analytics
What is Analytics?
The extensive use of data, statistical and quantitative
analysis, explanatory and predictive models and factbased management to drive decisions and actions.
The most recent step in increasingly sophisticated and
effective approaches to providing technical assistance
to decision-making.
Analytics Chronology
Scientific
Management
• 1890’s –
1920’s
Operational
Research
• World War
II
Operations
Research/
Management
Science
Enterprise
Resource
Planning
• 1950’s
• 1990’s
Decision
Support
Systems
• 1970’s
Related Methodologies
Business Intelligence
Competitive Intelligence
Business Process Management
Business Analytics
Information-based Strategy
Competitive Advantage
Hierarchy of Analytics Sophistication
Analytics
Competitors
Analytical
Companies
Analytical
Aspirations
Localized
Analytics
Analytically
Impaired
Sophistication
Consumer Products
• Anheuser Busch
• E & J Gallo
• Mars
• Proctor & Gamble
Industrial Products
• CEMEX
• John Deere & Company
Financial Services
• Barclay Bank
• Capital One
• Royal Bank of Canada
• Progressive Casualty
• WellPoint
Pharmaceuticals
• AstraZeneca
• Solvay
• Vertex Pharmaceuticals
Hospitality &
Entertainment
• Boston Red Sox
• Harrah Entertainment
• Marriott International
• New England Patriots
Retail
• Amazon
• JC Penny
• Tesco
• Wal-Mart
Who Uses Analytics (continued)
Telecommunications
• Sprint
• O2
• Bouygues Telecom
Transport
• FedEx
• Schneider National
• United Parcel Service
eCommerce
• Google
• Netflix
• Yahoo!
Netflix
Started in 1997 by an angry Blockbuster customer
Looked like another dot.com flop
Online ordering
Snail mail delivery
Competing against giant with $3 Billion in
Revenues
Grew from $5 Million 1999 revenue to $1 Billion
2006 revenue
Netflix- Competing Through Analytics
•Deliver a personalized web page for each customer
•Cinematch Movie Recommendation Engine
•Developed by a mathematician
•$1 Million prize for 10% improvement by an
outsider
•Discovering and focusing on most profitable
customers
Harrah’s Entertainment – Competing
Through Analytics
•
No override of revenue management system
•
Measuring customer loyalty and targeting
service levels accordingly
•
Optimize range and configuration of games
•
Provide options at bottlenecks
Characteristics of Analytical
Executives
Passionate believers in analytical and fact-based
decision making
In God we trust;
All others bring data.
Barry Beracha, CEO of Earthgrains
Do we think this is true?
Or do we know?
Gary Loveman, CEO of Caesar’s Entertainment
Characteristics of Analytical
Executives
Passionate believers in analytical and fact-based decision
making
Appreciate analytical tools and methods
Willing to act on the results of analyses
Willing to manage a meritocracy
Tools and Techniques in Analytics
Spreadsheets
Optimization Models
Explanatory & Prediction Models
Decision Analysis Models
Data warehousing and data mining
Online Analytical Processing (OLAP)
Digital Dashboards
Factors in Decision Making
Multiple (antagonistic) goals and/or objectives
Individual versus group decisions
Restrictions on decisions
Interactions among decisions
Objective probability measurements
Subjective probability assessments
Factors in Decision Making
Time pressures:
•
Potential deferment of some decisions
•
Possibility of obtaining additional information
One-time versus repetitive decisions
Attitudes regarding risk
Example
You meet a stranger who gives you $1000 in cash and
then offers you an opportunity to:
• Receive an additional $500, or
• Watch him flip a coin and receive an additional $1000 if
heads but receive nothing additional if it is tails.
Do you choose to...
• Take the sure thing?
• Gamble?
Example
You meet a stranger who gives you $2000 in cash but
requires that you:
• Return $500 immediately, or
• Watch him flip a coin and return nothing if heads but
return $1000 if it is tails.
Do you choose to...
• Take the sure thing?
• Gamble?
Decision Tree Analysis
$1,500
$2,000
EV = .5($2000)+.5($1000)=$1500
$1,000
Decision Analysis
An attempt to make decision making explicit by structuring the
decision process
Permits the analysis of the consistency of decision making
Assists supervision in evaluating the decision making of subordinates
Neither necessary nor sufficient to produce good results but may
increase the frequency of obtaining good results
Allows for consistent and unbiased comparisons of options
Good Decision – Bad Outcome
In 1965, Andre-Francois Raffray (age 47) agreed to pay
$500 a month
• To Jeanne Calment (age 90) until her death
• To buy her apartment in Arles “for life”
In December 1995, Raffray died at age 77 having paid
more than $180,000 for the apartment
• And Jeanne celebrated her 120th birthday
• Raffray’s widow and children are obligated to continue
payments
Decision Models
Assist decision makers by providing information helpful in
resolving a pending decision
Always involve a degree of abstraction and simplification of the
actual decision environment
Do not create information, but rather concentrate and focus
information
The Basic Proposal of Decision
Analysis
Improvements in the decision making process will occur if
the methods of science are applied to the decisions which
managers must make.
Something is to be gained from making the learningadaptive processes of management more nearly like those
of science.
Modeling and Decision Making
Problem
Recognition
Enrich Model
Simplify Model
Problem
Definition
Correct Model
Model
Formulation
Model Solution
And
Validation
Data Gathering
and
Processing
Implement
Model
Evaluate
Model
Effectiveness
Termination
DHL Application of Analytics for
Sustainability
The degradation of the environment has led many
governments and customers to pressure
businesses to make their operations more
environmentally friendly. The case illustrates an
effective example of corporate social
responsibility. Specifically, it demonstrates how a
small increase in a supply chain budget can
drastically reduce carbon dioxide emissions in the
transportation of LCD TVs from their
manufacturing bases to a distribution centre.
source: Ivey/NUS Cases
DHL Sustainability Case Cont…
Issues:
• Environmental Sustainability; Linear Programming; Logistics;
Optimization Analysis; Spreadsheet Modeling; Corporate
Social Responsibility; China
Disciplines:
• Management Science, Operations
Management, International
Industries:
• Transportation and Warehousing
Setting:
• China, Large, 2011
Please look at the Excel Spreadsheet Handout with the Data
Making Analytics Help in Strategic
Advantage
Make Analytics a Common Practice in the Company by
Expending Effort to Always Apply Analytical Tools for Decision
Making
Get Buy In from Top to Bottom of the Company That Will Help
Build an Operational Infrastructure for the Application of
Analytics
Employ the Workforce Necessary to Effectively and Successfully
Implement Analytics for Business Decision Making
Make Analytics a Common Practice in the Company by
Expending Effort to Always Apply Analytical Tools for Decision
Making
In order for a company to be successful in their
use of Business Analytics(BA) it must expend
the most effort to truly implement BA in their
organizational decision making
Get Buy In from Top to Bottom of the Company That Will Help
Build an Operational Infrastructure for the Application of
Analytics
The successful use of BA requires building
Operational Infrastructures within its Supply
chain and Intra-organizational entities that can
continuously support the most effective use of
BA.
Employ the Workforce Necessary to Effectively and Successfully
Implement Analytics for Business Decision Making
For successful use of BA in organizational
decision making the personnel must be in
place who are dedicated to and capable of
effectively employ BA for making decisions.
Make Analytics a Common Practice in
the Company
Permeate the Company’s Decision Making Process With
the Use of Business Analytics
Make Decision Making an Integrated Process Across The
Company
Make the Use of Analytics Strategically and Mission
Focused
Be More Aggressive in Acquiring/Learning Sophisticated
and Specific analytical Tools
Permeate the Company’s Decision Making Process With
the Use of Business Analytics
• Expand BA practices where feasible
• Companies with reliance on BA are more
successful
• Low reliance on BA for Decision making leads
to less effective implementation of BA
• Make BA second nature within the company
• Shift BA use from occasional to routine
leading to greater effectiveness
Reliance on BA and Effectiveness
Source: SAS Institute/Bloomberg
Research Services
Reliance on Analytics
Source: SAS Institute/Bloomberg
Research Services
Make Decision Making an Integrated
Process Across The Company
• Integrate Analytics organization wide
• Do not isolate BA to a single department
function
• Avoid siloing of data and functionalities
• Breakdown Silos
• Create cross-divisional data teams
• Create transparency through data sharing and
process cooperation across the company
Cross Divisional Integration
Source: SAS Institute/Bloomberg
Research Services
Make the Use of Analytics Strategically
and Mission Focused
• By bringing Analytics to task a company is
twice as likely to be successful then not
• Majority of companies using BA employ
Analytics heavily in Finance
• Majority of the successful companies use
Analytics in Marketing and Sales, SCM, and
Product Development
• Start with one or two BA initiatives because
employing Analytics can be complex.
Summary Data of Analytics use in
Functional areas
Source: SAS Institute/Bloomberg
Research Services
Be More Aggressive in Acquiring/Learning
Sophisticated and Specific analytical Tools
•
•
•
•
•
Use Business reporting, KPI, and Dashboards
Employ sophisticated Forecasting tool
Undertake Data and Text Mining
Use Simulations and Scenario development
Employ Web Analytics
Use of Analytics Tools by Companies
Source: SAS Institute/Bloomberg
Research Services
Build Operational Infrastructure
Articulate a Strategy for Managing and Accessing Data
Acquire and Implement the Appropriate Technology Needed for
Data Driven Analytics Activities
Formalize the Data Management Process
Data Access Summary
Source: SAS Institute/Bloomberg
Research Services
Articulate a Strategy for Managing and
Accessing Data
• Improve the Quality, Integrity, and Consistency of
Data
• Increase Accessibility of Data that leads to
Positive and Effective BA efforts
• Make Business Information Readily Available to
those who need it
• Make the Source of Information and Data Central
• Facilitate Data and Information Sharing across the
Company
Acquire and Implement the Appropriate
Technology needed for Data Driven Analytics
Activities
• Much of BA is fairly low-tech. Such as Standard
Electronic Spreadsheets
• Additionally acquire Software and other Technology
that is more Complex and Capable than Standard
Spreadsheets
• Integrate Software for Data Mining, Forecasting, and
Predictive Analysis into Business Processes
• Make the Technology for Accessing the Data needed
for Analysis
• Standardize the Technology for Accessing, Integrating,
and Analyzing information from all functional areas.
Use of Technology for Analytics in
Firms
Source: SAS Institute/Bloomberg
Research Services
Formalize the Data Management
Process
• Put in place Appropriate Data Management
Processes
• Put in place general Data-Governance Rules
and Policies
• Articulate Defined Data Stewardship
• Identify Master Data Definitions
Survey Information of Data
Management Process
Source: SAS Institute/Bloomberg
Research Services
Employ the Workforce Necessary
Get Buy In From The Company Hierarchy For Acquisition of the
right Personnel
Establish Clear Lines of Communication Thus Creating
Transparency in The Decision Making Process
Hire Talented Analysts and Also Develop Home Grown Ones
Get Buy In From The Company Hierarchy
For Acquisition of the right Personnel
• The Three Key areas to focus on are:

Senior Executive Buy in

Everyday Reliance on Analytics

Required Analytics Capability Enabled
Talent
Senior Leadership Buy in Data
Summary
Source: SAS Institute/Bloomberg
Research Services
Perceived Positive Impact
Source: SAS Institute/Bloomberg
Research Services
Analytics Capability Enabled Talent
Source: SAS Institute/Bloomberg
Research Services
Summary of the Steps Needed
• Permeate the Company’s Decision Making Process With the Use of
Business Analytics
• Make Decision Making an Integrated Process Across The Company
• Make the Use of Analytics Strategically and Mission Focused
• Be More Aggressive in Acquiring/Learning Sophisticated and Specific
analytical Tools
• Articulate a Strategy for Managing and Accessing Data
• Acquire and Implement the Appropriate Technology Needed For Data
Driven Analytics Activities
• Formalize the Data Management Process
• Get Buy In From The Company Hierarchy For Acquisition of the right
Personnel
• Establish Clear Lines of Communication s Thus Creating Transparency in
the Decision Making Process
• Hire Talented Analyst and Develop Home Grown Ones
Establish Clear Lines of Communication Thus Creating Transparency in The Decision Making Process
Formalize the Data Management Process
Striking a Balance
• Data versus Judgment
The Optimal Balance depends on the Number of Factors, the Tools, the
People, and the Decision at hand
• Modeling for Decision Making
The Modeling tool is Context Dependent.
• Analysis versus Intuition
There is no fixed balance between the reliance on Intuition and Experience
and reliance on Quantitative Data and Analysis
Thank you
?
?QUESTION?
?
Sample Quiz for CAP
•
•
•
•
A sample Quiz to Test Your Analytics Acumen
CAP stands for Certified Analytics Professional
The Certification is Given by INFORMS
I will Grade These and Return them to you at
the Banquet.
Thank You.

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