Porter introduction-a

Porter P. C. Lin

Outline

• Expertise & Major Achievements• P-S-P Challenges• Executing Action Plan• Expecting Contributions• Q&A

Expertise & Major

Achievements

Expertise:• Background includes: IE, Logistics, IT and MBA

– MBA M.S. of International MBA Program of NCCU– Logistics M.S. of NCTU– IE B.S. of Fen Chia University – IT Oracle ( OCP DBA) & Microsoft (MCSE) Certified

• Comprehensively and Systematically mind set.– Milestone JCA successfully from IT to Operation team.

• Operations and People management.• CT/ IPD Project Management experiences.

• Good practicing with people management theory. – Effective communication skill.– Enthusiastic, with Passions for people management.

Major Achievements

• FY08 APAC VP Purple Promise Award• People management

– Average SFA is over 4.0– GOLD candidate coaching– Sr. Courier Candidates set– Part time employees management.

• Breaking the rules by making impossible to be “I’m possible”:– Planning & Executing Early PUP Model in FY10. – CLH enhancement after CANH launching. – ASIA - One delay with PM service Level 100%.– Early push shuttle, during CNY rush hour.

P-P-P Personality

Integrator

P-S-P Challenges

• People – Efficiency vs. Safety– Safety Mind Set– Common Job Grading– Team members turn over

• Service– CANH + CLH Impact+ Flight late arrival– Model Ops Re-Enforcing – Customer feedbacks – Compliances and Rules

Challenges - Internal & External

• Profit– Global Economy recovery uncertainly – Customization evaluation with Cost surging – Market share varying with Cargo direct flight– Cross Strait Opportunities

• Others – People , Service & Profit Equilibrium– Change management– Synergy Generating and enduring– Moral sustain

Challenges - Internal & External

Executing Action Plan &

Strategy

Executing Action Plan & Strategy :

• STAR– Steering Team by Actions Reinforcing

• Leadership Strategy– Double E– B – A – C & U – A – C

-

•Right person, Right position, Right people

management

•Disciplines is to keep Ops on Track

•Performance Orienting to hold manager accountable

People Management

CostManagement

OperationManagement

ReportCustomerRelationship

Execution

STAR Steering Team by Actions Reinforcing

- -

• Plan annual budget with monitoring and managing Cost.

• Ensuring all the contract meet Corp compliances.

• Rational cost down with performance driving.

People Management

CostManagement

OperationManagement


Execution


-

• Model Ops , Model our way forward! Model our way / Model our mindset / Model our language Planning, Executing, & Fine Tuning Cooperate with Dispatch team FAMIS insight ! Discipline with correct method

• Safety above all Efficiency vs. Safety Safety culture evolving and enduring

People Management

CostManagement

OperationManagement


Execution


• Customer Feedback Be at cause Take it as Opportunity Hold Manager accountable

• Align with sales to meet customers’ need, & review the necessity

• Review with manager for ad hoc customer exceptions

People Management

CostManagement

OperationManagement


Execution


• Value will be created by Synergy with Effective Executions

•Keep the balance in mind and in place

• Only focus on what we can change, seeking opportunity of what we can influence

• Safety, Security, Regulatory & Compliance Compliance keeps us on track Commitments lead us ahead!

People Management

CostManagement

OperationManagement


Execution


Cost Management

Effective Executions

People Management

Customer Relationship

Operation Management

Balancing

Balancing

Bala

ncin

gBalancing

•Stretching out “Two Hands” :– Internally : People management– Externally : Customer relationship

•Standing firmly with “Two Legs ” – Operation management– Cost management

•Effective Executions in “Mind ” – Effective executions make it works.– Safety, Security, Regulatory & Compliance

Cost Management

SafetySecurity Regulatory Compliance

People Management

Operation Management Cost

Management

Effective Executions

People Management


Operation Management

Balancing

Balancing

Bala

ncin

g

Balancing


Leadership Strategy Double E : Execution & Engagement

•Executing by limiting the Knowing - Doing Gap– The Key element of Execution is to follow up– Harnessing the power of passion and simplicity to get result– To distill the most complex issues into simple

•“ E = MC 2 “

– Engagement = Motivation * Communication * Commitments • Aligning the common goal • Motivating by the Value contribution mindset• Effective communications by managing

commitments

Leadership Strategy B-A-C & U-A-C

•Be At Cause– Learning & reading the messages from employees’

interactions.– Help employees to see what they can see but not see.– It’s often not the mountain ahead, but the grain of sand in

shoes bothering.

•Understanding Alignment and Commitment– Effective Coaching by Listening ! – Seek first to understand, then to be understood. – What you talk does not matter, But What they really listen

does matter.

Expecting Contributions

•Emerge Internal Synergy by Culture Evolving– Right People asset Management– Purple DNA, Purple Mindset

•Create External Synergy by Well aligning– Effective Communications– Common Goal Setting

•Get Overall Operations Rationalizing & Balancing– Effectiveness, Efficiency and Safety– Model Ops Enforcing

Expecting Contribution :

Vision : L-E-DLeveraging, Engaging, & Delivering

SynergyEngagement

Competence

Organization capability

Front Line Managers

Competitiveness & Value

Team’s Value

Leveraging the ProfessionalEngaging the PeopleDelivering the Value

Resources: Revised from HRM Resources: Revised from HRM Lecture IMBA NCCU SH Lee Lecture IMBA NCCU SH Lee 20052005

Synergy

Generating

Driving the Force

Those Robust Bricks Build Up “Great Wall”


STAR Steering Team by

Actions Reinforcing

B-A-C

Execution

U-A-C

Integrator

Engagement

Vision L-E-D

The Porter PromiseLeading Team to be a “AAA ” Diamond Station : Aligned the team Actively, & Running team to be the most Aspiring team

DAMA-NCR

•Tuesday, November 13, 2001•Laura Squier

•Technical Consultant•[email protected]

What is Data Mining?

Agenda

• What Data Mining IS and IS NOT• Steps in the Data Mining Process

– CRISP-DM– Explanation of Models– Examples of Data Mining

Applications• Questions

Evolutionary Step Business Question EnablingTechnologies

Product Providers Characteristics

Data Collection(1960s)

"What was my totalrevenue in the lastfive years?"

Computers, tapes,disks

IBM, CDC Retrospective,static data delivery

Data Access(1980s)

"What were unitsales in NewEngland lastMarch?"

Relationaldatabases(RDBMS),Structured QueryLanguage (SQL),ODBC

Oracle, Sybase,Informix, IBM,Microsoft

Retrospective,dynamic datadelivery at recordlevel

Data Warehousing& DecisionSupport(1990s)

"What were unitsales in NewEngland lastMarch? Drill downto Boston."

On-line analyticprocessing(OLAP),multidimensionaldatabases, datawarehouses

SPSS, Comshare,Arbor, Cognos,Microstrategy,NCR

Retrospective,dynamic datadelivery at multiplelevels

Data Mining(Emerging Today)

"What’s likely tohappen to Bostonunit sales nextmonth? Why?"

Advancedalgorithms,multiprocessorcomputers, massivedatabases

SPSS/Clementine,Lockheed, IBM,SGI, SAS, NCR,Oracle, numerousstartups

Prospective,proactiveinformationdelivery

The Evolution of Data Analysis

Results of Data Mining Include:

• Forecasting what may happen in the future

• Classifying people or things into groups by recognizing patterns

• Clustering people or things into groups based on their attributes

• Associating what events are likely to occur together

• Sequencing what events are likely to lead to later events

Data mining is not•Brute-force crunching of bulk data •“Blind” application of algorithms•Going to find relationships where none exist•Presenting data in different ways•A database intensive task•A difficult to understand technology requiring an advanced degree in computer science

Data Mining Is

•A hot buzzword for a class of techniques that find patterns in data•A user-centric, interactive process which leverages analysis technologies and computing power•A group of techniques that find relationships that have not previously been discovered•Not reliant on an existing database•A relatively easy task that requires knowledge of the business problem/subject matter expertise

Data Mining versus OLAP

•OLAP - On-line Analytical Processing

– Provides you with a very good view of what is happening, but can not predict what will happen in the future or why it is happening

Data Mining Versus Statistical Analysis

•Data Analysis– Tests for statistical

correctness of models• Are statistical assumptions

of models correct?– Eg Is the R-Square

good?– Hypothesis testing

• Is the relationship significant?

– Use a t-test to validate significance

– Tends to rely on sampling– Techniques are not optimised

for large amounts of data– Requires strong statistical

skills

•Data Mining– Originally developed to act as

expert systems to solve problems

– Less interested in the mechanics of the technique

– If it makes sense then let’s use it

– Does not require assumptions to be made about data

– Can find patterns in very large amounts of data

– Requires understanding of data and business problem

Examples of What People are Doing with Data Mining:

•Fraud/Non-Compliance Anomaly detection

– Isolate the factors that lead to fraud, waste and abuse

– Target auditing and investigative efforts more effectively

•Credit/Risk Scoring•Intrusion detection •Parts failure prediction

•Recruiting/Attracting customers •Maximizing profitability (cross selling, identifying profitable customers) •Service Delivery and Customer Retention

– Build profiles of customers likely to use which services

•Web Mining

How Can We Do Data Mining?

By Utilizing the CRISP-DM Methodology– a standard process – existing data– software

technologies – situational expertise

Why Should There be a Standard Process?

•Framework for recording experience

– Allows projects to be replicated

•Aid to project planning and management•“Comfort factor” for new adopters

– Demonstrates maturity of Data Mining

– Reduces dependency on “stars”

The data mining process must The data mining process must be reliable and repeatable by be reliable and repeatable by people with little data mining people with little data mining background.background.

Process Standardization

CRISP-DM: • CRoss Industry Standard Process for Data Mining• Initiative launched Sept.1996• SPSS/ISL, NCR, Daimler-Benz, OHRA• Funding from European commission• Over 200 members of the CRISP-DM SIG worldwideOver 200 members of the CRISP-DM SIG worldwide

– DM Vendors - SPSS, NCR, IBM, SAS, SGI, Data Distilleries, DM Vendors - SPSS, NCR, IBM, SAS, SGI, Data Distilleries, Syllogic, Magnify, ..Syllogic, Magnify, ..

– System Suppliers / consultants - Cap Gemini, ICL Retail, Deloitte System Suppliers / consultants - Cap Gemini, ICL Retail, Deloitte & Touche, …& Touche, …

– End Users - BT, ABB, Lloyds Bank, AirTouch, Experian, ...End Users - BT, ABB, Lloyds Bank, AirTouch, Experian, ...

CRISP-DM

•Non-proprietaryNon-proprietary•Application/Industry Application/Industry neutralneutral•Tool neutralTool neutral•Focus on business issuesFocus on business issues

– As well as technical As well as technical analysisanalysis

•Framework for guidanceFramework for guidance•Experience baseExperience base

– Templates for Templates for AnalysisAnalysis

The The CRISP-CRISP-DM DM Process Process ModelModel

Why CRISP-DM?•The data mining process must be reliable and repeatable by people with little data mining skills

•CRISP-DM provides a uniform framework for –guidelines –experience documentation

•CRISP-DM is flexible to account for differences –Different business/agency problems–Different data

BusinessUnderstanding

DataUnderstanding

EvaluationDataPreparation

Modeling

Determine Business ObjectivesBackgroundBusiness ObjectivesBusiness Success Criteria

Situation AssessmentInventory of ResourcesRequirements, Assumptions, and ConstraintsRisks and ContingenciesTerminologyCosts and Benefits

Determine Data Mining GoalData Mining GoalsData Mining Success Criteria

Produce Project PlanProject PlanInitial Asessment of Tools and Techniques

Collect Initial DataInitial Data Collection Report

Describe DataData Description Report

Explore DataData Exploration Report

Verify Data Quality Data Quality Report

Data SetData Set Description

Select Data Rationale for Inclusion / Exclusion

Clean Data Data Cleaning Report

Construct DataDerived AttributesGenerated Records

Integrate DataMerged Data

Format DataReformatted Data

Select Modeling TechniqueModeling TechniqueModeling Assumptions

Generate Test DesignTest Design

Build ModelParameter SettingsModelsModel Description

Assess ModelModel AssessmentRevised Parameter Settings

Evaluate ResultsAssessment of Data Mining Results w.r.t. Business Success CriteriaApproved Models

Review ProcessReview of Process

Determine Next StepsList of Possible ActionsDecision

Plan DeploymentDeployment Plan

Plan Monitoring and MaintenanceMonitoring and Maintenance Plan

Produce Final ReportFinal ReportFinal Presentation

Review ProjectExperience Documentation

Deployment

Phases and TasksPhases and Tasks

Phases in the DM Process:Phases in the DM Process:CRISP-DMCRISP-DM

Phases in the DM Process (1 & 2)

•Business Understanding:– Statement of

Business Objective– Statement of Data

Mining objective– Statement of Success

Criteria

•Data Understanding– Explore the data and

verify the quality– Find outliers

Phases in the DM Process (3)

• Data preparation:– Takes usually over 90% of our time

• Collection• Assessment• Consolidation and Cleaning

– table links, aggregation level, missing values, etc

• Data selection– active role in ignoring non-

contributory data?– outliers?– Use of samples– visualization tools

• Transformations - create new variables


• Model building– Selection of the modeling

techniques is based upon the data mining objective

– Modeling is an iterative process - different for supervised and unsupervised learning• May model for either

description or prediction

Types of Models

•Prediction Models for Predicting and Classifying

– Regression algorithms (predict numeric outcome): neural networks, rule induction, CART (OLS regression, GLM)

– Classification algorithm predict symbolic outcome): CHAID, C5.0 (discriminant analysis, logistic regression)

•Descriptive Models for Grouping and Finding Associations

– Clustering/Grouping algorithms: K-means, Kohonen

– Association algorithms: apriori, GRI

Neural Network

Output

Hidden layer

Input layer

Neural Networks• Description

– Difficult interpretation– Tends to ‘overfit’ the data– Extensive amount of training time– A lot of data preparation– Works with all data types

Rule Induction•Description

– Produces decision trees:• income < $40K

– job > 5 yrs then good risk

– job < 5 yrs then bad risk• income > $40K

– high debt then bad risk– low debt then good risk

– Or Rule Sets:• Rule #1 for good risk:

– if income > $40K– if low debt

• Rule #2 for good risk:– if income < $40K– if job > 5 years

Cat. % nBad 52.01 168

Good 47.99 155Total (100.00) 323

Credit ranking (1=default)

Cat. % nBad 86.67 143

Good 13.33 22Total (51.08) 165

Paid Weekly/MonthlyP-value=0.0000, Chi-square=179.6665, df=1

Weekly pay

Cat. % nBad 15.82 25Good 84.18 133Total (48.92) 158

Monthly salary

Cat. % nBad 90.51 143

Good 9.49 15Total (48.92) 158

Age CategoricalP-value=0.0000, Chi-square=30.1113, df=1

Young (< 25);Middle (25-35)


Old ( > 35)


Age CategoricalP-value=0.0000, Chi-square=58.7255, df=1

Young (< 25)


Middle (25-35);Old ( > 35)


Social ClassP-value=0.0016, Chi-square=12.0388, df=1

Management;Clerical

Cat. % nBad 58.54 24

Good 41.46 17Total (12.69) 41

Professional

Rule Induction

Description• Intuitive output• Handles all forms of numeric data, as well

as non-numeric (symbolic) data

C5 Algorithm a special case of rule induction

• Target variable must be symbolic

Apriori

Description• Seeks association rules in

dataset• ‘Market basket’ analysis• Sequence discovery

Kohonen NetworkDescription• unsupervised• seeks to

describe dataset in terms of natural clusters of cases


• Model Evaluation– Evaluation of model: how well it

performed on test data– Methods and criteria depend on

model type:• e.g., coincidence matrix with

classification models, mean error rate with regression models

– Interpretation of model: important or not, easy or hard depends on algorithm


•Deployment– Determine how the results need to be

utilized– Who needs to use them?– How often do they need to be used

•Deploy Data Mining results by:– Scoring a database– Utilizing results as business rules– interactive scoring on-line

Specific Data Mining Applications:

What data mining has done for...

Scheduled its workforce to provide faster, more accurate

answers to questions.

The US Internal Revenue Service needed to improve customer service and...


analyzed suspects’ cell phone usage to focus investigations.

The US Drug Enforcement Agency needed to be more effective in their drug “busts” and


Reduced direct mail costs by 30% while garnering 95% of the

campaign’s revenue.

HSBC need to cross-sell more effectively by identifying profiles that would be interested in higheryielding investments and...

Final Comments• Data Mining can be utilized in any

organization that needs to find patterns or relationships in their data.

• By using the CRISP-DM methodology, analysts can have a reasonable level of assurance that their Data Mining efforts will render useful, repeatable, and valid results.

Questions?

Porter introduction-a

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