Predictive Analytics with Oracle Data Mining

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted

Predictive Analytics with Oracle Data Mining

Vinay DeshmukhSenior Director Oracle Applications [email protected]

Bryan HodgeGlobal Leader Customer IntelligenceCustomer Support [email protected]

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. |

Safe Harbor StatementThe following is intended to outline our general product direction. It is intended for information purposes only, and may not be incorporated into any contract. It is not a commitment to deliver any material, code, or functionality, and should not be relied upon in making purchasing decisions. The development, release, and timing of any features or functionality described for Oracle’s products remains at the sole discretion of Oracle.

Oracle Confidential – Internal/Restricted/Highly Restricted 2


We run the applications that run OracleWe drive enhancements based on our experienceWe share best practices with our customers

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal 4

Value Chain Opportunities and Risks

Large and Diverse Customer Base

Transition to the cloud

Complex Global Hardware Value Chain

600+ global spares warehouses

Opportunity & RiskAssessment


Opportunity and Risk assessment using ODM

Discover hidden/subtle data patterns

Augment Value Chain Planning –both forward and

reverse

Identify inter-relationshipsamong data elements

Quantify likelihood of opportunity/risk

Rewind the clock and compare model accuracy

against actuals.

Oracle Data Mining


Oracle Advanced Analytics Evolution

Analytical SQL in the Database

1998 1999 2002 2005 20082004 2011 2015

• 7 Data Mining “Partners”

• Oracle acquires Thinking Machine Corp’s dev. team + “Darwin” data mining software

• Oracle Data Mining 10g & 10gR2 introduces SQL dm functions, 7 new SQL dm algorithms and new Oracle Data Miner “Classic” wizards driven GUI

• New algorithms (EM, PCA, SVD

• SQLDEV/Oracle Data Miner 4.0 “work flow” GUI launched with SQL script generation and SQL Query node (R integration)

• OAA/ORE 1.3 + 1.4 launched adding several new scalable R algorithms

• Oracle Adv. Analytics for Hadoop Connector launched with scalable BDA algorithms

• Oracle Data Mining 9.2i launched – 2 algorithms (NB and AR) via Java API

• ODM 11g & 11gR2 adds AutoDataPrep (ADP), text mining, perf. improvements

• SQLDEV/Oracle Data Miner 3.2 “work flow” GUI launched

• Integration with “R” and introduction/addition of Oracle R Enterprise

• Product renamed “Oracle Advanced Analytics (ODM + ORE)


Data remains in the Database Scalable, parallel Data Mining algorithms in SQL kernel Fast parallelized native SQL data mining functions, SQL data preparation and efficient

execution of R open-source packages High-performance parallel scoring of SQL data mining functions and R open-source

models

Fastest way to deliver enterprise-wide predictive analytics Integrated GUI for Predictive Analytics Database scoring engine

Lowest TCO Eliminate data duplication Eliminate separate analytical servers Leverage investment in Oracle IT

Oracle Advanced AnalyticsPerformance and Scalability with Low Total Cost of Ownership

avings

Model “Scoring”Embedded Data Prep

Data Preparation

Model Building

Oracle Advanced Analytics

Secs, Mins or Hours

Traditional Analytics

Hours, Days or Weeks

Data Extraction

Data Prep & Transformation

Data Mining Model Building

Data MiningModel “Scoring”

Data Prep. &Transformation

Data Import


Insert Picture Here

• Easy to Use– Oracle Data Miner GUI for data analysts

– “Work flow” paradigm

• Powerful– Multiple algorithms & data transformations

– Runs 100% in-DB

– Build, evaluate and apply models

• Automate and Deploy– Save and share analytical workflows

– Generate SQL scripts for deployment

SQL Developer 4.0 Extension Free OTN Download

Oracle Data Miner


More Data Variety—Better Predictive Models

• Increasing sources of relevant data can boost model accuracy

Naïve Guess or Random

100%

0% Population Size

Resp

onde

rs

Model with 20 variables



Model with “Big Data” and hundreds -- thousands of input variables including:• Demographic data• Purchase POS transactional

data• “Unstructured data”, text &

comments• Spatial location data• Long term vs. recent historical

behavior• Web visits• Sensor data• etc.

100%


Function Algorithms Applicability

ClassificationLogistic Regression (GLM)Decision TreesNaïve Bayes Support Vector Machines (SVM)

Classical statistical techniquePopular / Rules / transparencyEmbedded appWide / narrow data / text

Regression Linear Regression (GLM)Support Vector Machine (SVM)

Classical statistical techniqueWide / narrow data / text

Anomaly Detection One Class SVM Unknown fraud cases or anomalies

Attribute Importance

Minimum Description Length (MDL)Principal Components Analysis (PCA) Attribute reduction, Reduce data noise

Association Rules Apriori Market basket analysis / Next Best Offer

ClusteringHierarchical k-MeansHierarchical O-ClusterExpectation-Maximization Clustering (EM)

Product grouping / Text miningGene and protein analysis

Feature Extraction

Nonnegative Matrix Factorization (NMF)Singular Value Decomposition (SVD) Text analysis / Feature reduction

Oracle Advanced Analytics Algorithms

A1 A2 A3 A4 A5 A6 A7

F1 F2 F3 F4


In-Database Advanced Analytics

• Query compares the mean of AMOUNT_SOLD between MEN and WOMEN Grouped By CUST_INCOME_LEVEL ranges

• Returns observed t value and its related two-sided significance (<.05 = significant)

Independent Samples T-Test

SELECT substr(cust_income_level,1,22) income_level,avg(decode(cust_gender,'M',amount_sold,null)) sold_to_men,avg(decode(cust_gender,'F',amount_sold,null)) sold_to_women,stats_t_test_indep(cust_gender, amount_sold, 'STATISTIC','F') t_observed,stats_t_test_indep(cust_gender, amount_sold) two_sided_p_value

FROM sh.customers c, sh.sales sWHERE c.cust_id=s.cust_idGROUP BY rollup(cust_income_level)

ORDER BY 1;

Copyright © 2014 Oracle and/or its affiliates. All rights reserved. | Oracle Confidential – Internal/Restricted/Highly Restricted 12

Case Study: Support Cancellation Early WarningBryan Hodge


Case Study: Support Cancellation Early Warning

Premier Support Revenue$21B Contracts to be Renewed

8M Product Lines

Very diverse customer base Broad range of products

550K


Challenge:

Predict the small percentage of contracts/lines that are at risk in order to focus resources appropriately , and minimize losses


Business Solution

• Developed a cancellation early warning system

• Embedded system generated risk assessment into Forecasting Tool

• Sales Rep uses to help forecast & engage management

• Manager uses to inform forecast judgement


Two Phase Approach

Tribal Knowledge• Used sales rep experience to

identify risk attributes– E.g. Age of product, size of deal

• Profiled contract base • Established thresholds for Low,

Medium & High risk per attribute• Algorithm to balance attributes into

overall risk assessment

Oracle Data Miner• Analyzed one year of outcomes to

Train decision tree model– Cancelled or Renewed

• ‘Wound the clock back’ on six months more data

• Scored the six months data to generate predictions

• Assessed Accuracy at 85%= (True Positive + True Negatives ) /

Number of Observations


Oracle Data Miner - Details

Warehouse star schema with enhanced attributes

Attribute Importance Analysis

Trained decision tree model & assessed accuracy

Saved results in warehouse fact for use in Forecasting Tool

ODM Analysis


Business Benefits• Identified hidden relationships

across many attributes• Improved quality of risk assessment• Early intervention for customer sat• Reduced cancellation rates

– Bottom line improvements

Challenges• Ensuring statistically significant data

volumes in tree branches• Preparation of data to ‘wind the

clock back’• Avoiding bias during data prep.• Handling partially populated

attributes


Case Study: Predicting Spare Parts@riskVinay Deshmukh [email protected] Director Oracle Applications Lab


Case Study: Identify Spare parts @risk of short supply

Hardware Service Revenue$2.3B Global Spares Warehouses

Large deployment of Value Chain Planning . Augment VCP capabilities with Oracle Data Mining

Very diverse customer base

Broad range of products

1.5 million part-location pairs

supersessions & substitutions

600+


Problem Statement

Ensure a high level of service to our hardware customers by identifying the parts at risk of short supply at the warehouses closest to them and take proactive steps to remedy the shortage risk . Augment current Value Chain Planning Capabilities to provide risk assessment of parts@risk.


Demand SignalManagement

DemandManagement and

AdvancedForecasting

CollaborativePlanning and VMI

ProductionScheduling

Global OrderPromising

ServiceParts

Planning

Trade PromotionPlanning andOptimization

Supply and DistributionPlanning and Event-driven

Simulation

Network Designand

Risk Management

Sales andOperationsPlanning

PlanningAnalytics

• Single source of truth• Integrated with ERP

Oracle Value Chain Planning SolutionTransformational Tools

Deployed

Deployed

Deployed

Deployed

Deployed

DeployedDeployed


Solution Approach

1. Augment Value Chain Planning using ODM Model2.Exception Reporting 3.Customer Report


Model Attributes

Forecast Accuracy• MAPE

• Volatility

• Intermittency

Demand• Safety stock mean/std dev

• Forecast mean/std dev

• Shipments

• Backorders

Supply• On hand

• External repair orders

• Projected available balance

• Days of supply

Item Attributes• Cost

• PLC Code


Prototype Assumptions for the Model: 1 of 2

• 4 Models based on – AMER (US), AMER (Non US), EMEA and APAC

• Data used for training the model was Feb , Mar and Apr 2014 with May 2014 as the target

• Input data used in the model - Apr, May, Jun 2014 with Jul 2014 as the target

• Average and Std Dev used for time phased inputs to the Model – Forecast and Safety Stock. Latest value for projected available balance used.

• Current Backorders and Onhand considered


Prototype Assumptions for the Model: 2 of 2• For remaining parameters, 3 month average value used

• Item is marked at RISK if

• (backorder > 0 OR pab_qty < 0 OR ss_qty > oh_qty)

• Item is marked as ‘Not at RISK’ if

• (pab_qty between 0 and 0.25 )OR (oh_qty - ss_qty) between 0 and 0.25

• Remaining records were deleted. This tolerance logic was applied to restrict the count of ‘NO’ records in the training data

• The final output shows the items at risk along with the orgs where planning exceptions are generated in the latest run of the corresponding Value Chain Plan for spares


Accuracy Analysis

APACAMEREMEA

Total Cases: 2097False YES: 0 (0%)False NO: 204 (10%)Accuracy: 90%

Latin America





Exception Reporting

• OBIEE Report to show priority exceptions generated for the parts-at-risk predicted by the ODM Model (built on Oracle Advanced Planning Command Center , Oracle Value Chain Planning Suite)

• ODM Output stored in Value Chain Planning data model by specifying the region and organization

• VCP (Advanced Planning Command Center) reports latest exceptions for the respective plan for the parts-at-risk predicted


Detailed Solution – APCC Report


Customer Report

• This report shows the contract, base-model and party impacted by the parts-at-risk predicted by ODM Model

• Based on the part-at-risk, the model is fetched utilizing Demantra Data.

• 'EXPIRED', 'CANCELLED', 'TERMINATED‘ contracts are filtered out

• Premier Customers impacted by the parts @ risk are identified


Case Study: Predicting Hardware OpportunitiesVinay Deshmukh [email protected] Director Oracle Applications Lab


Problem Statement

• Predict the outcome of (non-Cloud) Hardware Opportunities whose expected revenue is greater than $1 million–Includes both Direct and Indirect sales channel

• For the opportunities predicted to be won , provide early visibility to suppliers and contract manufacturers by leveraging the capabilities of Value Chain Planning Suite .


Solution Approach• Train the ODM model with the historical data

– Opportunities from 31-Mar-2011 to 31-Aug-2013– Trained the model with opportunities that were Won or Lost between 31-Mar-2011 and

31-Aug-2013– Additional computed attributes used - product weight, customer weight, partner weight

• Predict the likely outcome of opportunities open as of 1-Sep-2013 using the model

• Test the prediction by comparing against actual wins and losses for predicted opportunities

• Future: Use the predicted opportunities in Value Chain Planning as causal factors to improve forecast accuracy


Model Attributes

Industry• Top level Industry

Product• Product Line

• Primary Competitor

• Product Group

• Product Weight

Customer• Account Weight

• Annual Revenue Category

• Number of Employees

Opportunity• Cycle Time

• Opportunity Status

• Expected Revenue

• Opened Date

Partner• Channel Type

• Partner Type

• Partner Weight

Geography• LOB Code

• Region

• Country

• Sales Method


Calculating weights using Bayesian approach• ((Customer 'x' Won Opty / Total Won Opty) * (Customer 'x' Total Opty / Total Opty)) /

(Σ(Customer 'x' Won Opty / Total Won Opty) * (Customer 'x' Total Opty / Total Opty))

• ((Product 'y' Won Opty / Total Won Opty) * (Product 'y' Total Opty / Total Opty) ) /

(Σ(Product 'y' Won Opty / Total Won Opty) * (Product 'y' Total Opty / Total Opty))

• ((Partner 'z' Won Opty / Total Won Opty) * (Partner 'z' Total Opty / Total Opty)) /

(Σ(Partner 'z' Won Opty / Total Won Opty) * (Partner 'z' Total Opty / Total Opty))

Where x = number of customers, y = Number of products, z = Number of partners

• Note: Direct and Indirect Partner weights are calculated separately. For customer weights = 0 they are replaced by the median of the customer weights


The Metrics

• False Positives Model predicted that an event will occur but the event did not occur over the risk horizon

• False Negatives Model predicted than an event will not occur over the risk horizon but the event did occur

Model accuracy = 1 - [(false positives + false negatives)/ total observations]


Accuracy Achieved (Direct + Indirect channels)

• Average Accuracy– 73.0 %

• Overall Accuracy– 78.2 %

• Accuracy of winning the deal – 84.0 %

Lost Won Total Correct %

Lost 1813 1114 2927 61.9406

Won 1309 6882 8191 84.0191

Total 3122 7996 11118

Actual

Predicted


Conclusion

Challenge

• Predict Contract Lines@risk• Predict Spare Parts@risk • Predict H/W opportunity Wins

Benefits

Solution

• Oracle Data Mining for predictive analytics• Augment Oracle Value Chain Planning capabilities provided by Oracle

Demantra and Oracle Advanced Planning Command Center• OBIEE

• Reduced Cancellation Rates• Improve Service Delivery Performance to hardware spares customers• Early demand visibility to suppliers

Predictive Analytics with Oracle Data Mining

Oracle Applications Lab



Date post:	03-Jan-2016
Category:	Documents
Upload:	fatima-little
View:	65 times
Download:	0 times

Predictive Analytics with Oracle Data Mining

Documents