Deploying Predictive Analytics Solutions Dr. Stephan Gerali Lockheed Martin Dr. Rafael Pacheco SAP

Deploying Predictive

Analytics Solutions

Dr. Stephan Gerali – Lockheed Martin

Dr. Rafael Pacheco – SAP

SESSION CODE: BI1521

How Lockheed Martin Space Systems

Uses Predictive Analytics to Forecast

Supply Chain Management

Performance



Analyze the Business Case for Forecasting

Supplier Scheduling Performance



Learn How to Utilize SAP Technologies to Build a

Robust Predictive Analytics Capability



Provide Lessons Learned in the Development of

Lockheed Martin’s Predictive Analytics Solution

 Company:

 Headquartered in Bethesda, Md., Lockheed Martin is a Global Security and Aerospace Company that is Principally Engaged in the Research, Design, Development,

Manufacture, Integration and Sustainment of Advanced Technology Systems, Products & Services

 Employees:

 112,000 Domestic & International Employees

 Operations:

 Domestically, 542 Facilities in 500 Cities Throughout All 50 States

 Internationally, Business Locations in 70 Nations & Territories

LOCKHEED MARTIN OVERVIEW

 2014 Sales:

 $45.6 Billion

 Backlog:

 $80.5 Billion

 Cash Flow from Operations:

 $3.9 Billion

 Stock Ticker Symbol:

 Ranked 59th_{on the 2014 Fortune 500 List of}

Largest Industrial Corporations

 LMT, on the New York Stock Exchange

 Aeronautics, with Approximately $14.9 Billion in 2014 Sales which includes Tactical Aircraft, Airlift, and Aeronautical Research and Development Lines of Business

 Information Systems & Global Solutions (IS&GS), with Approximately $7.8 Billion

in 2014 Sales that Includes C4I, Federal Services, Government & Commercial IT Solutions

 Missiles and Fire Control, with Approximately $7.7 Billion in 2014 Sales that

Includes the Terminal High Altitude Area Defense System, Joint Light Tactical Vehicle, PAC-3 Missiles as some of its High-Profile Programs

 Mission Systems and Trainingwith Approximately $7.1 Billion in 2014 Sales,

which Includes Naval Systems, Platform Integration, Simulation and Training and Energy Programs Lines of Business

 Space Systems, with Approximately $8.1 Billion in 2014 Sales which Includes

Space Launch, Commercial Satellites, Government Satellites, and Strategic Missiles Lines of Business

LOCKHEED MARTIN BUSINESS AREAS

LOCKHEED MARTIN SPACE SYSTEMS

 Supply Chain Management Definition:

 Supply Chain Management (SCM) is "the Systemic, Strategic Coordination of the Traditional Business Functions & the Tactics Across these Business Functions within a Particular Company for the

Purposes of Improving the Long-Term Performance of the Individual Companies and the Supply Chain as a Whole.“

 Supply Chain Management Proposition:

 If You Can Better Predict (with Reliability), When a Part Will Arrive, You can Better Plan, Manage & Optimize Your Supply Chain to Improve Cost, Schedule & Quality Constraints

SUPPLY CHAIN MANAGEMENT

 Lockheed Martin SSC Supply Chain:

 Lockheed Martin SSC Manages Over 5,200+ Suppliers (1,500+ First Tier Suppliers)

 375,000 Annual Inbound / Outbound Shipments

 Shipments Originate from 22 Countries

 200+ Transportation Service Providers

 Highly Regulated Parts & Materials

 Represents ~70% of Final Product Cost

SUPPLY CHAIN MANAGEMENT LEAD TIMES

1. Run MRP to Generate Planned Order 2. Convert Planned Order to PR 3. Release / Approval PR 4. Convert PR to PO 5. Negotiate & Place PO with Supplier 6. Supplier Manufacturing 7. Source Inspection 8. Deliver Part / Material 9. Receive Part / Material at Dock 10. Inspect Part / Material 11. Place Part / Material on Floor or to Stock

Planner /

PR Release

Lead Time

Buyer

Lead Time

Supplier

Lead Time

Goods

Receipt

Lead Time

MRP Input: Program / Production Need Dates PR = Purchase Requisition PO = Purchase Order

LEAD TIMES BUSINESS CASE

• Lead Times Updated Quarterly Based on Buyer’s Knowledge & Average Lead Times

• Lead Times Require ETL (Extract, Transform & Load) from Lead Times Maintenance Tool to ERP

• Opportunities Exist for Automating Lead Times

• Lead Times Updated Daily to Reflect Current Market Conditions (with Delegate Support)

• Lead Times Updated Right to ERP

• Recommended Lead Times Created Based on Predictive Analytics Algorithms

• More Accurate (Real Time) Lead Times Available in ERP

• Better Forecasted Lead Times to Reduce Maintenance Overhead for Lead Time Updates

• Better Foresight into Delivery of Parts Supporting SSC Programs

Problems

Solutions

Benefits

Lead Time Maintenance Tool

• Provide the Ability to be Notified Daily When Parts are Received

• Provide the Ability to Update Lead Times After Parts are Received

• Provide the Ability to Recommend Lead Times using Historical Projections



Improve Material Master Lead Time Accuracy by 25%



Reduce SCA/Buyer Lead Time Maintenance Effort by

80%



Recommend Optimal Lead Times for Current Parts &

New Parts



Near Real-Time Report of Recommended “Optimal”

Material Master Lead Times with Direct Auto Update of

ERP



Descriptive Analytics

– Uses Data Aggregation & Data

Mining Techniques to Provide Insight into the Past

(“What Has Happened?”)



Predictive Analytics

– Uses Statistical Models &

Forecast Techniques to Understand the Future

(“What Could Happen?”)



Prescriptive Analytics

– Uses Optimization & Simulation

Algorithms to Advice on Possible Outcomes

(“What Should We Do?”)

DATA ANALYTICS



Predictive Analytics is the Practice of Extracting Information from

Existing Data Sets in Order to Determine Patterns & Predict Future

Outcomes & Trends



Predictive Analytics Forecasts What Might Happen in the Future with

an Acceptable Level of Reliability



Statisticians & Data Miners Utilize the R Programming Language for

Statistical Computing & Forecasting



SAP HANA Integrates the Power of the R Programming Language with

an In-Memory Database Capable of Performing Quick Data Analytics

CURRENT SAP ARCHITECTURAL LANDSCAPE

SAP HANA In-Memory Database and Platform for Predictive Analytics

SAP Client Tools

SAP Predictive Analysis, SAP InfiniteInsight, SAP Lumira

In-Memory Processing Engine

Application Function Library

R-Engine R Script R-Server Text-Analysis Spatial Processing Rules Engine Business Function Library Predictive Analysis Library Full-Text Search Graph Engine Automated Predictive Library Location

Data Machine_Data

Time-Series Data Transaction Data Unstructured Data Real-time (Stream) Data Data Connectors

SAP Business Suite SAP SLT ABAP Accelerators SAP ERP SAP Data Services

Application Function Modeler SAP HANA Studio • Cluster Analysis • Classification Analysis • Regression Analysis • Association Analysis • Time Series Analysis • Data Preparation • Statistical Algorithms • Social Network Analysis SAP ERP

Custom App

SAP ABAP

FOCUSED SAP ARCHITECTURAL LANDSCAPE

Database ABAP Program Database w rit e _read Real-Time Replication with SAP LT ABAP Accelerator

Read Lead Times

Source: ("SAP LT Replication Server Overview", 2014)

SAP HANA SAP ERP SAP DS Scheduled Job R Server R Runtime 1 Database 2 _ProcedureR Stored 3 _ProcessingStatistical Calc Engine w rit e _read 4

 SAP Advanced Business Application Programing (SAP ABAP):  Provides the Ability to Build Custom Applications for Managing

Lead Times

 SAP Enterprise Resource Planning (SAP ERP):

 Provides the Ability to Handle Purchase Requisitions Data (Including Lead Times)

 SAP Landscape Transformation (SAP SLT):

 Provides Real Time Data Replication from SAP ERP to our SAP HANA Database for all Purchase Requisitions to Allow Predictions to be Performed

 SAP HANA (SAP HANA):

 Provides an In-Memory Database to Handle High Transaction Rates & Complex Query Processing for Lead Times

 SAP Data Services (SAP DS):

 Provides Scheduling & Execution of Predictive R Code  R Server (R Server):

 Provides the Execution of R Code to Support Predictions  SAP ABAP Accelerator (SAP ABAP Accelerator):

 Provides ABAP Program with the Ability to Access Predicted Lead Time Data through SAP HANA

 SAP Advanced Business Application Programing (SAP ABAP):  Provides the Ability to Build Custom Applications for Managing

Lead Times

 SAP Enterprise Resource Planning (SAP ERP):

 Provides the Ability to Handle Purchase Requisitions Data (Including Lead Times)

 SAP Landscape Transformation (SAP SLT):

 Provides Real Time Data Replication from SAP ERP to our SAP HANA Database for all Purchase Requisitions to Allow Predictions to be Performed

 SAP HANA (SAP HANA):

 Provides an In-Memory Database to Handle High Transaction Rates & Complex Query Processing for Lead Times

 SAP Data Services (SAP DS):

 Provides Scheduling & Execution of Predictive R Code  R Server (R Server):

 Provides the Execution of R Code to Support Predictions  SAP ABAP Accelerator (SAP ABAP Accelerator):

 Provides ABAP Program with the Ability to Access Predicted Lead Time Data through SAP HANA

LEAD TIMES DATA FLOW

Buyer Part Received ERP 5 6 Email (Workflow) Scanned / Manually Entered

HANA

Lead Times Maintenance Tool (Web)

ABA P Acc ele ra to r 7 8 R Server Data Services 2 R Stored Procedure 3 Statistical Processing 4 Write Results 10 Commit 9 Re su lts 1 Lead Ti m e Dat a (SA P SLT )

LEAD TIME MAINTENANCE

Supplier Lead Time Maintenance Tool

http://servername/MaterialMasterLeadTime/GetSupplierLeadTimeView

Supplier Lead Time Maintenance Tool

Update Recommended Planned Delivery Time

Plant Part Number Part Number Desc Current Planned Delivery Time 80% Probability LT Avg LT Mid LT Mode LT Purchasing Group Purchasing Group Desc MRP Controller MRP Desc

DEN ABCDEFG00001 Bolt Pattern 1 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00002 Bolt Pattern 2 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00003 Bolt Pattern 3 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00004 Bolt Pattern 4 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00005 Bolt Pattern 5 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00006 Bolt Pattern 6 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00007 Bolt Pattern 7 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00008 Bolt Pattern 8 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00009 Bolt Pattern 9 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00010 Bolt Pattern 10 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00011 Bolt Pattern 11 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I

Save LT 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 2.00 Clear LT

LEAD TIME DELEGATATION

Supplier Lead Time Maintenance Tool

http://servername/MaterialMasterLeadTime/GetSupplierLeadTimeView

Supplier Lead Time Maintenance Tool

Update Recommended Planned Delivery Time

Plant Part Number Part Number Desc Current Planned Delivery Time 80% Probability LT Avg LT Mid LT Mode LT Purchasing Group Purchasing Group Desc MRP Controller MRP Desc

DEN ABCDEFG00001 Bolt Pattern 1 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00002 Bolt Pattern 2 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00003 Bolt Pattern 3 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00004 Bolt Pattern 4 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00005 Bolt Pattern 5 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00006 Bolt Pattern 6 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I DEN ABCDEFG00007 Bolt Pattern 7 5.00 2.00 1.01 0.70 1.50 43J Sys Pwr AV2 PROJ I

Save LT 2.00 2.00 2.00 2.00 2.00 2.00 2.00 Clear LT

Plant DEN Purchasing Group 43J MRP Controller AV2 CCAS

DEN

43I



Lockheed Martin Team Had Extensive Knowledge in:



Lockheed Martin Had Knowledge to Build an Integrated Solution for Lead

Times but Needed Assistance with Predictive Capability for Lead Times to

Help Meet Customer’s Aggressive Delivery Requirements



Partnered with SAP Data Science (John Sullivan & Rafael Pacheco) to Help

with the Data Science Aspect of Project & Help Deliver Predictive Capability



Now We Will Walk Through the Data Science Aspects of this Project

LOCKHEED MARTIN & SAP DATA SCIENCE



SAP ABAP



SAP ERP



SAP SLT



SAP HANA



SAP ABAP Accelerator

PREDICTIVE ANALYTICS

Regression

Analysis

Cluster

Analysis

Classification

Analysis

Outlier

Detection

Probability

Distribution

Link

Prediction

Predictive

Analytics

Time Series

Analysis

Association

Analysis

 Manufacturing  Retail/CPG

 Transport & Logistics  High Tech

 Oil and Gas  Public sector  Utilities  Sports  Banking

PREDICTIVE ANALYTICS PROCESS

Statistical

Techniques

Business

Rules

Historical

Data

Predictive

Models

Predictive

Analytics

Assumptions

Explanators



Example: Supplier Lead Time



The Time Between the Purchase Order Receipt Dock Date & the

Time the Purchase Order Item is Initially Placed (in Calendar

Days)

SOLUTION PROCESS (PROBLEM)

Purchase Requisition (PR) Release Date PO Item Placed Date PO Item Actual Receipt Dock Date

…

…



Clean Data – Outliers



Construct a Hierarchy Between Material and Part

Family, e.g.



Material: ABC123456789



Part Family 10: ABC1234567



Part Family 8: ABC12345



Construct Empirical Density Function for Material &

Part Family 12, 10, 8, 6



Determine the Cumulative Probability Distribution



Select the Desired Probability (the Item will be

Supplied on Time for the Material or Part Family

within Certain Probability)



Recommend the Lead Time Value from the Number

of Records in Material, Part Family 12, 10, 8, 6

SOLUTION PROCESS (BOX PLOT)

IQR

Q3 Q1



First Quartile

(Q

₁

) or the 25

th

Percentile



Q

₂

Called the

Median

or the 50

th

Percentile



Third Quartile

(Q

₃

) or the 75

th

Percentile



Interquartile Range

IQR = Q

₃

- Q

₁



Lower Fence

: Q1-1.5 X IQR



Upper Fence

: Q3-1.5 X IQR



Outliers

: Points Beyond the Fences

Q1-1.5 X IQR Q3-1.5 X IQR

Median



Probability Density Function PDF(

x

) Where

x

is a

Random Variable (Lead Time)



Cumulative Density Function CDF(

x

) from PDF(

x

)

SOLUTION PROCESS (PDF & CDF)

x x



Material Number



Family Part 6

SOLUTION PROCESS (FIT)

x x

PDF(x) CDF(x)

PDF(x) CDF(x)

x x

Data for Material Number is Sparse & CDF is not as Smooth as that of Family Part 6

Therefore, the Recommend Lead Time comes from Family Part 6



R Code is Embedded in SAP HANA

SQL Code in the Form of a RLANG

Procedure (See SQLScript Code on

Right)



SAP HANA Databases Uses External

R Server Environment to Execute R

Code (See Picture On Right)



SAP HANA Calculation Engine

Handles Communications Between R

Server & HANA

SOLUTION PROCESS (SAP HANA & R INTEGRATION)



Use R-Studio for Development & Testing of Code



Data can be Retrieved or Written from/to SAP

HANA via JDBC Connections



Deploy in HANA for Production: R code is

Embedded in SAP HANA SQL Code in the Form

of a RLANG Procedure (as Described Earlier)



Running SQL in R is Much Slower than Running SQL in

HANA & Forwarding Entire SQL Results to R



One Material Per Call ~ 10 seconds



Computer Time for About 60,000 Material Numbers ~ 7

Days! Not Feasible

PROCESS SOLUTION (LESSONS LEARNED)

xyz6 <- caracter_m6;

nxyz6 <- nchar(xyz6);

input6.dat <- fn$sqldf('select * from data_input where Material_Number like "$xyz6%" ', row.names =

FALSE);

xyz8 <- caracter_m8;

nxyz8 <- nchar(xyz8);

input8.dat <- fn$sqldf('select * from data_input where Material_Number like "$xyz8%" ', row.names =



SQL Queries Should Be Moved from R to HANA



Replace (Row Bind)

rbind

by Pre-Allocating Vectors

Instead of Data-Frames



Computer Time for about 60,000 Material Numbers

Reduced from 7 Days to 4 Hours!



Analyze the Business Case for Forecasting

Supplier Scheduling Performance



Learn How to Utilize SAP Technologies to Build a

Robust Predictive Analytics Capability



Provide Lessons Learned in the Development of

Lockheed Martin’s Predictive Analytics Solution

THANK YOU FOR ATTENDING

Thank You for Attending!

Dr. Stephan Gerali

Lockheed Martin Enterprise Business Services

[email protected]

Dr. Rafael Pacheco

SAP America Data Science

 Bertolucci, J. (2013, December 31). Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive -InformationWeek. Retrieved March 2, 2015, from http://www.informationweek.com/big-data/big-data-analytics/big-data-analytics-descriptive-vs-predictive-vs-prescriptive/d/d-id/1113279

 SAP HANA R Integration Guide. (2014, January 1). Retrieved March 2, 2015, from

http://help.sap.com/hana/sap_hana_r_integration_guide_en.pdf

 Who We Are · Lockheed Martin. (2015, January 1). Retrieved March 3, 2015, from

http://www.lockheedmartin.com/us/who-we-are.html

 Our Businesses · Lockheed Martin. (2015, January 1). Retrieved March 3, 2015, from

http://www.lockheedmartin.com/us/who-we-are/organization.html

 Space Systems Company Portfolio. (2013, January 1). Retrieved March 3, 2015, from

http://www.lockheedmartin.com/content/dam/lockheed/data/corporate/documents/suppliers/Space-Systems-Supplier-Conference-2014.pdf

 Supply Chain Management. (2015, February 27). Retrieved March 3, 2015, from

http://en.wikipedia.org/wiki/Supply_chain_management

 Supply Chain Threat Management. (2013, January 1). Retrieved March 3, 2015, from

http://www.lockheedmartin.com/content/dam/lockheed/data/corporate/documents/suppliers/Space-Systems-Supplier-Conference-2014.pdf

 Shi, X. (Director) (2014, November 1). SAP HANA Predictive Analysis Library. SAP Webcast. Lecture conducted from SAP.

 SAP LT Replication Server Overview. (2014, July 1). Retrieved March 5, 2015, from

http://www.sdn.sap.com/irj/scn/go/portal/prtroot/docs/library/uuid/70dd6e62-0113-3110-f58e-92aa5a784377?QuickLink=index&overridelayout=true&59511066929077

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SESSION CODE: BI1521

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