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

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2 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 2015 Lockheed Martin Corporation

3 LEARNING POINTS 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

4 LOCKHEED MARTIN OVERVIEW 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 2014 Sales: $45.6 Billion Backlog: $80.5 Billion Cash Flow from Operations: $3.9 Billion Stock Ticker Symbol: Ranked 59 th on the 2014 Fortune 500 List of Largest Industrial Corporations LMT, on the New York Stock Exchange Source: ("Who We Are Lockheed Martin", 2015)

5 LOCKHEED MARTIN BUSINESS AREAS 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 Training with 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 Source: ("Who We Are Lockheed Martin", 2015), ("Our Businesses Lockheed Martin", 2015)

6 LOCKHEED MARTIN SPACE SYSTEMS Source: ("Space Systems Company Portfolio", 2013)

7 SUPPLY CHAIN MANAGEMENT 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 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 Source: ("Supply Chain Management", 2015), ("Supply Chain Threat Management", 2013)

8 SUPPLY CHAIN MANAGEMENT LEAD TIMES 1. Run MRP to Generate Planned Order 2. Convert Planned Order to PR 3. Release / Approval PR 6. Supplier Manufacturing 7. Source Inspection 8. Deliver Part / Material MRP Input: Program / Production Need Dates Planner / PR Release Lead Time Buyer Lead Time Supplier Lead Time Goods Receipt Lead Time PR = Purchase Requisition PO = Purchase Order 4. Convert PR to PO 5. Negotiate & Place PO with Supplier 9. Receive Part / Material at Dock 10. Inspect Part / Material 11. Place Part / Material on Floor or to Stock

9 LEAD TIMES BUSINESS CASE 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 Provide the Ability to Delegate Lead Time Updates Problems Solutions Benefits 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

10 RETURN ON INVESTMENT 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

11 DATA ANALYTICS 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? ) Source: (Bertolucci, 2013)

12 PREDICTIVE 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

13 CURRENT SAP ARCHITECTURAL LANDSCAPE SAP HANA In-Memory Database and Platform for Predictive Analytics SAP Client Tools SAP Predictive Analysis, SAP InfiniteInsight, SAP Lumira SAP ERP Custom App SAP HANA Studio Application Function Modeler R-Server R-Engine Business Function Library Spatial Processing R Script In-Memory Processing Engine Application Function Library Automated Predictive Library Text-Analysis Graph Engine Predictive Analysis Library Full-Text Search Rules Engine Cluster Analysis Classification Analysis Regression Analysis Association Analysis Time Series Analysis Data Preparation Statistical Algorithms Social Network Analysis SAP ABAP SAP Business Suite SAP SLT ABAP Accelerators SAP ERP SAP Data Services Data Connectors Location Data Machine Data Time-Series Data Transaction Data Unstructured Data Real-time (Stream) Data Source: ("SAP HANA Predictive Analysis Library", 2014)

14 read read FOCUSED SAP ARCHITECTURAL LANDSCAPE ABAP Program write 4 ABAP Accelerator Read Lead Times Calc Engine write 2 R Stored Procedure Scheduled Job SAP DS Database SAP ERP 1 Real-Time Replication with SAP LT Database SAP HANA 3 Statistical Processing R Runtime R Server SAP Advanced Business Application Programing (SAP ABAP): SAP HANA (SAP HANA): Provides the Ability to Build Custom Applications for Managing Provides an In-Memory Database to Handle High Transaction Lead Times Rates & Complex Query Processing for Lead Times SAP Enterprise Resource Planning (SAP ERP): SAP Data Services (SAP DS): Provides the Ability to Handle Purchase Requisitions Data Provides Scheduling & Execution of Predictive R Code (Including Lead Times) R Server (R Server): SAP Landscape Transformation (SAP SLT): Provides the Execution of R Code to Support Predictions Provides Real Time Data Replication from SAP ERP to our SAP ABAP Accelerator (SAP ABAP Accelerator): SAP HANA Database for all Purchase Requisitions to Allow Provides ABAP Program with the Ability to Access Predicted Predictions to be Performed Lead Time Data through SAP HANA Source: ("SAP LT Replication Server Overview", 2014)

15 Lead Time Data (SAP SLT) ABAP Accelerator Results LEAD TIMES DATA FLOW 4 Write Results 2 3 Data Services R Stored Procedure HANA Statistical Processing R Server Part Received Scanned / Manually Entered 10 ERP (Workflow) 7 Buyer Commit Lead Times Maintenance Tool (Web)

16 LEAD TIME MAINTENANCE Supplier Lead Time Maintenance Tool 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 DEN ABCDEFG00001 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00002 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00003 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00004 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00005 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00006 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00007 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00008 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00009 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00010 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00011 Bolt Pattern J Sys Pwr AV2 PROJ I MRP Desc Clear LT Save LT

17 LEAD TIME DELEGATATION Supplier Lead Time Maintenance Tool Supplier Lead Time Maintenance Tool Plant DEN Purchasing Group 43J MRP Controller AV2 CCAS 43I AV1 DEN 43J AV2 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 DEN ABCDEFG00001 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00002 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00003 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00004 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00005 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00006 Bolt Pattern J Sys Pwr AV2 PROJ I DEN ABCDEFG00007 Bolt Pattern J Sys Pwr AV2 PROJ I MRP Desc Clear LT Save LT

18 LOCKHEED MARTIN & SAP DATA SCIENCE Lockheed Martin Team Had Extensive Knowledge in: SAP ABAP SAP HANA SAP ERP SAP ABAP Accelerator SAP SLT SAP Data Services 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

19 PREDICTIVE ANALYTICS Classification Analysis Regression Analysis Link Prediction Examples Manufacturing Retail/CPG Association Analysis Predictive Analytics Time Series Analysis Transport & Logistics High Tech Oil and Gas Outlier Detection Cluster Analysis Probability Distribution Public sector Utilities Sports Banking

20 PREDICTIVE ANALYTICS PROCESS Historical Data Business Rules Explanators Assumptions Statistical Techniques Predictive Models Predictive Analytics

21 SOLUTION PROCESS (PROBLEM) 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) Supplier Lead Time Purchase Requisition (PR) Release Date PO Item Placed Date PO Item Actual Receipt Dock Date

22 SOLUTION PROCESS (OVERVIEW) Clean Data Outliers Construct a Hierarchy Between Material and Part Family, e.g. Material: ABC Part Family 10: ABC Part Family 8: ABC12345 Construct Empirical Density Function for Material & Part Family 12, 10, 8, 6

23 SOLUTION PROCESS (OVERVIEW) 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

24 SOLUTION PROCESS (BOX PLOT) First Quartile (Q 1 ) or the 25 th Percentile Q1-1.5 X IQR Q1 IQR Q3 Q3-1.5 X IQR Q 2 Called the Median or the 50 th Percentile Outliers Outliers Third Quartile (Q 3 ) or the 75 th Percentile Median 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

25 SOLUTION PROCESS (PDF & CDF) Probability Density Function PDF(x) Where x is a Random Variable (Lead Time) Cumulative Density Function CDF(x) from PDF(x) PDF(x) CDF(x) x x

26 SOLUTION PROCESS (FIT) Material Number PDF(x) CDF(x) Family Part 6 x x PDF(x) CDF(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 x

27 SOLUTION PROCESS (SAP HANA & R INTEGRATION) 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 Source: ("SAP HANA R Integration Guide", 2014)

28 PROCESS SOLUTION (DEVELOPMENT) 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)

29 PROCESS SOLUTION (LESSONS LEARNED) Running SQL in R is Much Slower than Running SQL in HANA & Forwarding Entire SQL Results to R One Material Per Call ~ 10 seconds 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 = FALSE); Computer Time for About 60,000 Material Numbers ~ 7 Days! Not Feasible

30 PROCESS SOLUTION (LESSONS LEARNED) 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!

31 KEY LEARNINGS 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

32 THANK YOU FOR ATTENDING Thank You for Attending! Dr. Stephan Gerali Lockheed Martin Enterprise Business Services Dr. Rafael Pacheco SAP America Data Science

33 REFERENCES Bertolucci, J. (2013, December 31). Big Data Analytics: Descriptive Vs. Predictive Vs. Prescriptive - InformationWeek. Retrieved March 2, 2015, from SAP HANA R Integration Guide. (2014, January 1). Retrieved March 2, 2015, from Who We Are Lockheed Martin. (2015, January 1). Retrieved March 3, 2015, from Our Businesses Lockheed Martin. (2015, January 1). Retrieved March 3, 2015, from

34 REFERENCES (CONTINUED) Space Systems Company Portfolio. (2013, January 1). Retrieved March 3, 2015, from Systems-Supplier-Conference-2014.pdf Supply Chain Management. (2015, February 27). Retrieved March 3, 2015, from Supply Chain Threat Management. (2013, January 1). Retrieved March 3, 2015, from Systems-Supplier-Conference-2014.pdf

35 REFERENCES (CONTINUED) 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 92aa5a784377?QuickLink=index&overridelayout=true&

36 THANK YOU FOR PARTICIPATING Please provide feedback on this session by completing a short survey via the event mobile application. SESSION CODE: BI1521 For ongoing education on this area of focus, visit