After Market Service Auto Industry Leadership Forum. Copyright 2010 SAS Institute Inc. All rights reserved.

Transcription

1 After Market Service 2011 Auto Industry Leadership Forum Copyright 2010 SAS Institute Inc. All rights reserved.

2 Agenda Introduction In the Media Service Vision Customer Issues Solutions Value Questions & Discussion 2

3 seven Apple computers, two vehicles, a motorcycle and more than $571,000 all alleged to be fruits of the crime. 3

4 $1.7 million 4

5 $15.4 million 5

6 60 million bottles *Initial recall included 60 million bottles 6

7 $65 million in costs 11 lives 7

8 How does this relate to Service? 8

9 Service & Customers Companies expected their service organizations to: Run efficiently and effectively Keep costs low Contribute significantly to revenues and profits So much more Maintain, grow, and protect relationship Use events as an opportunity 9

10 Service & Customers Study: Customer experiencing positive service situations will be more satisfied with their product and more loyal to the brand Opposite is true as well, failure to act will drive customers away 10x harder to find a new customer than upsell existing Doing this is complex 10

11 Typical Service Chain Maintenance Scheduling Root Cause Analysis Supplier Recovery Suspect Claims Functions and systems integrated through business processes Parts Forecasting Inventory Management Call Center Early Warning Accrual Planning Service Contract Pricing Field Service Management Product Data Service Call Data Parts Data Customer Data 11

12 Using much of the same data and sharing functional outputs Diagnostic outputs Emerging issues impact Reliability Models Predicted failure rates Predicted stock Optimized service contract price Product Data Service Call Data Parts Data Customer Data 12

13 Opportunity Leverage technology that supports Data Integration» efficiently moving, cleaning and aligning data Advanced Analytics» predictive modeling, optimization, data and text mining Business Intelligence» method to deliver the information in a manner which is actionable and easy to use 13

14 After-Market Service Report Alert Predict Optimize 14

15 Customer Issue 1 An appliance manufacturer has maintained a leadership position in market share and experienced record growth on the basis that their products are the most reliable. A recent Consumer Reports study suggests that their rate of repair is actually higher than that of their competition and the quality of their product has been questioned openly on various social media websites. Economic pressures are forcing the company to find new ways to eliminate costs impacting the bottom line. SAS Warranty Analysis 15

16 After Market Service Intelligence Report Alert Predict Optimize 16

17 Production In Service Unit Fails Claim Submitted Claim Approved Issue Detected Issue Defined Issue Resolved Warranty Timeline Fraud Detection Issue Detection Problem Definition SAS Warranty Analysis 17

18 Solution Overview Enterprise Warranty Analysis Warranty Business Rules & Processes Claims Sales Data Product Data (origin, options, etc..) Dealer/Distributor Data Customer Call Center Warranty Information Store Customer Surveys Technician Hotlines Plant Audits Supplier Audits Corrective Actions 18

19 Solution Overview Dashboard Report Library Emerging Issues Reporting & Analysis Text Analysis Advanced Analysis Enterprise Warranty Analysis KPIs KPIs Traffic Lights Traffic Lights Warranty Business Rules & Processes Claims Sales Data Product Data (origin, options, etc..) Dealer/Distributor Data Customer Call Center Warranty Information Store Customer Surveys Technician Hotlines Plant Audits Supplier Audits Corrective Actions 19

20 Solution Overview Dashboard Report Library Emerging Issues Reporting & Analysis Text Analysis Advanced Analysis Enterprise Warranty Analysis Corporate Reports Custom Reports My Reports Warranty Business Rules & Processes Claims Sales Data Product Data (origin, options, etc..) Dealer/Distributor Data Customer Call Center Warranty Information Store Customer Surveys Technician Hotlines Plant Audits Supplier Audits Corrective Actions 20

21 Solution Overview Dashboard Report Library Emerging Issues Reporting & Analysis Text Analysis Advanced Analysis Enterprise Warranty Analysis Production Period Time in Service Time of Claim Warranty Business Rules & Processes Claims Sales Data Product Data (origin, options, etc..) Dealer/Distributor Data Customer Call Center Warranty Information Store Customer Surveys Technician Hotlines Plant Audits Supplier Audits Corrective Actions 21

22 Solution Overview Dashboard Report Library Emerging Issues Reporting & Analysis Text Analysis Advanced Analysis Enterprise Warranty Analysis 12 Out-of-the-box Warranty-focused Analyses, drillable, highly interactive Warranty Business Rules & Processes Claims Sales Data Product Data (origin, options, etc..) Dealer/Distributor Data Customer Call Center Warranty Information Store Customer Surveys Technician Hotlines Plant Audits Supplier Audits Corrective Actions 22

23 Solution Overview Dashboard Report Library Emerging Issues Reporting & Analysis Text Analysis Advanced Analysis Enterprise Warranty Analysis Advanced Text Dynamic Search Clustering Filtering Warranty Business Rules & Processes Claims Sales Data Product Data (origin, options, etc..) Dealer/Distributor Data Customer Call Center Warranty Information Store Customer Surveys Technician Hotlines Plant Audits Supplier Audits Corrective Actions 23

24 Solution Overview Dashboard Report Library Emerging Issues Reporting & Analysis Text Analysis Advanced Analysis Warranty Business Rules & Processes Claims Claim Fraud Detection Sales Data Environmental Product Data (origin, Influences options, etc..) Associated Dealer/Distributor Data Claims Predict Failures Customer Call Center Refine Codes Enterprise Warranty Analysis Advanced Ad Hoc Data Mining Text Mining Create Custom Models, Analytic Tasks, and Reports Warranty Information Store Customer Surveys Technician Hotlines Plant Audits Supplier Audits Corrective Actions 24

25 Solution Overview Dashboard Report Library Emerging Issues Reporting & Analysis Text Analysis Advanced Analysis Enterprise Warranty Analysis Warranty Business Rules & Processes Claims Sales Data Product Data (origin, options, etc..) Dealer/Distributor Data Customer Call Center Warranty Information Store Customer Surveys Technician Hotlines Plant Audits Supplier Audits Corrective Actions 25

26 Warranty Analysis Early Warning We are making constant progress and after six months, we are still discovering new ways of improving our warranty analysis process Mr. Nanxiang Gao, Field Performance Engineer Challenge Solution Results Issue identification cycle time was lengthy Problem solving cycle time was too long Data volumes were growing quickly and competition was getting more fierce SAS Warranty Analysis -SAS emerging issues component provides early warning of issues -SAS problem definition capabilities accelerate issue resolution. Shanghai GM has reduced issue identification and definition time by 70%, saving more than 4 months. Warranty costs have been reduced by 34% Shanghai GM saved over $2M USD in the first six months of using SAS Warranty Analysis. 26

27 Warranty Analysis Early Warning "SAS built us a comprehensive solution and offered us six more techniques they knew had worked for other manufacturers." John Kerr, General Manager of Quality & Operational Excellence Challenge Solution Results Issue identification cycle time was lengthy Need to reduce problem solving time to focus attention on innovation and design Terabytes of data in SAP R/3 and ServiceBench needed to be brought together for analysis. SAS Warranty Analysis & Text Miner -SAS emerging issues component provides early warning of issues -SAS problem definition capabilities accelerate issue resolution. Whirlpool has reduced the time required to detect and resolve issues by up to 90 days. SAS is an integral part of Whirlpool s plan to cut the cost of quality in half 27

28 Warranty Analysis Claim Coding & Early Warning We can detect and resolve issues much quicker before a large number of products ever reach customers' homes. David Bien, Corporate Director of Reliability Challenge Solution Results Issue identification cycle time was lengthy Manually processed each text based service order, introducing time and variation to the warranty process. Multiple different tools for root cause analysis SAS Warranty Analysis & Text Miner - Utilizing SAS text mining to automatically code warranty claims. - SAS emerging issues component provides early warning of issues and the root cause analysis capabilities accelerate issue resolution. Automated text coding using text mining has freed up resources for analysis and removed coding errors. Sub-Zero has reduced issue identification and definition time by more than 3 months. Reduced warranty costs by 14% 28

29 Customer Issue 2 Engineers at a computer manufacturer have noticed a large number of invalid warranty claims in their database. These claims make it very difficult for them to discover the true issues their products are having in the field and drive quality improvement projects. In addition, they are concerned with undetermined costs associated with these invalid claims. Suspect Claims Detection 29

30 After Market Service Intelligence Report Alert Predict Optimize 30

31 Production In Service Unit Fails Claim Submitted Claim Approved Issue Detected Issue Defined Issue Resolved Warranty Timeline Fraud Detection Issue Detection Problem Definition Suspect Claims Detection 31

32 Traditional Claims Auditing Approaches Manual Adjudication Automated (Rules Based) Adjudication Return Validation and/or Field Inspection Field Audits Analytics 32

33 SAS Hybrid Approach Business Rules Anomaly Detection Advanced Analytics Social Network Analysis Suitable for known patterns Suitable for unknown patterns Suitable for complex patterns Suitable for associative link patterns Examples: Used invalid failure code Part not possible for product model Exceed allowed labor hours Examples: Mileage driven exceeds norm Labor hours exceeds norm Number of parts used exceeds norm Examples: Part used does not match customer complaint text Similar service provider behavior as known fraud Examples: Service providers owned by same company showing similar patterns Technician with fraud history moves from one service provider to another Hybrid Approach Apply combination of all approaches 33

34 Value: Suspect Claim Reduction Reduced Service Claim Costs Claim costs are reduced up to 10% by denying payment, authorizing partial payment, or charging back for invalid claims. Audit processes are more efficient, allowing more value with the same staff. System acts as a deterrent for future claim fraud. Value is immediate 34

35 Case Study: Suspect Claims Detection We needed a tool that didn t require us to go fishing for the data. It needed to surface problems automatically. Richard Miller Field Service Solutions Challenge Solution Results Service providers learned to work around rules-based flags. Auditors couldn t see larger patterns and could only audit a subset of claims. Near real-time, analytic detection system was put in place to detect suspicious claims. Claims and Service Providers ranked based on likelihood of fraud. Focused auditors on the right claims and service providers. Saved over $5M in first year of operation and reduced the number of auditors. External Story 35

36 Customer Issue 3 One of the world s largest original design manufacturer companies producing information communication technologies (ICT) products was operating high inventory levels without the methodology and technology needed to forecast last-time buys and end-of-life products. Excel made planning tedious, manual and heavily reliant on experience and so they needed a solution to achieve effective forecast accuracy and reduce high inventory. Service Parts Optimization Case Study 36

37 After Market Service Intelligence Report Alert Predict Optimize 37

38 Case Study Kverneland, a Nordic leader in agriculture machinery, collects sales and demand data from SAP plus other marketing databases which stages into a SAS data mart. SAS SPO then plans inventory, replenishes low stock, priorities orders, alerts for exceptions such as over / understock, while lowering inventory and raising service levels. From the Executive Director of Supply Chain, We can carry 25% less inventory and fill 10% more orders. 38

39 Case Study AutoZone, a national auto-parts supplier, controls stock at over 3000 stores and 80 warehouses ensuring high customer service levels with minimal inventory. Store assortments are optimized based on unique store demand patterns and store cluster attributes using external data such as Polk to drive forecast accuracy. "SAS helps us accurately forecast consumer demand for service parts and accessories in our 3,000+ stores by sifting useful information from tens of billions of data points, so we can optimize our inventory and assets while serving our customers better. Rajeeve Kaul, Director of product and price optimization 39

40 Customer Issue 4 A FORTUNE 500 company provides mobile telecommunications service, products and features, and mobile web service, products and features to retail customers throughout the United States. With increasing consumer demand, more aggressive competition and higher cost of operations, the customer was looking for new ways to drive up customer satisfaction/retention and grow their customer base while reducing operations expense. Contact Center Planning & Optimization 40

41 After Market Service Intelligence Report Alert Predict Optimize 41

42 Case Study FedEx was challenged to provide consistent service level goals for all call centers with minimum answer times of all calls of 90% (within 20 seconds). Their use of SAS predictive analytics resulted in FedEx forecasts being consistently accurate to within a 1 to 2% of actual call volumes. Accurate forecasts have increased first call completes and has lowered call escalations. This solution allows us to schedule the network with precision, ensuring that coverage needs are met, service levels are maintained, and costs are controlled to the fullest extent possible. In accomplishing these accuracy objectives, our forecasting group has gained respect and trust from our customers both inside and outside the company. 1 Weidong Xu, LONG RANGE PLANNING FOR CALL CENTERS AT FEDEX THE JOURNAL OF BUSINESS FORECASTING 42

43 Customer Issue 5 A FORTUNE 500 company provides leasing, rental and programmed maintenance of trucks, tractors and trailers to commercial customers. With 70% of its revenue generated by Fleet Management Solutions (FMS), the customer was looking for new ways to reduce the high maintenance costs while improving the availability of trucks. Predictive Asset Maintenance 43

44 After Market Service Intelligence Report Alert Predict Optimize 44

45 Text & M&D Data Classification & Clustering Reliability Information Store Optional: Text Mining: Prepare text data of service reports for analysis Data Mining: Categorize incidents and conditions to service code clusters Scoring: Assign M&D data to service codes Known Incidents/Defects 45

46 Text & M&D Data Classification & Clustering Optional: Text Mining: Prepare text data of service reports for analysis Data Mining: Categorize incidents and conditions to service code clusters Scoring: Assign M&D data to service codes Known Incidents/Defects M&D Data, Conditions 46

47 Text & M&D Data Classification & Clustering Optional: Text Mining: Prepare text data of service reports for analysis Data Mining: Categorize incidents and conditions to service code clusters Scoring: Assign M&D data to service codes Known Incidents/Defects M&D Data, Conditions Near-Neighbors (Service Code Cluster) 47

48 Text & M&D Data Classification & Clustering Optional: Text Mining: Prepare text data of service reports for analysis Data Mining: Categorize incidents and conditions to service code clusters Scoring: Assign M&D data to service codes Conditions/sensor data similar to a known incident or defect Known Incidents/Defects M&D Data, Conditions Near-Neighbors (Service Code Cluster) 48

49 Manufacturing Roche Diagnostics Challenge Solution Results Implement a quality monitoring system for diagnostics solutions installed in laboritories around the world Reduce downtimes of customer s diagnostic equipment Calculate worldwide distribution of the accuracy of measurements for specific diagnostic tests SAS Predictive Analytics Nightly update of equipment performance data and measurement results Early Warning and automated alerting of service engineers via Automated root-cause analysis of 3,000+ different equipment alerts Drill-down capabilities geographically and system wise Reduced downtimes significantly by early warning and preventative maintenance Improvement of service quality Improvement of product quality The global quality monitoring solution added true intelligence to our systems. Now, we are far ahead of our competitors regarding product quality and customer service Martin Masch, Project Manager MITO(Market Introduction Tool for Performance Observation), Roche Diagnostics GmbH 49

50 Questions & Discussion Copyright 2010 SAS Institute Inc. All rights reserved.