Achieving Total Quality

Transcription

1 Achieving Total Quality 2011 Auto Industry Leadership Forum Copyright 2011, SAS Institute Inc. All rights reserved.

2 Agenda Introduction Quality Today Quality Vision Customer Issues Solutions Value Questions & Discussion 2

3 Quality Today Manufacturing Marketing Customer Engineering Service 3

4 The Problem Competition & Market Dynamics & Media Consumers are more informed Margins are razor thin How do you stack up? Buyer s market Knowledgeable Demand satisfaction Only satisfied customers are profitable 4

5 The Problem Substandard product quality Compromises customer satisfactions Negative quality perceptions Erodes brand image Damage to brand equity Become a commodity Hot potato quality 5

6 The Problem Cost? 15% - 40% Cost of goods sold Scrap, Rework, Returns, Reduced Service Levels, Lost Revenue 5X greater to eliminate in the field versus development or mfg Made sense to focus on Manufacturing Process ANDON / Jidoka Six Sigma Lean Kaizan Problems Persist Recalls, Engineers spend 80% time integrating data, lack of automation Must take a broader view of quality 6

7 Customer Driven Quality Manufacturing Marketing Customer Engineering Service 7

8 Customer Driven Quality Enables entire organization to raise a flag Fosters collaboration and participation across enterprise Identifying Issues, Defining Issues, Solving Problems Results in Dramatic improvement in return on quality Improved customers satisfaction Positive perceived quality Valuable brand equity Less customers that experience an issue 8

9 Customer Driven Quality How is the achieved? Utilize data inside and outside the organization Both standard and non-standard Integrating all data sources Available to all those involved in maintaining quality Leverage Advanced Analytics to exploit these data» Automate, Automate, Automate» time is money (find issues sooner) 9

10 Customer Driven Quality Production Assets Social Media Manufacturing Marketing Customer Continuous Product Improvement Engineering Service Warranty Call Center 10

11 Customer Driven Quality - Examples Quality starts early with product development Major mobile phone manufacturer» Short product lifecycle» 90% component reuse» Alarm prior to release of new products Automotive manufacturer» Longer product lifecycle» Service, Quality, Product Engineering working together on continuous improvement» Battery leaks & Association analysis 11

12 Customer Driven Quality - Examples Increased use of machines and automation in process Great improvements to productivity and quality Many more subtle ways quality can be compromised Can only be seen in vast amounts of data Must apply analytics and automated alerting» Solve problems fast» Eliminate scrap, rework, production disruptions Semiconductor manufacturer» Yield issues» Many parameters» Predictive models, regression, multivariate analysis 12

13 Customer Driven Quality - Examples Major capital assets used to manufacture product Properly maintained assets will» Perform longer» Have a positive impact on quality» Eliminate maintenance costs» Maximize availability Natural gas producer» Large amounts of capital assets (compressors, pumps)» Unexpected compressor shut down = 38 days lost» Gathered data across 400 assets» Developed predictive models, score new data» Planned shutdown = 20 day improvement 13

14 Customer Driven Quality - Examples Defect will make it customers More costly than fixing during development/manufacturing Customer satisfaction, perceived quality, brand image at stake Service organization is at the forefront Properly handled issue can entrench positive brand image Service must be part of the integrated team Service data must be leveraged High-End luxury appliance manufacturer» Relies entirely on brand image» Existing process inefficient» Utilize text analytics and automated early warning» Detect, define, prioritize improved by 90 days» 15% reduction in warranty costs 14

15 Customer Driven Quality - Examples What do the iphone, xbox, and Drymax diapers have in common? Each suffered a quality issue made worse by social media Social media is the newest challenge Dramatic impact on quality perception and image Opportunity for manufacturers Insights never before possible Both good and bad Put the Customer in Customer Driven Quality 15

16 Customer Driven Quality - Examples P&G Drymax diapers Networking blogging moms Baby s skin burns P&G initially challenged moms $8B business segment at risk Resolution through social media partnering 16

17 Customer Driven Quality - Examples Social media analytics Detecting Issues Prioritizing Issues Calibrating/optimizing internal quality standards Detect Facebook Fan Noise Prioritize New Product Launch, Crisper Drawers Optimize» What is quality but the customers view of your product» Cosmetic standards» Fan noise issue passed quality specification 17

18 Customer Driven Quality - Summary Must be an integrated organization Focus on the customer AND quality Leverage all available data Apply automated advanced analytics Will maintain positive perceptions of products Will maintain brand image (must valuable asset) 18

19 SAS Quality Lifecycle Analysis Overview Dashboard Reports Alerts Analytical Workbench Quality Lifecycle Analysis Predictive Modeling Model Management Business Rules and Processes Parts Movement Measurements Testing Inspection Products & Hierarchies Information Store Financial & Organization Equipment Repair & FA Field Failures Supplier Data 19

20 Yield Time to Market Development Yield Ramp Volume Production 90% 70% 25% Time 20

21 Yield Issue Detection & Resolution Development Yield Ramp Volume Production 90% 70% 60% Duration (before) 25% Duration (after) Time T x-a T x T y-a T y 21

22 Yield Performance Improvement Development Yield Ramp Volume Production 92% 90% 70% 25% Time 22

23 Benefits to Customers With SAS, manufacturers will significantly reduce costs during the entire lifecycle of products: Shortened time to market Establishing control and reduce variation Determining process capability Shortening cycle times Improved Problem Resolution Pinpointing critical problems quickly Gaining process understanding. Performance Improvement Reducing waste and rework Improving products and processes 23

24 SAS Quality Lifecycle Excellence: Close the Loop SPC Quality Management Lab Info. Mgmt. Sys. Event Notification Operational Quality Management MES ERP QMS Monitoring and Alerting Quality Lifecycle Analysis Interactive Discovery Document Management Scheduling Dispatching Inspection Planning SPC MES ERP Reporting and Collaboration Root Cause Analysis QMS 24

25 SAS Quality Lifecycle Analysis POSCO Challenge Solution Results Make the most of Six Sigma performance strategy to enhance production quality and improve earnings. Click here to access customer success story The SAS Enterprise Intelligence Platform uncovers new insights into physical processes for significant ROI. "SAS has directly contributed an ROI of $14 million on Six Sigma projects and an additional $1.5 million on other projects. That s an impressive result in less than two years, and we have anticipated ways to gain even greater returns in the future." Ill-Chul Shin, Manager and Master Black Belt at POSCO s Six Sigma Academy 25

26 SAS Quality Lifecycle Analysis W.L. Gore Business Issue Solution Results Enhance product quality and increase yields through process knowledge discovery. Click here to access customer success story SAS analytics and quality applications help Gore leverage manufacturing information, processes and controls to make intelligent decisions and realize better results. In one case, we increased the already exceptional reliability of a particular fiber optics technology by more than 70 percent. We could not have reached that level of success without SAS and the collaboration with the SAS developers and the technical support teams." Dr. José Ramirez, Industrial Statistician W.L. Gore & Associates 26

27 SAS Predictive Maintenance Overview Dashboard Monitoring Reports Alerts Analytical Workbench Predictive Modeling Predictive Asset Maintenance Model Management Business Rules & Processes SCADA data Maintenance Records Asset Data (origin, options, etc.) Historian Data Operations Data Reliability Information Store Financial/Risk Data Operator Data & Complain Inspection Records Technician/Engineer Data External data 27

28 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 28

29 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 29

30 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) 30

31 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) 31

32 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 32

33 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 33

34 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 34

35 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 35

36 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 36

37 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 37

38 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 38

39 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 39

40 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 40

41 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 41

42 Failure Relationship Analysis Links indicate relationships among codes. The table includes information on the strength of the links, as well as the cost.

43 Failure Relationship Analysis Each code is linked to itself and shows how often the same code occurs on the same unit.

44 Text Analysis Slide Title Notes Notes Notes

45 Multivariate Analysis

46 Multivariate Analysis

47 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. 47

48 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 48

49 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% 49

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