Predictive Analytics at the Edge. Achieving Enduring Value in the Connected World

Transcription

1 Predictive Analytics at the Edge Achieving Enduring Value in the Connected World

2 SAS in the Industry Serving Manufacturing since 1980 Over 2,000 manufacturing customers in more than 52 countries. 82% of Manufacturing Companies in the Fortune 500 rely on SAS 47 of the Fortune Top Global of the Gartner Global Supply Chain Top 25 Automotive, Aerospace, Chemicals, Energy, Consumer Products, High Tech, Industrial, and Process

3 Creating Value with Analytics Its not magic its math Sense Understand Act Value Business Efficiency, Safety and Optimization New Business Models

4 Industry 4.0 The digitally inter-connected world Quality Service Supply Chain Customer Experience Predict quality excursions Detect customer issues Avoid unplanned downtime Increase production yield Detect field issues earlier Delight customers Optimize service parts inventory Increase service level commitments while minimizing risk Understand demand signals Shape demand to balance supply Improve operations planning with greater forecast accuracy Optimize inventory Create relevant, individualized customer experiences in real time Know and share data from every customer interaction across channels

5 Quality One of top computer manufacturer achieved over a 50 percent reduction in issue detection time and a 10 to 15 percent reduction in warranty costs from out-of-norm defects. We help electronics maker Murata Manufacturing save $50M/year through improved yield. SAS gives Western Digital engineers the insights they need to identify possible failures early in the production process and make timely decisions to avoid a yield excursion, ensuring Western Digital hard drives are of the highest quality. -- KH Sim

6 Service VR Group developed a predictive program that focuses on monitoring the condition of parts at all times. In this program, mathematical models predict when parts are likely to fail so that they can be replaced before they cause unplanned downtime. SAS Analytics gives VR Group a real-time overview of its fleet. SAS enabled a medical device manufacturer to predict device failure 5 to 10 days in advance, with >70% accuracy and <20% false alarms. They used these insights to profitably exceed SLA commitments on their program.

7 Supply Chain With SAS, a global appliance manufacturer improved forecast accuracy by 10% and 100,000 units, reduced inventory >12%, and had a 1% increase in revenue. They improved their product availability to >93% to achieve NPV in excess of $20m and payback within 1 year. Amway China improved replenishment time by 20 percent, and customer satisfaction soared to 97 percent. A major automotive company used SAS to achieve 3% growth in 7 months.

8 Customer Experience HP has seen a 20% incremental ROI across campaigns. Orders shipped have increased by 50% in three years, and the overall operating profit of the HPDirect.com store has increased by more than 50%. SAS helps Maruti Suzuki cross-sell, up-sell and repeat sell to 10 million customers. Janssen Pharmaceuticals increased marketing channel lift by 20% to 30% with SAS.

9 Predictive Analytics Data IOT data stream Unstructured Data Maintenance records Internal Data Set Predictive Analytics Event Prediction RCA Situation Simulation Performance Optimization Asset Lifetime prediction Take Decision Value Business Efficiency, Safety and Optimization New Business Models Quality Service Supply Chain Customer Experience

10 Value of Predictive Maintenance Independent reports* indicate the following industrial average savings resultant from the initiation of a functional predictive program: Return on investment: 10 times Reduction in costs: 25% - 30% Elimination of breakdowns: 70% - 75% Reduction in downtime: 35% - 45% Increase in production: 20% - 25% *Source: Operations and Maintenance Best Practices Guide. US Department of Energy

11 Benefits Maintenance Maturity Roadmap Maintenance Strategies Maintenance Best Practices Effective Strategy Performance based Risk based 5 D Condition based Maintenance Define root causes of failures Monitor asset condition Set warning and alarm levels Determine p-f interval Condition based 4 C Usage based Maintenance Introduce timers e.g. run hours, mileage Logical meters Nesting of tasks Breakdown Planned 0 A Preventive 1 B C D 3 2 Efficient Operations Capability Maturity B A Preventive Time based Regulatory tasks Vendor recommended actions Operator Asset Care Work initiation & prioritizing Clear notifications Gatekeeping on work requests Work Request Prioritization Equipment Criticality

12 Benefits Maintenance Maturity Roadmap Breakdown Advanced Maintenance Strategies Effective Strategy Planned Preventive Condition based 2 Performance based Risk based 3 E F 4 G H 5 H G F Maintenance Best Practices Predictive Asset Maintenance Integrate AM, SCADA and operations data sources Improve efficiency of performance monitoring Data driven models to generate early warnings Asset lifetime prediction Optimize planning and scheduling Performance monitoring Define performance KPIs Assure data quality & integrity Monitor plant performance in real time Combine operating and decision functions in one operations centre Living reliability centered Criticality Ranking on value drivers Define failure modes and assess risks Optimize intervals Close improvement loop using failure codes 0 1 Efficient Operations Capability Maturity E Preventive Maintenance Optimization Optimize strategy based on bad actor Pareto analysis Reduce consequences Schedule compliance

13 SAS Platform THE PERSONA BASED INTERFACE Dashboard Monitoring Reports & Visualization Alerts Analytical Workbench Predictive Modeling Model Management Case Management Analytics Layer Business Rules & Processes Sensor Data Maintenance Records Asset Data (origin, options, etc.) Historian Data Operations Data Data Model Financial/Risk Data Operator Data & Complaints Inspection Records Technician/Engineer Data External data

14 Enrich Store Deploy Analytics Lifecycle Stream Understand Act Data Data Storage ETL Deploy Alerts - Reports Decisioning Streaming Data Streaming Model Execution

15 Edge Getting Analytics closer to the Sensor Edge Analytics, Distributed Analytics Sensor Sensor Readings Devices Gateways Operational Data Stores Operational Apps Edge of Network Operational Technology Information Technology Analytics By 2019, about 40% of IoT-data will be stored, processed, analyzed, and acted upon close to, or at the edge of the network. IDC 2016 IoT Futurescape Cloud, On Premise

16 Key Partnerships and Industry Participation

17 Achieving Enduring Value in the Connected World Open Analytics Platform Industry Thought-Leaders Global Presence Analytic Expertise

18 David Dozoul SAS Institute Thank You