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New Technologies For The Sustainable Enterprise; keynote @Wharton

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Dinner keynote at Wharton May 9th 2011 @ 11th Annual Strategy and the Business Environment Conference (SBE) jointly with the 3rd Annual Research Conference Alliance for Research on Corporate Sustainability (ARCS)

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New Technologies For The Sustainable Enterprise; keynote @Wharton

  1. 1. New Technologies For The Sustainable Enterprise<br />Paul Hofmann, SAP Labs North America<br />Wharton, May 9th 2011<br />
  2. 2. What Does SAP Do? <br />Financial/Mgmt Accounting <br />Sales Order Management<br />Talent Management<br />Production Planning<br />Business Intelligence<br />© SAP AG 2010. All rights reserved. / Page 2<br />
  3. 3. SUPPLIER<br />CUSTOMER<br />DISTRIBUTIONCENTER<br />CFO<br />CRO<br />Sourcing<br />CUSTOMS/<br />REGULATORY AGENCY<br />CUSTOMS/<br />REGULATORY AGENCY<br />Customs Operations<br />COO<br />Fulfillment<br />Receiving<br />Manufacturing<br />EVERY 2ND DOLLAR OF WORLD TRADE RUNS ON SAP<br />Suppliers and<br />Customers<br />Exports<br />Imports<br />Export Compliance<br />GLOBAL<br />ENTERPRISE<br />
  4. 4. 100,000 Companies Run SAP<br />
  5. 5. Summary of SAP Today<br />SAP AG in 2010 revenues: $16.5 billion<br /><ul><li>~53,000 SAP employees
  6. 6. 100,000 companies run SAP software
  7. 7. 121,000 installations
  8. 8. Provides 26 industry solutions
  9. 9. 1 Suite</li></ul>12 million users in 140+ countries<br />Unique partner ecosystem<br /><ul><li>More than 1.3 million community members (SDN and BPX)
  10. 10. More than 3,850 industry partners</li></li></ul><li>Consumer Products <br />1.4 million sales order line items per day<br />Business Intelligence> Analytics over > ~50 TB data in memory with BIA<br />Human Capital Management<br />Payroll calculations for 500,000 employees in 3 hours<br />Portal<br />1 million users at one customer and 500,000 at many customers<br />Our Resource Consumption Requirements MMassive and Diverse<br />SAP Business Suite<br />Supply Chain Management4.5M characteristic combinations (SKUs) & 512 GB - 1TB memory in live cache<br />Banking<br />40 million customers – up to 8 million transactions an hour<br />SRM<br />SCM<br />ERP<br />PLM<br />CRM<br />Engineering & Construction <br />5,000 concurrent active users<br />~ 400 Million lines of code<br />Utilities<br />25 million business partners – 85 million service and sales orders per year<br />Order Management<br />10 million on-line item orders invoiced per day<br />© SAP AG 2010. All rights reserved. / Page 6<br />
  11. 11. What Can ICT Industry Do?<br />“The ICT (Information and Communications Technology)industry is responsible for 2% of global CO2 emissions.<br />ICT solutions have the potential to be an Enabler to reduce 30-50% of the 98% CO2 emitted by non-ICT industries.”<br />
  12. 12. Path to Sustainability Challenges Leading to Innovation Opportunities<br />Greening IT<br />IT for Greening<br />Greening SAP<br />Challenge<br />Reducing IT related CO2 emissions by optimizing energy footprint of SAP-related software and hardware<br />Challenge<br />Providing integrated solutions for:<br /><ul><li>measuring,
  13. 13. aggregating,
  14. 14. reporting,
  15. 15. analyzing and
  16. 16. optimizing environmental data</li></ul>Challenge<br />Identifying, structuring and coordinating programs for a targeted reduction of SAP’s environmental impact combined with communicating the success<br />© SAP 2008 / Page 8<br />
  17. 17. SAP’s Role In The Clean Tech Movement<br />Final Product<br />ENVIRONMENTAL ACCOUNTINGFor carbon impactCarbon just another currency<br />CARBON CAP AND TRADEAcross the Supply Chain<br />CO2<br />CO2<br />CO2<br />CO2<br />© SAP 2008 / Page 9<br />
  18. 18. Three Technology Mega Trends<br />Mobile - Pervasive Connectivity<br /><ul><li>5.3 B cell phones worldwide (77%) - 1.4 B sold in 2010 vs.15 B shoes</li></ul>Data Growth Follows Moore’s Law<br /><ul><li>1.2 million Petabyte in data have been created 2010 up from 160 Exabyte in 2007
  19. 19. By 2020 it will be 35 Zetabyte (IDC, UC Berkeley and UC San Diego)
  20. 20. Stack of DVDs halfway to Mars</li></ul>High Performance Computing – Real Time Analytics/Decision Making<br /><ul><li>In-Memory and multi-core for the enterprise at 1/30 of the price of mainframe
  21. 21. Mainframe power at desktop</li></li></ul><li>Multicore – No More Free Lunch<br />CPU Clock Speeds Over Time<br />4 GHz<br />Heat<br />Power Leakage<br />Physical Limitations<br />© SAP 2008 / Page 11<br />
  22. 22. The Big Challenge of Parallelism/Concurrency<br />From my perspective, parallelism is the biggest challenge since high-level programming languages. It’s the biggest thing in 50 years because industry is betting its future that parallel programming will be useful.<br />– David Patterson, UC Berkeley [ACM06]<br />Key messages <br /><ul><li>Parallelism/concurrency is a big challenge for the IT industry
  23. 23. Multicore combined with cheap memory is a big opportunity for in-memory computing and real time analytics</li></li></ul><li>SAPs In-Memory Technology <br />Analytics at the speed of thought - HANA (High Performance Analytic Appliance)<br />“SAP’s in-memory technology has the potential to threaten Oracle by producing faster transactions”, SAP Uses Hardware To Hit Oracle’s Database Hegemonyin Forbes, March 22nd<br />Tape is Dead, Disk is Tape, Flash is Disk, RAM Locality is King, J Gray, MS 12/06<br />A Modern CPU waits a lot<br />For RAM – 100 to 400 cycles translated in miles to the next statefor flash – 5000 cycles countryfor disk – 1,000,000 cycles Mars<br />
  24. 24. Big Iron - Commodity HPCDesign by SAP<br />Enterprise Supercomputer - 1/30 Price of Mainframe<br />5 X 4U Nodes (Intel XEON x7560 2.26Ghz)<br />160 cores (320 Hyper-threads) 5 X 32<br />5 TB memory total, 30TB solid state disk<br />160 GB/s InfiniBand interconnect per node<br />Scalable coherent shared memory (via ScaleMP)<br />Developers don’t need additional skills for in-memory<br />Data base becomes data structures<br />Scalable DB on virtualized HW – Alternative to Cloud<br />
  25. 25. Warren Powell et al.<br />Princeton University - Operations Research and Financial Engineering<br />Optimal Learning & In-Memory Handle Uncertainty <br />
  26. 26. Solve Very Compute Intensive ProblemsLike Stochastic Optimization @Princeton<br />Juggle intermittent energy from wind, solar & volatile electricity prices to meet time-varying loads – Princeton has the algorithms<br />With BigIron we can reduce compute time from days to minutes!<br />Wind speed<br />Load<br />Electricity prices<br />
  27. 27. Modeling uncertainty in power scheduling<br />The effect of modeling uncertainty in wind<br /> 2% wind<br /> 40% wind<br />Uncertain forecast<br />Perfect forecast<br />Constant wind<br />
  28. 28. Modeling Uncertainty In Power Scheduling<br />Designing energy portfolios….<br />… is like building a stone wall. You can do a perfect job with a perfect forecast. The challenge is dealing with uncertainty.<br />
  29. 29. John Williams et al.<br />MIT Auto ID Lab<br />Multithreading Real Time Event Platform <br />
  30. 30. Rapid Growth of Events and Messaging Platforms<br />Verizon and T-Mobile: 2-3 days to generate phone bill<br />iTunes: 24 hours to generate bill<br />Uninterrupted Growth of online billing systems (Hulu, Netflix…)<br />Dynamic Pricing on SmartGrid requires design of infrastructure capable of ingesting millions of events in quasi-real time<br />Goal: Design a multi-threaded system that produces the electricity consumption bill of a city of 1M households<br />8 hours  seconds<br />A Comparative Study of Data Storage and Processing Architectures<br />
  31. 31. Smart Meter Reading Problem<br />Data Generation<br />Data Persistence<br />Data Processing<br />
  32. 32. Electronic Nervous System<br />GPS SIM Card<br />
  33. 33. Multithreading Real Time Events & Messaging Platform<br />Platform that handles billions of events/day AND large numbers of threads on one machine (> 1 million), e.g. Siemens 500k events/s<br />RDBMS (used by today’s MDUS vendors) provides good query performance but does not scale to millions of households (8 h)<br />Prototype for SmartGrid allowing to ingest smart meter data in real time, do dynamic pricing (4 buckets), store in DFS & do real time analytics<br />Bill for 1 M households in seconds<br />A Comparative Study of Data Storage and Processing Architectures<br />
  34. 34. Pacific Northwest National Labs (PNNL)<br />GridLAB-D For Comprehensive Grid Simulations<br />
  35. 35. California Statewide Cumulative Investment Through 2020 To Achieve Renewable Portfolio Standard Goals<br />Governor Schwarzenegger signed Executive Order S-21-09 to adopt regulations increasing California's Renewable Portfolio Standard (RPS) to 33% by 2020.  <br /> Need to forecast financial and operational impacts before investing<br />
  36. 36. CalPower – A Hypothetical California Utility with 15% Renewable Generation Today<br />CalPower generation portfolio today<br />CalPower RPS goal in ten years<br />2010<br />2020<br />Total Renewables: 33%<br />Total Renewables: 15%<br />Geo Thermal: ?%<br />Geo Thermal: 4%<br />Biomass: 3%<br />Biomass: ?%<br />Solar: 3%<br />Solar: ?%<br />Natural Gas: ?%<br />Wind: 5%<br />Wind: ?%<br />Nuclear: 18%<br />Natural Gas: 48%<br />Nuclear: ?%<br />Coal: ?%<br />Coal: 19%<br />Traditional Technologies: 85%<br />Traditional Technologies: 67%<br />
  37. 37. Study Future Options For CalPower’s Generation Portfolio<br />Plan C<br />2020 Portfolio C<br />Plan B<br />Questions:<br />Larry Nolan<br />Peak Total Capacity: 5GW<br />CAPEX:$1405/MWh<br />OPEX: $167/MWh<br />Total Cost:$15,566M<br />Total CO2 emission:5MT<br />Avg. CAIDI:1.63 Hours<br />2020 Portfolio B<br />Questions:<br />Operations, <br />Sr. Analyst<br />CalPower LLC<br />Peak Total Capacity: 5GW<br />CAPEX:?$ M <br />OPEX: $368/MWh<br />Total Cost:$15,566M<br />Total CO2 emission:5MT<br />Avg. CAIDI:1.63 Hours<br />Plan A<br />Goals<br />2020 Portfolio A<br />Questions:<br /><ul><li>Balance financial performance, quality of service, and operational risks</li></ul>Peak Total Capacity: 5GW<br />CAPEX: ? $M through 2020<br />OPEX: ?$M<br />Total Cost:? $M<br />Total CO2 emission:? Tons<br />CAIDI:? hours/year <br />Tasks<br /><ul><li>Investigate future options of generation capacity plans for the next 10 years
  38. 38. Analyze potential KPI changes and risks</li></ul>Pain Points<br />“Which plan offers the best expected total cost?”<br /><ul><li>Lack of supporting evidence to evaluate future performance – need data of how the RPS change might affect the company in the long run</li></ul>“Which plan minimizes financial & service quality risks?”<br />“How do we mitigate these risks?”<br />CAIDI: Customer Average Interruption Duration Index <br />
  39. 39. Step 1: Use GridLAB-D To Model Objective & Constraints<br /> Today’s Power Sale Portfolio<br />Goal – Year 2020<br />Weather Model<br />Renewable Portfolio Standard<br />33%<br />Natural Gas <br />48% <br />2.4 GW<br />Coal<br />19% <br />CalPower’s Load Models<br />Constraints<br />Total Peak Capacity<br />Other<br />7%<br />Maximum Wind<br />Maximum Coal<br />Wind<br />5%<br />Nuclear<br />18% <br />Solar<br />3%<br />(GW)<br />
  40. 40. Step 2: Compare Different Plans<br />
  41. 41. Step 3: Drill Down Analysis Of Exception Days And Risks<br />
  42. 42. Step 3: Drill Down Analysis Of Exception Days And Risks<br />
  43. 43. Exception Day Risk Mitigation Strategies<br />Use stored power to close the gap<br />Decrease demand in response to supply drop<br />1. Adopt demand response<br />OPEX<br />Exception Day Risk<br />2. Invest in power storage technologies<br />CAPEX<br />Exception Day Risk<br />
  44. 44. RPS Study Takeaway: GridLAB-D Solution Provides Larry The Answers He Needs<br />Plan C<br />Comprehensive model of utility operations, including the distribution level. Can model distributed generation, and can model loads at high resolution to make more precise forecasts of operations KPIs (e.g. CAIDI, CO2) and financial KPIs (OPEX, CAPEX).<br />SAP User Experience Team helps business customers access results, and increase precision of their KPI forecasts. <br />Plan B<br />Plan A<br />2020 Portfolio C<br />Questions:<br />Peak Total Capacity: 5GW<br />CAPEX:$1405/MWh<br />OPEX: $167/MWh<br />Total Cost:$15,566M<br />Total CO2 emission:5MT<br />Avg. CAIDI:1.63 Hours<br />2020 Portfolio B<br />Questions:<br />Larry’s questions answered<br />Peak Total Capacity: 5GW<br />CAPEX:$15,306.77 M<br />OPEX: $368/MWh<br />Total Cost:$15,566M<br />Total CO2 emission:5MT<br />Avg. CAIDI:1.63 Hours<br />“Which plan offers the best expected total cost?”<br />Questions:<br />2020 Portfolio A<br />“Which plan minimizes financial and<br /> service quality risks?”<br />Peak Total Capacity: 5GW<br />CAPEX:$1,414.04 M<br />OPEX: $13,726.04 M<br />Total Cost:$15,140.08 M<br />Total CO2 emission:<br />145,765,543.95 T<br />CAIDI:1.63 hours/year<br />“How do we mitigate these risks?”<br />
  45. 45. © SAP 2008 / Page 34<br />
  46. 46. Thank You!<br />Contact information:<br />Paul Hofmann<br />SAP Labs, Palo Alto<br />paul.hofmann@sap.com<br />www.paulhofmann.net<br />

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