Howto Deliver Business Driven Demand Planningv1

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Now more than ever there is a need for organisations to ensure there is optimal infrastructure capacity in place to support business services. Excess capacity can result in unacceptable capital and operating costs, impacting business profitability. Conversely, insufficient capacity can impact service performance and business competitiveness. The Capacity Plan should determine the optimal capacity required and a key input into it is forecast service demand. This presentation details a number of techniques to forecast service demand using a business-driven approach.

A number of important considerations are addressed, including business seasonality, forecast error and techniques for translating business demand to service and component demand. The techniques are demonstrated with case studies based on real-life client engagements.

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Howto Deliver Business Driven Demand Planningv1

  1. 1. itSMF 2009 Annual Conference How to Deliver Business-Driven Demand Planning Danny Quilton, COO, Capacitas
  2. 2. Agenda Overview Business-driven demand planning Challenges associated with business demand planning Demand forecasting techniques Demand management Benefits of business-driven demand planning 2
  3. 3. Demand Planning Overview A key input into the Capacity Management process is the anticipated level of demand expected of the system Demand planning can be carried out at different ‘layers’ These layers are defined by ITIL: Business Service Component Note that these layers apply to a single information and communication technology (ICT) service 3
  4. 4. Demand Planning Overview • Understood by the business Business demand • May be forecast by the business • Functionality presented to the user • May not be understood by the business Service demand • Technology independent • The link between business and component demand • Not understood by the business Component • Technology specific; “bits and bytes” demand • The actual consumer of capacity 4
  5. 5. Demand Planning – Online Banking Number of accounts Make Check Show transfer balance statement Server Server CPU Server CPU Server I/O Server CPU memory demand demand demand demand demand 5
  6. 6. Demand Planning – Corporate Messaging Service Number of users Create Send Receive journal emails emails entries Server Server Server Network Network Server I/O CPU CPU CPU demand demand demand demand demand demand 6
  7. 7. Demand Planning – e-commerce Service Product Inventory Add to Search Checkout Basket Server Server Server Network Network Server I/O CPU CPU CPU demand demand demand demand demand demand 7
  8. 8. Demand Planning – Mobile Phone Pre-Pay Service Number of Pay-as-you-go subscribers Number of Number of Number of calls SMS top ups Server Server Server Serer Server Server I/O CPU memory CPU memory CPU demand demand demand demand demand demand 8
  9. 9. 0 100 10 20 30 40 50 60 70 80 90 2007-12-02 12:00:00 AM 2007-12-09 12:00:00 AM 2007-12-16 12:00:00 AM 2007-12-23 12:00:00 AM 2007-12-30 12:00:00 AM 2008-01-07 12:00:00 AM 2008-01-14 12:00:00 AM 2008-01-21 12:00:00 AM 2008-01-28 12:00:00 AM 2008-02-05 12:00:00 AM 2008-02-12 12:00:00 AM 2008-02-19 12:00:00 AM 2008-02-26 12:00:00 AM 2008-03-05 12:00:00 AM 2008-03-13 12:00:00 AM 2008-03-20 12:00:00 AM 2008-03-27 12:00:00 AM 2008-04-04 12:00:00 AM 2008-04-11 12:00:00 AM Common Pitfall 2008-04-18 12:00:00 AM 2008-04-25 12:00:00 AM 2008-05-03 12:00:00 AM 2008-05-10 12:00:00 AM 2008-05-17 12:00:00 AM 2008-05-24 12:00:00 AM 2008-05-31 12:00:00 AM 2008-06-08 12:00:00 AM 2008-06-15 12:00:00 AM 2008-06-22 12:00:00 AM 2008-06-29 12:00:00 AM 2008-07-07 12:00:00 AM 2008-07-14 12:00:00 AM 2008-07-21 12:00:00 AM Database Server - CPU Utilisation - Max (%) 2008-07-28 12:00:00 AM 2008-08-05 12:00:00 AM 2008-08-12 12:00:00 AM 2008-08-20 12:00:00 AM 2008-08-27 12:00:00 AM 2008-09-04 12:00:00 AM 2008-09-11 12:00:00 AM 2008-09-18 12:00:00 AM 2008-09-25 12:00:00 AM 2008-10-03 12:00:00 AM 2008-10-10 12:00:00 AM 2008-10-17 12:00:00 AM 2008-10-24 12:00:00 AM 2008-10-31 12:00:00 AM 2008-11-07 12:00:00 AM 2008-11-14 12:00:00 AM 2008-11-21 12:00:00 AM 2008-11-29 12:00:00 AM Database Server Load; December 2007 - November 2008 Linear (Database Server - CPU Utilisation - Max (%)) 9
  10. 10. Business-Driven Demand Planning Demand deconstruction Business demand Service demand Component demand 10
  11. 11. Demand Deconstruction: Business to Service One unit of business demand will often map to many units Business demand of service demand Build an empirical understanding the relationships Service demand Consider the relationship over the peak period Component demand 11
  12. 12. Challenges Planning Service Demand Number of accounts Make Check Print Change Change Order Pay credit transfer balance statement Password address credit card card bill Rich functionality – which service demand do I focus on? Poor instrumentation of the service 12
  13. 13. Demand Deconstruction: Business to Service Number of accounts Consider the peak rate of check balance Consider using segmentation: – Different account types will use the Check system differently balance – E.g. Retail and Business accounts 13
  14. 14. Demand Deconstruction: Service to Component A unit of service demand will be implemented by one or more technical transactions Business demand The component capacity planner must identify these technical transactions A technical transaction will Service demand traverse a number of components (infrastructure components) Each component in the path Component demand will be subjected to some component demand 14
  15. 15. Business Demand Planning Business demand is termed ‘business volume indicators (BVIs) Forecast Measure BVIs Agree BVIs suitable BVIs Identify business stakeholders 15
  16. 16. Criteria for Defining BVIs BVIs must be understood by the business BVIs must have a direct bearing on system capacity Selected BVIs must have ‘buy in’ from the business BVIs must be measurable 16
  17. 17. Example BVIs from Client Engagements Broadband Internet Stock Airline Service Retailer Broadcaster Bank Broker Provider Stock keeping units (SKUs) Trading Aircraft Subscribers staff Number of Stores Subscribers accounts Airports Exchanges Trades Lorry Deliveries 17
  18. 18. Tips for Measuring BVIs It is essential that BVIs are measured in production BVIs cannot be forecast if current BVI levels are unknown Sources of BVI data: – Database systems are likely to hold BVI information – Application monitors – Audit logs BVIs are typically measured at coarse sample intervals, e.g: – Monthly – Quarterly Service acceptance process must demand BVI monitoring 18
  19. 19. Business Forecasting Challenges Business demand is Lack of engagement from confidential or the business commercially sensitive Over optimistic forecasts Lack of forecasting skills from the business within the business 19
  20. 20. Establishing Business Demand Forecasts The preference is always to work with the business to establish a business demand forecast There will however be occasions where business demand forecasts are not forthcoming Then the capacity management function will need to establish a business demand forecast 20
  21. 21. Sources of Business Demand Forecasts Sales and marketing revenue forecasts HR headcount projections Business cases for new services Research from external bodies, e.g: – Ofcom http://www.ofcom.org.uk/research/telecoms/reports/ – Office for National Statistics http://www.statistics.gov.uk/ – Research companies (Gartner, Ovum, Forrester, etc.) – Competitors (via annual company reports) 21
  22. 22. Forecasting Techniques Linear trending Time series decomposition Forecast error 22
  23. 23. Registered Users 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 Service Jan-2002 Mar-2002 May-2002 Jul-2002 Sep-2002 Nov-2002 Jan-2003 Mar-2003 May-2003 Jul-2003 Sep-2003 Nov-2003 Jan-2004 Mar-2004 May-2004 Jul-2004 Oct-2004 Dec-2004 Feb-2005 Apr-2005 Jun-2005 Aug-2005 Oct-2005 Dec-2005 Feb-2006 Apr-2006 Jun-2006 Historical Business Demand Since Go-live Aug-2006 Oct-2006 Dec-2006 R² = 0.9766 y = 7438.3x Feb-2007 Apr-2007 Jun-2007 Aug-2007 Dec-2007 Feb-2008 May-2008 Jul-2008 Nov-2008 Linear Trend Forecast for an Internet Banking 23
  24. 24. Registered Users 0 100,000 200,000 300,000 400,000 500,000 600,000 700,000 Service Jan-2006 Feb-2006 Mar-2006 Apr-2006 May-2006 Jun-2006 Jul-2006 Aug-2006 Sep-2006 Oct-2006 Nov-2006 Dec-2006 Jan-2007 Feb-2007 Mar-2007 Apr-2007 Jun-2007 Jun-2007 Jul-2007 Aug-2007 R² = 0.9947 Oct-2007 y = 10538x + 312801 Dec-2007 Jan-2008 Historical Business Demand - 36 months to Dec 2008 Feb-2008 Mar-2008 May-2008 Jun-2008 Jul-2008 Sep-2008 Nov-2008 Dec-2008 Linear Trend Forecast for an Internet Banking 24
  25. 25. Registerd Users Service 0 200,000 400,000 600,000 800,000 1,000,000 1,200,000 Jan-2002 Apr-2002 Jul-2002 Oct-2002 Jan-2003 Apr-2003 Jul-2003 Oct-2003 Jan-2004 Apr-2004 Jul-2004 Nov-2004 Feb-2005 May-2005 Aug-2005 Nov-2005 Feb-2006 Historical Registered Users May-2006 Aug-2006 Nov-2006 Feb-2007 Jun-2007 Oct-2007 Forecast Business Demand Feb-2008 Forecast Registered Users May-2008 Sep-2008 Jan-2009 Apr-2009 Jul-2009 Oct-2009 Jan-2010 Apr-2010 Jul-2010 Oct-2010 Jan-2011 Apr-2011 Jul-2011 Oct-2011 Linear Trend Forecast for an Internet Banking 25
  26. 26. Number of Owned Aircraft 20 40 60 80 100 120 140 160 180 0 Mar-04 Jun-04 Sep-04 Dec-04 Mar-05 Jun-05 Sep-05 Dec-05 Mar-06 Jun-06 Sep-06 Dec-06 Delivery Date Fleet Plan to April 2009 Mar-07 Jun-07 Sep-07 Dec-07 GB Mar-08 Airways acquisition Jun-08 Business Demand of www.easyJet.com Sep-08 R² = 0.9668 Dec-08 y = 0.0461x - 1673.1 Mar-09 26
  27. 27. Daily Purchases 26/03/2004 26/04/2004 26/05/2004 26/06/2004 26/07/2004 26/08/2004 26/09/2004 26/10/2004 26/11/2004 26/12/2004 26/01/2005 26/02/2005 26/03/2005 26/04/2005 26/05/2005 26/06/2005 26/07/2005 26/08/2005 26/09/2005 26/10/2005 26/11/2005 26/12/2005 26/01/2006 Actual Daily Purchases 26/02/2006 Demand Seasonality 26/03/2006 26/04/2006 26/05/2006 26/06/2006 26/07/2006 26/08/2006 26/09/2006 26/10/2006 Trend 180day 26/11/2006 26/12/2006 26/01/2007 26/02/2007 26/03/2007 26/04/2007 26/05/2007 26/06/2007 26/07/2007 26/08/2007 26/09/2007 26/10/2007 26/11/2007 Linear (Trend 180day) 26/12/2007 Historical Service Demand for an e-commerce Service 26/01/2008 26/02/2008 26/03/2008 26/04/2008 26/05/2008 26/06/2008 26/07/2008 26/08/2008 26/09/2008 26/10/2008 R² = 0.9436 26/11/2008 26/12/2008 y = 12.088x - 436266 26/01/2009 26/02/2009 26/03/2009 26/04/2009 26/05/2009 26/06/2009 27
  28. 28. 01/01/2006 01/04/2006 01/07/2006 01/10/2006 01/01/2007 01/04/2007 01/07/2007 01/10/2007 01/01/2008 01/04/2008 01/07/2008 Actual Daily Purchases 01/10/2008 01/01/2009 01/04/2009 01/07/2009 Time Series Decomposition 01/10/2009 01/01/2010 Forecast Daily Purchases 01/04/2010 Forecast Service Demand for an e-commerce Service 01/07/2010 01/10/2010 01/01/2011 01/04/2011 01/07/2011 01/10/2011 01/01/2012 28
  29. 29. Forecast Error Any forecast you make will be wrong! The key step is to measure your forecast error 29
  30. 30. Forecast Error Forecast error is the difference between what was forecast and what actually occurred Forecast error, et is given by: e t = A t − Ft – At is the observed value at time period t – Ft is the forecast value at time period t 30
  31. 31. Forecast Error A t −F t Percentage error: PE t = At n ∑ PE t= t =1 t MPE = Mean percentage error: n n ∑| PE t =1 t | Mean absolute percentage error: MAPE = n 31
  32. 32. Forecast Error Forecast Service Demand vs. Actual Service Demand 16,000 14,000 12,000 10,000 Purchases 8,000 6,000 4,000 2,000 0 Oct-07 Nov-07 Dec-07 Jan-08 Feb-08 Mar-08 Apr-08 May-08 Jun-08 Jul-08 Aug-08 Sep-08 Oct-08 Nov-08 Dec-08 Actual Bookings (Peak Hour) Forecast Bookings (Peak hour) 32
  33. 33. Forecast Error Here the MAPE is 12% Forecast Error Dec-08 Nov-08 Oct-08 Sep-08 Aug-08 Jul-08 Jun-08 May-08 Apr-08 Mar-08 Feb-08 Jan-08 Dec-07 Nov-07 Oct-07 -20% -15% -10% -5% 0% 5% 10% 15% 20% 33
  34. 34. Other Forecasting Techniques Moving average smoothing methods Exponential smoothing methods 34
  35. 35. Extraordinary Peak Demand Service Extraordinary Peak Scenario Internet banking service Run on a bank News service Major news event, e.g. 9/11 Mobile Major news event, e.g. 7/7 telecommunications New Years Eve E-commerce service Unexpected demand resulting from a promotion 35
  36. 36. Demand Management 36
  37. 37. Demand Management 37
  38. 38. Benefits of Business-Driven Demand Planning Demand forecasts can be Capacity plans can be driven signed off by the business directly by business volumes Business-driven demand planning Justification for capacity Justification for SLA upgrades modifications 38
  39. 39. Summary Business driven capacity planning requires planning activities at all 3 ITIL layers: – Business – Service – Component Business demand drives the Capacity Management process – Must have business ‘buy in’ Component demand dictates the capacity requirements Service demand provides the translation between business and component demand Demand deconstruction This approach may be warranted for your important ICT services only 39
  40. 40. Questions? Please visit us at our stand at P09 for any further questions Presentation will be available for download from www.capacitas.co.uk dannyquilton@capacitas.co.uk 40

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