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.

1. itSMF 2009 Annual Conference
How to Deliver Business-Driven
Demand Planning
Danny Quilton, COO, Capacitas

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. 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. 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. 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. 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. 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. 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
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9. 0
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Database Server Load; December 2007 - November 2008
Linear (Database Server - CPU Utilisation - Max (%))
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10. Business-Driven Demand Planning
Demand deconstruction
Business demand
Service demand
Component demand
10

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. 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. 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. 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. Business Demand Planning
Business demand is termed ‘business volume indicators (BVIs)
Forecast
Measure BVIs
Agree BVIs
suitable
BVIs
Identify
business
stakeholders
15

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. 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. 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. 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. 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. 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. Forecasting Techniques
Linear trending
Time series decomposition
Forecast error
22

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. 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. 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. 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. 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. 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. Forecast Error
Any forecast you
make will be
wrong!
The key step is
to measure your
forecast error
29

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. 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. 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. 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. Other Forecasting Techniques
Moving average smoothing methods
Exponential smoothing methods
34

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. Demand Management
36

37. Demand Management
37

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. 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. 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