McGill University

1
Introduction

• McGill Universitys experience in planning
  lessons that might be learned.

2
Planning lessons learned

• Planning, along with decision making, is an
  integral part of the job of the Vice-chancellor,
  the Provost, the Vice-presidents, the Deans, the
  Chairs, the Directors.

3
Planning lessons learned

• Planning is based on
• Vision, wisdom, judgment, and experience
• Solid and clearly understood data.
• The role of the data is to inform, not to drive,
  the planning, be it strategic or tactical.

4
Planning lessons learned

• It is a challenge to turn a vast amount of fine
  grained data into useful business intelligence.
• The senior administrator needs to really
  understand the data,
• IT project manager needs to really understand the
  needs of the user.
• The only good answer is that they must both
  invest quality time and effort.

5
Planning lessons learned

• Open access to information
• to fine grained data
• to aggregated/analyzed/filtered data
• to analytic tools
• Downside some people will do incorrect analyses
  with the data
• Upside everybody can check that MY analyses are
  correct.

6
Planning lessons learned

• Administrative and reporting structure is very
  important.
• Reporting lines
• Assignment of responsibility
• Flexibility
• Good communication at every level across silos

7
Planning lessons learned

• Warning never forget to take into account
• Institutional politics
• Personal ambitions
• Defense of turf
• Human nature has always been a major factor in
  success as well as in failure.

8
About me

• Core position
• Professor of Physics ( now emeritus )
• Past administrative positions
• Chair, Department of Physics
• Associate Dean of Science
• Interim Dean of Science
• Associate Provost responsibilities included
• Courses and academic programs
• Student affairs, Admissions, Registry,
  Residences,
• Interim Registrar

9
About McGill University

• Founding date of McGill University 1821
• Degrees granted in 2008-09 7675
• 35,300 students (19 international, 22
  graduate )
• 1,627 tenured/ tenure-track faculty

10
About McGill University

• Enrolment by Place of Origin

11
About McGill University

• International Rankings
• THES 2009 18
• Shanghai Jiao Tong 2009 65
• Canadian Rankings
• Macleans (medical/doctoral) 1

12
The Quebec context

• Bachelor Masters Doctoral
• Bishop's U. 567 6 0
• Concordia U. 4546 1012 115
• U. Laval 4920 1438 283
• McGill U. 4090 1198 399
• U. Montreal 6804 2280 409
• U. Sherbrooke 2523 1020 110
• U. Quebec 9272 2328 268
• Total 32722 9282 1584

13
The Quebec context

• Government (MELS) university budget envelope
  cadM 2490
• Distribution of operating grants
• most (4/5)normed by weighted or straight FTE
• Some targeted by general priorities,
• Some targeted by special priorities.
• Value of a weighted FTE available envelope
  divided by the total weighted FTEs

14
The Quebec context

• Government data bases (input is audited)
• Students GDEU
• complete (except for grades) SRS
• Staffing EPE and SYSPER
• Research grants/contracts SIRU
• Buildings and facilities SILU
• Finance SIFU

15
The Quebec context
16
Vice-Principals (Planning) at McGill University

• 1966-1969 Carl A. Winkler, Planning
  Development
• 1973-1976 Dale C. Thomson, Planning
  
• 1976-1981 E.J. Stansbury, Planning
• 1981- 1986 E.J. Stansbury, Planning Academic
  Services

17
Vice-Principals (Planning) at McGill University

• 1986-1989 Paul Davenport, Planning and Resources
  
• 1989-1997 François Tavenas, Planning and
  Resources
•  

18
Heads of Planning at McGill University

•  
• 2006-2008 Helène Perrault, Associate Provost
  (Planning Budgets)
• 2009- Pierre Moreau, Executive
  Director, Planning and Institutional
  Analysis (PIA) and Senior Advisor (Policy
  Development)

19
University Planning Office 1990-2000

• Institutional analysis scope
• Space and facilities
• Organization table
• Student enrolment
• staffing, especially academic staff
• Research grants
• Data exchanges and benchmarking AAUDE, G10
  (later G13), etc.

20
University Planning Office 1990-2000

• Institutional Analysis methodology
• Fixed census dates
• Precise and standardized data definitions
• Transparency and open access
• McGill Fact Book open on the web
• Metrics
• Space database and tools

21
Budget process before Vice-Principal Tavenas
22
The PBA budget model

• Data and analysis UPO / VP(Planning ).
• Faculty budgets
• Initial budget cut to reduce deficit and generate
  free money
• Substantial part of the allocation is enrolment
  driven
• Smaller part is a discretionary allocation
• Changes smoothed over some years

23
The PBA budget model

• Service unit budgets
• Historical plus adjustment.
• No formula
• Total scaled to predicted income.
• Process iterated several times between November
  and May.

24
The PBA budget model

• Undergraduate FTE is based on courses
• FTE (number of student credit hours taught by
  a unit)/30
• Graduate FTE (non-thesis) is as above.
• Graduate FTE (thesis) is based on enrolment
• Time limits masters 1.5 years, PhD 3 or 4
  years.

25
The PBA budget model

• Weighted student units
• WSU (weight of the degree level)FTE
• Current weights are
• Bachelor 1
• Masters 2
• PhD 3.3

26
The PBA budget model

• Budget allocated to a faculty (faculty
  weight)(total WSU of the faculty)
• Faculty weight is a measure of the cost of the
  disciplines in the faculty

27
The PBA budget model

• Departmental budgets are determined by the Dean,
  not central administration
• The PBA model is not believed to be sensible for
  units smaller than faculties.

28
The PBA budget model

• Strength
• Predictability
• fairness
• Connection with the government funding model

29
The PBA budget model

• Weakness
• Needs driven rather than vision driven
• Quantity driven rather than quality driven
• Open to manipulation
• Transparency

30
Budget 2003?

• The PBA budget model was found to be
  increasingly unsatisfactory for McGills
  purposes, and was abandoned around 2003.

31
Senior Administration

• 1997.
• The position of Vice-Principal (planning) is left
  unfilled.
• University Planning Office reports to both VP
  Academic and VPAdmin Finance).
• The budget is back in the hands of VP(Admin
  Finance)

32
Senior Administration

• The Provostial model of university
  administration is introduced.
• lt2001 Vice-Principal(Academic)
• 2001 Vice-Principal(Academic) and Provost
• 2003 Provost

33
Senior Administration

• The Provostial model of university
  administration is completed.
• 2007 Provost is responsible for the budget
• 2009 new Office of the Budget - reporting to
  the Provost

34
Current budget model

• Current budget model. Its pillars are
• a strategic plan (vision),
• faculty compacts, including
• academic renewal
• enrolment targets
• multi-year budget (incl. deficit reduction
  targets)

35
Current budget model

• Compared to PBA model
• The form very different
• The substance much the same
• The difference is the starting point for the
  budget iterations
• Budget was and is Research driven
• ( please explain)

36
Current budget model

• Faculty compacts
• Iterative discussions between Provost and Deans
• Informed by data analysis ( and projections ???
  ), performance indicators, benchmarks, .
• Constrained by operating budget from the
  government

37
3 Science Departments
Dept. Professors UG FTE GR FTE
Computer Science 28 123 45
Earth Planetary 11 107 10
Psychology 33 500 33
38
Central offices -- planning

• Office of Planning and Institutional Analysis
• ?Executive Director?Provost
• Office of the Budget
• ?Director?Provost
• Campus and Space Planning Office
• ?AVP(Univ. Services)? Vice-Principal(AdminFi
  nance)

39
Central offices -- planning

• Enrolment Services
• ?Registrar?Deputy Provost?Provost
• Information Technology Services
• ?CIO?Vice-Principal(AdminFinance)

40
Observations

• Our current budget model

41
Observations

• In the old days.
• the best information was based on direct contact
  with reality
• the information, understanding and analytic power
  were all in the head of the decision maker.

42
Observations

• At the present time.
• Enterprise data systems with a vast amount of
  fine grained data
• It would be irresponsible not to use this
  information
• Business Intelligence tools to reduce and package
  this information are not (yet?) really
  satisfactory

43
Observations

• Is the quantitative information generated by the
  BI tools
• a solid and accurate representation of parts of
  reality?
• I think yes, if we work hard at it
• a reliable representation of full reality?
• I think not, at least not for a long while.

44

• http//mymcgill.mcgill.ca
• http//www.mcgill.ca/students/courses/calendars/
  
• http//www.mcgill.ca/pia/mcgillfactbook