To the future and back again:
Long-Term
Forecasting
1.
THE CHALLENGE
Are we ready for
build-out?
As Gilbert approaches build-out,
Council and city manager have
placed emphasis on long-term
planning
Conversation starts with review of
long-term financial picture
Result:
- Long-term financial
forecast
- Staffing model
“
Start by confronting the brutal facts. One good-to-great CEO
began by asking, “Why have we sucked for 100 years?” That's
brutal—and it's precisely the type of disciplined question
necessary to ignite a transformation. The management climate
during a leap from good to great is like a searing scientific
debate—with smart, tough-minded people examining hard facts
and debating what those facts mean. The point isn’t to win the
debate, but rather to come up with the best answers….
- Jim Collins, author of Good to Great
How will the decisions of today impact tomorrow?
Critical questions to ask:
▷ Does current level of
infrastructure maintenance set us
up for long-term success?
▷ Will we have staff necessary to
complete critical tasks as service
base grows?
▷ Personnel ≈ 70% of operating
expenses; will we have resources
to hire and maintain staff?
2.
EARLY ROADBLOCKS
Original forecast:
- Five year plans for financials
- Staff projected based on FTE per 1,000
New projections:
- Wanted more sophisticated approach
- Hired consultant in 2013/2014
Ideal:
A. B. C. D. E.
Reality:
A. Q. G. W. B.
Consultant – staffing model
▷ Excel-based
▷ Quickly discovered fatal flaws
- Population based
- All positions projected same
- Lack of operational knowledge
- Graphs ≠ meaning
Gilbert decision
▷ Take offline and rebuild
▷ Research + take what we liked and didn’t like
▷ Use the players you have on the field
“
“There’s no way to project
that.”
- Everybody
“
“There’s
no way
to project
So, our manager
cleared
the path
and helped the
that.” of and our
organization see the importance
commitment to both
projects.
- Everybody
3.
BUILDING A DYNAMIC MODEL
Long-Term Financial and Staffing Forecasts
Process and Framework
▷
Project through 2030
▷
Project on baseline services
- Do not assume Council will elect to add/change services
- Build in flexibility to change services if they so choose
▷
Revisit assumptions annually
▷
No black box calculations; met with department directors
and division managers for input
▷
Devil is in the details, but utility is at 10,000-foot level
Long-Term Financial Forecast:
Three key components
1.
Basic
Assumptions
2.
Revenues
3.
Expenditures
Assumptions
▷ What inflation index should we
use?
▷ What are population growth
assumptions?
▷ How will personnel-related costs
change?
Q: What key
components
may change
over time?
Forecast task:
 Build a section for overarching assumptions that can be
updated easily
Assumptions
Revenues
▷ Use existing formats
▷ Spend time on highest value
▷ Remember some revenues
decrease over time
Forecast task:
 Start forecasting using general trends
 Add detail for specific high-profile scenarios
Q:How will
revenues likely
change?
Revenues
Expenditures
▷
▷
▷
Keep it simple!
Group items that trend the same
Spend the most time and thought
on personnel
Forecast task:
 Decide groupings first
 Fill in data
 Look at trends
Q: What if
current trends
continue?
Expenditures
Staffing Forecast:
Three key projections
1. Ratio +
anticipated
growth
2. Position
weights
3. Manual
adjustments
Current ratios
+ anticipated growth
▷ What drives new FTE?
▷ Division-specific indicators
▷ Reasonable growth assumption
0-5% in five-year spans
Forecast task:
 Look at position level
 Project based on these assumptions
Q: What is an
appropriate
ratio or input
that drives FTE
needs?
Ratio example: Court
Court
Cases:
30,000
Division
Court
Services
Clerks:
10
Metric
Ratio:
1 to 3,000
Input
Growth
Assumption:
2%
Growth Assumption
Ratio example: Court
Court
Cases:
30,000
Court
Services
Clerks:
10
Forecast result:
 1 new FTE 2022
 1 new FTE 2027
Ratio:
1 to 3,000
Growth
Assumption:
2%
Position weights
▷
Categories:
1. New service level
2. Increased demand
3. Legal or risk
4. Infrastructure / CIP
5. Counterbalance / negative weight
▷
Q: Should an
individual
position be
weighted to
recognize extra
demand?
Capture what’s not in ratio and shifts between service lines
Forecast task:
 Weight positions identified
 Reset every five years
Weight example: GIS Technician
Review
weights for
5 areas
Position
Increased
Demand:
Low
CIP:
Medium
Select weight
Other
Weights:
Stable
Result
Weight example: GIS Technician
Review
weights for
5 areas
Increased
Demand:
Low
CIP:
Medium
Other
Weights:
Stable
Forecast result:
 1 new FTE 2020
 Additional layer added to show growth beyond ratio; may also
be used to delete over time
Manual adjustments
▷
▷
▷
▷
What would we need if large
projects come online?
Also helpful for items where staffing
is known, e.g. fire station
Should be the exception, and not the rule
Build in flexibility for scenarios
Forecast task:
 Look at position level
 Project based on these assumptions
Q: Is there a
policy-level
decision that
must be made
before this
service is
added?
Manual example
New
Regional
Park
Identify
Need and
Years
Forecast result:
 23 FTE spread out over five years
 Utility built in to turn on or off this
Populate
Include?
Y/N
How do the models interact?
They talk!
Time to go live
4.
LESSONS LEARNED
Lessons
Learned
5.
TIPS FOR GETTING STARTING
How to get started
▷ Begin with the end in mind
▷ Start with how you’ll need to report out
▷ Outline a manageable project and timeline
▷ Build a project team that can see the forest AND the trees
▷ Build assumptions that can be reset or changed over time
▷ Keep one master that populates everything
Remember why you started
Set community up for long-term success, and better understand
impact of decisions
Thanks!
Any questions?
For more information contact:
Kelly Pfost, Management and Budget Director
[email protected]
480-503-6828
Mary Vinzant, Assistant to the Town Manager
[email protected]
480-503-6756