Tips and Tricks for Optimising Excel

www.hanrickcurran.com.au
Optimising Excel™
Using Excel™ to optimise decision making and avoiding
common problems with spreadsheets and financial analysis
June 2014

2experience. new thinking
Todays presenters

Today’s agenda
 Learning’s from implementing FAST at
Wilmar with Gerard Cooney
 Common problems seen in practice with
Matthew Green
 Spreadsheet Detective and its review
abilities with Anthony Berglas

4experience. new thinkingexperience. new thinking 4
Insights from implementing the
FAST standard at Wilmar Sugar
Gerard Cooney
Implementing FAST

A review of the implementation of the
FAST modelling standard at Wilmar
Sugar – Gerard Cooney
(fomerly Sucrogen and CSR Sugar)
Implementing FAST

The Problem
 Excel is ubiquitous in business
 User friendly
 Very flexible
 These attractive attributes cause problems:
 Everyone thinks they can use excel
effectively
 Models structured using the idiosyncrasies of
the user
 The result is often a mess

Key Problems
 Every model is structured differently
 Difficult to review and audit
 Prone to errors
 Difficult to modify
 Difficult to understand

1.4 metre long formula
=IF(AND($N97<>1,$O97<>1),$F97*$I97*IF(AND(HeavyVehInd=2,X
$4>=RoadStartYr),(1-$L97),(1-$K97))*(1-INDEX
(X$5:X$7,MATCH($Q97,$Q$5:$Q$7,0))),
IF(AND($N97=1,$O97=1),IF(OR(BiomassInd=1,AgInd=1),0,IF(X$4>
=MAX(BiomassStartYr,AgStartYr),
$F97*$I97*IF(AND(HeavyVehInd=2,X$4>=RoadStartYr),
(1-$L97),(1-$K97))*(1-
INDEX(X$5:X$7,MATCH($Q97,$Q$5:$Q$7,0))),0)),
IF($N97=1,IF(BiomassInd=1,0,IF(X$4>=BiomassStartYr,$F97*$I97*I
F(AND(HeavyVehInd=2,X$4>=RoadStartYr),(1-$L97),(1-$K97))*(1-
INDEX(X$5:X$7,
MATCH($Q97,$Q$5:$Q$7,0))),0)),IF($O97=1,IF(AgInd=1,0,IF(X$4>
=AgStartYr,$F97*$I97*IF(AND(HeavyVehInd=2,X$4>=RoadStartYr),(
1-$L97),(1-$K97))*(1-
INDEX(X$5:X$7,MATCH($Q97,$Q$5:$Q$7,0))),0))))))

THE FAST STANDARD
What does it stand for?
 Flexible
 Accurate
 Structured
 Transparent
http://www.fast-
standard.org/

FLEXIBLE
 Design and modelling techniques must allow
models to be both flexible in the immediate
term and adaptable in the longer term.
 Flexibility is born of simplicity.

APPROPRIATE/ACCURATE
 Models must reflect key business
assumptions directly and faithfully without
being over-built or cluttered with
unnecessary detail.

STRUCTURED
 Rigorous consistency in model layout and
organization is essential to retain a model’s
logical integrity over time, particularly as a
model’s author may change.

TRANSPARENT
 Simple, clear formulas that can be
understood by other modellers and non-
modellers alike. Confidence in a financial
model’s integrity can only be assured with
clarity of logic structure and layout.

Key Attributes of FAST model
 Use of calculation blocks where the calculation ingredients are shown explicitly
and appear directly above the calculation
 Calculation ingredients link directly to the source (either input data or
precedent calculation block). There is no daisy changing of links.
 Link labels and units as well as numbers, and enter only once
 Use of timing flags
Also:
 Use of short formulae
 Constants only entered once.
 Parameters only calculated once.
 Consistent formulae across a row.
 Consistent use of columns within a sheet
 All inputs are collected on input-only sheets and colour coded to show explicitly
 Formatting consistency
 Diligent use of units
 Separate calculation engine from presentation output.

Key Attributes of FAST model
 Use of calculation blocks where the
calculation ingredients are shown explicitly
and appear directly above the calculation
 Calculation ingredients link directly to the
source (either input data or precedent
calculation block). There is no daisy
changing of links.
 Link labels and units as well as numbers,
and enter only once
 Use of timing flags

Secondary Attributes of FAST model
 Use of short formulae
 Constants only entered once.
 Parameters only calculated once.
 Consistent formulae across a row.
 Consistent use of columns within a sheet
 All inputs are collected on input-only sheets
and colour coded to show explicitly
 Formatting consistency
 Diligent use of units
 Separate calculation engine from presentation
output.

Excel example
REVENUE
USING A TYPICAL APPROACH
Sugar Revenue 5,870 $
Molasses Revenue 120 $
Total Revenue 5,990 $

Common Complaints
 Doesn’t a standard approach stifle creativity
 It takes too long to model using the FAST
Standard

Resources
http://www.fast-standard.org/
http://info.f1f9.com/31-day-financial-modelling-
course
http://www.fi-mech.com/
http://www.financialmodellinghandbook.com/

Understanding the common issues
we see with Excel™ spreadsheets,
some conceptual insight into how
errors occur and some suggestions
on how to prevent them…
Matthew Green
Common issues with Excel™
spreadsheets and models

My agenda
 How prevalent is Excel™
 Common problems
 Error research
 The need for graphical presentation of data
 7 steps to review your spreadsheets
 Practical learning's and takeaways

Typical balance sheet
Excel used in the following key areas
and calculations:
 Account reconciliations
 Other asset listings and
amortisation
 Fixed asset registers and
depreciation
 Deferred and current tax
 Intangible asset reconciliations
 Impairment models
 Debt covenants
 Interest accruals
 Employee benefits
 Derivative reconciliations and to
cross check bank valuations
 Spreadsheets for transactional
reports with Pivot tables for
further analysis
 Consolidation schedules

"I suppose it is tempting, if the
only tool you have is a hammer, to
treat everything as if it were a
nail."
Abraham Maslow, 1966

Excel & Accountants …
“like giving your kid a chainsaw, powerful tool, but
does he really understand what he’s got in his hands
and how to use it?” You have to ask yourself: “Is this
going to end well?” (And “No, he’s not getting one for Christmas!”)

How many problems really exist?
“In our experience, most spreadsheets are
poorly developed. This is probably because
there isn’t much formal training on how to
build a spreadsheet and people don’t have
time to build them so that they are optimised
for their purpose and to support decision
making.”
Want to see some examples?

Common problems
 Confusing, complicated formula
 Unstructured layout
 Formula & Function errors
 Range & Pointing errors
 Hard coding
 Remote references
 Empty precedents

Confusing and complicated formula
 A real life discussion thread in an Excel™
specialist LinkedIn group.
 Why would anyone want to nest more than
1 “If” statement, let alone 8?
 Can you imaging how hard it would be to
unravel an error in these “If” statements?

Inconsistent build
 Sundry income inconsistently treated in six operating
sites in budget file taken from board papers.
 In some sites, sundry income was included in total
revenue and gross profit.
 In other sites, sundry income is excluded from both, but
is factored into net profit.
 Site analysis based on GP would favour those with the
sundry income in their GP.
 This error would not have been apparent to directors in
their decision making.
Source: corporate transaction, target budget file

Inconsistent build
 Budget model for 6
operating sites in board
papers.
 Site A calculated direct
wages % based on net
metered win.
 Site B calculated direct
wages % based on gross
profit.
 Not a big difference, but
makes comparison
across sites difficult.
 Why is there a business
reason for the two sites
to have different basis of
calculation
Site A
Site B
Source: corporate transaction, target budget file

Hard coding and format
 Common example of a
quickly built spreadsheet.
 Note the hard coded
information buried in
formula, in this case the
CPI increase rate for the
leases.
 Note the inconsistent
number formatting, doesn’t
help the reader with
assessing information.
 Whilst the outcome is
quantitatively accurate, the
poor design makes in hard
to review and leaves the
worksheet prone to error.
Lease commitments:
No later than 1 year
Office premises to 14 October 2018
annual rent 100000
Monthly Rent to 31 October 14 8333.333 83333.3333
annual rent after 1st increase 103000
Mothly rent to 31 December 14 8583.333 17166.6667
100,500.00
1 year to 5 years
Year 2 2015
Monthly rent to October 15 8583.333 85833.3333
annual rent after 2nd increase 106090
Mothly rent to 31 December 14 8840.833 17681.6667
Total rent yr 1 103,515.00
Source: Hanrick Curran audit file

Hard coding
 Commonly seen on ad hoc spreadsheets.
 Typically involves a formula like:
= C5 * (A36 + 1.03) – 408 + 12
 The reviewer can usually decipher that the 1.03 is
probably CPI, but what about the other
adjustments.
 A better way is to put the CPI number in its own
cell as an input.
 Hard coded adjustments should also be avoided. If
needed, build data entry cells for adjustments.
Example of problem use of hard coding
Source: Hanrick Curran client board reporting file

Remote references
 A “remote reference” is a reference in a formula to cells that
are remote from the worksheet the formula is on, either to
another worksheet in the same file or to a different file.
 Remote references are difficult to review and prone to errors.
 We recommend they be avoided by using a ‘links sheet’ in a
workbook for all ‘cross-file links’.
 Formula within a workbook should also avoid remote
references by gathering all needed data and then using the
formula
Example of problem use of remote references
Example of corrected approach to use of remote references

How big can the errors get?
 In January 2010, academics
Carmen Reinhart and Kenneth
Rogoff published “Growth in a
Time of Debt”.
 Their report was widely cited by
politicians as theoretical and
research based support for
reducing public debt and public
spending.
 Later analysis reveals errors with
the underlying spreadsheet
analysis; countries are excluded
from the average because of a
‘range error’.
 Great Brittan slashes spending by
£10 billion, in response to the
research and increase in debt
following in the GFC.
Source: Quartz website, http://qz.com/75119
Pasted from <http://qz.com/75119/how-to-avoid-making-
an-excel-mistake-like-rogoff-and-reinhart/>

Reinhart & Rogoff’s public problems
Source: Australian Financial Review, 23 April 2013

Allied Irish Bank – US$691 m. Fraud
 AIB is one of the Irish big 4 commercial banks,
parent of Allfirst Bank, based in Baltimore,
Maryland, US.
 John Rusnak, committed a US$691 million
currency trading fraud.
 Rather than pay $10,000 fee for a Reuters feed
to the treasury compliance team (back office)
the data feed to the VaR calculation was based
on data from Rusnak’s computer.
 The data was loaded into a spreadsheet which
Rusnak manipulated to ensure that trading
losses were otherwise hidden from the VaR
assessment.
Source: AIB p.l.c. SEC filing, March 12, 2002
Source: R. Butler, “The role of Spreadsheets in the Allied Irish Bank / Allfirst Currency Trading Fraud” (2009)
VaR = Value-at-Risk

Allied Irish Bank – US$691 m. Fraud
 AIB SEC Filing: “A simple check to see if the
holdover figures were captured in the next
day's trading activity would have caught
this scheme.”
At least two points of failure:
1. Data in spreadsheets was open to
manipulation
2. Compensating controls were not
strong enough to detect the
manipulation
Source: AIB p.l.c. SEC filing, March 12, 2002
Source: Hanrick Curran research

Enabling Fraud
 In summary, the inclusion of a spreadsheet in a reporting
chain enabled hiding of fraud, especially without adequate
compensating detective controls and reconciliations.
 A similar fraud occurred in a Brisbane company between 2011
and 2013, resulting in a $2.4 million loss to the company,
related to overstatement of inventory balances (16% of PY
reported inventory).
Source: Hanrick Curran research
Data
spreadsheet
Reporting
Missing control checks and reconciliations

Raymond Panko, University of
Hawaii, has undertaken significant
research into spreadsheet errors.
Panko’s research informs the
classification of errors in
spreadsheets.
Error taxonomy and research

Error research
 There is a significant amount of research into
human error from fields as diverse as
mathematics, programming, aircraft accidents,
nuclear incidents, proofreading and linguistics.
 A key insight from these fields is that
“human cognitive processes produce the
correct result nearly all the time but have a
small inherent error rate that stems from the
same processes that produce correct results.
In other words, the way we actually think …
is the heart of the problem, not simple
sloppiness.”
Source: Raymond Panko and Salvatore Aurigemma
“Revising the Panko-Halverson Taxonomy of Spreadsheet
Errors” (February 2010) p.4

Mistakes, Slips and Lapses
When working with spreadsheets, errors can be
categorised as follows (Reason, 1990):
 Mistake – an error in planning
 Slip – an error during a sensory-motor action, such
as typing the wrong number in a cell (e.g.,
$120,000 instead of $210,000)
 Lapse – a failure in memory, usually caused by
overloading the limited human memory capacity
In terms of error detection, planning and memory
errors that occur ‘off spreadsheet’ leave little if any
evidence for error detection.

Error frequency
Research from Allwood used students solving a mathematical
problem. Error rates identified included:
 327 errors as they worked
 60% of errors were execution errors (slips and lapses)
 83% of execution errors were spontaneously identified
and corrected during work – the result, execution errors
only accounted for 29% of final errors
 Logic errors (mistakes) accounted for only 25% of
errors, but low detection rates resulted in these
mistakes contributing to 40% of final errors.
 Skip errors (missing a part of the solution) accounted
for only 9% of all errors made, but a nil detection rate
meant they contributed to 29% of final errors.
In short: “We don’t see what we don’t see”
29%
40%
29%

Qualitative error impacts
When considering errors, we need to assess their
impact on the final result. Panko suggests two
approaches:
1. Error magnitude – how big is the error
compared to the final correct bottom-line
number
2. Would a different decision be taken based on
correct versus incorrect results.
Panko and Halverson conclude that “most errors
are either too small to be important or still give
answers that lead to the correct decisions”.

Panko & Halverson error taxonomy
 A revised error taxonomy is described by
Panko and Halverson.

Panko & Halverson error taxonomy
 A revised error taxonomy is described by Panko
and Halverson.
 Domain type errors (e.g., misunderstanding
requirements or not correctly reflecting
business requirements) are the most likely
error to remain undetected and to result in an
undetected error in the spreadsheet.
 Execution errors (e.g., incorrect formula
references) are most likely to be corrected
during spreadsheet development and review,
but can also leave undetected errors in
spreadsheets (e.g., Reinhart & Rogoff).

Graphing outputs of spreadsheets
is important. Some examples of
why follow…
A segue into the graphic display
of information

Anscombe’s Quartet
 Four data sets with
similar characteristics
 X average = 9.0
 Y average = 7.5
 X sum = 99.0
 Y sum = 82.5
x y x y x y x y
10.00 8.04 10.00 9.14 10.00 7.46 8.00 6.58
8.00 6.95 8.00 8.14 8.00 6.77 8.00 5.76
13.00 7.58 13.00 8.74 13.00 12.74 8.00 7.71
9.00 8.81 9.00 8.77 9.00 7.11 8.00 8.84
11.00 8.33 11.00 9.26 11.00 7.81 8.00 8.47
14.00 9.96 14.00 8.10 14.00 8.84 8.00 7.04
6.00 7.24 6.00 6.13 6.00 6.08 8.00 5.25
4.00 4.26 4.00 3.10 4.00 5.39 19.00 12.50
12.00 10.84 12.00 9.13 12.00 8.15 8.00 5.56
7.00 4.82 7.00 7.26 7.00 6.42 8.00 7.91
5.00 5.68 5.00 4.74 5.00 5.73 8.00 6.89
sum
99.0 82.5 99.0 82.5 99.0 82.5 99.0 82.5
Average
9.0 7.5 9.0 7.5 9.0 7.5 9.0 7.5
I II III IV
Source: Wikipedia

Anscombe’s Quartet
-
2.00
4.00
6.00
8.00
10.00
12.00
- 5.00 10.00 15.00
Series I
-
1.00
2.00
3.00
4.00
5.00
6.00
7.00
8.00
9.00
10.00
- 5.00 10.00 15.00
Series II
-
2.00
4.00
6.00
8.00
10.00
12.00
14.00
- 5.00 10.00 15.00
Series III
-
2.00
4.00
6.00
8.00
10.00
12.00
14.00
- 5.00 10.00 15.00 20.00
Series IV

A common audit test
 A common audit test is to graph revenue, looking for
spikes, trends and seasonality.
 In these examples, two audit clients, displaying
seasonality in accordance with underlying business
model.
 One factor we look for is a year-end spike.
$-
$50,000
$100,000
$150,000
$200,000
$250,000
$300,000
Letting fees
$-
$20,000
$40,000
$60,000
$80,000
$100,000
$120,000
$140,000
$160,000
$180,000
Management Fees
Source: Hanrick Curran audit file

Discontinuity
 Other common issues
include
discontinuities such
as spikes, slope
changes and steps.
 Graphing outputs can
also help with
identifying spikes
from data entry or
formula errors.
Source: F1F9, 31 day on-line learning

Stephen Few, Perceptual Edge
 Stephen Few’s work on visual communication is well
worth investigating as part of developing your team’s
use of excel.
 Typically a board paper might include a table such as
exhibit A. The problem with this is that the data does
not provide the reader with any insight into the data.
 Using Excel’s graphs, providing a visual presentation of
the graph allows insights (see next slide).
Sales ($'000) Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec
Domestic 1,893 2,343 2,593 2,283 2,574 2,838 2,382 2,634 2,938 2,739 2,983 3,493
International 574 636 673 593 644 679 593 139 599 583 602 690
2,467 2,979 3,266 2,876 3,218 3,517 2,975 2,773 3,537 3,322 3,585 4,183
Exhibit A: Sales data table
Source: Stephen Few “Visual Communication” IBM
Whitepaper, April 2009 (p. 2)

Stephen Few, Perceptual Edge
From the data at right for a typical sales graph,
we can observe:
 Domesitc sales trend upwards across the
year
 International sales are relatively flat across
the year
 An exception in international sales is noted
in August
 There is a cyclical pattern in domestic sales,
being lowest in the first month of the
quarter and then growing through the
quarter
From the graph, we might infer:
 Sales staff may be going light in the first
month of the quarter and start working
harder as the quarter progresses in order to
meet their quarterly targets.
 Perhaps there is an element of ‘channel
stuffing’ happening at the end of the
quarter.
 Why the year-end spike?
Using Excel’s full potential enables this analysis. -
500
1,000
1,500
2,000
2,500
3,000
3,500
4,000
Jan Mar May Jul Sep Nov
Domestic International
Source: Stephen Few “Visual Communication” IBM
Whitepaper, April 2009 (p. 2)

All is not lost, here is a 7 step
process to review your
spreadsheets.
7 steps to assure your
spreadsheets

7 steps to spreadsheet assurance
Control environment
Model design
Inputs and assumptions
Formula design and calculations
Output assessment
Change and version control
Reporting

Control environment
 Review what spreadsheets exist and how
they are controlled and developed
 Review access and security arrangements
for spreadsheets
 Consider risk assessment for in-use
spreadsheets

Model design
 Consider overall design and implementation
of spreadsheets
 Consider domain related information
needed to understand model designs (e.g.,
are experts required such as geologists)
 Consider periodicity and format consistency

Inputs and assumptions
 Review inputs and assumptions, consider
approval requirements for assumptions
included in spreadsheets
 Consider inputs for data-entry errors
See: ASIC v MacDonald (No 11) [2009] NSWSC 287
(James Hardy case)
Do key assumptions need board level approval?

Formula design and calculation
 Check formula for calculation consistency and
accuracy
 Examine model for potential errors including:
hard coding, reference failures
How long can this take?
Hanrick Curran were recently asked to review a
transaction model with 1,173 unique formula and
50,028 total formula.
At 1 minute per unique formula, that equates to
19.5 hours of review time or 2.6 days of just
looking at formula. And this doesn’t even allow
time to consider domain errors.

Output assessment
 Review model outputs for consistency
 Consider information accuracy
 Does the model promote effective decision
making

Change and version control
 Consider change controls implemented over the
reviewed spreadsheet, including password
protection
 Consider version controls implemented over the
model and to whom ownership of the
spreadsheet is delegated
Tip: use a descriptive file name that includes information
regarding status and version or date of the spreadsheet.
Tip: put dates in YYYYMMDD order to enable auto-sorting
Example: “division budget review (v2.3)(DRAFT).xlsx”
Example: “20140612”

Reporting
 Prepare and communicate with
management regarding the outcomes of the
spreadsheet review

Some suggestions on how to
implement some key lessons from
todays topics…
A brief summary and some
practical solutions

In summary
 Errors will happen, if you plan well, their
impact can be minimised.
 Most errors are not material, but do you
want to be the example that proves the
rule?
 Most errors are not actually on/in the
spreadsheet.

Practical solutions (I)
 Set organisational spreadsheet standards;
have a “this is the way we do it here.”
 Implement a best practice standard
(i.e., FAST).
 Implement training in how to use Excel and
how to design spreadsheets.

Practical solutions (II)
 Demand better presentation of information … in
a way that supports decision making.
(“But this requires better training to start with.”)
 Stocktake where you are using spreadsheets …
assess where your vulnerability lies and
address key risks.
 Implement a review process for key
spreadsheets with external review if needed
(e.g., internal audit, external audit or domain
specialists).

Practical solutions (III)
 At a basic level, for ad hoc spreadsheets:
 Layout your work
 Use styles
 Format sheet well/properly
 Don’t hard code
 Don’t use remote references
 Include graphs
 Take time to check and review your work
 Document information sources

Practical solutions (IV)
 For more complex spreadsheets:
 Use a standard format & style
 Involve review and signoff of key inputs &
assumptions in the spreadsheet
 Build-in error checks
 Keep cross links to a minimum
 Keep all links between worksheets on a
single page
 Implement version and change controls

Self-checking your spreadsheets to
avoid errors in decision making.
Anthony Berglas
Spreadsheet Detective

Spreadsheet Detective
 Sits as an add in within Excel™

What can it do…

What SD can do…

Map report

Workbook report

Formula report

Questions?

More resources
www.f1f9.com
“Well worth trying their 31 day free online course for a brush-up on your
excel skills.”
www.spreadsheetdetective.com
“Use the tools we use, to understand and self-audit your model.”
www.asap-utilities.com
“Great tools for every excel user. If you ever work with data, you need
these tools.”
www.perceptualedge.com
“For enlightening analysis and communication.”

Disclaimers
This document contains information in summary form and is therefore intended for
general guidance only. It is not intended to be a substitute for detailed research or the
exercise of professional judgement. It does not purport to be comprehensive or to render
professional advice. The reader should not act on the basis of any matter contained in
this publication without first obtaining specific professional advice.
We believe that the statements made by us in this document are accurate but no
warranty of accuracy or reliability is given. Our conclusions are based on interpretations
of accounting standards and other relevant professional pronouncements and legislation
current as at the date of this document. Should the interpretations, accounting
standards, other relevant professional pronouncements or legislation change, our
conclusions may not be valid. We are under no obligation to update the matters
considered in this document after its publication.
© Hanrick Curran, June 2014
All rights reserved

Thank you

Tips and Tricks for Optimising Excel

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