Key Driver Analysis
Key Driver Analysis …
© Access Analytic Solutions Pty Ltd 2013
Sean Vincent Introduction:
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Senior Business Analyst at Access Analytic
Access Analytic is an Australian consultancy that provides modelling,
reporting and analysis for medium and large companies to enable
them to improve their financial and operational performance.
Focus: Australia/Oceania, Asia, Middle East, Africa
Clients: Chevron, Woodside, BHP Billiton, Rio Tinto, PwC, KPMG,
Toyota, Wesfarmers, and many other medium/large companies
Masters (Economics) – current, MPA, BCom (Management &
Marketing), Cert IV (Property), Excel Expert
© Access Analytic Solutions Pty Ltd 2013
Agenda
- Part 1: About Key Driver Analysis
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Non-financial information: key drivers
Deterministic vs probabilistic
Simple Simulations
- Part 2: Putting it into Practice
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Simulations without expensive software
Key driver reporting & analysis best practice
Excel & beyond
- Part 3: Questions
© Access Analytic Solutions Pty Ltd 2013
Part 1: About Key
Driver Analysis
Financial or Non-Financial?
Financial
Inventory turnover
Profit per unit
Customer satisfaction
Wastage cost per unit
Ore grade
Strip ratio
% of on-time deliveries
Days lost due to injury
Product quality metrics
Productivity per hour
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Non-Financial
Non-Financial Information: It’s More
Important Than you Think!
• Often more important than financial information!
• Financial = quantitative?
Non-financial = qualitative?
•  Drives financial  Critical for decision-making
• Key Drivers for financial models
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Impact Stakeholder Decisions
“External stakeholders look at a range of non-financial performance
measures to assess how well a company is meeting its corporate
responsibility objectives.
These are seen as a proxy for good management.
To communicate this information, companies require relevant nonfinancial key performance indicators, or KPIs.”
-- Deloitte, Canada
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Non-financial Information in Results
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Inputs
Business Analysis:
risks, context
Assumptions, forecasts, base
data (eg historical financials,
non-financial, economic data)
Scope
Purpose
Audience
Sensitivities
Outputs &
Logic
Calculations
Forecast
Cashflows
High-level structure
Modules
Detail/Summaries
Charts
Documentation
Error Checking
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Discount Rate
Valuation
Analysis &
Interpretation
Deterministic vs Probablistic
Deterministic
Probabilistic
Standard Model
Monte Carlo
Single answer(s)
Range of answers
Allow for uncertainty:
outputs
Scenario(s)
Range of answers
Allow for uncertainty:
inputs
Estimated values &
sensitivities
Estimated values, probable
ranges, distributions
Key result
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Introduction to Monte Carlo
• Simulate sources of uncertainty that affect value, &
calculate a representative range of values
• Used to handle multiple sources of uncertainty
• Typically have an idea of the range of numbers expected
but not the exact number
• Excel alone or with add-ins such as “Crystal Ball” or @ Risk
© Access Analytic Solutions Pty Ltd 2013
Monte Carlo Method
1. Construct financial model as normal
2. Identify variables impacted by uncertainty
3. Identify expected range of values & distribution type:
historical data or estimate (1%  99% range)
4. Generate large number of random values using selected
distribution
5. Run multiple simulations, record the results, analyse.
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Single Variable
Variable
e.g. Sales Price
Model Result Distribution
(depending on model’s calculations)
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Multiple Variables
Inflation (narrow)
Sales Price (normal)
Exchange rate (wide)
Model Result Distribution
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Variable Distributions
• Not all variables are normally distributed
– Positively skewed: won’t be below a specified minimum, but
unlimited upside e.g. oil price, property prices, stock prices
– Uniform: all values in the range are equally likely e.g. heads
or tails
– Triangular: values near the most likely are more likely
• Examine historical data or best estimates to decide
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Key Drivers & Performance Analysis
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What are KPIs?
• Key Performance Indicators
• Key: focus on important stuff ONLY
• Performance: measure how things are going  goal
• Indicators: not always precise, not guaranteed
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What’s important?
Aim: align individuals with organisation  bonus
Often roll-up
Presented in dashboard
• Distributed throughout the organisation
© Access Analytic Solutions Pty Ltd 2013
Why you need KPIs
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Don’t “just get it done”!
Focus: costs, productivity, profitability, optimisation
“What gets measured, gets done”
Can’t achieve meaningful focus without measurement
& reporting
• Competitive advantage
© Access Analytic Solutions Pty Ltd 2013
Key Driver Reporting & Analysis in the Enterprise
Analysis & Reporting
Budgeting, & Forecasting
What happened?
What will happen?
Report
Forecast
Single Data
Model
What could happen?
Scenarios
What is happening?
What do I want to happen?
Dashboard/Scorecard
Budget
Why?
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Analyse
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© Access Analytic Solutions Pty Ltd 2013
Dashboard Best Practice Principles
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One screen of about 5-6 relevant measures
Basics: data is accurate, timely, reliable, clean
Easy to update, ideally automatically
Define what KPIs are & what they mean
Objective is communication
Summarised with drill to detail available
Highlight exceptions & important information
Careful use of colours that go together
Choose most appropriate charts, data  clear & uncluttered,
logical grouping & linking
• Support taking action
© Access Analytic Solutions Pty Ltd 2013
© Access Analytic Solutions Pty Ltd 2013
Limitations of Excel
# of Users
Hard to involve more than 1-2 people in inputs, hard to
distribute
Workflow
Managing multiple versions, getting people to provide
input, splitting/consolidating  nightmare!
Data in/out
Difficult to get historical data in, or send information to
ERP/accounting package
Security
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Excel’s security is very weak: no audit trail, sensitive data
is easily exposed and unauthorised changes can occur
Key Driver Reporting & Analysis Systems
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Part 2: Putting it into Practice
Putting it into Practice
• Simulations in Excel (without add-ins)
• Key driver reporting & analysis best practice
• Excel & beyond
© Access Analytic Solutions Pty Ltd 2013
Part 3: Questions