Forecasting and
Scenario Planning
The Challanges of Uncertainty and Complexity
Agenda
 Introduction on forecasting
Introduction
 Methods of forecasting depend on two variables
 Uncertainty
 The degree of available knowledge about the target
variable
 Complexity
 The number of variables and the extent to which they are
interrelated
Introduction
 Low uncertainty / Low complexitt (LL)
 Point estimates
 A single number
 Forecasting the demand of a known product in a stable
market
Introduction
 High uncertainty / Low complexity (HL)
 Confidence ranges
 Upper and lower boundaries
 Estimate the oil reserves in a new offshore field
Introduction
 Low uncertainty / High complexity (LH)
 Deterministic models
 Identify, measure and relate different influencing
variables
 Fundemental forecasting of exchange rates
Introduction
 High uncertainty / High complexity (HH)
 Scenario planning
Introduction
High
complexit
y
Low
complexit
y
Deterministic
models
Scenario
planning
Point
estimates
Confidence
ranges
Low
Uncertain
ty
High
Uncertain
ty
The Challange of
Uncertainty
 Definition of uncertainty
 As to the correct value of an unknown quantity of
interest:
 disagreement among forecasters
 doubts within a single forecaster
The Challange of
Uncertainty
Why disagreement?
 Overconfidence
 (marriage, business success)
 Illusion of control
 (choose lottery number)
 Information distortion
 (availability heuristic)
 Risk perception
 Assessing or weighing probabilities
The Challenge of
Complexity
 Definition of complexity
 How many variables
 How deeply they interact
The Challanges of
Complexity
 Cognitive simplifications
 The abbility to identify all variables and combine them
sufficiently
 Bounded rationality
 Cognitive devices (for instance schema’s and associateive
networks)
The Challanges of
Complexity
 Dynamic complexities
 Cross-sectional complexities
 How variables interrelate at a given point in time
 Dynamic complexity
 How variables interrelate over time
 Research shows that people have a difficulty learning over time
=> internalisation of the concept not to give up and try and try
again.
The Challenges of
Complexity
“We should make things as simple as possible,
but not simpler” (Albert Einstein)
Uncertainty / Complexity
 LL (Point estimates)
 Problems
 Focus extensively on one value
 Reluctant to diverge from an initial estimate
 Use of heuristics and consequent bias
 Improve
 Extrapolating forecasting
 NPV analysis
 Decision analytic models
Uncertainty / Complexity
 HL (Confidence ranges)
 Problems
 Overconfidence which makes ranges to narrow
 Ignoring base rates
 Ignoring sample size
 Improve
 Event diagrams
 Fault trees
 Simulation
Uncertainty / Complexity
 LH (Deterministic models)
 Problem
 Humans simplistic notion about cause and effect
 Think about everything that is said during election campaigns
 Improve
 Assumption analysis
 Influence diagrams
Uncertainty / Complexity
 HH (Scenario Planning)
 Problem
 Inability of human beings to deal with ambiguity
 Improve
 Focus on exploration and learning
 Feedback loops
 Posing disconferming questions
Scenario Planning
 Focus on the joint effect of many factors
 Helps us understand how various strands of a complex
tapestry move if on or more threads are pulled
 The art of scenario planning lies in blending the known
and the unknown into a limited number of internally
consistent views of the future than span a very wide
range of possibilities.
How to develop scenarios
 Define the scope and time frame
 Identify the major stakeholders
 Overview of players, there interests and power position
 Identify basic trends
 DESTEP
 Why is it relevant
 Direction of the trend (positive, neuteral, negative)
How to develop scenarios
 Identify key uncertainties
 Aimed at issues concerning the company
 Determine possible outcomes for each uncertainty
 Construct initial scenario themes
 Identify two extreme worlds by putting all the positive
outcomes in one and all the negative outcomes in the other
scenario
 Or, select the two most significant uncertainties and
postulate two rather extreme outcomes for each uncertainty
How to develop scenarios
 Build a scenario blueprint
 Create a matrix that represents the identified uncertainties and
plot the scenarios you constructed
 Check whether all scenarios are:
 Plausible
 Internally consistent
 Sufficiently relevant
 Develop full-fledged scenarios
 Identify general themes
 The overall goal is to identify storylines and themes for the
scenarios that cover a full range of possible outcomes for the
uncertain issues at hand
 Highlight competing perspective
How to develop scenarios
 Identify research needs
 To flesh out your understanding of uncertainties and trends,
as well as perhaps their interrelationship over time
 Develop influence diagrams
 Develop quantitative models
 Simulations
 Usually done with specific software
How to develop scenarios
 Evolve towards decision scenarios
 Converge towards a limited number of distinctive different
scenarios that you will eventually use to test your strategies
and generate new ideas
 Windtunnel the scenarios with the business ides
Assignment / Break
‘Your personal scenario’s’
 Take 20 minutes to answer the following questions:
 Which two variables in your environment will be most
important in the way your life will develop over the next 5 –
10 years?
 For both variables, determine (realistically) what the most
negative an most positive outcome would be for you.
 Combine the 4 outcomes => 4 possible futures
 How well are you prepared for these 4 possible futures and
why?
 What do you possible have to do the be better prepared for
these 4 possible futures?
 After 20 minutes I would like to ask 3 -4 students to elaborate
on their scenario’s.