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 Deterministic models Scenario planning Point estimates Confidence ranges High complexit y Low complexit y 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.