Methodology - PowerPoint PPT Presentation

Methodology

Not easily quantifiable – no data, uncertain data, incomplete data

Problem is continually developing and mutating

Full of ambiguities, contradictions and vicious circles

Stakeholder oriented with strong political, moral and professional issues

Reactive: the problem fights back

Wicked problems are multi-faceted, multi-dimensional

‘Wicked problems’

H. Rittel & M. Webber (1973)

Dilemmas in a General Theory of Planning

Wicked problem (Rittel et al 1973)

Compare Tame Problem

Has a relatively well-defined and stable problem statement

Has a definite stopping point i.e. we know when the solution is reached

Binary solutions: objectively evaluated as being right or wrong

Has solutions which can be tried and abandoned

• Not easily quantifiable as uncertain or incomplete information
• Continually developing and mutating
• Full of ambiguities, contradictions and vicious circles
• Stakeholder oriented with strong political, moral & professional issues
• Reactive: the problem fights back

• What should be our negotiating response based on the various possible positions adopted by the other party?
• How should we deal with tax-related private banking challenges?
• How do we pre-empt the risk to corporate reputation in a complex, mutating environment?
• What are the core issues that a corporate governance strategy should try to incorporate?
• How much should the emerging sciences – synthetic biology, stem cells, nanotechnology, pediatric trials – be regulated?

Morphological analysis is a problem-structuring method ideal for tackling wicked problems in multiple dimensions using extended typology analysis

Fritz Zwicky (1898-1974)

Professor of Astronomy, Caltech (1942-68)

Co-founder Aerojet Engineering Corporation

Developed Morphological Analysis as a problem-structuring method to address genuine uncertainty and stress test boundary conditions, resulting in discovery :

Dark matter (1934)

Triple hypothesis - supernova, neutron stars, and cosmic rays (1934)

Gravitational lensing (1937)

Point of origin

Where do you place the 4th, 5th…..nth dimension?

How to build a

morphological model

Example:

What to do about the Swedish

Nuclear Bomb Shelter Program

following collapse of Soviet Union

Dimensions

• What’s the problem (focus question)?
• Convene a subject – matter specialist team
• Stakeholders facilitated to agree
• on most important Dimensions of the problem complex

Quantitative

scale

2 x 2 matrix

Normative,

non-quant scale

Agree and define a range of ‘values’ or ‘conditions’ for each dimension

A morphological model of 2034 configurations –

how to reduce to a workable number?

Consider every pair - facilitate team to knock out

illogical, or empirical, contradictory pairs

- or 0 possible

x = not possible

S or F = not optimal

Cross

Consistency Matrix

Note reduction

> 90%

Solution space: list of surviving, internally consistent combinations – all blue cells are compatible

• Input and outputs interchangeable - manipulate both cause and effect
• Ability to freeze and compare scenarios and strategy alternatives
• Reducing alternatives does not require re-developing scenarios
• Easily updatable - visual, real time systematic group exploration
• Speed, efficiency and cost of facilitation and model development fraction of traditional consultancies – enhances entire value chain

Managing genuine uncertainty in real time by placing comparative judgements on a sound methodological basis

Accommodating multiple, alternative perspectives to anticipate unintended consequences vs. prescribing single solution

Anticipating consequences of decisions made under conditions of high uncertainty, incomplete data and high decision stakes

SFP uses multi-methodology to give clarity to messy situations and decision-support in ranking solutions

Bayesian Belief Networks

Assigning a probability to an event to give indication how strongly client believes an event will occur

Morphological Analysis

Structure (dimensionalise) the problem complex

Analytic Hierarchy Process

Decision-making process for prioritising alternatives when multiple criteria must be considered

• Give a brief description of the methodology
• Will avoid the mathematics (Eigenvector)!
• Provide examples of where AHP has been used
• Illustrate principle by way of simple example – choosing a car

• Developed by Thomas Saaty in the 70’s for rational decision support for complex decision situations with multiple criteria
• Why? Observed the lack of practical, systematic, approach for priority setting and decision making by groups when dealing with uncertainty
• Crucial decision situations, forecasts or resource allocations involve too many dimensions for humans to synthesize intuitively

• Investigating the effect of website quality on e-business success
• Assessing supply chain risks for the off-shoring decision by a US manufacturing company
• Involving patients in decisions regarding preventive health interventions
• Decision support for selecting exportable nuclear technology
• A departmental approach to apportion co-author responsibility

Goal

Criteria

- Audi A3

- VW Golf

- Megane

- Ford Focus

- Audi A3

- VW Golf

- Megane

- Ford Focus

- Audi A3

- VW Golf

- Megane

- Ford Focus

Alternatives

Given uncertainty or incomplete data, relative weights are agreed by

the working group or the managerial team and led by a facilitator

• Sum ratios in each column
• Divide each ratio by the column sum
• Compute the row averages

Qualitative judgements

and Quantitative measures

can be incorporated in the

same decision matrix

Audi A3 0.12

VW Golf 0.25

Megane 0.06

Focus 0.58

Audi A3 0.38

VW Golf 0.30

Megane 0.07

Focus 0.26

Audi A3 0.26

VW Golf 0.28

Megane 0.24

Focus 0.22

Which car did you choose?

• Eigen Vector
• mathematical function used in prioritising elements of different sizes and scale in a matrix
• Consistency ratio
• a measure how consistent the judgements havebeen relative to large samples of purely random judgements
• must be less than 10% (dependent upon team expertise and quality of facilitation)

The Facilitated Process

The Rationale

For multi-inclusive modelling

i.e. ‘and’ rather than ‘or’ (use morphological analysis)

Allows rational group decision making where stakeholders use experience, data/knowledge to address uncertainty

Gives decision support to complex, mutating problems

• Deconstruct problem into a hierarchy
• Make pairwise comparison and establish priorities of elements in the hierarchy
• Synthesise the results (to obtain the overall ranking of alternatives w.r.t. goal)
• Evaluate consistency of judgements