The rather complicated-sounding term Morphological Analysis (MA) is a particularly useful, albeit hidden, method for helping to structure problems and support decision making, notably when they are complex, “wicked” and inherently contain high levels of uncertainty. It has also been called “totality research”, an “idea factory”, and  “strategic options analysis”.

 

Morphological analysis (MA) systematically structures and examines the total set of “possible relationships in a multidimensional, usually non-quantifiable, problem space. It helps to reduce the chance that events will play out in a way that the analyst has not previously imagined and considered. Such problems are complex and exacerbated by high levels of interconnectivity adding complexity, and as highlighted above, have been called “wicked problems”. A ‘wicked problem’ is one that is difficult or impossible to solve because of incomplete, contradictory, and changing requirements that are often difficult to recognise. The challenge is how to deal with such problems where the relationship between the multitude of variables is poorly defined leading to sub-optimal decision making and spurious correlations. MA addresses this conundrum.

 

MA allows for all ideas to be considered as a first stage in the analysis process and as such is an exploratory method par excellence. It is well suited to handle conditions of uncertainty (not to be confused with risk), and complexity – conditions ever present at the early stages of the decision making process. In the strange days of the post Brexit era, “Uncertainty” is no longer a conceptual slogan but a reality we are living through.

 

MA supports decision making under such conditions by helping to  improve identification of viable solutions AND avoid making bad decisions.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MA is a problem-structuring method that defines a problem as a set of key parameters. Each parameter is broken down into a set of discrete states or dimensions, being qualitative or quantitative in nature. The scale of the problem is represented as a product of all the selected parameters and their individual states expressed as a total number of configurations. This is the problem space. Configurations where each state within a parameter is deemed consistent with every other state across the other parameters are identified and isolated. Software helps to filter out inconsistent configurations or options reducing the problem space by 95% or more. This reduced output is called the solution space.

 

Although considered a sound approach to problem structuring and decision-making MA’s uptake has been patchy, and latterly overlooked. This is mainly due to the user experience being compromised by three interrelated factors: poor access to support software which can address the combinatorial explosion generated by multi-parameter problem spaces inherent in the use of MA; insufficiently flexible processes that address users’ operational constraints; seen to be overly generic, disguising identification of specific application areas of interest.

 

Recent empirical research and product development has majorly addressed these constraints, making the product robust enough for commercially viability with NATO having purchased a licence. On and off-line versions are available combined with more flexible processes to take into account user’s own operational resources and constraints.

 

So where can MA be deployed?  Being a generic method means that it can used across a wide spectrum of early stage problem areas. Specifically MA works with the following main disciplines.

 

 

 

 

 

 

 

 

 

 

 

 

 

 

MA can be used discretely within a single domain such as Ideation & Technological forecasting or in any combination of the other domains. Below is a sample of areas where it has and can be used. Applicable to any business or organisation it is well suited to sectors such as Life Science/Pharma, Healthcare, Defence and Security, Engineering and Creative as well as with organisational issues such as diversity, CSR, governance, social exclusion etc.

 

(A): Ideation & Technological Forecasting – Development of new types of combustion or jet engines, electrical torque devices (after Ayres), concept design for a new vacuum cleaner.

(B): Foresight & Scenario Planning – Projecting alternative, contingent, regional and national futures such as the quality and nature of political integration, identifying weak signals relating to various technologies, criteria for military engagement in large urban conurbations in 2035.

(C): Systems Uncertainties – How to develop policy to combat social exclusion, integrating organisations post merger or acquisition, how to embed diversity practices within an organisation, immigration and integration.

(A+B):Ideation and Foresight – How to develop a product which can be updated on a modular basis to reflect changing technological capabilities over the next 10 years, of bio-mimicry and its future impact.

(A+C): Ideation and Systems Uncertainties – How will humans adapt to and control technological advancement in artificial intelligence?, how will innovations in social media impact human behaviour and social responses?

(B+C): Foresight and Systems Uncertainties – what are future options for the role of the state? What are the dangers of an over concentration of power in the hands of media conglomerates?

(A+B+C): Ideation, Foresight and Systems Uncertainties – What might be the unintended consequences on privacy of the “internet of things” by 2030 and Long-Term Policy Analysis (LTPA)

 

Using MA in practice: A recent case study in the NHS Health sector described how MA can play a role in developing and improving the efficacy of hand hygiene (HH) measurement in healthcare. Results from the study highlighted the key aspects of lack of meaningful data to the current measurement systems and opportunities to develop the system to address this. Additionally, secondary benefits of applying the technique were also noted, namely the use of MA as a participatory tool with which to engage and involve users in a co-creative process to address HH measurement.  The results demonstrate the significance of the approach in understanding different aspects of the HH measurement system and how the method allows a user centred participatory approach to complex system design and improvement. The process and supporting software reduced a problem space of some 9720 different configurations to some 600 possible solutions – a reduction of 94%.