Morphological analysis is a technique developed by Fritz Zwicky (1966, 1969) for exploring all the possible solutions to a multi-dimensional, non-quantified problem complex.
In linguistics it refers to identification of a word-stem from a full word-form. (See Morphemes).
As a problem-structuring and problem-solving technique, morphological analysis was designed for multi-dimensional, non-quantifiable problems where causal modeling and simulation do not function well or at all. Zwicky developed this approach to address seemingly non-reducible complexity. Using the technique of cross consistency assessment (CCA) (Ritchey, 1998), the system however does allow for reduction, not by reducing the number of variables involved, but by reducing the number of possible solutions through the elimination of the illogical solution combinations in a grid box. A detailed introduction to morphological modeling is given in Ritchey (2002).
Morphology comes from the classical Greek word morphe, which means shape or form. MA concerns the arrangement of objects and how they conform to create a whole of Gestalt. The objects in question can constitute a physical system (e.g. anatomy), a social system (e.g. an organisation) or a logical system (e.g. a language or system of ideas).
A complex problem has several characteristics:
Moreover, at least one expert in the area of expertise of a dimension of the problem must be included in the team composition. However it is suggested that a team must not have more than eight members. Adequate amount of time also has to be allocated depend on the complexity of the problem. A research conducted by Swemorph suggests that the length of a morphological analysis might vary from 1 – 30 full workshop days.
Figure1: Morphplogical Analysis process-data model
The left figure shows the meta-process model, which is the representation of the activities involved in performing morphological analysis. These activities are explained in more depth in section 2. On the right hand of the figure you see the meta-data model of morphological analysis, which is the presentation of the data that are produced by the activities of the method. Each of these data or concepts is detailed in section 3. The dashed lines in between the two models indicate the relationship between the activities and the produced data. An example of Morphological Analysis project to clarify the application of Morphological Analysis’ activities is then presented in section 4. The aim of this example is to perform analysis and explore the possibilities to grow a start up company.
Activity 1: Describe the problem. In describing the problem, you identify all issues (Sub-activity 1-1a) that might relate to/ be caused by/ cause the problem. In line with that, you need to define the dimensions (Sub-activity 1-1b) that affect the problem. Accordingly, you gather the result of these activities into a policy problem 1-2. You might use any other idea generation method, such as brainstorming, voting, etc, in performing this step. See policy problem sample for more concrete description.
Activity 2: Analyze the possible solution’s parameters. Once you have a policy problem in hand, you decompose it into some problem’s values (Sub-activity 2-1). Each dimension that you have defined in the policy problem might consist of one or more values. Then you decompose the values into more specific concepts, called parameters (Sub-activity 2-2). Again, you might use any other idea generation method, such as brainstorming, voting, etc, in performing these (sub) activities. See policy problem sample for more concrete description.
Activity 3: Construct morphological box. This activity, constructing morphological box, forces you to document/ organize what you have thought/ discussed so far. You create a two dimensional matrix, transform the pre-defined values as the column header and list the corresponding parameters under each values. See morphological box sample for more concrete description.
Activity 4: Evaluate possible solution. The first sub-activity you need to perform in evaluating possible solution is performing cross-consistency assessment (Sub-activity 4-1) to all parameters against each other. If you find two parameters are contradictive, you put a cross (x) mark into the morphological field. Secondly you determine one or more input constraint (Sub-activity 4-2) that is parameter(s) that must be included in the future solution or solution space. (Sub-activity 4-3) The last activities is to combine the pre-constructed morphological box (Activity 3), the cross consistency assessment result (Sub-activity 4-1), and the predefined input constraint (Sub-activity 4-2). You highlight the morphological field on the morphological box that fulfills the input constraint with a certain color, e.g. red. Then you map the cross consistency assessment result of the input constraint to the parameters on the morphological box and finally you highlight the blank morphological field with another color, e.g. blue. The parameters highlighted in red and blue are composing the solution space. See solution space sample for more concrete description.
Activity 5: Apply the selected solution. Based on the solution space that is resulted from performing activity 4, you can decide whether to put the suggested solution into a real action or adapt one or more previous activity.
The figure 2 below serves as a preliminary introduction of the concepts that will be described in section 3.5 – 3.9.
Figure 2: Morphological analysis concepts introduction.
For example we could detail ‘financial’ dimension which we derived in the previous example, into ‘financial sources’. We call this decomposition as a value. Other examples of values are ‘relation with existing business’ and ‘educational level’, which are result of decomposing the ‘social’ dimension. We list this value into the column header as depicted in figure 3.
Further decomposition of a value will result in parameters definition, which we list in the rows under the related value as depicted in figure 3. For example, the value ‘sector’ has parameters such as ‘automation service’, ‘automotive industry’, ‘business consultancy’, ‘electric apparatus’, etc. This gives you a complete list of available sectors and you can start a new business in any of these sectors. The sector’s parameters are derived from registered industry KVK website, which is the official chamber of commerce of the Netherlands.
Another example of value is ‘personal characteristics’. It has parameters, such as ‘extroversion’, ‘introversion’, ‘aggressive’, ‘open to new ideas’, etc. This gives you a complete list of possible characters that every human has. Since personality is closely related to profession, this personality list could advice you about what personal character is lacking in yourself in order to be an entrepreneur. The personal characteristics parameters are derived from another wikipedia entry, Personality.
The resulted two-dimensional matrix is derived from identifying the values of dimensions and parameters of values, is shown in the figure 3 below. The column header lists the values and the rows under each column-header represent the decomposition of a value (i.e. the parameters).
Figure 3: The morphological box – Entrepreneur to be
Sample of a complete Cross Consistency Assessment (CCA)t result The purpose the cross consistency assessment is to check a parameter against other parameters. The contradictive judgment of two parameters are shown with the cross (x) mark in a morphological field in the figure 4 below. The figure is an excerpt of a CCA result.
Figure 4: Excerpt of Cross Consistency Assessment result - Entrepreneur to be
In the following paragraph, the two cells in the blue circle are explained further as the example of CCA.
We checked whether the parameter ‘automation service’ contradicts to parameter ‘personal saving’. We do not find any contradiction between these two parameters, so we leaved the box empty. Our motivation was, the start up budget required to establish a new business in the sector of automation service, e.g. software house, is affordable by only using personal saving. In the other hand, we found a contradiction between the parameter ‘automotive industry’ and parameter ‘personal saving’, since the capital required to establish an automotive industry is (most of the time) too much for a personal saving. Therefore we put the contradiction/ cross (x) mark into the corresponding cell. Example of an Input Constraint definition and the corresponding Solution Space Input Constraint: Competitor (relation with existing business) of existing IT companies (sectors). These input constraints are shown with the red highlight in the figure 4 and figure 5. These input constraints are chosen based on our needs. In this example we wanted to establish a software house, however we do not want to be part of a larger company (subsidiary or joint venture), therefore we decided to be competitor of the existing business.
The solution space that fulfill the input constraints are shown by the blue boxes in figure 5, which are derived from combining the non-contradict parameters (empty cells) of our input constraints (highlighted in red color) in the figure 4. In other word, a parameter could be in a solution space is and only if it does not contradict with the input constraint.
This also means that we could derive more than one solution by varying the input constraint.
Figure 5: The Input constraint and solution space – Entrepreneur to be
The lessons learned from the solution space shown in figure 5 are, in order to be an ‘independent’ (no join venture nor become sub diary) entrepreneur in IT industry:
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