Quote from sachinm on 6 November 2023, 12:52 pm

 

The Stacey Matrix was developed and published by Ralph Douglas Stacey. It is designed to help understand the factors that contribute to complexity and choose the best management actions to address different degrees of complexity.

Source:Stacey RD. Strategic management and organisational dynamics: the challenge of complexity. 3rd ed. Harlow: Prentice Hall, 2002.

The basis of the matrix is two dimensions: agreement and certainty.

Projects are close to certainty when cause and effect relationships are well known and similar projects have been performed in the past. Projects that are far from certainty are required to deliver something that is new and innovative (or at least new to the host organisation performing the project).

Within the two axes, Stacey identified five areas:

1. Close to agreement, close to certainty (Agree on HOW and agree on WHAT)

• In this area of the matrix, data can be gathered from the past that can be used to predict the future. Construction and engineering projects typically have a wealth of technical data that allows them to be well specified and scheduled before delivery work starts. Work is controlled by monitoring against detailed plans.
• This is where past data is collected that can be used to predict the future. Much of the management literature and theory focuses on this area of ​​the matrix where certainty and agreement are both high.

2. Far from agreement, close to certainty (Disagree on WHAT, but Agree on HOW)

• Some projects are very certain about which objectives are possible and how they can be delivered but there is less agreement about which objectives are of greatest value. This may be exemplified by a project manager who has trouble developing a business case that is acceptable to multiple stakeholders who have differing views on value.
• In this area, skills such as negotiation, reaching a compromise and developing coalitions are important. Decision making becomes political rather than technical.
• The intended activity is in this part of the matrix when certain challenges have high certainty about how the results are created, but there is significant disagreement about which results are desirable.

3. Close to agreement, far from certainty (Agree on WHAT, but disagree on HOW)

• Projects may have a high level of agreement about the desired objectives but not much certainty about the cause and effect linkages that will result in the desired objectives. Relationships between outputs, outcomes and benefits can often fall into this category when assumptions are made about how outputs will lead to benefits. In cases like this a strong sense of shared vision among the stakeholders is necessary and a flexible and realistic approach to planning.

4. The zone of complexity (Not even about WHAT, but about HOW)

• This zone covers an area where the combination of low levels agreement or low levels of certainty make the project a complex management problem. This is the area that often triggers poor decision-making practices, when what it really needs are high levels of creativity, innovation and freedom from past constraints to create new solutions. Adaptability and agility are key skills here, not just for the project manager but for the sponsor, team members and stakeholders.
• There is a high degree of agreement, but there is not much certainty about cause and effect to achieve the desired results.

5. Far from agreement, far from certainty (No idea about HOW, no idea about WHAT)

• Where there is little agreement and little certainty, anarchy can prevail. Individuals and organisations sometimes resort to avoidance but such situations cannot always be avoided. Strategies are needed to address these situations should they occur.
• This zone is often referred to as chaos rather than anarchy and the boundary between this and the zone of complexity is known as the ‘Edge of chaos’. The area between traditional management approaches and chaos or anarchism.
• With Chaos you talk about situations in which there is low certainty and low agreement. Approached the other way around: an area of ​​great uncertainty and/or great disagreement. Sufficient reasons for 'fallout' or anarchy.

"There are changing customer wishes, we cannot eliminate uncertainty...

So we use frequent feedback and will avoid changes in course."

 

The four areas relate to the Stacey Matrix relate to the four areas of the Cynefin Framework (simple, complicated, complex and chaotic) helps understand the way to respond to circumstances of complexity as shown below:

Quote from sachinm on 6 November 2023, 1:13 pm

According to Frederic Laloux's cultural model, Chaos is the red environment, where the 'pack of wolves' with a strong leader and a 'terrifying' style comes into its own.

"The survival of the group depends on the decisions of the leader. In a real chaos environment you see tribal forms: mafia, street gangs and militias."

At an organisational level, traditional methods of planning, visioning and negotiation do not work sufficiently in this context of chaos. So many believe that this part of the matrix should be avoided by organisations. In accordance with traditional management theories.

The second, more recent approach is the proposition that innovation takes place along the edges of chaos. It is argued that avoidance may be a protective strategy in the short term, but this strategy will prove disastrous in the long term. There is an increasing group of supporters of this idea. People are convinced that real innovation and creativity take place along the edges of chaos. Proponents believe that the edges of chaos belong in 'Complex'.

 

Laloux's culture model has 5 phases, with the fifth phase representing the description of a new organisational form Teal.

• Red: chaos – tribal – impulsive

Very reactive view and short-term focus. Loyalty and fear connect this type of organisation.

• • Order by authority for division of work.
  • Impulsive survival urgency.
  • 'Hunter and prey' management style.

• Amber: traditional – agricultural – conformist

Very formal roles within a hierarchical pyramid. Ethics, stability through strict processes and predictability. Top down command and control systems: command and control, both what and how. Status is very important. The hierarchy provides control over things that happen in a lower order.

• • Authoritarian, safety protocols, formal roles that allow a long-term perspective.
  • Formal roles and hierarchy that can be scaled up.

• Orange: scientific – industrial – successful

Innovative, meritocratic management style. A form of command and control over 'what' and partial freedom over 'how'.

• • Innovation, responsibility and the socio-economic position of an individual in society are important. This is reflected in personal responsibility.
  • Innovation and renewal in an organisation is the way to lead. This is reflected in: whoever wins is rewarded. (also known as who deserves it..)

• Green: post-modern – information – pluralism

The focus in the green phase is on culture and empowerment to motivate employees. Although there is still a pyramid structure, the focus is on making customers happy. Choices are made based on shared values. Looking for high involvement of all employees and associated exceptional motivation.

Characteristics:

• • Social responsibility.
  • Sharing power, authorities and responsibilities.
  • Consensus/participative style.
  • Shared values-driven motivation culture.
  • The 'right value-having' investor model is important.

• Cyan – Teal: Self-management, wholeness, and evolutionary purpose

Self-management replaces the hierarchical pyramid. Organisations are seen as a cellular living organism, with the aim of fully developing their potential.

No one is in charge and there is no hierarchy. Hierarchy is not strong enough to be confronted with complexity. If you take a goal seriously, there is nothing holding you back and competition does not exist.

Characteristics:

• • Self-management
  • Wholeness
  • Evolutionary purpose

Quote from sachinm on 23 December 2023, 1:43 pm

To explore the concept of gamification on Strategy Deployment Approach, and Agile Transformation, you can use the X-Matrix TASTE model.

By printing of an A3 version, the X-Matrix template can work through the TASTE stimuli, as follows:

• True North:  the orientation which informs what should be done. This is more of a direction and vision than a destination or future state. Decisions should take you towards rather than away from your True North.
• Aspirations: the results we hope to achieve. These are not targets, but should reflect the size of the ambition and the challenge ahead.
• Strategies: the guiding policies that enable us. This is the approach to meeting the aspirations by creating enabling constraints.
• Tactics: the coherent actions we will take. These represent the hypotheses to be tested and the work to be done to implement the strategies in the form of experiments.
• Evidence: the outcomes that indicate progress. These are the leading indicators which provide quick and frequent feedback on whether the tactics are having an impact on meeting the aspirations.

 TASTE Success with an X-Matrix Template by Karl Scotland

Source: https://availagility.co.uk/2017/01/06/taste-success-with-an-x-matrix-template/

So, here you can use Facilitation tools like the "Vector theory of Change" and Back-briefing and Experiment template to identify aspirations, and start making small changes and monitoring what’s happening to create an iterative loop.

So, the idea behind the Back-briefing and Experiment template is to ensure a tactical team has understood their mission and mission parameters before they move into action. This A3 template is heavily inspired by Stephen Bungay’s Art of Action to charter a team working on a tactical improvement initiative. The sections are:

• Context: why the team has been brought together
• Intent: what the team hopes to achieve
• Higher Intent – how the team’s work helps the business achieve its goals
• Team: who is, or needs to be, on the team
• Boundaries: what the team are or are not allowed to do in their work
• Plan: what the team are going to do to meet their intent, and the higher intent

The vector theory of change, explains how we can map the system’s current state, run experiments to shift in that direction, measure the system's state again and assess what interventions might be most useful. This is an iterative process that incorporates feedback and allows for continually taking stock of a dynamic environment so we can take advantage of new opportunities as they arise. Hence, this is a ToC (Theory of Change) that is most suitable for complex environments.

With this approach, we talk about vectors, rather than explicit goals and end points. Hence, the name vector theory of change. A vector integrates three factors: the direction you want to move in, how quickly, and how much effort will be required. We start journeys with a sense of direction in which we want to move, we consider the intensity of effort that specific paths will take. Then speed is set by identifying available resources and natural enabling constraints to achieve the lowest energy costs to move in the desired direction.

The most common approach to ToC involves visioning what you want to achieve, and then working backwards in steps outlining the actions needed to achieve the subsequent step until you reach the present moment. This is a useful approach when working within a system that could be categorised as clear or complicated within the Cynefin domains. However, this approach to ToC will not be useful when operating within a complex system given its dynamic nature.

Quote from sachinm on 26 February 2024, 4:58 am

Risk-related decision problems are the problems that influence the risk of accidents. In most cases, the probability of major accidents is very low, but their consequences can be extreme, resulting in numerous fatalities and extensive environmental damage. An example is the Deepwater Horizon disaster in the Gulf of Mexico in 2010; where decision making was under high stake and complexity circumstances.

An improved understanding of risk-related decisions problem can be aided by developing a decision analysis approach to enhance decision makers’ understanding on the features of their decision problems.

By developing a decision analysis approach to enhance decision makers’ understanding on the features of their decision problems; Risk Managers can predict the human decision-making process, potential decision-making errors, and identifying decision support requirements.

This can in turn form the basis for tailoring the information needed for decision making. The proposed approach is a multi-dimensional approach, which includes seven dimensions: criticality, uniqueness, structuredness, complicatedness, dynamic, residual uncertainty, and problem trigger.

Characterisations of decision problems

In the field of problem solving and decision making, there are extensive discussions about different problem types:

• dynamic problems
• complex problems
• dynamic complex problems
• ill-structured problems
• complex and ill-structured problems
• wicked problems
• risky decisions

These problem feature-based classifications provide indications on how to solve the problem and how the problem will be solved. For example, utility theory or the analysis of choices among risky projects with multiple (possibly multidimensional) outcomes (e.g., St. Petersburg paradox as first proposed by Nicholas Bernoulli in 1713) should be used for risky decisions, while microworld simulation should be used for complex problems.

Visual representation of the key dimensions

Tiantian Zhu, Xue Yang, Stein Haugen, Yiliu Liu, in their paper “A multi-dimensional approach for analyzing risk-related decision problems to enhance decision making and prevent accidents”; propose the following seven dimensions to describe the features of risk-related decision problems:

1. Criticality (negligible to critical)
2. Uniqueness (common to unique)
3. Structuredness (well-structured to ill-structured)
4. Complicatedness (simple to complicated)
5. Dynamic (static to dynamic)
6. Problem trigger (proactive vs reactive)
7. Residual uncertainty (low to high)

Source: Tiantian Zhu, Xue Yang, Stein Haugen, Yiliu Liu, "A multi-dimensional approach for analyzing risk-related decision problems to enhance decision making and prevent accidents", Journal of Loss Prevention in the Process Industries, Volume 87, 2024, 105235, ISSN 0950-4230, https://doi.org/10.1016/j.jlp.2023.105235.

Here a qualitative radar map can be used to intuitively visualise the properties of a risk-related decision problem.

We can map typical risk-related decision problems into these proposed key dimensions as below. The radar map for a category of problems, as a collection of many problems with some similarities, can be shaped by a certain range in each dimension.

The mapping of key dimensions for the characterisation of risk-related decision problems, suggests that each dimension will have features that demand alternative decision-making process and tactics.

Another observation is that highlighted problem features in different sectors are not the same. For example:

• Climate Change problems: characterised by long time-lag between action and effect, concerning a complex system that is not well understood, and the objective being for global collective good.
• Environmental health risk: complex, dynamic, or wicked problem solving.
• New technology: new and emergent are the focused features, with many decisions in socio-technical systems considered very complex and highly uncertain.

Recognition of different problem-types, needing different decision-making has been recognised by the UK Oil and Gas Industry Association (2014) which classifies risk-related decisions into 3 types (A, B, C) considering 3 decision context factors: type of activity, risk and uncertainty, and stakeholder influence.

"Complexity" vs "Complicatedness"

Readers will note that one of the seven dimensions in the radar map is "complicatedness" and NOT “"complexity". This is an interesting distinction to explore, where complexity refers to a non-linear (and compounding) casual-effect relationship, so it's unpredictable and unknown; whereas the degree of complicatedness is a more empirical. It defines the number of elements in the problem space (e.g., number of possible future states, objectives, possible actions, constraints, relationships between the variables).

And so, using "complicatedness" instead as a measurement dimension identifies solutions to reduce complicatedness through simplification by 1) increasing the level of abstraction, 2) decomposing the problem into a set of simpler problems to solve these problems almost independently, or 3) rule-based decision-making process.

Unique problems are often ill-structured. Complicatedness, uncertainty, and dynamics are positively associated with each other. For example, when decision makers are not sure which state will be the future state, they tend to assign a likelihood to each future state. The likelihood of each future state is an added variable and increases the complicatedness of the problem space. When people perceive that the system state is changing/dynamic, a function is needed to describe the mechanism (relationship between system states in successive time steps). With imperfect knowledge about the dynamic mechanism, uncertainty is introduced and increases the complicatedness of the problem.

The benefits of the proposed multi-dimensional characterisation of risk-related decision problems can be:

1. Predict the “right” decision-making process to follow
2. Avoid oversimplification and generalisation and make decision support more accurate, flexible, and operationally oriented.
3. Provide more detailed elaboration of when to apply or abandon which tactic (e.g. rely on heuristics) for decision makers to ensure a higher chance of successful decision making.
4. Provide feedback to formulate the right problem to solve. reformulating the problem when it is found that the problem is too opaque after characterisation.
5. Allows the grouping of different risk-related decision problems in the same context setting.