Our level of understanding and ability to predict systems aims to be increased by technologies; it is still necessary to have the structure of reflection allowing us to navigate there.
Accelerating decentralized decision-making bringing overall value is a constant quest for organizations. Staying competitive is a priority in an increasingly uncertain, complex, and interdependent ecosystem.
This article shares the different models helpful in analyzing the context to adapt our actions. The applicability of each paradigm is identified before concluding on the implementation of a Quality Engineering initiative.
Let’s start with VUCA, an acronym so popular with consultants.
VUCA, from military to consulting
This acronym appeared in 1987 based on the leadership theories of Warren Bennis and Burt Nanus. Initially used in a military context, it aimed to describe the evolution of the more global and uncertain context after the Cold War. Its distribution in companies started in 2002 mainly by consultants.
VUCA identifies 4 possible contexts oriented on the 2 axes of the degree of knowledge and anticipation: Volatility, Uncertainty, Complexity, Ambiguity.
Volatility represents a high degree of unpredictable and unstable changes for situations where we have knowledge. Uncertainty is a context with less prediction capability but a better level of knowledge. Complexity consists of having less information, a large number of possibilities, and a limited analytical capacity. Ambiguity is the “unknown unknown” case, with a lot of uncertainty, little historical data, requiring a step-by-step approach.
VUCA helps bring value when translated into concrete actions.
Why use VUCA in a business context?
The application of VUCA aims to improve an organization’s decision-making by adapting its approach according to the situation.
VUCA’s model is one way of choosing “the right tool for the right job”. For example, a company wanting to launch a new product in the context of Ambiguity has every reason to iterate with its teams on a model of Lean Startup rather than adding additional resources most appropriate to the case of Volatility.
The complementary contributions of VUCA are to improve the identification of “unknowns”, be better prepared for the future, and recognize the different facets of the context in which the company operates. The parallel army with companies is reflected in globalization and digitization, making our ecosystems more challenging to understand and predict.
VUCA is also a key acronym taken in agility models. I recommend the book of Jorgen Hesselberg, Unlocking Agility.
Meanwhile, a VUCA challenger also appeared, RUPT.
RUPT, the disruption alternative to VUCA?
Today we are far from the past where we had to process 10 letters a day while having months to plan our work. The term DisRUPTion is often associated with the acronym RUPT™. Rumpere means a break in Latin, where the suffix ab-, for “abruptus”, is the origin of the word “abrupt”.
RUPT is the acronym for Rapid, Unpredictable, Paradoxical, Tangled. Its objective is to change the approach of organizations facing the evolution of these contexts. Unlike VUCA, one consolidated method is resulting from the RUPT model.
The model advocates the use of metaphorical reasoning to navigate rapid waves of change. Shared Sense Making is a technique for alignment and collaborative decision-making found in agile rituals. The need for a systematic approach with a holistic perspective is reflected in Integrative Thinking, aiming at generating new possibilities. Finally, the difficulty in discerning the cause and effect in a mountain of information requires recognizing Patterns supported by transparency.
RUPT is a valuable model for several practices of Quality Engineering.
What relevance for RUPT in our context?
RUPT is applicable in various situations of Quality Engineering that are more and more frequent. The case of companies favoring prediction to the detriment of adaptation is a good example, where the adoption of agile practices is one of the available levers.
Our ecosystem is full of opposing tensions: accelerating with stability, improving quality while reducing costs. It is the concept of cognitive dissonance that can sometimes freeze leadership in decision-making. The notion of error-budget from Google’s SRE is a concrete example to balance speed and stability.
RUPT is also relevant in a complex system, where cause and effect relationships are difficult to perceive and even more difficult to explain. We speak in this case of subjective chain reactions rather than linear or logical reactions. We try to recreate that ecosystem by building reactive event-driven distributed architectures operated by data science.
Chaos Theory can give us some hope.
Recognizing our limits with Chaos Theory
The lack of information, the identification of cause and effect relationships, the inability to identify patterns: so many problems formalized in Chaos Theory. It is a mathematical-based discipline assuming a large share of the unknown in our understanding of systems.
Chaos: When the present determines the future, but the approximate present does not approximately determine the future.Edward Lorenz, The Chaos Theory
The Chaos Theory defines that a complex system, even if little understandable on the surface, reacts with underlying patterns and laws influenced by its initial state. The consequence of this assumption is that a small change or mistake upstream can cause very different results downstream. The volume of data combined with the delay between cause and effect is the main difficulty for humans to detect them. We can understand the craze around Data Science and Quantum Computing.
A more widely held image of Chaos Theory is the butterfly effect.
The Innovation Butterfly, our current reality
Companies’ innovation and growth cycles are getting shorter and shorter, with unicorns appearing in cycles of less than 2 years. Startups are emerging on new business models and disrupting existing players.
This dynamism creates as many additional reaction points in an ecosystem in digitalization. Innovation Butterfly takes on its full meaning; a change can have significant consequences inside or outside the system. The Chaos Theory is often represented by the butterfly, connected with the famous “butterfly effect”.
We are still far from being able to understand the entirety of such dynamic and complex systems. Our Quality Engineering is directly concerned by these subjects. The analysis of the Value Stream supported by Process Mining to model and improve the development process is one possible application. It is a more or less easy exercise depending on the context, which we can try to measure.
The Global Simplicity Index is an indicator aimed at materializing the simplicity of an organization. Beyond the commercial interest of its creators, a study measured that organizations accumulate on average 10% of unnecessary complexity. It is a good indicator of internal value levers in our Quality Engineering initiatives.
We must therefore learn to navigate in uncertainty.
Accept uncertainty, iterate, and adapt
VUCA, RUPT, Chaos are all acronyms that highlight the evolution of our contexts which do not go into simplification. Even if technologies promise to help us, human skills will remain fundamental.
Developing our capacity for judgment and intuition in problem-solving will remain usable in these contexts. For example, we can learn to recognize the FUD marketing tactic (Fear, Uncertainty, Doubt), inspired by the propaganda.
In an unstable environment, our personal and organizational resilience capacities make a difference. Cyber-security, for example, has evolved into cyber-resilience. Our engineering must also develop these skills, applying the concept of anti-fragility.
We can apply the following points to our Quality Engineering approach :
- Accept that we cannot foresee everything in the evolution of the current context
- Recognize the typologies of situations in which we find ourselves to use the most suitable response model
- Use acronyms and their motivations to defend the approach based on agility, like consultants
- Identify the cognitive dissonances we have to face (e.g., speed/stability, quality/cost) and what practices can come to support them (e.g., DevOps, SRE)
- Develop a continuous improvement plan for anti-fragility, resilience, and incremental approaches to provide value with flexibility.
Structuring our reasoning allows us to improve the quality of our problem-solving. The Cynefin framework is an example of a process related to VUCA. We can also apply McKinsey’s methodology to Quality Engineering.
Are you ready to make Chaos our new comfort zone?
Jorgen Hesselberg, Unlocking Agility https://unlockingagility.com/
LEAN Startup Official Website http://theleanstartup.com/
Kirsi Pyhältö, Janne Pietarinen & Tiina Soini, Shared Sense Making https://www.tandfonline.com/doi/abs/10.1080/09585176.2018.1447306?journalCode=rcjo20
Roger L. Martin, Integrative Thinking, HBR https://hbr.org/2007/06/how-successful-leaders-think
Edward Lorenz, The Chaos Theory https://en.wikipedia.org/wiki/Edward_Lorenz
Edward G. Anderson Jr & Nitin R. Joglekar, The Innovation Butterfly https://www.amazon.com/Innovation-Butterfly-Opportunities-Distributed-Understanding/dp/1461431301
The Global Simplicity Index https://simplicityindex.com/
Mind Tools, The Cynefin Framework https://www.mindtools.com/pages/article/cynefin-framework.htm
Nassim Nicholas Nicholas Taleb, Antifragile: Things That Gain from Disorder https://www.amazon.com/Antifragile-Things-That-Disorder-Incerto/dp/0812979680