In light of major challenges such as the resilient, digital, and ecological realignment of companies, industries, and regions, the proportion of problem-oriented learning in education and training must increase. The ability to design solutions for complex management problems is crucial for success. Management theory for complex evolutionary systems provides an important foundation for this. The application of this approach, which has developed over the last few decades, has hardly been taught at business schools to date. One practical approach is the DSCMP method, which we have tested in many projects. DSCMP stands for Designing Solutions for Complex Management Problems.
In this new blog post, I explain the theoretical foundations of the DSCMP method and a general framework that can be adapted to specific problem situations.
What students and practitioners find difficult
An exam question that most students find difficult is: Using an example, explain a concept and approach for AI-based strategic realignment. It makes little difference whether the question asks for a digital or ecological realignment. However, designing solutions for complex management problems is not only difficult for students, but also for experienced practitioners in companies. Apparently, neither group is familiar with suitable theoretical foundations or a practical approach that they could apply. I would therefore like to begin by explaining the theory.
Management theory of complex evolutionary systems
Management theory of complex evolutionary systems has emerged from the combination of three lines of development, the application of which in management enables better strategic realignments.1 These paths, which I outline below, are:
- The path from ecosystems to system theories
- the application of evolutionary theories in economics, and
- a transfer of the theory of complex adaptive systems to the solution of management problems.
The term ecosystem was defined by biologist Arthur Tansley (oikos, the house, and systema, the connected) in 1935, among others. The field of cybernetics, which has been shaped by Norbert Wiener and others since the 1940s, deals with the control of systems. In 1959, Stafford Beer defined the term management cybernetics.2 Since the 1950s, a general systems theory has developed. According to this theory, open systems are in dynamic exchange with their environment. The sociological systems theory founded by Talcott Parsons regards actions as constitutive elements of social systems. In 1951, Parson developed the AGIL scheme (adaptation, goal attainment, integration and latency) for the structural and functional analysis of social systems.3 At the University of St. Gallen, Hans Ulrich has been developing the concept of system-oriented management theory since the 1960s.4 Since the 1990s, the terms economic, stakeholder, start-up, and AI ecosystems have gained importance.
Evolutionary theories (from evolvere, to develop) have a long history in various disciplines. Of particular importance for economics is the concept of spontaneous order developed in the 1960s by Friedrich August von Hayek, who later won the Nobel Prize in Economics (1974).5 This is the result of self-organizing processes that emerge over time and are based on rules that can change. An important basis for the concept of an evolutionary organizational theory of the Munich School around Werner Kirsch6 is the theory of communicative action developed by the philosopher Jürgen Habermas.7 An evolutionary process in management is characterized by a dynamic sequence of imbalances.
The interdisciplinary theory of complex adaptive systems was developed at the Santa Fe Institute in New Mexico (USA), which was co-founded in 1984 by Nobel Prize winner in physics Murray Gell-Mann (1969). Such a system absorbs information about its environment and its own interaction with this environment, recognizes patterns, and condenses them into competing models. The resulting actions feed back into these models. An important management recommendation is to create suitable conditions for more self-organized interaction between competent actors.8 Agile methods are based on this. The theory of complex responsive relationship processes emphasizes the importance of local, nonlinear interactions between actors, which give rise to patterns that are difficult to predict.9
The theory of complex evolutionary systems has initiated a paradigm shift in strategic management, which has been driven primarily by successful digital companies since the 1990s.10 The characteristics of this management theory, which is new to many established companies, are openness, dynamism, connectedness, non-linearity, emergence, path dependency, adaptivity, self-organization, and learning loops.
The question now is how this theoretical foundation can help teachers and learners in practice to design solutions for complex management problems.
Approach using the DSCMP method
The DSCMP method was developed as part of our consulting, teaching, and research activities. Consultants are usually called in when organizations are looking for support in solving complex management problems. However, consulting only leads to lasting success if managers and employees are successfully taught the relevant skills.
Designing solutions for complex management problems requires a conceptual framework that project teams can adapt to the situation at hand. The following figure shows the main phases of the iterative process used in the DSCMP method. The didactic challenge lies in teaching the ability to adapt this approach to new complex problems.
In the DSCMP method, the first phase involves forming interdisciplinary program and project teams that report to a management committee. The structure and composition of these teams may change as the work progresses.
In phase 2, the task is to bring together relevant information and different perspectives. The goal is to understand a complex problem and its causes and describe it as accurately as possible.
This is followed by the important step of designing creative solutions that bring everything together. Examples include the development of a hydrogen value chain and the conversion to climate-resilient cities. Communication with stakeholders from politics, science, and society is becoming increasingly important but is difficult to implement.11 Such cooperation requires dialogue-based action based on a position of strength, e.g., with the help of new methods such as connective design.
Depending on the type of problem, possible approaches can be tested in pilot solutions, prototypes, or minimum viable products.
A variety of methods have been developed for testing the „pilots.“ It is also important to identify implementation difficulties and adapt the solutions in rapid learning loops.
The implementation of a promising solution begins with planning the scaling and further financing. The Objectives and Key Results (OKR) method has proven useful for formulating goals and key results.
The final phase is then the implementation of scaling within the framework of programs and projects, a review of success, and regular reflection on interim results.
The success of such an approach depends crucially on the skills and mindset of the teams and managers involved.
Implications for management education
Important implications for management education can be summarized in three points:12
- Learning from complex current problems
- interdisciplinary work on these problems in projects
- the use of teachers with leadership experience.
The reality at most universities is far from this. However, this opens up a wide range of opportunities for innovative education providers. In our practical work, we use the DSCMP method in bachelor’s and master’s degree programs as well as directly in companies as part of customized management education. One focus of our research is on supporting problem-oriented project-based learning through artificial intelligence (AI). The benefit for all learners lies in improving their chances in a labor market that is currently undergoing dramatic change.
Artificial intelligence (AI) is changing the world of work
Dario Amodei, founder of the AI start-up Anthropic, predicts that AI will destroy half of all entry-level office jobs. Classic clerical work, which is characterized by repetitive, analytical, and administrative tasks, will be particularly affected. This makes it all the more important for job seekers to be able to demonstrate AI skills. Past experience with digitalization shows that jobs tend to change rather than disappear completely. It is therefore important to first learn how to use AI to perform time-consuming tasks more productively, thereby gaining more time to solve complex problems. In training and continuing education on the path to becoming a manager, the use of AI to manage complexity is crucial for success.13
Conclusion
- Strategic realignments require a suitable theoretical foundation, innovative approaches, and problem-oriented learning
- One such foundation is the management theory of complex evolutionary systems
- A creative phase in the Designing Solutions for Complex Management Problems (DSCMP) approach is the connecting design
- Innovative education providers play an important role in applying these foundations and approaches within the framework of problem-oriented learning.
Literature
[1] Servatius, H.G., Triple Strategic Realignment. In: Competivation Blog, June 7, 2024
[2] Beer, S., Cybernetics and Management, English Universities Press 1959
[3] Parsons, T., The Social System, The Free Press 1951
[4] Ulrich, H., The Enterprise as a Productive Social System, Haupt 1968
[5] von Hayek, F.A., The Constitution of Liberty, University of Chicago Press 1960
[6] Kirsch, W., Seidel, D., van Aaken, D., Evolutionary Organization Theory, Schäffer-Poeschel 2010
[7] Habermas, J., Theory of Communicative Action (2 volumes), Suhrkamp 1984
[8] Gell-Mann, M., The Quark and the Jaguar: From the Simple to the Complex, Piper 1994, p. 53
[9] Stacey, R.D., Complex Responsive Processes in Organizations – Learning and Knowledge Creation, Routledge 2001
[10] Servatius, H.G., Learning from Successful Digital Companies. In: Competivation Blog, July 12, 2024
[11] Servatius, H.G., Designing Innovative Stakeholder Ecosystems. In: Competivation Blog, January 10, 2023
[12] Servatius, H.G., AI and the Future of Management Education. In: Competivation Blog, April 9, 2025
[13] Knees, L., Maier-Brost, H., How secure is my job from AI? In: Handelsblatt, July 4/5/6, 2025, pp. 54-55