Complexity Management Methods Research Articles

The Energy Transition as a Super Wicked Problem: The Energy Sector in the Era of Prosumer Capitalism

The main objective of this paper is to demonstrate that the energy transition as part of prosumer capitalism is a socio-economic process whose complexity increases over time, which makes it an example of a super wicked problem. It comprises many new phenomena emerging spontaneously, and often unpredictably, in the energy markets. The main contemporary challenge involves such an energy sector transformation which will prevent climate change and will ensure the sustainable development of the global economy. However, this requires solving a large number of sub-problems in areas such as legislation, energy distribution, democracy, and cybersecurity. Therefore, this is a multidisciplinary issue. Moreover, the situation is complicated by the frequently omitted fact that energy transition is not part of the standard capitalism model, extensively described in handbooks and scientific literature, but it is conducted as part of a new economic system—prosumer capitalism, which has not been properly explored yet. However, a solution to this super wicked problem has to be found soon, as the energy system may be threatened with complexity catastrophe, which denotes exceeding the upper complexity limit associated with the breakdown of its adaptability. Therefore, developing effective techniques for alleviating the complexity catastrophe, including redefining the change management and complexity management methods to the global scale, becomes the top priority among the tasks faced by science.

A modeler's guide to handle complexity in energy systems optimization

• Holistic review of complexity management methods in energy systems optimization. • Data availability drives model sizes and therefore the model complexity. • Systematic temporal down-sampling or aggregation are widespread and easily accessible while spatial aggregation has only a small community. • Decomposition methods still need to be customized to the model wherefore exploitation of parallel compute infrastructure requires high efforts. • Level of details in system modeling drives non-convexity and is often avoided. Determining environmentally- and economically-optimal energy systems designs and operations is complex. In particular, the integration of weather-dependent renewable energy technologies into energy system optimization models presents new challenges to computational tractability that cannot only be solved by advancements in computational resources. In consequence, energy system modelers must tackle the complexity of their models by applying various methods to manipulate the underlying data and model structure, with the ultimate goal of finding optimal solutions. As which complexity reduction method is suitable for which research question is often unclear, herein we review different approaches for handling complexity. We first analyze the determinants of complexity and note that many drivers of complexity could be avoided a priori with a tailored model design. Second, we conduct a review of systematic complexity reduction methods for energy system optimization models, which can range from simple linearization performed by modelers to sophisticated multi-level approaches combining aggregation and decomposition methods. Based on this overview, we develop a guide for energy system modelers who encounter computational limitations.

Enhancing Collaboration between Design and Simulation Departments by Methods of Complexity Management

The significance of CAD-CAE coupling has grown with the increasing use of simulations in development processes. With the focus on technical aspects like simulation data management in literature, however, there is a lack of research on the implications on collaboration. This paper uses methods of structural complexity management to improve communication and collaboration between simulation and design departments. Design structure matrices and multiple domain matrices are derived from system graphs that come from interviews. A case study uses these methods to handle data to enhance collaboration between departments. The results are techniques to deal with lacking information and low degrees of connectivity in the matrices. After an overlay of different matrices, standard procedures like triangulation and clustering can be applied that would otherwise not have been sensible. This leads to knowledge clusters and sequences of documents and task that help to integrate simulations more smoothly into the product development process.

Identifying Complexity Drivers in Discrete Manufacturing and Process Industry

Complexity management methods in the field of strategic and operational complexity management are usually conceived for the discrete manufacturing industry and are hardly transferable to the process industry. In this paper, complexity management practices, as well as complexity drivers, in the discrete manufacturing industry and the process industry are identified and collected. Furthermore, an analysis is conducted regarding similarities and differences between the approaches in the two industries. In future research suitable evaluation criteria will be identified and combined in an evaluation model, which may help to show applicable complexity management methods from the discrete manufacturing industry for the process industry. Through a survey of 136 participating companies, the implementation degree of complexity management methods and the impact of complexity drivers within various industries were investigated. Based on these results, the process industry was identified as lagging behind in the field of complexity management. By conducting an interview study with five successful companies identified from the survey's sample, the researchers gained detailed information on the applied methods. Supported by a Delphi study with three companies of the process industry, specific complexity drivers and the main challenges to apply existing complexity management methods in their businesses were identified. To show the difference between the applied methods in the process industry and the discrete manufacturing industry, three cases are described. Two of them are from the process industry. The first one is representative for complexity management in the process industry, comes from a participating company from the Delphi study and is rated as less experienced in this field. On the other hand, the other case, rated as a successful practice case from the survey's sample, shows a high implementation degree of complexity management methods from the discrete manufacturing industry. In the third case, the approach of an automotive supplier with long experience in complexity management and a high implementation degree of complexity management methods is described. First results show, that the challenges in the process industry are mainly resulting from external complexity drivers, like legal and political factors. Whereas internal complexity drivers like product structure as well as the dynamic of technology, are rather influencing the discrete manufacturing industry. Hence methods for gaining transparency on materials and resources seem to be more valuable for companies in the process industry than others.

Evaluation of Complexity Management Systems – Systematical and Maturity-based Approach

The way to deal with the strongly increasing complexity of the company itself and its environment has become a key competitive factor. The complexity within a production company is characterized by the challenges encountered in daily business processes and can be described by the four dimensions of complexity: variety, heterogeneity, dynamics and non- transparency, as well as their interrelationships.Despite this increasing importance, only few companies have access to adequate tools for complexity management. Most companies have not introduced or implemented yet a complexity management system/approach or they do not know, if the used complexity management methods are efficient and adequate. Therefore, the question rises: How can a company be reviewed and evaluated regarding its complexity management skills?Maturity models can be used to support the analysis and assessment of skills and development-levels of products, processes or organizations by defining different levels of maturity, in order to assess the extent to which an object fulfills defined qualitative requirements. The various levels of maturity within such models can be used to describe the different achievable skill levels. Maturity models not only include methods for the assessment of skill levels, but also provide incentives and measures to increase the degree of maturity. After the introduction of measures to increase the skill level of maturity these models are also suitable to measure and evaluate the progress made.This paper presents an approach for an evaluation model of complexity management systems. First, the basics of the so-called advanced Complexity Management are given, highlighting the difference between complexity and complicacy as well as the comparing external and internal complexity. The fields and dimensions of complexity are presented as well. After that, maturity models fundamentals are presented by showing state-of-the-art maturity model approaches. Furthermore, the overall maturity-based approach for evaluation of complexity management systems, especially the maturity of their functionalities and capabilities regarding the needs and purposes of manufacturing enterprises, is presented by adapting and combining existing methods and models. The maturity-based approach describes different levels of complexity management systems within a production company, also taking into account recommendations and measures to increase the degree of maturity.

Requirements for the design of a complexity management method in new product development of integral and modular products

Complexity influences the success of new product development (NPD) and thus the competitiveness of a company, e.g. product complexity influences supply chain management and NPD process itself. There is therefore the need of a complexity management method to support managers in taking proper NPD decisions. In a complexity management method different requirements are important. Our research aims at assessing the relevance of the different requirements of the complexity management methods depending on the type of NPD project, i.e. according to the development of integral or modular product. The main result of our research is that the importance levels of the requirements do not differ strongly among each other. However, there are slight differences regarding the product architectures – complexity management method requirements for modular product NPD projects have generally a higher importance level than those for integral product NPD projects. Keywords : Complexity Management, New Product Development, NPD Project Complexity, Product Architecture.

A scenario-based approach to airport security

Mobility, particularly air transport, is vital to the economic stability and growth of a nation. It symbolizes national self-confidence and self-conception. As a result commercial aviation remains a preferred target for attacks by terrorists and other offenders. Security measures intended to render these threats harmless have mostly been introduced in response to specific occurrences, thus allowing the potential attackers to always remain one step ahead. As this approach seems inappropriate for dealing with future security threats, this paper provides a proactive approach to identification of future threats and their coverage by airport security processes and technologies. To meet the requirements of a highly complex and at the same time critical system, such as airport security, a standard scenario process has been enhanced by matrix-based methods of complexity management. This approach allows analysis of threat scenario clusters with respect to the number of potentially effective security measures. The method developed allows proactive detection of weak points in the security architecture and thus reveals potential for improvement.

Cognitive Centres: technology for designing the future

PurposeThe purpose of this paper is to introduce and conceptualize the topic of Cognitive Centres. It emphasizes the importance of complexity management and cognitive technologies in management practice.Design/methodology/approachIt elaborates the relevance of different types of models for management: Stafford Beer's operations room, viable system model, subject‐object and subject‐subject management models, expert‐based mathematical modelling and computational experiment, organizational‐activity games theory and cybernetic (Viplan) methodology.FindingsThe paper suggests models and approaches for strategic management. It shows the inadequacy of a subject‐object management model for industrial or regional governance. It integrates a communicative management approach, complexity management methods, the USSR's Gosplan experience of long‐term planning, the Keldysh Institute of Applied Mathematics experience of modelling extremely complex systems and modern cognitive and information technologies into the concept of a Cognitive Centre.Practical implicationsCognitive Centres can enhance the adaptation and viability of a large network of small organizations: public enterprises, business and not‐for‐profit regional industries. The paper describes two cases of practical implementation.Originality/valueThe paper contributes to research on management models that integrates cybernetics, cognitive technologies and computational experiments.