Complexity Management and System Dynamics Thinking

Grösser, Stefan N.

doi:10.1007/978-3-319-45438-2_5

Cited by 30 publications

(26 citation statements)

References 51 publications

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“…The last stage in the modelling procedure is the model use stage, that is, model application in decision-making and policy design that incorporates a system perspective for when there is uncertainty [15,20,25]. In this research, outputs from the conducted structural analysis with MICMAC and consultations with stakeholders were used to define and evaluate the potential strategic pathways to apply the policies to overcome innovation diffusion challenges in the Russian construction industry.…”

Section: Stage 5: Model Use and Recommendationssupporting

confidence: 43%

“…Application of one or another participatory modelling approach depends on many factors such as context of application, research goals, stakeholder involvement structure, to name a few [16,18,25]. The important aspects to consider during the selection of a particular participatory modelling approach can be categorised into the following parameters: time and cost needed for a model development; ability to incorporate different stakeholders' opinions and ensure successful social learning among participants of the modelling process; ability to deal with complex problems; expected level of consensus among stakeholders; and size of stakeholder community.…”

Section: Mediated Modelling (Mm)mentioning

confidence: 41%

“…Furthermore, it is necessary that a loop polarity be determined. Reinforcing (also known as positive) loops accelerate change within systems to produce growth or decline, while balancing (also known as negative) loops counteract change within systems to produce stabilising behaviour [1,25,26].…”

Section: Stage 2: Conceptualisationmentioning

confidence: 47%

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An Integrated Participatory Systems Modelling Approach: Application to Construction Innovation

Suprun

Sahin

Stewart

et al. 2018

Systems

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This paper presents a novel five-stage integrated participatory systems modelling (IPSM) approach that can be used for a range of systems dynamics (SD) applications. The IPSM approach was formulated considering the advantages and disadvantages of existing SD modelling approaches, as well as balancing the competing goals of SD model development efficiency and robustness. A key feature of the IPSM approach is that stakeholders are central to each of the five stages of the modelling process from problem scoping, to scenario analysis and strategy implementation recommendations. Each stage of the IPSM approach was demonstrated through a case study of the innovation diffusion process in the Russian Federation construction industry. This highly complex innovation system could only be sufficiently understood using a SD model that was conceptualised, critiqued, codified, tested and utilised, by the relevant actors within that system (i.e., stakeholders). The IPSM approach facilitated the efficient formulation of the SD model for the case study application. The case study SD model simulation results indicate that sufficient government incentives and the active promotion of strong collaborative linkages between construction companies and universities are two key enablers of innovation development in the Russian Federation construction industry.

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Section: Stage 5: Model Use and Recommendationssupporting

confidence: 43%

Section: Mediated Modelling (Mm)mentioning

confidence: 41%

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An Integrated Participatory Systems Modelling Approach: Application to Construction Innovation

Suprun

Sahin

Stewart

et al. 2018

Systems

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“…First, the system is delimited from the environment and cause-effect links between the system's variables and elements are established (Causal Context Modelling, or CCM). S. N. Grösser (2017) emphasizes that this approach is an integrative, qualitative, cross-disciplinary, able to produce qualitative description of the system, including the key variables of correlation and system scope [25]. A special focus is put on reactions.…”

Section: Resultsmentioning

confidence: 99%

Use of system dynamics tools in value-oriented approach in management

Barabanova¹,

Lebedeva²,

Rastova³

et al. 2018

EA-XXI

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The article is based upon assumption that in search for best sustainability any organization is leaning towards the value-oriented approach in management. This approach is grounded in the stakeholder theory, and addresses needs and expectations of consumers and other stakeholders on a long-term basis. When contributing to the best practices, companies produce shared values, able to act as a social power, to enter into the public consciousness, and to transform into behavioural norms. At the same time, these practices support best handling of resources and opportunities thus leading companies to success. The system dynamics model presents enterprises with potential strategies grounded in the target indicators, provides objective assessment of the target indicators sensitivity to changes at one or several parameters, sets and corrects their controllable limits, identifies specific causes of variation and, thus, insures necessary adjustments to the business management. Today, the system dynamics modelling is supported with mathematical software: Maple, MathCAD, Maxima, AnyLogic, Vensim, iThink (computer experiments software). In our research, we support the idea to develop methodology and improve practices of value-oriented approach in organization management based upon key tools of process efficiency, i.e. the system dynamics model. To build a system model and to run simulation analysis, we used AnyLogic software system. In this research, system dynamics model is tested against the case of supply chain management in the food industries, which include production facilities of high seasonal demand and products distribution network. To meet volatile demand, the company keeps certain stock of products in the chain of regional warehouses, and shipping the products as soon as new order is received. The market conditions and customer requirements are such that if the products cannot be shipped to the customer on time, then they buy these products from other sources. the system works steadily and does not require adjustments. Minor fluctuations in demand also do not break the system's balance. The system works steadily and does not require adjustments. Minor fluctuations in demand also do not break the system's balance. Simulation of seasonal demand increase (40% seasonal upsurge) proved the rate of new production shifts is almost twice higher than that of demand and two times smaller than the production cycle length. The experiment draws us to the following conclusions. The first response to increased demand is the shrink of stocks due to the delivery delays because of the duration of production cycle. A natural reaction of the company to such sharp reduction in stocks is striving for extreme intensification in reply to the demand upsurge. This causes a multiplicative effect. Since the stocks are initially shrinking, the only way to meet such shortfall is to raise the production rate above the shipments rate. The production shall exceed the shipments rate by sufficiently in volume and time to replenish the stocks in full....

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“…• to provide the foundations for further simulation modelling, exploring the dynamics of the variables relationships; • to visually communicate the problem of construction innovation to potential stakeholders; and • systematically present important scenario and policy variables associated with guidelines, formulated and enforced by decision makers (Grösser, 2017;Sterman, 2000).…”

Section: Resultsmentioning

confidence: 99%

Innovation policy analysis and decision making: a systems approach

Suprun

Sahin

Stewart

et al. 2017

Syme, G., Hatton MacDonald, D., Fulton, B. And Piantadosi, J. (Eds) MODSIM2017, 22nd International Congress on Modelling and Si

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The construction industry is one of the largest and most diverse of the sectors with continuous development. The momentum and impact of the industry's development is highly influenced by a complex system of different elements including innovation. However, the sector is relatively weak at all stages of the innovation process and the strength of collaboration among government, industry and academia is insufficient. It is generally accepted that construction companies invest less in new knowledge and process development as well as engage significantly less in innovation related activities than firms in other sectors. Hence, it is very important to control and manage the significant factors that affect success in innovation taking into account the complexity and the inherent dynamics of the construction innovation diffusion. By doing so, this study addresses the decision making process within the complex innovation process in the construction industry by employing a step-by-step modelling process consisting of a multi-stages analysis, stakeholder-based and modelling approaches. Construction is closely connected to the national social structure and is highly influenced by various institutional actors and interactions among components and, consequently, can be presented as a sectoral system. Hence, the overarching objective of the paper is to introduce a systems approach aiming to conceptualise and formulate an initial simulation model of a complex construction innovation system representing correct and continuous interactions between government, the construction industry and academia. Moreover, the research underpins future development of scenariogenerating modelling in order to reveal potential pathways to rational decision-making along with potential policy recommendations and various innovation planning strategies that improve construction innovation performance in the Russian Federation.

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Complexity Management and System Dynamics Thinking

Cited by 30 publications

References 51 publications

An Integrated Participatory Systems Modelling Approach: Application to Construction Innovation

An Integrated Participatory Systems Modelling Approach: Application to Construction Innovation

Use of system dynamics tools in value-oriented approach in management

Innovation policy analysis and decision making: a systems approach

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