The paper describes development of a continuous and discrete model of human resources transitions in a large organization. The model considers eight different ranks. The calibration of the model was performed where the historical data was used to determine time constants of transitions and fluctuations. Several simulation runs were performed in order to complete predictive validation of the model. Optimization of the model was performed in order to achieve desired structural dynamics. Pattern search algorithm was applied at this stage while considering the key parameters limitations. By performing the optimization appropriate strategy of the system structural development could be determined. Development and comparison of the continuous and discrete event model was performed. The discrete event model was applied in the validation phase. The hybrid approach to the problem provided higher level of confidence. System dynamics methodology proved to be appropriate as the tool for initial development of the model and structural validation reference.
Lake Superiors fishery resources have been subject to management control for more than a century. A goal of achieving stability of fish populations has been elusive. Present goals stated by management authorities have been expressed as hopes of achieving fish- community objectives, some of which may be impossibly exclusive in practice. A system dynamics model of major predator and prey fish populations of Western Lake Superior is discussed and demonstrated. Model simulations of fish population changes are compared to historical estimates. Model-implied results of alternative management policies are explored. Experiments applying past and alternative management policies indicate that a policy of reducing current high rates of predator stockings together with moderately increasing predator harvestings would contribute to long term population stability among both predator and prey fish populations of Lake Superior.
Strategic Human Resources Management (HRM) is a crucial factor of companies in which knowledge plays a vital role. But Germanys shrinking and aging population and their effects on the labor force potential within the next 10 to 15 years seem to be ignored by HR managers. Layoffs are still common practice and even early retirement schemes were common a short time ago. Supposably, managers do not have informative tools neither to evaluate ex ante specific HR strategies nor to forecast the development of stock and age structure of their workforce by simultaneously taking into account the intrasystem complexity and dynamic.
This case study in progress elaborates on the underestimated effects of employee fluctuation and different recruiting policies on the value of human capital in conjunction with a detailed aging chain. The author uses the feasible Transparent Human Capital Valuation approach to assess this human capital value and implements the approach as a co-flow within the system dynamics model.
The promotion of an applicable and holistic HRM tool is both a contemporary and overdue issue.
The purpose of this paper is to present a case study which illuminates the role of dy-namic models as enablers of better general management in the face of complexity. That role is usually accounted for either by the logic of the models or by the process of building them, namely in group model-building. Here, the relationship and interaction of the two, model logic with modeling process, is considered. We maintain that the conceptual understanding of managers is the crucial lever for better management. Our focus is on the role of models in improving such understanding. The empirical base and object of reflection is a large case study from an ultra-complex firm, where a model building and training venture was carried out. The main concern which aimed the project was to facilitate the ability of managers to cope with complexity and to enable effective organizational change. The venture enhanced both the systemic view and awareness among participants of the project, and therefore proved to be a good investment in management quality. In essence, it was an impor-tant move toward model-based management. A core group had been captivated by the power of systemic thinking in general and the use of models in particular. A seed had been sown.
System dynamics modelers face a broad spectrum of risks toward achieving project objectives. As they gain experience, their risk identification and management capabilities increase. By applying classification techniques from taxonomy development, the collective knowledge of previous modelers has been captured in a classification scheme for system dynamics modeling risks. The classification scheme allows modelers to more efficiently and effectively consider modeling risks by reducing the variation in their knowledge levels. The classification structure is focused on the steps of the system dynamics modeling methodology and the achievement of system knowledge and improvement objectives. As part of a broader modeling risk management approach, the risk classification scheme assists modelers in identifying and prioritizing the anticipated sources of modeling risks for a project. With that knowledge, they can more effectively identify the appropriate techniques for managing risks and then efficiently apply those techniques in a timely fashion through the entire project cycle.
Like other organizations, United States intercollegiate athletics departments face the challenge of operating efficiently and effectively. Performance measurement in this environment is made more challenging by the need to be successful both on and off the playing fields. With its focus on structural performance contributions, system dynamics modeling works well with data envelopment analysis, which is focused on input-output relationships, to provide a more complete understanding of performance measurement and assessment. This combined understanding supports policy analysis that contributes to performance improvement opportunities. This research outlines the success achieved by linking these two approaches, even with the system dynamics contribution limited to a qualitative model.
Growing concern about climate change and energy security has led to increasing interest in developing domestically available renewable energy sources for meeting the electricity, heating and fuel needs in the United States. Illinois has significant potential to grow perennial grasses that can provide bioenergy. Recent research on miscanthus has shown that this low-input perennial may have biomass yields that are twice that of switchgrass and corn. Land requirements from growing biomass crops compete with existing profitable land uses, which in the case of Illinois, is primarily in row crop agriculture. This study examines the conditions of switching land from row crops to energy crops which are expected to vary across the landscape in Illinois, depending on soil quality and climatic conditions. To find the optimal land allocation among competing uses we will use spatial dynamic modeling tools combined with data from Geographic Information Systems (GIS) on land quality, climate and land use. The Spatial Modeling Environment (SME) allows inclusion of spatially enabled dynamic models to combine system-dynamics and agent-based modeling approaches. Four major crops are compared, including corn, soybeans, miscanthus, and switchgrass.
This years peer review dialog meeting will take up what participants have articulated last year and what the Policy Council has undertaken in the meantime.
Briefly put: even though the final decision is up to the programme committee, formally deficient papers would be rejected without revision, formally deficient reviews would lead to suspending the reviewer for one year, authors of accepted papers would evaluate the reviews usefulness and reviewers would have a discussion forum in order to collaborate.
One conference later, we shall ask how the actual process related to the planned one, on the basis of last years report and the PCs response to it. Well also assess how it worked this time, indicate what has been achieved and what seems to need improvement. We shall conjointly set up a set of recommendations, too.
Since the 50s, there have been voices that governments should cease to operate schools and limit themselves to financing it via a voucher system and controlling schools compliance to quality standards. In the early 80s, this has been implemented in Chile. There are three types of schools: private ones freely charge fees, private subsidized that have a limited fee and public. The quality and equality of the school system fall short of expectations. This paper proposes a qualitative model to explain what is going on. Families are assumed to prefer higher performing schools, teachers prefer better labor conditions and schools prefer favored pupils and better teachers. Richer schools attract more favored families that enable improved results due to the favored-pupil effect; additionally their ability to charge higher fees allows them to attract the best teachers, which further enhances their advantage. We find 5 positive feedback loops. The result is a process of concentration of favored pupils and good teachers that increases inequality. It is concluded that there are unequal conditions amongst the types of schools, and as long as they persist, no initiative in favor of more equality will succeed
Polarity and causality are important concepts but have not received much attention in the system dynamics literature. The great effort it takes students to properly understand them has motivated this inquiry. In the framework of a conceptual model of interacting with complex systems, several cognitive tasks are proposed. This paper concentrates on one of them that deals with causal links polarity. An examination of other approaches that deal with causality and use more or less similar diagram languages shows that usually causality is only very broadly defined, and where it is operationally defined, this is done with respect to events rather than behavior. In contrast to these approaches, system dynamics is about behavior rather than events. We then revisit the traditional criticism of causal loop diagrams and show a way out, but add two new criticisms related to the inability of causal loop diagrams to address behavior: in fact it seems that they are closer to the event-related definition of causality. Also, the impossibility to execute them in simulations means that executable concept-models are to be preferred: they express important information a causal loop diagram cannot represent and on top of it they render the behavioral consequences visible (as opposed to the events).