In todays business environment it is essential to be able to gain insights in a yet unfamiliar industry under tough time constraints. The 7 step framework we suggest addresses this issue. Following the standardised sequence of steps will direct the user to identifying the industrys main influencing factors. The historical development of the industrys output variable will be approximated by model simulations. An analysis of the peaks in relative deviations between real and simulated data will spotlight the industrys significant events and influencing factors. Compared to traditional market and environment analysis techniques our framework takes the particular industrys development dynamics into account. Thus we choose System Dynamics as underlying methodology which has already proven useful for understanding market dynamics and gaining structural insights. The 7 step framework will be illustrated with the development of passenger traffic at German airports.
Over the past few years there has been an increasing interest in using computer simulation models in order to create learning laboratories (Interactive Learning Environments - ILEs), for management education. Particularly when combined with System Dynamics simulation models, ILEs have proved their validity in a variety of different fields. Starting from the previous considerations, this paper focuses on the use of System Dynamics based ILEs for processes of individual learning. In particular, the paper presents and discusses the main features of an ILE based on a case study related to service quality management. The effectiveness of the ILE in fostering individual learning has been assessed through a computer based experiment run in a master course classroom. Additional comments and data were gathered through a feedback questionnaires that was delivered to the participants. Among its findings, the paper shows that the ILE supported players to learn to: a) balance the growth of demand-side and supply-side resources; b) simultaneously control tangible and intangible resources; c) take into account the presence and the effects of time-delays; d) develop and apply policies, understanding the short and long term consequences of their decisions.
Experimental decision making studies are typically done in environments where subjects have plentiful time before making decisions. In this research, a scuba diving simulator is developed for experimental analysis of decision making under real-time pressure, in dynamic feedback environment. In our clock-driven scuba diving simulator, subjects make decisions in real-time, continuously, which enables us to study effect of game speed (time pressure) on performance and learning. Results show that game speed has significant effect on subjects performances. Material and information delays are further incorporated to evaluate effects of delays. Both information and material delays are found significantly influential on performance. However, performance differences between delay and no-delay games decrease with practice. Since games attempt to simulate experiential learning, subjects having real diving experience may be expected to perform better than inexperienced ones. Interestingly, no significant difference is found between those with scuba-diving experience and those without. A feature of the game is the fact that the control problem that subjects face is under strong influence of a positive feedback loop. Combined with delays and nonlinearity, the game illustrates how complex the control problem can become even for a small model. Performances of subjects in most trials are strongly oscillatory
Hydrogen, an energy vector, displays remarkable versatility with regards to the ways it can be produced. State-of-the-art technologies allow almost every energy source to be converted into hydrogen. What is more challenging, however, is the feasibility of building a new infrastructure to overlap with and, possibly, substitute existing one. This investigation aims to assess what it would entail to add 5% of hydrogen fuel to road transport energy consumption through 2050. The comparison spans five technologies: steam methane reforming, coal gasification, and water electrolysis where power is generated from wind, solar, and nuclear sources. The simulation provides two sets of estimates: calculations on physical infrastructure requirements and its related variable and fixed costs. With regards to facility requirements, the considered technologies show different degrees of feasibility. Coal and nuclear power are not as land-intensive as solar and wind power, but bear problems with pollution and waste disposal, respectively. Economically, coal is least expensive, followed by wind. Natural gas loses competitiveness because of high hydrocarbon prices. The sheer economic rank of preferable energy sources for generating hydrogen should be put into question when internalizing environmental impact of the considered options.
Hemodialysis-induced hypotension is still a severe complication in spite of all the progress in hemodialysis treatment. Because of its multifactor causes, hemodialysis-induced hypotension cannot be reliably prevented by conventional ultrafiltration and sodium profiling in open-loop systems, as they are unable to adapt themselves to actual decreases in blood pressure. Therefore, it is the ultimate goal to provide automatic control in hemodialysis. Furthermore, the treatment should improve patient comfort and be carried out without use of additional body sensors and without additional medication. Automatic control of hemodialysis has the potential to provide a better treatment to the ever increasing number of ESDR patients who present with more complicated co-morbid conditions
The total dialysis dose, expressed as Kt/V, has been widely recognized to be a major determinant of morbidity and mortality in hemodialyzed patients. Many different factors influence the correct determination of Kt/V, such as urea sequestration in different body compartments, access and cardiopulmonary recirculation. These factors are responsible for urea rebound after the end of the hemodialysis session, causing poor Kt/ V estimation. In this work, system dynamics model was combined with a neural network (NN) method for early prediction of the Kt/V dose. Two different portions of the urea concentration-time profile provided by the system dynamics (on-line urea monitor) were analyzed: the entire curve A and the first half B, using an NN to predict the Kt/V and compare this with that provided by the system dynamics model. The NN was able to predict Kt/V is the middle of the 4h session (B data) without a significant increase in the percentage error (B data: 6.65%±2.51%; A data: 5.62%±8.65%) compared with the system dynamics Kt/ V.
Human resource crises by collective retirement of hospital physicians are a critical issue in Japanese health care systems. System Dynamics modeling is a feasible way to understand these phenomena. Japanese health care system is confronted with not only exogenous environments but also endogenous feedbacks to build up the situation. Increasing busyness by physicians and risk of medical lawsuits and decreasing average productivity and quality of physician by hiring new physicians reinforce retirements of physicians and the retirements change the situation for the worse. To keep sustain level of physician we could find essential policies by simulation. First strategy is changing desired number of physicians with increasing of number of patients per physician. Second way is decreasing delay between retirement and hiring. This was accomplished by early recognition of physicians busyness by hospital managers and abundant of physicians in a health care system.