The primary issue addressed in this research is how to schedule clients as they call for appointments, without knowing which "types" of clients will call at a later time. The main goal is to compare various scheduling rules in order to minimize the waiting time of the clients as well as the idle time of the service provider. Interviews with receptionists verified that they have knowledge regarding differences between clients' service time characteristics. This information is used both to differentiate between clients and to develop various scheduling rules for those clients. A simulation model of a dynamic medical outpatient environment is developed based on insight gained from the interviews and from prior research.Two decision variables are analyzed ("scheduling rule" and "position of appointment slots left unscheduled for potential urgent calls") while two environmental factors are varied ("expected mean of the clients' service time", and "expected percentage of clients with low service time standard deviation compared to those with high service time standard deviation"). This resulted in 30 combinations of decision variables, each tested within 15 combinations of environmental factors. By using multiple performance measures, it is possible to improve considerably on some of the "best" rules found in the current literature. The "best" decisions depend on the goals of the particular clinic as well as the environment it encounters. However, good or best results can be obtained in all cases if clients with large service time standard deviations are scheduled toward the end of the appointment session. The best positioning of slots left open for urgent clients is less clear cut, but options are identified for each of a number of possible clinic goals.

Many disasters have occurred because organizations have ignored the warning signs of precursor incidents or have failed to learn from the lessons of the past. Normal accident theory suggests that disasters are the unwanted, but inevitable output of complex socio-technical systems, while high-reliability theory sees disasters as preventable by certain characteristics or response systems of the organization. We develop an organizational response system called incident learning in which normal precursor incidents are used in a learning process to combat complacency and avoid disasters. We build a model of a safety and incident learning system and explore its dynamics. We use the model to motivate managers to implement incident learning systems as a way of moving safety performance from normal accidents to high reliability. The simulation model behavior provides useful insights for managers concerned with the design and operation of incident learning systems.

We report on the use of discrete event simulation modeling to support process improvements at an orthopedic outpatient clinic. The clinic was effective in treating patients, but waiting time and congestion in the clinic created patient dissatisfaction and staff morale issues. The modeling helped to identify improvement alternatives including optimized staffing levels, better patient scheduling, and an emphasis on staff arriving promptly. Quantitative results from the modeling provided motivation to implement the improvements. Statistical analysis of data taken before and after the implementation indicate that waiting time measures were significantly improved and overall patient time in the clinic was reduced.

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Considering the strategic importance of business process improvement, it is imperative that educators, students, and practitioners be familiar with this topic. Thus, the intention of this tutorial is to provide a guiding framework for carrying out improvements of business processes. We present numerous illustrative examples, taken from our personal experiences and those of our students, as well as from the literature. An extensive reference list is provided, thus pointing the interested reader to sources of further detail on specific topics.