Managers of large industrial projects often measure performance by multiple attributes. For example, our paper is motivated by the simulation of a large industrial project called a land seismic survey, in which project performance is based on duration, cost, and resource utilization. To address these types of problems, we develop a ranking and selection procedure for making comparisons of systems (e.g., project configurations) that have multiple performance measures. The procedure combines multiple attribute utility theory with statistical ranking and selection to select the best configuration from a set of possible configurations using the indifference-zone approach. We apply our procedure to results generated by the simulator for a land seismic survey that has six performance measures, and describe a particular type of sensitivity analysis that can be used as a robustness check.Simulation, Ranking and Selection, Multiple Attribute Utility Theory

In this paper, we investigate the dynamic behavior of service supply chains in the presence of varying demand and information sharing. Each stage holds no finished goods inventory, rather only backlogs that can be managed solely by adjusting capacity. These conditions reflect the reality of many service (and custom manufacturing, such as capital equipment) supply chains. While there is a growing literature on finished goods inventory management in supply chains, relatively little research exists on managing capacity in the absence of finished goods inventory. To address this problem, we develop a capacity management model for a serial chain. At each stage in the supply chain, our model relates capacity, processing, backlog, and service delays to capture the aggregate dynamic interactions between the different stages. Using a system dynamics simulation model in an experimental design as well as a formal analysis of a simplified, yet representative, model using control theory and signal analysis techniques, we characterize the conditions under which a "bullwhip effect" (i.e., an increase in demand and backlog variability as one looks up the supply chain) can occur. We then study the impact of different management strategies and levels of information visibility on capacity and service delay variability in a two-stage model. Conventional wisdom, derived from studies of make-to-stock manufacturing supply chains, strongly supports lead-time reduction in order to mitigate the bullwhip effect. We show that lead-time reduction can exacerbate the bullwhip effect in a service or custom manufacturing setting if it is not carefully coordinated with capacity adjustment. In particular, lead-time reduction generally reduces backlog variance locally but often increases backlog variances at higher stages. Further, sharing end-customer demand reduces backlog variances as in inventory supply chains but over-reliance on it relative to local information may actually increase demand variance at higher stages. Finally, we show that the natural tendency to pursue system-wide process improvement by imposing uniform parameter targets across the supply chain exacerbates demand, capacity, and backlog variances at higher stages. Instead, we show that a superior policy is asymmetric, holding the bulk of system backlog at the stage closest to the point of end customer demand.

For decades, the Beer Game has taught complex principles of supply chain management in a finished good inventory supply chain. However, services typically cannot hold inventory and can only manage backlogs through capacity adjustments. We propose a simulation game designed to teach service‐oriented supply chain management principles and to test whether managers use them effectively. For example, using a sample of typical student results, we determine that student managers can effectively use end‐user demand information to reduce backlog and capacity adjustment costs. The game can also demonstrate the impact of demand variability and reduced capacity adjustment time and lead times.

A novel intervention or new clinical program must achieve and sustain its operational and clinical goals. To demonstrate successfully optimizing health care value, providers and other stakeholders must longitudinally measure and report these tracked relevant associated outcomes. This includes clinicians and perioperative health services researchers who chose to participate in these process improvement and quality improvement efforts (“play in this space”). Statistical process control is a branch of statistics that combines rigorous sequential, time-based analysis methods with graphical presentation of performance and quality data. Statistical process control and its primary tool—the control chart—provide researchers and practitioners with a method of better understanding and communicating data from health care performance and quality improvement efforts. Statistical process control presents performance and quality data in a format that is typically more understandable to practicing clinicians, administrators, and health care decision makers and often more readily generates actionable insights and conclusions. Health care quality improvement is predicated on statistical process control. Undertaking, achieving, and reporting continuous quality improvement in anesthesiology, critical care, perioperative medicine, and acute and chronic pain management all fundamentally rely on applying statistical process control methods and tools. Thus, the present basic statistical tutorial focuses on the germane topic of statistical process control, including random (common) causes of variation versus assignable (special) causes of variation: Six Sigma versus Lean versus Lean Six Sigma, levels of quality management, run chart, control charts, selecting the applicable type of control chart, and analyzing a control chart. Specific attention is focused on quasi-experimental study designs, which are particularly applicable to process improvement and quality improvement efforts.

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In this paper, we provide a quantitative approach to Frequency Domain Methodology (FDM) using harmonic analysis. For a certain class of metamodels, we give the frequency domain hypothesis and develop the corresponding hypothesis test. Minimum simulation model run length information for FDM is provided for a subclass of these metamodels. We discuss factor screening designs to increase the power of the test and illustrate these designs by an example.computer simulation, factor screening, experimental design, harmonic analysis, frequency domain methodology

Outpatient services account for more than four-fifths of patient care in the United States, and most patients access these services via appointment scheduling. Because of the possibility of patient no-shows, healthcare providers usually rely on overbooking. If very few patients show up, it will leave hospital resources underutilized, whereas too many showing up will increase patient wait times and increase staff overtime costs. In a hospital or a network of clinics, where patients could enter and exit at various stations and have complex flow patterns, scheduling becomes even more of a challenge. In “Coordinated Patient Appointment Scheduling for a Multistation Healthcare Network,” D. Wang, K. Muthuraman, and D. Morrice propose a multistation network model that carefully strikes a balance between assumptions that allow tractability and assumptions that disallow real-world adoption. Given the complexity involved in solving the model, they explore a sequence of approximations and find one that offers a significant computational advantage.

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