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ABSTRACTAppointment policy design is complicated by patients who arrive earlier or later than their scheduled appointment time. This article considers the design of scheduling rules in the presence of patient unpunctuality and how they are impacted by various environmental factors. A simulation optimization framework is used to determine how to improve performance by adjusting the schedule of appointments. Prior studies (that did not include patient unpunctuality) have found that a scheduling policy with relatively consistent appointment interval lengths in the form of a dome or plateau dome rule to perform well in a variety of clinic environments. These rules still perform reasonably well here, but it is shown that a combination of variable‐length intervals and block scheduling are better at mitigating the effects of patient unpunctuality. In addition, performance improves if the use of this policy increases toward the end of the scheduling session. Survey and observational data collected at multiple outpatient clinics are used to add realism to the input parameters and develop practical guidelines for appointment policy decision making.

PurposePhysician lateness and service interruptions are a significant problem in many health care environments but have received little attention in the literature. The purpose of this paper is to design appointment systems that reduce waiting times of the patient while maintaining utilization of the physician at a high level.Design/methodology/approachEmpirical data from time studies and surveys of medical professionals from multiple outpatient clinics are used to motivate the study. Simulation optimization is used to simultaneously account for uncertainty and to determine (near) optimal scheduling solutions.FindingsAs lateness increases, it is shown that, in general, appointment slots should be shorter and pushed later in the session. Conversely, as interruptions rise, appointments in the middle of the session should be longer. These findings are fairly consistent over a variety of environmental conditions, including clinic sizes, service time variance, and costs of physician time compared to patients' time.Practical implicationsThis paper demonstrates that the dome/plateau‐dome scheduling patterns that have been found in prior studies work well under many of the new factors modeled here. This is encouraging because it suggests that a generalizable pattern is emerging in the literature for the range of environments studied in these papers and this research provides guidance as to how to adjust the pattern to account for the factors studied here. In addition, it is shown that some environments will perform better with a different pattern, which the authors denote a "descending step" pattern.Originality/valueThis paper differs from most prior studies in that the complexity of environmental variables and stochastic elements of the model are simultaneously accounted for by the simulation optimization algorithm. The (very few) prior papers that have used simulation optimization have not addressed the factors studied here.

PurposeSelf‐serve technologies (SSTs) provide many benefits such as speed, time and place convenience for the customer and reduced labour costs for the firm. The aim of this study is to consider whether these benefits are denoted by changes in the firms' stock price when SSTs are introduced.Design/methodology/approachUsing data from banking, retail (grocery and gas), and airline industries, this event study considers overall effects of SST implementation on stock price, and also considers effects in three sub‐categories: industry, time period, and scope of announcement (i.e. corporate vs. regional).FindingsSST announcements had a positive effect on firm value during the late 1990s. However, for the most part, financial markets do not respond to SST announcements. This is in line with the strategic necessity hypothesis and the resource‐based view of the firm, but may also be partly due to the phased rollouts that are typical of these implementations (which dilute the impact over time).Research limitations/implicationsIt proved quite difficult to locate original public announcements of SST investments in publicly traded companies; thus, the sample size is smaller than desired. However, a bootstrapping method was used to crosscheck the findings.Practical implicationsFirms should not promise investors immediate increase in firm value, but rather demonstrate the benefits from a longer term, competitive and customer‐oriented perspective.Originality/valueThis is the first study to consider the effect of implementing SSTs using event study methodology. Most prior SST studies have considered behavioural aspects of the implementation, while most prior event studies that have considered IT implementations have done so in a general sense, not focusing on a specific technology. Using a new dataset collected from two decades of SST implementations, this study focuses on the impact of SSTs from a different perspective.

Service managers are continually challenged with balancing customer demand and service capacity. Recent studies have raised awareness of various demand and capacity management practices available to services, but little numerical work has been done to identify how these decisions work together and how they relate to one another. For instance, reducing prices may attract customers during a slow period, but the extent of impact this should have on cross‐training staff is not clear. A simulation based on theoretical and empirical insights explores the impact of various decisions on profitability and operations. The decisions modelled include the impact of: automation, customer participation, cross training employees, informing customers about the operation, and others. It is shown that demand and capacity decisions do indeed impact on each other – sometimes in ways that are not initially obvious. Results provide useful thought‐starters for service managers striving to improve their operations.