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Risks can easily disrupt the demand–supply match targeted by sales and operations planning (S&OP). As surprisingly little is known of how organizations identify, assess, treat and monitor risks through tactical planning processes, this paper zooms in on the S&OP set-up and process parameters to explore how risks are managed through S&OP.

A multiple case study analyzes the S&OP processes of seven organizations in the process industry, drawing on 17 in-depth interviews with high-ranking representatives, internal and external documents, and a group meeting with participating organizations.

The study finds that organizations proactively design their S&OP based on their main risk focus stemming from the planning environment. In turn, such designs proactively support organizations' risk identification, assessment, treatment and monitoring through their S&OP execution. Reactively, a crisis S&OP meeting – making use of the structure of S&OP – can be used as a risk-treatment tool, and S&OP design can be temporarily adapted to deal with emerging risks.

This study is among the first to empirically elucidate risk management through S&OP. S&OP design, execution and adaption are identified as three interconnected strategies that allow organizations to manage risks. The design enables risk management activities in the monthly execution of S&OP. The reactive role of S&OP in risk management is particularly novel.

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.

An important and challenging question for air transportation regulators and airport operators is the definition and specification of airport capacity. Annual capacity is used for long-term planning purposes as a degree of available service volume, but it poses several inefficiencies when measuring the true throughput of the system because of seasonal and daily variations of traffic. Instead, airport throughput is calculated or estimated for a short period of time, usually one hour. This brings about a mismatch: air traffic forecasts typically yield annual volumes, whereas capacity is measured on hourly figures. To manage the right balance between airport capacity and demand, annual traffic volumes must be converted into design hour volumes, so that they can be compared with the true throughput of the system. This comparison is a cornerstone in planning new airport infrastructures, as design-period parameters are important for airport planners in anticipating where and when congestion occurs. Although the design hour for airport traffic has historically had a number of definitions, it is necessary to improve the way air traffic design hours are selected. This study aims to provide an empirical analysis of airport capacity and demand, specifically focusing on insights related to air traffic design hours and the relationship between capacity and delay.

By reviewing the empirical relationships between hourly and annual air traffic volumes and between practical capacity and delay at 50 European airports during the period 2004–2021, this paper discusses the problem of defining a suitable peak hour for capacity evaluation purposes. The authors use information from several data sources, including EUROCONTROL, ACI and OAG. This study provides functional links between design hours and annual volumes for different airport clusters. Additionally, the authors appraise different daily traffic distribution patterns and their variation by hour of the day.

The clustering of airports with respect to their capacity, operational and traffic characteristics allows us to discover functional relationships between annual traffic and the percentage of traffic in the design hour. These relationships help the authors to propose empirical methods to derive expected traffic in design hours from annual volumes. The main conclusion is that the percentage of total annual traffic that is concentrated at the design hour maintains a predictable behavior through a “potential” adjustment with respect to the volume of annual traffic. Moreover, the authors provide an experimental link between capacity and delay so that peak hour figures can be related to factors that describe the quality of traffic operations.

The functional relationships between hourly and annual air traffic volumes and between capacity and delay, can be used to properly assess airport expansion projects or to optimize resource allocation tasks. This study offers new evidence on the nature of airport capacity and the dynamics of air traffic design hours and delay.

This paper sets out to investigate whether demand for gynaecological theatre time could be described in terms of the time required to undertake elective operations booked for surgery, and so help match the capacity to this.

A questionnaire assessed the estimates for total operation time for seven common operations, sent to surgeons, anaesthetists and nursing staff in one tertiary referral and one district general hospital (total 49 staff; response rate 58 per cent), and estimates were obtained from theatre computer logs. Average timings for each operation were then applied to cases added from clinics to the waiting list at the district general, to yield the mean demand for elective surgery, and were also applied to emergencies to estimate emergency workload. Finally these demand estimates were compared with the theatre capacity available.

The paper found no difference between the estimates of the three staff groups or between these and the theatre logs (p=0.669), nor did it find that estimates differed between the two centers (p=0.628). Including emergencies, the mean (95 per cent confidence intervals) demand at the district general was 2,438 (1,952‐2,924) min/week.

Although the paper modelled the variation in demand using the relevant variation in operation times, any additional variation caused by differences in booking rates from clinics over time was not nodelled. The minimum period over which data should be collected was not established.

The paper finds that the existing capacity of 1,680 min/week did not match these needs and, unless it was increased, a rise in waiting lists was predictable.

The paper concludes that time estimates for scheduled operations can be better used to assess the need for surgical operating capacity than current measures of demand or capacity.

Airport capacity constraints lead to operational congestion and delays, which have become major threats to the aviation industry. They impose large costs on airlines and their passengers. Uncertainty in demand or unexpected events can cause a mismatch between capacity and demand, resulting in either capacity oversupply, with a decrease in efficiency, or airport congestion over an extended period. Moreover, airport capacity is rather difficult to define due to its multifaceted and dynamic nature, and it depends both on the available infrastructure and on operating procedures. Additionally, traditional capacity management methods do not consider relevant behavioral economic challenges to conventional analysis, particularly failure of the expected utility hypotheses and dependence of valuations on reference points. This study aims to develop a preliminary framework to include economic concepts when evaluating expansions of airport capacity.

This paper reviews major opportunities in airport demand and capacity management from an economic perspective while appraising the challenges involved in airport capacity expansion processes that have not been fully completely in past studies. Although welfare economics provides the conceptual foundations for demand/capacity analyses, the authors integrate the findings regarding capacity definition, uncertainty management and behavioral economics into standard economics to guide the measurement of the airport capacity expansion problem.

The authors obtain several insights regarding airport capacity and demand management. First, airport capacity is a complex metric when evaluating airport expansion, and it depends both on the available infrastructure and on operating procedures. Furthermore, airport throughput is highly conditioned by factors that shape capacity and delay and shows significant variability when these factors are modified. Second, a marginal change in capacity at congested airports may have a great impact on demand distribution, airline competition, aircraft types, fares, operating revenues, route map and other characteristics of a given airport. Behavior after capacity expansion is highly reliant on the slot allocation models. Additionally, overall social welfare is usually affected after changes in infrastructure in terms of increased connectivity, economic benefits and negative externalities, including noise and local pollution. Third, on-time performance is clearly nonlinear, and thus sensitive to variations in demand and capacity. Finally, airport capacity and demand management involve a trade-off between mitigating congestion and maximizing capacity utilization, so decision-making tools are required to support and enhance policy and managerial choices. Three main challenges arise when developing new methods for evaluating airport expansions: the definition of capacity, the management of uncertainty in demand and the need to consider economic concepts.

This paper explores and produces an in-depth understanding of the problem of airport capacity and demand balance. The authors propose a preliminary framework that considers the challenges that have been previously identified and that, particularly, provides an economic perspective for airport capacity expansion processes. This framework is completed with a theoretical model to help policymakers and airport operators when faced with a capacity development decision.

The main purpose is to describe and analyse the impact that the implementation of an advanced planning system (APS) has on the tactical planning level at a steel processing company. This is done in terms of analysing changes in the tactical planning processes, effects on company performance, and how the APS is used in a practical planning context.

This research is based on a longitudinal case study in the process industry. The case company, a high‐end steel producer, has been studied during several years using a combination of data sources: literature reviews, interviews, archival records, and also attendance at meetings, workshops, seminars, etc.

This case study points to the fact that implementing an APS and reorganizing the planning department and the planning processes are mutually dependent. The positive effects at the tactical planning level (in terms of service levels, fast and reliable order promises, more accurate forecasts) could not have been realized without the APS. On the other hand, the APS could not have been effectively utilized without the organizational change.

The results presented in this paper are based on a single case study, but in the context of our literature review and other case studies the findings are still valid and an important step towards better understanding of the practical use of APSs.

The process descriptions, lessons learnt, and issues encountered in case studies like this should be helpful to practitioners on their way to implement APSs, and companies seeking new ways to improve their planning can use this research to investigate the use of an APS.

Studies on the practical use of standard APS software are still scarce. As such this paper provides enhanced knowledge and understanding on the use of APSs in industry settings.

The purpose of this paper is two-fold: first, to bring into sharper focus the role of organisational performance management both for “exploring the future to deliver better outcomes” and “learning from the past to improve the future”; and second, to introduce the control tower approach to optimising complex service delivery performance, explaining how this approach is derived from a unique combination of lean thinking and connected performance.

This is a practitioner paper based on extensive practical experience.

While the tools and techniques of “exploring the future to deliver better outcomes” may be less common than those for “learning from the past to improve the future”, they offer significant benefits, particularly in complex service delivery situations.

It has not, so far, been possible to find organisations willing and able to participate in a quasi-controlled experiment to explore how organisations which implement the control tower approach actually perform in relation to others that do not. However, the authors hope this paper will help move thinking forward in the field of complex service delivery, and perhaps inform future academic research.

The control tower approach offers significant opportunities to improve service delivery performance, not just within healthcare but across all sectors where service delivery is complex and important.

The improvement of complex service delivery performance offers huge social benefits for all stakeholders, including customers, providers (and their staff) and society as a whole, notably through improved outcomes and efficiency.

Although the value of “learning from the past to improve the future” in organisational performance management terms is widely understood, the value of “exploring the future to deliver better outcomes” is much less well known. Hence, this paper highlights a perspective of real practical significance.

The paper aims to investigate the cardiology department’s operational system for improving flexibility by minimizing the patient waiting time and simultaneously maximizing the utilization of service capacity in an uncertain environment. This article also proposes a policy framework that suggests a pool of additional resources and inter-firm collaboration can boost healthcare service delivery excellence.

A discrete event simulation (DES) approach is followed for modeling patient flow and determining the service capacity to respond to demand variability and uncertainty. The model's outputs are used to minimize patient waiting time, maximize the utilization of the resources and match the service capacity with the patient demand.

This research has tested two hypotheses and proved that an increase in waiting time decimates the throughput rate, and additional resources deployment in bottleneck activity positively impacts the throughput rate. The simulated scenarios prescribe an enhanced service capacity with quality care and further contribute to operational performance in reduced waiting time and cost. The results indicate that flexibility reduces the patient waiting time and maximizes the throughput rate.

The study guides the healthcare policymakers to develop flexible competence and facilitate service mechanisms that are adaptive and robust while operating under a volatile environment. The article contributes to the healthcare literature that conjoins flexibility through simulation and resource utilization.

This research is based on real-life primary data collected from healthcare providers. This study adds value to the healthcare systems to adopt strategic decisions to build flexibility through resource allocation, sharing and coordinated care.

The purpose of this paper is to explore what potential benefits may be achieved by using advanced planning and scheduling (APS) systems in the sales and operations planning (S&OP) process.

The paper investigates benefits at the S&OP process level by interviewing APS experts and APS users. Several methods have been used; literature review, Delphi study, and a case study at a company in the chemical industry which uses APS system support in the S&OP process.

Three types of potential benefits were found to be achieved when using APS systems in the S&OP process; benefits concerning decision support, planning efficiency and learning effects. The most common type was decision support benefits according to APS users and APS experts. The results from the case company showed that the benefits perceived in the different S&OP activities differed. In the activities concerning the preparation and generation of delivery plans, the perceived benefits mainly concerned learning effects. In the activities concerning the generation of a production plan, the benefits were foremost found in planning efficiency. In the S&OP meeting decision support benefits were highest valued. The reason for the different results can be explained by the aim of the activity, how APS was used in the activity, the user characteristics and the design of the model and access and quality of planning data.

The focus of this paper is on potential benefits of APS systems in the S&OP process only, not the costs. It has established a typology of potential benefits. No validation in form of statistical analysis has been done. The empirical analysis is mainly based on findings from a single case study.

The findings about the types of APS potential will assist companies in understanding the benefits they can expect from its use in the S&OP process. The case study analysis gives further insight into how APS can be employed and what benefits different APS user categories can expect when it is used in an appropriate way.

The knowledge about which benefits that can be achieved when using APS in the S&OP process is quite unexplored. This paper fills some of these gaps.

Service experiences and waiting lines are often – unfortunately – seen to go hand in hand. This chapter explains why this is the case. Beginning with an exploration of capacity and operating constraints, discussion then delves into both the mathematical origins and psychological implications of waiting lines. The final section offers hope to managers and guests alike, with a survey of different operations strategies and tactics that can eliminate or abate the need to wait.