Why casinos need capacity planning

In their Capacity Management for hospitality and tourism: a review of current approaches, Pullman and Rodgers acknowledge that many leisure enterprises, such as casinos and massive integrated resorts, require substantial capital investment and expenditure. Pullman and Rodgers contend that, “While capacity management has generally fallen within the domain of operations management, and demand management within the domain of marketing, intersecting methodologies such as yield and revenue management, the partitioning of visitors (by length of stay), and/or the adjustment of visitor participation levels rely on expertise from both of these functional areas to work effectively."

Although capacity management has been referred to as ‘‘demand management’’ by Crandall and Markland and ‘‘managing capacity and demand’’ by Fitzsimmons and Fitzsimmons, capacity and demand management are, in fact, distinct concepts. In his How to match plant with demand: a matrix for marketing for the International Journal of Tourism ManagementTaylor believes that demand management attempts to influence when and how many visitors attend or use a service, whereas Klassen and Rohleder contend that capacity management ensures that sufficient capacity exists to meet this demand.

Pullman and Rodgers argue that, capacity strategy is a key operational function for all leisure-related enterprises. The extent to which capacity satisfies demand has an impact on visitor experience, employee satisfaction, profitability, and long-term sustainability of both the resources and the enterprise itself,” they contend.

In the leisure industry, excess capacity not only underutilizes the workforce and other physical resources, but can also lead to increased waste and demand stimulating and profit reducing price reductions. “Inadequate capacity, on the other hand, can impair the visitor experience through degradation of facilities, overuse of natural resources, crowding, and increased waiting time, warn Pullman and Rodgers. It can also open the door for competitors to enter the market as well as overtax the workforce, leading to employee burnout and increased turnover. “Fortunately, firms can borrow a number of ideas from other industries to facilitate better matching of supply to demand in their particular enterprise,” advise Pullman and Rodgers.

As Pullman and Rodgers argue, capacity "can be separated into two distinct perspectives, the strategic or long-term perspective and the tactical or short-term perspective." Strategic capacity decisions are made during the planning stages for a project as managers consider macro-level responses to existing and potential future demand (PorterDavis and HeinekeSchroeder). For Pullman and Rodgers, “Such decisions may include the number of hotel rooms an area can or should support; the land space, energy, and water required for a project; the available labour and skills; the anticipated overall size of the enterprise in terms of parking, seating, and production requirements (in food services); and/or the carrying capacity issues for natural resources.”

Later in the planning process, the capacity focus shifts to the set of short-term actions taken to fulfill the planned strategies, often called the “tactics.”viii “Examples of these decisions include the determination of the number of employees needed to meet peak demand during a summer lunch period and the best mix of table configurations to accommodate dinner demand given different party sizes,” say Pullman and Rodgers. As per Figure 1, “Long versus short term decisions are distinctly different, and the methodologies for addressing these problems often involve alternate approaches” (Pullman and Rodgers).

Figure 1: Capacity decisions

Source: Pullman and Rodgers, Capacity management for hospitality and tourism: A review of current approaches

According to Porter, “In the strategic capacity planning phase, enterprises make decisions to ensure achievement of their desired objectives based on an assessment of their current situation or position, capabilities, shortcomings, and overall competitive position; the alternatives and risks involved; and the timing.”

For Pullman and Rodgers, “Strategic capacity decisions include capacity size and expansion, carrying capacity or optimal use of the resource, and capacity flexibility. Capacity size refers to the maximum physical size of a facility or the optimal size of a workforce.” Carrying capacity is the desired or optimal level of utilization of a given resource; beyond that capacity, both Lawson and Baud-Bovy as well as Mathieson and Wall argue that negative impacts exceed levels specified by evaluative standards, such as those for physical deterioration or minimum quality of recreational experience. “Capacity flexibility represents the ability to respond to fluctuating demand using additional labour or adjustments to physical space.”i For Schroeder, “These strategies address the amount and timing of capacity changes and types of facilities needed for the long run.” “For physical facilities, capacity strategy is part of the total operations strategy; it is not merely a series of incremental capital-budgeting decisions,” argue Pullman and Rodgers. “Both physical and labour capacity strategies include proactive planning for future growth, reactive response to existing demand, or a mix of these two strategies,” claim Davis and Heineke.\

“Short-term capacity management comprises the set of actions taken to fulfill the firm’s strategy — specifically, the methods of implementation or the requirements for the strategic plan to take effect,” say Pullman and Rodgers; for example, diners or visitors served per hour, rooms cleaned per shift, or skiers per day. “Managers and planners are concerned with meeting demand in real time by means of such things as labour scheduling, flexible seating or partitioning, or opening up more seating capacity at lower price points as an event or departure date approaches,” explains Pullman and Rodgers. Other decisions can address such things as “the design of the service process, including the service interaction time (the average amount of time an employee spends with a visitor); the level of visitor participation or self-service; the capacity cushion (extra space or labour added as a contingency); and queue reservations or partitioning.” Disney’s Fast-Pass program or Universal’s VIP pass for special fast lines are examples of reservations systems put in place to ease traffic and customer headaches, say Pullman and Rodgers. “The appropriate focus for these capacity decisions depends on the overall size and type of leisure enterprise,” argue Pullman and Rodgers.

It is useful to view leisure-related enterprises in terms of the service process matrix, which was originally developed by Schmenner for the field of operations management. As per Table 1 from Pullman and Rodgers, this framework classifies enterprises “according to their degree of labour intensity versus degree of interaction/customization.” “Labour intensity is defined as the ratio of labour cost incurred to the value of the facilities and equipment,” as per Pullman and Rodgers. In general, for Pullman and Rodgers, “highly labour-intensive businesses require relatively little capital expenditure for facilities and equipment, while businesses with low labour intensity have low levels of labour cost relative to costs for facilities and equipment.” “The degree of interaction and customization reflects the level of service customization for the visitor and the level of visitor interaction with the service process. When both of these levels are high, visitors actively participate in the service process and the business works to satisfy the visitors’ particular preferences,” say Pullman and Rodgers

Degree of labour intensity

Degree of interaction and customization




Service Factory


Standard hotels


Cruise ships


Theme parks

Service Shop

Luxury hotel

Luxury restaurants

Luxury cruise ships



Mass Service



Fast food

Professional Services

Tour guides



Table 1: Application of the service process matrixxi to the field of hospitality and tourism

Source: Pullman and Rodgers, Capacity management for hospitality and tourism: A review of current approaches

“For enterprises with low ratios of labour to capital intensity — that is, Service Factory and Service Shop processes — physical capacity decisions are paramount. Here, capacity cannot be augmented easily, so it is essential to utilize each capacity unit effectively (each airline seat, hotel room, restaurant or gaming table seat, amusement park ride seat, etc.),” contend Pullman and Rodgers. “Strategic physical capacity decisions encompass the long-term planning of construction or renovation of buildings, planes, or cruise ships, or the purchasing of new equipment, rides, vehicles, etc. Short-term capacity management, in contrast, includes approaches that enable firms to react to short-term fluctuations in demand,” argue Pullman and Rodgers. “The latter is typically achieved through scheduling, yield or revenue management, and the addition or withdrawal of support services. Mass Service and Professional Service processes, on the other hand, have high degrees of labour intensity,” explain Pullman and Rodgers. “Strategic capacity management for these enterprises focuses more on the management of labour capacity, including the hiring and selection of employees with the appropriate skill mix,” note Pullman and Rodgers. “Short-term capacity management focuses on the implementation of scheduling and other incremental capacity methods that allow the enterprise to react to short-term fluctuations in demand with greater flexibility,” conclude Pullman and Rodgers.

"Capacity measures serve as the foundation of all analytical approaches for all capacity decisions,” explain Pullman and Rodgers. “For example, a capacity measure could provide the planning objective (e.g., an 80% employee utilization goal). Or the measures could show the relationship of capacity to the overall performance of a system (e.g., at 80% and 90% maximum capacity, a park experienced 89% and 75% customer satisfaction ratings, respectively),” add Pullman and Rodgers. But as pointed out by Wall, capacities are difficult to measure because of definitional problems. “The appropriate design and outcome measures depend on whose perspective is emphasized,” note Pullan and Rodgers. Table 2 provides some examples of operational indicators that are particularly relevant to managers and visitors.

Visitors’ perspective

Managers’ perspective


Right time

Right price

Right type

Revenue or profits

Number of visitors

Crowding and space

Too many people/too little space

Too few people/too much space



Percentage of cost of depletion or wear


Excess capacity or waste

Perceived service time

Labour costs

Perceived wait time

Variable and fixed physical resources

Actual service time

Actual wait time

Table 2: Examples of capacity measures relevant to visitors’ versus managers’ perspective

Source: Pullman and Rodgers, Capacity management for hospitality and tourism: A review of current approachesi

“In most for-profit environments, management is concerned with maximizing revenue through the number of paying visitors using the enterprise or revenues generated per visitor and the firm’s capacity utilization of resources,” explain Pullman and Rodgers. “Visitor objectives (including less crowding and shorter perceived waiting times) and their other specific measures of relevance differ from those of the enterprise management,” add Pullman and Rodgers. For Wall, “In hospitality and tourism, both biophysical (the quality of the environment) and behavioural components are recognized.” An increase in physical capacity utilization can lead to queuing and a general reduction in the perceived quality of service warn Davis and Heineke. This can have profoundly negative effects on customer satisfaction, obviously.

In the mainstream literature, the critical levels that are needed to support certain facilities are often referred to as capacity thresholds, says TaylorMuller provides the example of the high occupancy of an airplane or a quick-service restaurant, which can lead to untidiness because the service crew simply doesn’t have time to maintain cleanliness; from the customer’s perspective this can be a big negative. A nearly empty cruise liner, on the other hand, does not provide the expected social ambience and affects the employees’ interactions with customers argues Kandampully.

“Clearly, managers must monitor visitors’ perceptions and preferences and try to best meet both management’s and their customers’ expectations,” advise Pullman and Rodgers. For example, Kilby and Fox explain in Casino Operations Management, “an Australian casino improved the level of patrons’ comfort by decreasing the playing spots offered on each gaming table, which subsequently increased the blackjack hold. However, as Muller illustrates, a reduction in the number of customers can be compensated for by higher revenues only if the demand is price inelastic, which is not always the case.

“Different capacity indicators linking the ability of an enterprise to control the environment with the overall customer experience can be used. For example, in a restaurant setting, capacity-related indicators include average customer waiting time and average duration of each service,” explain Pullman and Rodgers. These indicators have been shown to have “a causal relationship with the number of incidents/complaints, the general ratings of the service, and the food quality.” [vii] “For a recreational site, capacity indicators can include the following: the satisfaction of the visiting public, the intention to return, the percentage of people feeling overcrowded, and the number of complaints,” say Simón et Al. Several researchers have shown that the links between capacity decisions and other performance measures such as profit, quality (overall visitor satisfaction), and market share are complex and nonlinear, including work by Easton and Pullman and Sill.

As previously mentioned, “strategic capacity planning of physical resources is of primary concern for Service Factory and Service Shop processes (hotels, resorts, cruise ships, recreational sites and theme parks).”i In practice, this planning is part of the detailed design that includes capacity estimation, an integral part of a feasibility study[xi] and the main indicator of the price of a capital asset.[xii] Strategic physical capacity decisions include size, configuration, layout, and built-in flexibility. These decisions made during the planning stage have long-term implications for the overall resource productivity of a property.[xiii]

The capacity-related criteria for the ‘‘goodness’’ of a design includes the grossing factor, which is calculated as the proportion of income generating areas relative to the total floor space. According to Pullman and Rodgers, “The grossing factor is reduced by the need to provide technical areas (business centres, sport and recreational areas, etc.) and functional features (architectural elements such as atriums, balconies, and grand staircases), as well as by the need to meet legislative and environmental requirements such as land zoning and usage restrictions, building codes, fire and maritime safety requirements, and disability access regulations.”i

“Codes for food-service premises, for example, stipulate the allocation of adequate space for each food production area and regulate the physical layout of the facility to ensure the ease of cleaning/sanitation (minimum gaps between equipment units and walls, minimum number of hand-washing sinks and maximum walking distance to reach them, etc.), explain Pullman and Rodgers. These requirements for land-based[xiv] [xv] and cruise-ship[xvi] kitchens lead to an increase in the size of the production areas, which are non-revenue-generating.i “By means of the grossing factor, various areas of a facility can be assessed for the contribution margin of the different service products they support,” explain Pullman and Rodgers. For example, Kimes and McGuire show that in the Raffles City Convention Centre in Singapore, the contribution margin is 30–35% for food services, 85–95% for room rentals, and 50–95% for audio–visual equipment.[xvii]

“In addition to size, the actual physical capacity depends on the configuration and layout of a facility, since the combination of these affects the flow of products and customers,” notes Pullman and Rodgers. For example, in restaurant planning, table types and locations (banquettes, booths, or anchored or unanchored tables) affect the customer experience and, hence, the average spending per minute.[xviii] [xix] Table 14 provides other examples of other design solutions for different industries and sectors.

The capacity-related criteria for the ‘‘goodness’’ of a design include the grossing factor, “which is calculated as the proportion of income generating areas relative to the total floor space.”i “The grossing factor is reduced by the need to provide technical areas (business centres, sport and recreational areas, etc.) and functional features (architectural elements such as atriums, balconies, and grand staircases), as well as by the need to meet legislative and environmental requirements such as land zoning and usage restrictions, building codes, fire and maritime safety requirements, and disability access regulations,” note Pullman and Rodgers.




Airlines, theme parks, ski resorts

Hub and spoke system

The hub is a central facility offering restaurants, shopping arcades, entertainment, conference rooms, and other amenities, while the spokes are chosen destination areas.[xx]

Point to point

The space is designed for people to move from one destination area to the next without moving through a central location.[xxi]

Virtual queue

Customers sign up for virtual queue, go to other destination areas and return at appointed time.[xxii]


Increased bathroom size

The decrease in the net bedroom size coupled with an addition of a walk-in shower and dressing area resulted in a 13% improvement of the design efficiency ratio.


Process flow principle

A steady, controlled process flow reduced complexity and confusion in a kitchen [xxiii]

Better distribution of serving capacity between cold and hot sections in restaurant buffets resulted in faster table turns, shorter lines, better food temperature control, and decreased construction costs.xx

Table type and location

Customers in booths spent more on average than those in banquettes; those in poor table locations spent more.xxviii

Reduce table spacing

Creating a busy feel (small dining spaces) increased customer satisfaction.

Cruise ships

Horizontal class sandwich

Positioning of the upper category of cabins at the lower desk to minimize rolling, which is amplified at the upper deck as well as at the bow and the aft.[xxiv]

‘‘Cocooned isolation’’

Vertical class barrier with the provision of a view, a private balcony, and no need to change floors to reach the nearest pool.xxxiv

Table 3: Examples of layout applications for physical resourcesi

Source: Pullman and Rodgers, Capacity management for hospitality and tourism: A review of current approachesi

Food production is the only manufacturing function in the hospitality sector[xxv], “but it faces more unpredictable demand patterns than most goods production.”i This production is manageable, however; Creed recommends short shelf-life systems (cook-hot-hold and cook-chill), which offer a relatively small time buffer between food preparation and consumption and are suitable for settings with predictable demand, such as banqueting or in-flight catering.[xxvi]

“Extended shelf-life systems (cooked-in-a-bag and cook-freeze), on the other hand, which allows for a longer time buffer, are suitable for unpredictable demand patterns such as those of railway food services or restaurants that are open to the public,” note Pullman and Rodgers. “The practical implication of this for tourism-oriented food services is that the capacity of their physical resources can be better utilized through the production of packaged meals for new markets (local cafes and restaurants as well as the retail sector) during a slow season or periods of tourism downturn,” argue Pullman and Rodgers.

“The relationships between demand, capacity, managerial objectives, and the visitor experience are complex and difficult to evaluate without appropriate analytical techniques,” assert Pullman and Rodgers. Table 4 lists several different methods used in strategic capacity estimation for industries such as hotels, restaurants, gaming, theme parks, convention and exhibition centers and cruise ships, as per Pullman and Rodgers. The range of strategic estimation approaches run “from the planning of overall tourism or hotel carrying capacity in a city (for long range tourism or special events such as the Olympics) to such micro-level decisions as the sizing of hotel water heaters,” note Pullman and Rodgers.





Beds, rooms, percentage of occupancy


Regressions analysis[xxviii]

Economic order quantity[xxix]


Available seats per hour

Number of seats x hours of service/service-cycle timexiv

Degree of table “combinability”

TABLEMIX computer program[xxx]

Production equipment units

Empirical formulae[xxxi]


Average blackjack hands/hour

Minimum break-even betsxvi

Theme parks and resorts


Break-even analysisxxx

Ski lifts

Simulation modeling[xxxii]

Convention and exhibition centers

Square meters of exhibition area, tables, flatware, chairs

Benchmarking against the consumption patterns (number of customers per event, space, time requirements, etc.) and future trends in demand[xxxiii]

Cruise ships


Dynamic game theory[xxxiv]

Table 4: Examples of strategic physical capacity estimation techniques

Source: Pullman and Rodgers, Capacity management for hospitality and tourism: A review of current approaches

Generally, the major capacity indicators – the number of berths on a cruise ship, rooms in a hotel, seats in a restaurant, etc.) are determined during the initial stages of major infrastructure projects. These indicators are then used later in the estimation of other capacity elements,” contend Pullman and Rodgers. “Closed-form/cost-oriented methods are typically used in the accommodation sector, which is capital intensive, since the capital costs, such as construction costs, furnishings, property taxes, and insurance, vary with capacity” add Pullman and Rodgers. An example of a closed-form solution is a typical break-even formula such as the cost-volume-profit method[xxxv] put forth by Liu and Var:

where P = total profit; Q = volume of sales (e.g., visitor-days); S = sale price per unit volume; V = variable cost per unit volume; F = total fixed cost.

“Here, if a target profit is set, the volume of sales (Q) can be derived from this equation, which together with the expected occupancy level would indicate the capacity needed to meet the given profit objective. With this approach, the capacity depends on the targeted sales price,” claim Pullman and Rodgers. “Another closed-form approach, proposed by Careyxxxviii uses regression models to determine overall hotel carrying capacity for specific locations. Here the dependent variable — optimal room occupancy — is a function of the independent variables, price and overall market capacity,” note Pullman and Rodgers.

“A feasibility assessment of a new project also includes the determination of variable or semi-variable costs affected by furniture/equipment needs and layouts,” say Pullman and Rodgers. They add that, “These can be classified as ‘capacity elements’ and are usually estimated using empirical formulae with results expressed in a variety of units (square meters for different functional areas; number of parking places/rides/boats, etc.; customers per lift cycle for a ski resort; standing spots for casino games; and others).”i “These approaches are usually simpler than the methods used in determining the major capacity indicators and reflect the factors shaping the capacity on the micro-level, which are usually not directly linked to profits,” explain Pullman and Rodgers. For example, Muller provides a very simple empirical formula to estimate capacity for a restaurant based on estimates of hours of service, number of seats, cover count, and planned service-cycle timexiv:

Pullman and Rodgers argue that, “The closer a capacity element is to the customer interface in a physical sense, the more customer-related variables are used in capacity estimation — the number of customers served per hour for a dining-room or the percentage of people travelling by car for parking, for example.”i “To estimate the number of rides in a theme park, Wanhillxxx provides a closed-form solution integrating the catchment’s population and penetration rate, demand fluctuations between weekdays and weekend, average ‘consumption’ of entertainment units per customer, and average ride throughput,” claim Pullman and Rodgers.

“Typically closed-form solutions must assume that variables such as pricing or demand are constant or that certain conditions must be ignored to create problem tractability. As an alternative, probabilistic models allow for more complexity and realism. These approaches range from modifications of closed-form approaches to large simulation models,” claim Pullman and Rodgers. For In his Analysis of Las Vegas strip casino hotel capacity: an inventory model for optimizationxxxix, Gu gives an example of a modified closed-form solution, that  “treated the supply of room nights of Las Vegas Strip casino hotels as inventory units and integrated a probabilistic model based on the economic order quantity (EOQ) equation to reflect the perishable nature of hotel services.”i “This approach allowed for the prediction of the cycles of over- and undercapacity,” state Pullman and Rodgers.

“In previous research, simulation modelling has been applied to capacity problems such as restaurants, cruise ships and ski resorts,” note Pullman and Rodgers. For example, Wall uses simulation modelling to account for multiple inputs and the cyclic nature of capacity in tourism. For restaurant capacity, the TABLEMIX model developed by Thompson

“provides the optimal percentage of combinable tables in a restaurant by integrating the number and duration of peak dining periods, the probabilities of different numbers of party arrivals for the dining period, the probabilities of different size parties, the maximum number of waiting parties, the distribution of dining periods (normal or lognormal), and the choice of table arrangement rule (e.g., assign an available table to the largest party or to the party waiting the longest).”xl

“Using probabilistic models of market share and computer simulation to more accurately integrate the relationship between capacity and demand,” Pullman and Thompson developed a method for determining the profit-maximizing capacity strategy for different hospitality environments.xlii “This method requires customer utility models (reflecting real customer’s preferences for multiple capacity-related aspects of a service such as queue duration) with the potential costs and revenues from the implementation of different capacity management strategies,” explain Pullman and Rodgers. The writers continue, saying that, “Using discrete-event simulation models of a resort’s subsequent flow performance under different capacity scenarios, the profit maximizing strategy simultaneously addresses both management and customer preferences as shown below:

where t = time period (set T periods); Nt = number of potential visitors in period t; Pjt = price of service j during time period t; V = variable cost per visitor for service j; F = fixed cost of service j; pjt = potential market share or probability that a customer group will select service j from the set of J services in period t.”i

The above market share is derived from actual customer preference models based on choice experiments by Verma et al.[xxxvi] In this case, the probability that an aggregate group chooses service j out of a set of J competitive service establishments is as follows (Pullman and Rodgers):

where Uj = aggregate customers’ overall systematic utility for service design j.

Pullman and Rodgers see the advantages of these mixed-model approaches are as follows:

“first, complex enterprises can be modelled with a great deal of realism, including variable costs, dynamic customer arrivals and flow patterns, multiple combinations of strategies, and heterogeneous customer mixes. Alternate forecasts of visitor behaviour and customer flows can be tested. Second, utility models are typically based on actual customer preferences for different attributes of an enterprise (for example, crowding level, average queue time, price, retail and amenities level, mix of activities, or skill level). Third, by integrating the customer and enterprise perspectives, diverse criteria can be optimized, including profit, revenue, and customer satisfaction. This approach allows for the testing of various capacity management strategies, enabling management to evaluate their estimated costs prior to the capital investment.”

For enterprises, strategic labour capacity decisions cover a long-term planning horizon, usually of 6-18 months.viii “As with physical capacity decisions, firms’ functional capacity strategies should reflect their competitive priorities,” argue Pullman and Rodgers. From an operations management perspective, there are three typical strategies for labour capacity planning: level, chase, or mixed.[xxxvii] Pullman and Rodgers provide the following example: “hotels with a high service/competitive priority might want to retain the majority of their staff throughout the year (a level strategy), regardless of demand, to keep both the service level and the morale high. A cost-focused hotel, on the other hand, might only staff as needed (a chase strategy) to minimize labour costs.”i

Sasser argues that, a level strategy is more appropriate when demand is quantifiable before its time of use, or the firm has strict requirements about service availability, and/or the employees are scheduled according to pre-existing criteria such as strict union contracts.xlvii “Typically, Service Factory processes with low interaction, customization, and labour intensity use this strategy. Demand is scheduled to fit into an existing plan, such as airline flight or tourist bus schedules. The visitor or guest reserves a space in advance and the labour capacity is fixed for the flight or bus trip regardless of the number of people on that trip,” explain Pullman and Rodgers. “This approach is best suited to an environment in which labour requirements are predetermined based on physical capacity, such as under safety regulations (e.g., the number of flight attendants on a plane is fixed regardless of the number of passengers),” state Pullman and Rodgers.

A chase strategy tries to meet demand as it occurs.xlvii Pullman and Rodgers contend that, “For this type of strategy, the labour requirements change to match the demand forecast throughout the planning horizon. This approach is best suited to an environment in which visitors cannot be prescheduled and there is a high degree of labour interaction and customization (Professional Services), or to other environments with unpredictable visitor arrivals such as hotel front desks, shops, parks, and non-reservation restaurants.”i

Mixed strategies use some combination of level and chase approaches.xlvii “Because of the seasonality of many tourism enterprises, it makes sense to use level strategies for certain staff areas (e.g., cruise ship operators and maintenance or other departments that must function regardless of the number of visitors) and chase strategies for others (maid service, ski instructors, front desk personnel, wait-staff, bell-staff, etc.),” recommend Pullman and Rodgers.

“Often a leisure enterprise will change its labour capacity strategy as the company grows,” claim Pullman and Rodgers. “For example, Port Aventura, a Spanish theme park, used a chase strategy for their first season to match employees to demand. During that season, the workforce reached 2800 on the busiest days but went down to 200 employees during the winter season.”i Later, Huete and Segarra report, “the company signed a collective agreement with their unions that offered permanent or permanent-seasonal contracts to 50% of the long-term workforce, thus requiring a mixed strategy.”[xxxviii]

“Labour capacity estimation for various strategies depends on the industry and often on the planned physical capacity,”i even with integrated resorts that are now some of the biggest buildings in the world. “Here, industry standards play a significant role, dictating how many employees of each type are required for a plane, restaurant or hotel of a certain size,” argue Pullman and Rodgers. Sill provides examples of this approach for hospitality: An enterprise first estimates a key demand input (e.g., number of expected guests, menu-mix history, or potential bar sales), multiplies that by a given standard (staffing standard, recipe, table-setting used) and gets a capacity output (staffing for the dining-room, kitchen, dishwashing, food production, or bar).[xxxix] “To cite another example, in the hotel industry, union contracts might dictate how many rooms a housekeeper may clean in a shift (perhaps 12–14 standard rooms), and the required number of housekeepers can then be directly calculated from the expected room occupancy,” explain Pullman and Rodgers

“For capacity estimation that involves a partial or full chase strategy, a typical approach to labour staffing for long-term planning employs aggregate planning techniques that calculate the economic trade-off between full- and part-time employees, overtime, and hiring and lay-off costs to determine the labour requirements,” argue Pullman and Rodgers.i In their paper Introduction to Operations and Supply Chain Management[xl], Bozarth and Handfield believe, “These problems are solved optimally using linear programming in which the objective is to minimize cost or maximize profit subject to constraints such as the meeting of demand, limits on materials and equipment time, and labour requirements for standard and overtime wages.” In a typical inventory-less model formulation, Vollman et al.[xli] argue that the linear objective function for cost minimization is the following:

subject to the following constraintsli:

·       that regular-time hours must not exceed the allowable maximum to be worked per employee per month;

·       workforce level change between months Ht Ft; and Xt Ot Dt

where CH, CF, CR, Co, and CU = the cost of hiring an employee, the cost of firing an employee, the cost per labour-hour of regular time, the cost per labour-hour of overtime, and the cost per labour-hour of idle regular time, respectively; Ht, Ft, Xt, Ot, and Ut = the number of employees hired, employees fired, regular-time hours, overtime hours, and idle regular-time hours in month t, respectively; Dt = the number of hours of service to be sold in month t; and M = the number of months in the planning horizon.li

Vollman et al. also believe that, “Similar linear programming models can be set up to model more complex environments, such as different product or customer classes, different employee classes (part-time and full-time), and many other service situations.”li

Pullman and Rodgers argue that, “During the actual operation of an enterprise, the ability to meet demand depends on both the flexibility of the physical resources and the willingness of the price/market segment to pay for an available capacity unit.”i

Physical capacity

Physical flexibility

Rent capacity

Share capacity

Hire sub-contractors

Change resource allocations

Change hours of operation            

Provide off-site access

Use automation

Price/segment flexibility

Partition visitors (status and length of transaction)

Yield management

Revenue management

Human capacity

Labour flexibility

Schedule employees

Allow overtime

Allow idle time

Cross-train employees

Change work speed and process

Hire permanent employees

Lay-off employees

Use temporary employees

Use part-time employees

Visitor flexibility

Allow waiting

Allow balking

Turn away visitors

Provide rewards or incentives

Provide diversions or complementary services

Camouflage the queue

Pay for VIP queues

Change level of visitor participation

Schedule visitors/take reservations

Inform/educate about alternative options


Table 5: Short-term capacity management approaches

Source: Pullman and Rodgers, Capacity management for hospitality and tourism: A review of current approaches

Table 5 provides a comprehensive list of the different physical resource approaches, most of which are self-explanatory.iv xvii “The majority of price/segment flexibility approaches use capacity partitioning or yield/revenue management techniques,” say Pullman and Rodgers. “Traditionally, yield management has been used by airlines (price and class), lodging enterprises (price and stay duration), restaurants (price, style, and duration of service/meal), and more recently, by gaming venues (gaming spots per table and minimum bets) and the golf-course industry (no-show fees, rental fees, playing times),” state Pullman and Rodgers. The most basic methodology is the linear modelling approach, which attempts “to maximize revenues from different customer classes subject to the capacity constraints of the enterprise as illustrated below:

where i = rate class or segment, xi = number of items sold in rate class i, di = demand for rate class i,ri = revenue from selling items in rate class i, and c = capacity of the service.”i

The work of Lindenmeier and Tscheulin[xlii] and Sanchez and Satir[xliii] have shown that yield management methodologies have evolved to include probabilistic demand variables, economic approaches, network approaches, customer satisfaction implications, etc. “In addition, yield management has been applied to almost all leisure and hospitality sectors, claim Pullman and Rodgers. Table 17 provides examples of the various short-term capacity estimation techniques used in different sectors.i

“The capacity requirements of different customer groups cannot only affect the revenue but the overall utilization of a facility,” say Pullman and Rodgers. They provide the example of a sky resort that attempted to increase utilization; “a ski resort with a large percentage of terrain for expert or advanced skiers increased its promotions for the ‘family skier’ segment (skiers with beginner-level and intermediate skills).”i Although the change reduced the service quality in terms of queuing, a resulting increase in spending on lessons and amenities was observed. xlii In a golf course setting, experienced golfers usually play faster than novices.i Kimes found that offering separate playing times for beginners relieved the experienced players’ frustration and improved overall flow.[xliv] Simón et Al. discovered that, for outdoor recreation enterprises, the attraction of environmentally aware tourists in order to minimize ecological damage and increase tourism carrying capacity had a similar effect on the operations.

“Tactical human capacity approaches involve labour and/or customer flexibility. From the labour perspective (Table 17), most of the flexibility comes from hiring an appropriate number of employees, scheduling employees to meet demand as it occurs, and adequately training employees to do their respective jobs or cross-training them to do multiple jobs,” say Pullman and Rodgers. “The majority of these labour-related tactical capacity management approaches derive from the operations area and are based on queuing theory,” note Pullman and Rodgers. According to Sundarapandian, queueing theory is the mathematical study of waiting lines, or queues.[xlv]





Packages: budget, moderate, and deluxe


Categories: couples, singles, and families


Subcategories for children: kids, petit, and mini baby/nursery



Off-peak discounts


Increase production rate by application of more efficient equipment and simpler


Reduce randomness via well-designed reservation system

Kimes et al.[xlvii]

Increase sophistication through application of queuing theories, modelling and simulations, linear programming, and forecasting



In low-demand periods: assign multiple duties to the host, increase menu variety, offer regular or reduced prices, increase promotion, have servers bring condiments


Ski resort

Capacity upgrades: improved lifts

Pullman and Thompsonxlii

Capacity expansion: additional terrain


Queuing information


Inter-daily demand smoothing: surcharge during weekends


Class variation: mix of beginners and intermediate


Golf course

Reduce the uncertainty of arrival by means of forecasting, overbooking, requiring guaranteed reservations, reconfirming reservations by phone, instituting a no-show fee and/or a fixed fee for reservations


Reduce the uncertainty of duration by selling rounds of predictable length, flexible course design with a global positioning system to direct flow, requiring own golf carts or reducing the cart rental fee during busy times, using course marshals and caddies, posting golfers’ playing times


Reduce the tee-time interval


Small Inn/B&B

Coping: close part of year, maintain the premises, ‘reduce staff’ seek other income, borrow money

Getz and Nilsson[xlviii] (2004)

Combating: stay open all year, develop other tourist segments, cater to residents, augment the product’s appeal, add value, develop export products


Capitulating: shrink, sell, or terminate the business


Table 6: Capacity estimation in different hospitality and tourism sectorsi

Source: Pullman and Rodgers, Capacity management for hospitality and tourism: A review of current approachesi

In Labour scheduling, part 1: forecasting demand [xlix], Thompson explains that, matching visitor demand to labour capacity involves a four-step process:

1.       forecasting demand;

2.       translating the demand forecasts into requirements for employees;

3.       developing a workforce schedule that, ideally, only has employees working when necessary to deliver the service; and

4.       controlling the delivery of the service in real time.

“From the customer perspective, flexibility tactics involve the management of waiting lines or ‘capacity buffers,’” says Pullman and Rodgers. The process, Pullman and Rodgers contend, “can be designed to allow visitors to merely wait; to let them leave rather than keeping them ‘captive’ in roped areas; to provide diversions to improve the line experience; to hide the queue; to offer higher prices in exchange for a shorter queue; or to allow virtual queuing (e.g., electronic queuing, such as Disney’s Fast Pass system).”

Using queuing theory and the appropriate model, analysts can determine the appropriate number of workers based on line configuration, desired length of lines, customer wait duration, and average service times, says Kleinrock.[l] “Alternatively, visitors can increase their own level of participation in the process, allowing the enterprise to increase the available labour capacity,” offer Pullman and Rodgers. They provide the example of a self-guided audio-tour system for museums, parks, convention centers, arenas, and other tourism sites to reduce the number of guides required.i

A wide range of labour capacity techniques are available that address these problems. Thompsonlix [li] [lii] [liii] offers a comprehensive tutorial on the development of optimal workforce schedules for multiple employee classifications (part-time, breaks, and reliefs). Thompson’s main goal is to minimize employee cost, while meeting customer service-time goals.i Adenso-Dia´zxvii offer an alternative approach that establishes the minimum labour staffing levels below which quality may be affected. In that study, quality was measured according to visitors’ perceptions (see Table 14), based on historical data in a catering sector.i Along those lines, Goodale et al.[liv] (2003) illustrate “a method that schedules labour to maximize profit (creating the most desirable quick-service restaurant configurations from both the visitors’ and management’s perspectives) using consumer utility-based models in an international airport foodcourt.”i “Both of the latter labour-scheduling methods have the ability to include multiple measures of visitor-preferences (such as crowding level, visual displays, layouts, and food variety) in addition to capacity costs and revenues,” contend Pullman and Rodgers.

Klassen and Rohleder ran multiple simulated scenarios that combined a number of labour and visitor flexibility options simultaneously to determine the best combinations of approaches.iv They found that enterprises with limited flexibility in terms of maximum schedulable staff should make use of customer participation, employee cross-training, and price segmentation and should inform or educate customers about other alternatives.iv

Finally, the real-time adjustment of labour scheduleslxiii “refers to methods in which managers observe actual visitor demand and correct any capacity problems as they occur. Here the goal is to improve service and cost performance in real time” (Pullman and Rodgers). For example, Pullman and Rodgers recommend, “if a restaurant’s customer flow is slower than expected, employees can go on break or leave early; if it is busier than expected, employees can defer breaks until a later point in their schedule.”i Real-time schedule adjustments include changing employees’ station assignments, cancelling or changing their breaks, starting their shifts early/late or asking them to leave early/ late, calling in more workers, or cancelling shifts.[lv] Thompson indicates that schedules developed with preset employee breaks outperformed those with real-time break adjustment in terms of overall cost.lxiii However, Hur et al. found that the active adjustment of work schedules is beneficial as long as the direction of demand change is accurately identified.lxv


[i] Schmenner, R., 1986. How can service businesses survive and prosper? Sloan Management Review 27 (3), 21–32.

[ii] Wall, G., 1982. Cycles and capacity: incipient theory or conceptual contradiction? Tourism Management (September), 188–192.

[iii] Saveriades, A., 2000. Establishing the social carrying capacity for the tourist resorts of the east coast of the Republic of Cyprus. Tourism Management 21, 147–156.

[iv] Muller, C.C., 1999. A simple measure of restaurant efficiency. Cornell Hotel and Restaurant Administration Quarterly 40 (June (3)), 31–37.

[v] Kandampully, J., 2000. The impact of demand fluctuation on the quality of service: a tourism industry example. Managing Service Quality 10 (1), 10–18.

[vi] Kilby, T., Fox, G., 1998. Casino Operations Management. Wiley, New York.

[vii] Adenso-Dia´z, B., Gonza´les-Torre, P., Garcia, V., 2002. A capacity management model in service industries. International Journal of Service Industry Management 13 (3), 286–302.

[viii] Simón, F., Narangajavana, Y., Marque´s, D., 2004. Carrying capacity in the tourism industry: a case study of Hengistbury Head. TourismManagement25 (2), 275–283.

[ix] Easton, F., Pullman, M., 2001. Optimizing service attributes: the seller’s utility problem. Decision Sciences 32 (2), 251–275.

[x] Sill, B., 2000. Capacity management: engineering the balance between customer satisfaction, employee satisfaction and company profit. The Consultant, 2nd quarter, 2–11.

[xi] Lawson, F., 1995. Hotels & Resorts: Planning, Design and Refurbishment. The Architectural Press, Oxford, UK.

[xii] Roubi, S., Litteljohn, D., 2004. What makes hotel values in the UK? A hedonic valuation model. International Journal of Contemporary Hospitality Management 16 (3), 175–181.

[xiii] Brown, J., Dev, C., 1999. Looking beyond RevPAR: productivity consequences of hotel strategies. Cornell Hotel and Restaurant Administration Quarterly 40 (2), 23–33.

[xiv] Standards Australia, 1998. Construction and fit-out of food premises. AS/NZS 1234. Sydney, Australia.

[xv] U.S. Food and Drug Administration [USDA], 2005. Equipment, utensils and linen. In: Food code (Chapter 4).

[xvi] Center for Disease Control and Prevention (US), 2001. Vessel sanitation program construction guidelines. Retrieved June 6, 2008 from http://www.cdc.gov/nceh/ vsp/pub/

[xvii] Kimes, S., McGuire, K., 2001. Function-space revenue management: a case study from Singapore. Cornell Hotel and Restaurant Administration Quarterly (December), 33–46.

[xviii] Kimes, S., Robson, S., 2004. The impact of restaurant table characteristics on meal duration and spending. Cornell Hotel and Restaurant Administration Quarterly (November), 333–346.

[xix] Robson, S., 1999. Turning the tables: the psychology of design for high-volume restaurants. Cornell Hotel and Restaurant Administration Quarterly 40 (3), 56–63.

[xx] Wanhill, S., 2003. Economic aspects of developing theme park. In: Fyall, A., Garrod, B., Leask, A. (Eds.), Managing Visitor Attractions: New Directions. ButterworthHeinemann, Oxford, UK, pp. 39–57.

[xxi] Hanlon, J.P., 1989. Hub operations and airline competition. Tourism Management 10 (2), 111–124.

[xxii] Dickson, D., Ford, R., Laval, B., 2005. Managing real and virtual waits in hospitality and service organizations. Cornell Hospitality Quarterly 46 (52), 52–68.

[xxiii] Brown, D.M., Hoover, L.W., 1990. Productivity management in food service: past accomplishments—a future alternative. Journal of the American Dietetic Association 90 (7), 973–979.

[xxiv] Junko, D., 2004. Changes in social strata on the Cunard’s Queen Sisters. In: Proceedings from 10th Asia Pacific Tourism Association Conference, Sasebo, Nagasaki, Japan.

[xxv] Jones, P., Lockwood, A., 2002. The Management of Hotel Operations. Continuum, London.

[xxvi] Creed, P.G., 2001. The potential of foodservice systems for satisfying consumer needs. Innovative Food Science & Emerging Technologies 2, 219–277.

[xxvii] Liu, J., Var, T., 1980. The use of lodging ratios in tourism. Annals of Tourism Research 7 (3), 406–427.

[xxviii] Carey, K., 1989. Tourism development in LDCs: hotel capacity expansion with reference to Barbados. World Development 17 (1), 59–67.

[xxix] Gu, Z., 2003. Analysis of Las Vegas strip casino hotel capacity: an inventory model for optimisation. Tourism Management 24 (3), 309–314.

[xxx] Thompson, G.M., 2002. Optimizing a restaurant’s seating capacity: use dedicated or combinable tables? Cornell Hotel and Restaurant Administration Quarterly 43 (4), 48–57.

[xxxi] Berdichevskiy, V.C., Kapsekin, V.I., 1988. Design of Catering. High Education, Kiev, Ukraine.

[xxxii] Pullman, M., Thompson, G., 2003. Strategies for integrating capacity with demand in service networks. Journal of Service Research 5 (3), 169–183.

[xxxiii] Schnitzler, H., 1982. Purchasing for a new hotel. Cornell Hospitality Quarterly 23 (2), 83–91.

[xxxiv] Wie, B.W., 2005. A dynamic game model of strategic capacity investment in the cruise line industry. Tourism Management 26 (2), 203–217.

[xxxv] Liu, J., Var, T., 1980. The use of lodging ratios in tourism. Annals of Tourism Research 7 (3), 406–427.

[xxxvi] Verma, R., Plaschka, G., Louviere, J., 2002. Understanding customer choices: a key to successful management of hospitality services. Cornell Hotel and Restaurant Administration Quarterly 43 (6), 15–24.

[xxxvii] Sasser, E., 1976. Match supply and demand in service industries. Harvard Business Review 54 (6), 133–140.

[xxxviii] Huete, L., Segarra, J., 1997. Port Aventura. IESE Case #697-054-1. University of Navarra, Barcelona-Madrid.

[xxxix] Sill, B., 1991. Capacity management: making your service delivery more productive. Cornell Hotel and Restaurant Administration Quarterly 31 (4), 76–88.

[xl] Bozarth, C., Handfield, R., 2006. Introduction to Operations and Supply Chain Management. Pearson-Prentice Hall, Upper Saddle River, NJ.

[xli] Vollman, T., Berry, W., Whybark, D.C., Jacobs, F.R., 2005. Manufacturing Planning and Control for Supply Chain Management. McGraw-Hill/Irwin, New York.

[xlii] Lindenmeier, J., Tscheulin, D., 2008. The effects of inventory control and denied boarding on customer satisfaction: the case of capacity-based airline revenue management. Tourism Management 29 (1), 32–43.

[xliii] Sanchez, J., Satir, A., 2005. Hotel yield management using different reservation modes. International Journal of Contemporary Hospitality Management 17 (2), 136–146.

[xliv] Kimes, S., 2000. Revenue management on the links. Cornell Hotel and Restaurant Administration Quarterly (February), 120–127.

[xlv] Sundarapandian, V. (2009). “7. Queueing Theory”. Probability, Statistics and Queueing Theory. PHI Learning. ISBN 8120338448.

[xlvi] Kandampully, J., 2000. The impact of demand fluctuation on the quality of service: a tourism industry example. Managing Service Quality 10 (1), 10–18.

[xlvii] Kimes, S., Barrash, D., Alexander, J., 1999. Developing a restaurant revenue-management strategy. Cornell Hotel and Restaurant Administration Quarterly (October), 18–29.

[xlviii] Getz, D., Nilsson, P.A., 2004. Responses of family businesses to extreme seasonality in demand: the case of Bornholm, Denmark. Tourism Management 25, 17–30.

[xlix] Thompson, G.M., 1998a. Labour scheduling, part 1: forecasting demand. Cornell Hotel and Restaurant Administration Quarterly 39 (5), 22–31.

[l] Kleinrock, L., 1975. Queuing Systems: Theory, vol. 1. Wiley, New York.

[li] Thompson, G.M., 1998b. Labour scheduling, part 2: knowing how many on-duty employees to schedule. Cornell Hotel and Restaurant Administration Quarterly 39 (6), 26–37.

[lii] Thompson, G.M., 1999a. Labour scheduling, part 3: developing a workforce schedule. Cornell Hotel and Restaurant Administration Quarterly 40 (1), 86–96.

[liii] Thompson, G.M., 1999b. Labour scheduling, part 4: controlling workforce schedules in real time. Cornell Hotel and Restaurant Administration Quarterly 40 (3), 85–96.

[liv] Goodale, R., Verma, R., Pullman, M., 2003. A market utility-based model for capacity scheduling in mass services. Production and Operations Management 12 (2), 165–185.

[lv] Hur, D., Mabert, V., Bretthauer, K., 2004. Real-time work schedule adjustment decisions: an investigation and evaluation. Production and Operations Management Journal 13 (4), 322–339.

[i] Pullman, Madeleine E., Rodgers, Svetlana. (2010). International Journal of Hospitality Management. Capacity management for hospitality and tourism: A review of current approaches. March 2010. Volume 29(1): 177-187. https://www.researchgate.net/publication/247123990_Capacity_management_for_hospitality_and_tourism_A_review_of_current_approaches (Accessed 8 November 2019).

[ii] Crandall, R., Markland, R., 1996. Demand management: today’s challenge for service industries. Production and Operations Management 5 (1), 106–120.

[iii] Taylor, G., 1980. How to match plant with demand: a matrix for marketing. International Journal of Tourism Management (March), 56–60.

[iv] Klassen, K., Rohleder, T., 2002. Demand and capacity management decisions in services: how they impact on one another. International Journal of Operations & Production Management 5 (6), 527–548.

[v] Bozarth, C., Handfield, R., 2006. Introduction to Operations and Supply Chain Management. Pearson-Prentice Hall, Upper Saddle River, NJ.

[vi] Davis, M., Heineke, J., 2005. Operations Management: Integrating Manufacturing and Services, fifth ed. McGraw-Hill/Irwin, New York.

[vii] Porter, M., 1996. What is strategy? Harvard Business Review (November–December), 61–78.

[viii] Schroeder, R., 2006. Operations Management: Contemporary Concepts and Cases, third ed. McGraw-Hill/Irwin, New York.

[ix] Lawson, F., Baud-Bovy, M., 1977. Tourism and Recreational Development: A Handbook of Physical Planning. The Architectural Press, Oxford, UK, pp. 183–186.

[x] Mathieson, A., Wall, G., 1982. Tourism: Economic, Physical, and Social Impacts. Longman, New York.