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Managing Capacity and Demand

• Managing dynamic demand

• Service capacity is perishable

• Yield Management

Shin‐Ming GuoNKFUST

Case: Disneyland Paris

Established in 1992

Overestimate the demand

Too many hotel rooms lead to high operating cost

Need more space for bus parking

Unbalanced workforce scheduling

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Service Capacity

Participation: Need to be near customers

Simultaneity: Inability to transport services

Perishability: Inability to store services

Heterogeneity: Volatility of demand

Capacity: amount of output over a period of time

Often use resource input  to measure capacity

Focus: Matching Capacity with Demand

• Demand can vary and is unpredictable.

• Capacity is inflexible and maybe costly.

• Demand < Capacity  Impossible to stock service

• Demand > Capacity  Customers may not wait for service

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Economic Consequences of Mismatch

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Air travel Emergency Room Retailing

Supply Seats on specific flight

Medical service Consumerelectronics 

Demand Travel for specific time & destination 

Urgent need for medical service 

Kids buying video games

SupplyExceedsDemand 

Empty seat Doctors, nurses, and infrastructure are under‐utilized 

High inventory costs

DemandExceeds Supply

Overbooking; Profit loss

Crowding and delaysin the ER, Deaths

Foregone profit; 

Consumer dissatisfaction  

Capacity Utilization vs. Service Quality

Optimal operating level  70% of Design capacity

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Matching Supply and Demand for Services

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DEMANDStrategies

2 Partitioningdemand

5 Developingcomplementary

services

3 Differential Pricing

6 Developingreservationsystems 

12 YieldManagement

CapacityStrategies

9 Cross‐training

employees

8 Franchising

7 Increasingcustomer

participation 

11 Schedulingwork shifts10 Using

part‐timeemployees

1 Managing Variability

4 Promoting Off Peak Demand

1. Managing Customer-induced Variability

Type of Variability

Accommodation Reduction

Arrival Provide generous staffing Require reservations

Capability Adapt to customer skill levels

Target customers based on capability

Request Cross‐train employees Limit service breadth

Effort Do work for customers Reward increased effort

SubjectivePreference

Diagnose expectations and adapt

Persuade customers to adjust expectations

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2. Segmenting Demand

0

20

40

60

80

100

120

140

Mon. Tue. Wed. Thur. Fri.

BeforeSmoothingAfterSmoothing

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Smoothing Demand by AppointmentScheduling

Day Appointments

Monday 84Tuesday 89Wednesday 124Thursday 129Friday 114

Too many walk‐in patients on Mondays at a health clinic.

3. Differential Pricing

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4. Promoting Off-Peak Demand

Different sources of demandHotel: conventions for business or professional groups during the off‐season.

Avoid waiting timesDepartment store: shop early and avoid the rush.

5. Developing Complementary Services

• A new service is the complementor if customers value your service more when they already have purchased the existing service.

• Movie theaters offer popcorns and soft drinks.

• A new service is the complementor if it results in a more uniform demand.

• Restaurants offer the “afternoon tea” service.

• Travel agency: Australia and New Zealand Tours

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6. Reservation and Overbooking

• Taking reservations is like preselling the service.

• Reservations may benefit consumers by reducing waiting and guarantee service availability.

• Approximately 50% of reservations get cancelled.

• Multiple reservations, late arrivals, no‐shows.

• Customers can cancel or postpone reservations— with a penalty

• Airlines and hotels can overbook reservations— with a penalty

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Overbooking to Protect Revenue

Overbooking—accept more reservations than supply

Example: On average there would be 10 cancellations or no‐shows. So the hotel can accept 10 more reservations.

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Too much overbooking: some customers may have to be denied a seat even though they have a confirmed reservation.

Too little overbooking: waste of capacity, loss of revenue

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Example: Surfside Hotel

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expected number of no‐shows = 0(0.07)+1(0.19)+…+9(0.01)=3.04

Expected opportunity loss = 3.04 × $40 = $121.60

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Cost of too many overbooking: Co=$100 for accommodation at some other hotel and additional compensation.

Cost of not enough overbooking: Cu=$40 per room.

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Overbooking Solution

• Critical ratio 

• Find x such that x is the largest number that satisfies P(number of no‐shows < x) ≤ 0.286

• Optimal number of overbooking = 2

• There is about a 26% chance that the hotel will have more customers than rooms.

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286.010040

40 ou

u

CCC

Strategies for Managing Capacity

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7. Customer Participation

Customer participates actively in the service process.

Objectives:

• Cost reduction (less personnel is needed)

• Capacity becomes more “variable”, according to demand

Disadvantages:

• Customer expects quicker service

• Customer expects low prices (compensation for his help)

• Quality of customers “work” cannot be controlled by company (e.g., customer can leave his waste on the table)

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8. Franchising

Benefits to the FranchisorLess financial investment

Quick expansion to other marketsEconomies of Scale

ProblemsFranchisee does not receive proper trainingFranchisee fails to follow the contract or regulations

Franchisor does not have new product development

Franchisor fails to provide support

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Economies of Scale for Service Industry

• Chain stores lead to buying power.

• Travel agency buy airline tickets and hotel rooms in bulks to get deeper discount.

• Small business can form an alliance to increase the bargaining power against big suppliers.

Competing retail stores or restaurants located in the same area may attract more consumers.

Commuter cleaning

Economies of scale may hurt service quality 

9. Cross-training & Part-time Employees

Training employees to be able to do different tasks

• Demand peaks: Each employee performs his specialized work (e.g., cashier in a supermarket)

• Low demand: Employee performs additional tasks: Job is 

enlarged (e.g., filling the shelves in a supermarket)

Using part‐time employees

• When demand peaks can be foreseen: Additional staff can be employed for these times (e.g., lunchtime in restaurants)

• Skills needed low: Students can be taken (e.g., bakery)

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10. Adjustable Capacity

• Airlines: Different aircrafts

• Rental Cars: ability to move cars around. 

Workshift Scheduling 

• The peak to valley variation is 125 to 1.

• Carefully schedule the workforce so that the required service level can be maintained with the minimal cost.

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Convert Demand and Schedule Shifts

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Scheduling Consecutive Days Off

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Mon Tue Wed Thu Fri Sat Sun

forecast 4 3 2 4 3 1 24 3 2 4 3 1 2

3 2 1 3 2 1 2

2 1 0 2 2 1 1

1 1 0 1 1 0 0

A

B

C

D

12. Revenue Management

• Return = Revenue – Operations Cost

= Throughput  Price – Fixed Costs –Throughput  Variable Costs

– Reduce fixed costs

– Reduce variable costs

– Increase price

– Increase throughput

• If capacity is fixed and perishable, fixed costs are high and variable costs are low, increasing price and/or throughput to improve profitability.

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Case: Increase Revenue with Fixed Capacity

• The Park Hyatt Philadelphia, 118 King/Queen rooms.

• Regular fare is rH= $225 (high fare) targeting business travelers.

• Hyatt offers a rL= $159 (low fare) discount fare for a mid‐week 

stay targeting leisure travelers. 

• Demand for low fare rooms is abundant.

• Most of the high fare demand occurs 

only within a few days of the actual stay.

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Booking Limits and Yield Management

• Choice 1: Do not accept low fare reservation.  Hope that high fare customers will eventually show up.

• Choice 2: Accept low fare reservations without any limit. 

• Choice 3: Accept low fare reservations but reserve rooms for high fare customers

• Objective: Maximize expected revenues by controlling the sale of low fare rooms. 

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Yield Management: Airline Pricing

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• Carriers typically fill 72.4% of seats and have a break‐even load of 70.4%.

• Very high fixed costs and perishable capacity.

Example: Blackjack Airline

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d = demand for full fare ($69) ~ N(60, 152)

Expected revenue=6960=$4140

Demand for “gamblers fare” ($49) is abundant

Expected revenue=4995=$4655

Decision:

x = seats reserved for full fare passengers

95 seats

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Optimal Booking Solution

•

• (z)=P(d < x)=0.29 z= -0.55

31

)1,0(~15

60 Nddz

5115)55.0(60

55.01560

x

xz

29.04920

20)( ou

u

CCC

xdP

Cost of too many full fare seats reserved: Co=$49

Cost of not enough full fare seats reserved: Cu=$20

Optimal Revenue for Blackjack Airline

• Z= ‐0.55  Normal Loss Function L(z)

=NORMDIST(z,0,1,0)‐z*(1‐NORMSDIST(z)) =0.7328

• For full fare customer 

expected loss  (due to not enough seats reserved) =L(z)∙=0.7328=10.99

expected sales + expected loss = expected full fare demand

expected sales=expected demand‐expected loss   =60‐10.99=49.01

• Expected total revenue=49.01*69+(95‐51)*49 =$5537

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Yield Management for a Resort Hotel

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Ideal Characteristics for Yield Management

• Relatively Fixed Capacity

• Ability to Segment Markets

• Perishable Inventory

• Product Sold in Advance

• Fluctuating Demand

• Low Marginal Sales Cost and High Capacity Change Cost

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