Capacity and Demand Management

MD254

Service Operations

Professor Joy Field

Strategic Role of Capacity Decisions in Services

A capacity expansion strategy can be used proactively to: Create demand through supply (e.g. JetBlue, Dunkin Donuts) Lock out competitors, especially where the market is too small for

two competitors (e.g. WalMart) Get down the learning curve to reduce costs (e.g. Southwest

Airlines) Support fast delivery and flexibility (e.g. Mandarin Oriental)

A lack of short-term capacity can generate customers for the competition (e.g. restaurant staffing)

Capacity decisions balance costs of lost sales if capacity is inadequate against operating losses if demand does not reach expectations.

Strategy of building ahead of demand is often taken to avoid losing customers.

Capacity Planning Challenges in Services

Inability to create a steady flow of demand to fully utilize capacity

Enforced idle capacity if no customers are in the service system

Customers are participants in the service and the level of congestion impacts perceived quality.

Customer arrivals fluctuate and service demands also vary.

Capacity is typically measured in terms of (bottleneck) resources rather than outputs (e.g. number of airplane seats available per day rather than number of passengers flown per day).

Customer-Induced Demand and Service Time Variability

Arrival: customer arrivals are independent decisions not evenly spaced.

Capability: the level of customer knowledge and skills and their service needs vary

Request: uneven service times result from unique demands.

Effort: level of commitment to coproduction or self-service varies.

Subjective Preference: personal preferences introduce unpredictability.

Modeling Service Delivery Systems

Using Queuing Models Customer population The source of input to the service system Whether the input source is finite or infinite Whether the customers are patient or impatient

The service system Number of lines - single vs. multiple lines Arrangement of service facilities – servers, channels, and phases Arrival and service patterns – e.g. for many service processes,

interarrival and service times are exponentially distributed (arrival and service rates are Poisson distributed)

Priority rule (queue discipline) Static

First-come, first-served (FCFS) discipline Dynamic

Individual customer characteristics: e.g. earliest due date (EDD), shortest processing time (SPT), priority, preemptive

Status of the queue, e.g. number of customers waiting, round robin

Queue Configurations and Service Performance

Multiple Queue Single queue

Take a Number 3 4

8

2

6 10

1211

5

79

Enter

Arrangement of Service FacilitiesChannels and Phases

Service facility Server arrangement

Parking lot Self-serve

Cafeteria Servers in series

Toll booths Servers in parallel

Supermarket Self-serve, first stage; parallel servers, second stage

Hospital Many service centers in parallel and series, not all used by each patient

Distribution of Patient Interarrival Times

for a Health Clinic

0

10

20

30

40

1 3 5 7 9 11 13 15 17 19

Patient interarrival time, minutes

Rel

ativ

e fr

eque

ncy,

%

Patient interarrival times approximate an exponential distribution.

Temporal Variation in Arrival Rates

0

0.5

1

1.5

2

2.5

3

3.5

1 3 5 7 9 11 13 15 17 19 21 23

Hour of day

Aver

age c

alls p

er h

our

60708090

100

110120130140

1 2 3 4 5

Day of weekPe

rcen

tage

of a

vera

ge d

aily

ph

ysic

ian

visi

ts

Ambulance Calls by Hour of Day

Physician Arrivals by Day of Week

Queue Discipline

Queuediscipline

Static(FCFS rule)

Dynamic

Selectionbased on status

of queue

Selection basedon individual

customerattributes

Number of customers

waitingRound robin Priority Preemptive

Processing timeof customers

(SPT or cµ rule)

Single-Server, Exponential Interarrival

and Service Times (M/M/1) ModelAssumptions: Number of servers = 1 Number of phases = 1 Input source: infinite, no balking or reneging Arrivals: mean arrival rate = ; mean interarrival time = Service: mean service rate = ; mean service time = Waiting line: single line; unlimited length Priority discipline: FCFS

/1

/1

Single-Server Operating Characteristics

Average utilization:

Probability that n customers are in the system:

Probability of less than n customers in the system:

Average number of customers in the system:

Average number of customers in line:

Average time spent in the system:

Average time spent in line:sq WW

nn )1(P

nn 1P

sL

sq LL

1

Ws

Multiple-Server (M/M/c) Model

Assumptions: Number of servers = M Number of phases = 1 Input source: infinite, no balking or reneging Arrivals: mean arrival rate = ; mean interarrival time = Service: mean service rate = ; mean service time = Waiting line: single line; unlimited length Priority discipline: FCFS

/1

/1

Multiple-Server Operating Characteristics

Average utilization:

Probability that zero customers are in the system: Probability that n customers are in the system:

Average number of customers in line:

Average time spent in line/system:

Average number of customers in the system:

Average waiting time for an arrival not immediately served:

Prob. that an arrival will have to wait for service:

M

1M1M

0n

n

0 ])1(!M

)/(

!n

)/([P

Mnfor PM!M

)/( ,Mn0for P

!n

)/(0Mn

n

0

n

2

M0

q)1(!M

)/(PL

1WW,

LW qs

qq

ss WL

M

1Wa

a

qw W

WP

Capacity Utilization and Capacity Squeeze A capacity squeeze is the breakdown in the ability of the operating

system to serve customers in a timely manner as the capacity utilization approaches 100%. As the variability in arrival and service rates increases, a capacity squeeze occurs at a lower capacity utilization.

100

10

8

6

4

2 0

0 1.0

With:

Ls 1Then:

Ls

0 00.2 0.250.5 10.8 40.9 90.99 99

Capacity utilization

System line length

Service System Cost TradeoffTotal Cost of Service

The total cost of service reflects both the firm’s capacity cost as well as the customers’ cost of waiting. Service processes should be designed to minimize the sum of these two costs.

How can the economic cost of customer waiting be determined?

Let: Cw = Hourly cost of waiting customer

Cs = Hourly cost per server

C = Number of servers

Total cost/hour = Hourly service cost + Hourly customer waiting cost

Total cost/hour = Cs C + Cw Ls

Queuing Model Takeaways Variability in arrivals and service times contribute equally to

congestion as measured by Lq. Even though servers will be idle some of the time, there will be

customer lines and waits, on average. These lines/waits will get very long very quickly as capacity utilization approaches 100%. Given the potential for a capacity squeeze as capacity utilization

approaches 100%, service firms typically design their processes with a capacity cushion (i.e., the amount of capacity above the average expected demand). The greater the variability in arrival/service rates, the larger the capacity cushion needed for a given service level.

To improve system performance (waits and line lengths): A single queue vs. multiple queues with multiple channels. More servers can be added (reducing capacity utilization but at a

higher operating cost). A fast single server is preferred to multiple-servers with the same

overall service rate.

Managing Waiting Lines

SIX MONTHS Waiting at stoplights

EIGHT MONTHS Opening junk mail

ONE YEAR Looking for misplaced objects

TWO YEARS Reading E-mail FOUR YEARS Doing housework

FIVE YEARS Waiting in line

SIX YEARS Eating

In a lifetime, the average person will spend:

The Psychology of Waiting

People dislike “empty” time – Fill this time in a positive way.

Service-related diversions convey a sense that the service has started (e.g. handing out menus).

Waiting can induce anxiety in some customers – Reduce anxiety by providing information to the customer (e.g. expected wait times).

Customers want to be treated “fairly” while waiting – First-come-first-served (FCFS) queuing discipline or logical prioritization process (e.g. triage)

Managing the Customer Waiting Experience

Conceal the queue from the customer. Engage the customer in co-production tasks during

the wait. Provide diversions during the wait. Serve priority customers or customers who are

willing to plan ahead faster. Automate standard services to enable self-service. Manage waiting time perceptions – under promise,

over deliver.

Managing Demand and Capacity to Reduce Lines and Waiting

Times

Yieldmanagement

MANAGINGDEMAND

SegmentingdemandDeveloping

complementaryservices

Offeringprice

incentivesReservationsystems andoverbooking

Promoting off-peakdemand

MANAGINGCAPACITY

Cross-training

employees

Increasingcustomer

participationSharingcapacity

Schedulingwork shifts

Creatingadjustablecapacity

Usingpart-time

employees

Managing Demand

Segmenting demand (e.g. random vs. scheduled arrivals)

Offering price incentives (e.g. lower matinee pricing at movie theaters)

Promoting off-peak demand (e.g. use of a resort hotel during the off-season for business or professional groups)

Developing complementary services (e.g. HVAC) Reservation systems and overbooking (tradeoff

between opportunity cost of unused capacity and costs of not honoring an overbooked reservation)

Managing Capacity

Increasing customer participation (e.g. e-commerce) Scheduling work shifts (based on historical demand

patterns and desired service level) Creating adjustable capacity (e.g. Tesco online

grocery fulfillment) Using part-time employees (e.g. during tax season) Cross-training employees (to increase workforce

flexibility and leverage capacity to provide additional value-added services)

Sharing capacity (e.g. gate-sharing arrangements)

Flow Management

Flow management focuses on relieving bottlenecks so that customers can move more smoothly and quickly through the service process. How can the flow of this service process be improved?

Resource-side Demand-side

CustomersCustomers

(highly variable arrival rate, average=20/hour)

40/hour 40/hour20/hour

Three stage service process, average service rates:

Maximizing Utilization vs. Flow Management

Compare and contrast the process performance with a maximizing utilization vs. flow management approach. Why does flow management usually improve capacity

utilization, but maximizing utilization often results in poor flow?

CustomersCustomers 40/hour 40/hour20/hour

Yield Management

Yield management attempts to dynamically allocate fixed capacity to match the potential demand in various market segments to maximize revenues and profits.

Although airlines were the first to develop yield-management, other capacity-constrained service industries (e.g. hotels, car rental firms, cruises) also use yield management.

Possible ethical issues associated with yield management? (http://en.wikipedia.org/wiki/Yield_management)

Ideal Characteristics for Yield Management

Relatively fixed capacity Ability to segment markets (i.e., discount

allocation) Perishable inventory (i.e., potential for

“spoilage”) Product sold in advance Fluctuating demand Low marginal fulfillment costs and high

marginal capacity change costs