Service DesignService Design

Beni AsllaniUniversity of Tennessee at Chattanooga

Operations Management - 6th EditionOperations Management - 6th Edition

Chapter 5Chapter 5

Roberta Russell & Bernard W. Taylor, IIIRoberta Russell & Bernard W. Taylor, III

Lecture outlineLecture outline

Service economy Characteristics of services Service design process Tools for service design Waiting line analysis for service

improvement

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Service EconomyService Economy

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Source: U.S. Bureau of Labor Statistics, IBM Almaden Research Center

GDP – Employment by sectorGDP – Employment by sector

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Characteristics of ServicesCharacteristics of Services

Services acts, deeds, or performances

Goods tangible objects

Facilitating services accompany almost all purchases of goods

Facilitating goods accompany almost all service purchases

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Continuum from Goods to ServicesContinuum from Goods to Services

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Source: Adapted from Earl W. Sasser, R.P. Olsen, and D. Daryl Wyckoff, Management of Service Operations (Boston: Allyn Bacon, 1978), p.11.

Characteristics of Services (cont.)

Services are intangible

Service output is variable

Services have higher customer contact

Services are perishable

Service inseparable from delivery

Services tend to be decentralized and dispersed

Services are consumed more often than products

Services can be easily emulated

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Service Design Process

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Service Design Process (cont.)

Service concept purpose of a service; it defines target market and

customer experience Service package

mixture of physical items, sensual benefits, and psychological benefits

Service specifications performance specifications design specifications delivery specifications

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Service Process MatrixService Process Matrix

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High v. Low Contact Services

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Convenient to customer

Facility location

Design Decision

High-Contact Service Low-Contact Service

Near labor or transportation source

Source: Adapted from R. Chase, N. Aquilano, and R. Jacobs, Operations Management for Compensative Advantage (New York:McGraw-Hill, 2001), p. 210

Facility layout

Must look presentable, accommodate customer needs, and facilitate interaction with customer

Designed for efficiency

High v. Low Contact Services (cont.)

Quality control More variable since customer

is involved in process; customer expectations and perceptions of quality may differ; customer present when defects occur

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Design Decision

High-Contact Service Low-Contact Service

Measured against established standards; testing and rework possible to correct defects

Source: Adapted from R. Chase, N. Aquilano, and R. Jacobs, Operations Management for Compensative Advantage (New York:McGraw-Hill, 2001), p. 210

Capacity Excess capacity required to handle peaks in demand

Planned for average demand

High v. Low Contact Services (cont.)

Worker skills Must be able to interact well with customers and use judgment in decision

making

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Design Decision High-Contact Service Low-Contact Service

Technical skills

Source: Adapted from R. Chase, N. Aquilano, and R. Jacobs, Operations Management for Compensative Advantage (New York:McGraw-Hill, 2001), p. 210

Scheduling Must accommodate customer schedule

Customer concerned only with completion

date

High v. Low Contact Services (cont.)

Service process

Mostly front-room activities; service may change during delivery in response to customer

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Design Decision

High-Contact Service Low-Contact Service

Mostly back-room activities; planned and executed with minimal interference

Source: Adapted from R. Chase, N. Aquilano, and R. Jacobs, Operations Management for Compensative Advantage (New York:McGraw-Hill, 2001), p. 210

Service package

Varies with customer; includes environment as well as actual service

Fixed, less extensive

Tools for Service DesignTools for Service Design

Servicescapes space and function ambient conditions signs, symbols, and

artifacts

Quantitative techniques

Service blueprinting line of influence line of interaction line of visibility line of support

Front-office/Back-office activities

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Service BlueprintingService Blueprinting

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Service Blueprinting (Con’t)Service Blueprinting (Con’t)

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Elements of Waiting Line AnalysisElements of Waiting Line Analysis

Operating characteristics average values for characteristics that describe

performance of waiting line system Queue

a single waiting line Waiting line system

consists of arrivals, servers, and waiting line structure

Calling population source of customers; infinite or finite

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Elements ofElements ofWaiting Line Analysis (cont.)Waiting Line Analysis (cont.) Arrival rate (λ)

frequency at which customers arrive at a waiting line according to a probability distribution, usually Poisson

Service time (μ) time required to serve a customer, usually described by

negative exponential distribution Service rate must be shorter than arrival rate (λ < μ) Queue discipline

order in which customers are served Infinite queue

can be of any length; length of a finite queue is limited

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Elements ofElements ofWaiting Line Analysis (cont.)Waiting Line Analysis (cont.)

Channels number of

parallel servers for servicing customers

Phases number of

servers in sequence a customer must go through

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Operating CharacteristicsOperating Characteristics

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Operating characteristics are assumed to approach a steady state

Traditional Cost RelationshipsTraditional Cost Relationships

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as service improves, cost increases

Psychology of WaitingPsychology of Waiting Waiting rooms

magazines and newspapers

televisions Bank of America

mirrors Supermarkets

magazines “impulse purchases”

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Disney costumed characters mobile vendors accurate wait times special passes

Psychology of Waiting (cont.)Psychology of Waiting (cont.)

Preferential treatment Grocery stores: express lanes for customers with

few purchases Airlines/car rental agencies: special cards available

to frequent-users or for an additional fee Phone retailers: route calls to more or less

experienced salespeople based on customer’s sales history

Critical service providers services of police department, fire department, etc. waiting is unacceptable; cost is not important

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Waiting Line ModelsWaiting Line Models

Single-server model simplest, most basic waiting line structure

Frequent variations (all with Poisson arrival rate) exponential service times general (unknown) distribution of service times constant service times exponential service times with finite queue exponential service times with finite calling

population

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Basic Single-Server ModelBasic Single-Server Model

Assumptions Poisson arrival rate exponential service

times first-come, first-

served queue discipline

infinite queue length infinite calling

population

Computations λ = mean arrival rate μ = mean service rate n = number of

customers in line

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Basic Single-Server Model (cont.)Basic Single-Server Model (cont.)

probability that no customers are in queuing system

probability of n customers in queuing system

average number of customers in queuing system

average number of customers in waiting line

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( )( )PP 00 = 1 – = 1 – λλ

μμ

( ) ( )( )( ) ( )( )PPnn = ∙= ∙ PP 00 = = 1 – 1 –

λλ nn λλ nn λλ

μμ μμ μμ

LL = = λλ

μμ – – λλ

LLqq = = λλ 22

μμ ( (μμ – – λλ ))

Basic Single-Server Model (cont.)Basic Single-Server Model (cont.)

average time customer spends in queuing system

average time customer spends waiting in line

probability that server is busy and a customer has to wait (utilization factor)

probability that server is idle and customer can be served

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1 L1 L

μμ – – λλ λλWW = = = =

λλ

μμ ( (μμ – – λλ ))

WW qq ==

λλ

μμρρ = =

II = 1 – = 1 – ρρ

= 1 – = = 1 – = PP 00

λλ

μμ

Basic Single-Server Model ExampleBasic Single-Server Model Example

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Basic Single-Server Model Example Basic Single-Server Model Example (cont.)(cont.)

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Service Improvement AnalysisService Improvement Analysis

waiting time (8 min.) is too long hire assistant for cashier?

increased service rate hire another cashier?

reduced arrival rate

Is improved service worth the cost?

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Basic Single-Server Model Example: Basic Single-Server Model Example: ExcelExcel

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Advanced Single-Server ModelsAdvanced Single-Server Models

Constant service times occur most often when automated equipment or

machinery performs service

Finite queue lengths occur when there is a physical limitation to length of

waiting line

Finite calling population number of “customers” that can arrive is limited

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Advanced Single-ServerAdvanced Single-ServerModels (cont.)Models (cont.)

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Basic Multiple-Server ModelBasic Multiple-Server Model

single waiting line and service facility with several independent servers in parallel

same assumptions as single-server model sμ > λ

s = number of servers servers must be able to serve customers faster than

they arrive

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Basic Multiple-Server Model (cont.)Basic Multiple-Server Model (cont.)

probability that there are no customers in system

probability of n customers in system

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1 λ n

s!sn – s μ

P0, for n ≤ s

1 λ n

n! μ( ){ ( )P0, for n > s

Pn =

1 λ n 1 λ s sμ

n! μ s! μ sμ - λ( ) ( )( )n = s – 1

n = 0

1P0 = ∑

+

Basic Multiple-Server Model (cont.)Basic Multiple-Server Model (cont.)

probability that customer must wait

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( )( ) 1 1 λ λ s s ssμμ

ss! ! μμ ssμμ – – λλ

PPww = = P P 00

λμλμ ( (λλ //μμ )) s s λλ

((ss – 1)! ( – 1)! (ssμμ – – λλ )) 22 μμ

LL = = PP 0 0 + +

LL

λλWW = =

LLqq = = LL – –λλ

μμ

1 L1 Lqq

μμ λλWWqq = = WW – = – =

ρρ = = λλssμμ

Basic Multiple-Server Model ExampleBasic Multiple-Server Model Example

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Basic Multiple-Server Model Example Basic Multiple-Server Model Example (cont.)(cont.)

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Basic Multiple-Server Model Example Basic Multiple-Server Model Example (cont.)(cont.)

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Basic Multiple-Server Model Example Basic Multiple-Server Model Example (cont.)(cont.)

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Basic Multiple-Server Model Example Basic Multiple-Server Model Example (cont.)(cont.)

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Basic Multiple-Server Model Basic Multiple-Server Model Example (cont.)Example (cont.)

To cut wait time, add another service representative now, s = 4

Therefore:

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Multiple-Server Waiting LineMultiple-Server Waiting Linein Excelin Excel

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