OM-08-ServiceProcessesWaitingLines.ppt

8/10/2019 OM-08-ServiceProcessesWaitingLines.ppt

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Services Processes and

Waiting Line AnalysisSelected Slides from Jacobs et al, 9thEdition

Operations and Supply Management

Chapter 8 and 8AEdited, Annotated and Supplemented by

Peter Jurkat


2/23

Service Businesses

Customer is the entire focus of attentiona common

definition of quality is satisfaction of the customer (moreon quality later)

Facilities-based services: Where the customer must go tothe service facility

Field-based services: Where the production andconsumption of the service takes place in the customersenvironment

A service businessis the management of organizations whose primary

business requires interaction with the customer to produce the service

8-2


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Characteristics of Workers, Operations, and Innovations Relative to the Degree of

Customer/Service Contact

8-3


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8-4Service Blueprint, Failure Anticipation, and Poka-Yokes

Complete blueprint (p262-3)identifies 16 failure opportunities


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8-5


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Three Contrasting Service Designs

The production line approach (ex.McDonalds)

The self-service approach (ex. automaticteller machines)

The personal attention approach (ex.Ritz-Carlton Hotel Company)

8-6


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Well Designed Services

1. Each element of the servicesystem is consistent withthe operating focusof thefirm

2. It is user-friendly

3. It is robust(avoid failures,poka-yokes)

4. It is structured so that

consistent performancebyits people and systems iseasily maintained

5. It provides effective linksbetween the back officeand the front office so thatnothing falls between[sic]the cracks

6. It manages the evidenceofservice quality in such away that customers see thevalue of the service

provided

7. It is cost-effective

Lets consider Problem 8.4, p272


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Behavior and Guarantees

The front-end and back-endof the encounter are notcreated equal

Segment the pleasure,combine the pain

Let the customer control theprocess

Pay attention to norms andrituals

People are easier to blame

than systems

Let the punishment fit thecrime in service recovery(task error vs. treatmenterror vs.

Recent research suggests:

Any guarantee is betterthan no guarantee

Involve the customer aswell as employees in thedesign

Avoid complexity orlegalistic language

Do not quibble or wrigglewhen a customer invokesa guarantee

Make it clear that you arehappy for customers toinvoke the guarantee


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Waiting Lines

Almost all services can have waiting lines, evenalong manufacturing line

Waiting lines involve both layout and service

management Can be the most damaging of service failures

since customer never gets to experience the

service Waiting lines also called queues (first in, first out)

Trade-off: more service (cost) vs. longer waits

(customer dissatisfaction)


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Managing Queues

1. Determine an acceptablewaiting time for yourcustomers

2. Try to divert your

customers attentionwhen waiting

3. Inform your customers ofwhat to expect

4. Keep employees not

serving the customersout of sight

5. Segment customers

6. Train your servers to befriendly

7. Encourage customers tocome during the slack

periods

8. Take a long-termperspective towardgetting rid of the queues


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Components of the Queuing System

Customer

Arrivals

Servers

Waiting Line

Servicing System

Exit

Queue or


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Customer Service Population Sources

Population Source

FiniteInfinite

(without known bound)

Example: Number of

machines needing

repair when a

company only has

three machines.

Example: The

number of people

who could wait in a

line for gasoline.

Arrival Processes: (usually measured by time between arrivals)

Constant (e.g., assembly line)

Deterministic (e.g., based on occurrence of another event)

Random/Stochastic (e.g., Exponential, Erlang)

Batched (e.g., elevator, bus load at rest stop)Depends on number in system (e.g., machine repair)


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Service Pattern

Service

Pattern

Constant Variable

Example: Items

coming down anautomated assembly

line.

Example: People

spending timeshopping.

Same classification as arrival process


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The Queuing System

Queue Discipline

Length

Number of Lines &

Line Structures

Service Time

Distribution

Queuing

System

Single Q, single S

Single Q, multiple S

Multiple Qs, multiple Ss,

w/ Q switching

First in, first out (FIFO)First in, last out (LIFO)

Various prioritiesConstant inter-arrival times

Random

Event dependent


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Examples of Line Structures

Single Channel

Multichannel

Single

Phase

Multiphase

(Sequential Servers)

One-personbarber shop

Car wash

Hospital

admissions

Bank tellers

windows


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Degree of Patience

No Way!

BALK

No Way!

RENEG


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12/9/2014 MPJ/UNM CS452/Mgt 532 I. Introduction 17

Examples

Service Systems: Traffic on Networks: messages to/from computers, cars on

roads/rails, airplanes to/from airports/gates, ships to/fromharbors/piers, elevators

Retail/Service: stores selling goods, service/repair shops Manufacturing Systems:

Primarily job shops, piece work, mass customization

Appliances, Automobiles/Trucks, Toys, Clothing

Logistics/inventory/distribution/MRP


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Notation

Many combinations of arrival and serviceprocesses, queue disciplines, populations, etc.

Standard notation: A/S/c/N/K/QdisciplineA: Arrival Process ; e.g.,C for constant, M for Markov (exponential),

Ekfor Erlang, G for arbitraryS: Server Process ; e.g.,C for constant, M for Markov (exponential),

Ekfor Erlang, G for arbitraryc: Number of ServersN: System Capacity ; both queues and server stationsK: Size of Calling PopulationQueue Discipline: FIFO, LIFO, various priorities

M/M/1///FIFO default, shown as M/M/1 Various A/S distributions possible; most frequent

are constant, exponential, Gamma, empirical


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Poisson Process

Inter-arrival time isexponentially distributed

Completely determined byaverage time between arrivals

Easy to specify (count arrivalsand divide by time period)

Equivalent to exponential

inter-arrival time

Provides probability of a

given number of arrivals in

unit time


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Notation: Infinite Queuing: Models 1-3

lineintingnumber waiAverage

serversingleafor

ratesevicetoratearrivaltotalofRatio==

arrivalsbetweentimeAverage

timeserviceAverage

rateService=

rateArrival=

1

1

Lq

See Exhibit 8A.8, p26


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Infinite Queuing Models 1-3 (Continued)

lineinwaitingofyProbabilit

systeminunitsexactlyofyProbabilit

channelsserviceidenticalofNumber=

systemin theunitsofNumber

served)betotime(includingsystemintimetotalAverage

lineinwaitingtimeAverage=

served)beingthose(including

systeminnumberAverage=s

Pw

nPn

S

n

Ws

Wq

L


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Notice how sharply

the average length ofthe queue grows with

increasing average

utilization

For average > .7short term increases in

arrival () and/or

service () can make

queues so long thatrecovery is very long

or may never happen

Utilization


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Calculating Performance Different models and conditions will generally

dictate different equations for each performancemeasure

Most situations fall into one of four models (allassume FIFO):

Single server, single queue (SSQ: M/M/1) Multiple servers, single queue (M/M/c)call center

Finite system capacity (M/M/c/N)

Finite population (M/M/c/K/K)maintenance crew of cfor K machines

Most included in available tables and software andapproximations - see Ch08A_Queue.xlsx,Ch08A_Queue_Models.xlsx

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