ch09

1 1 2002 South-Western/Thomson Learning 2002 South-Western/Thomson Learning TMTM

Slides preparedSlides preparedby John Loucksby John Loucks

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Chapter 9Chapter 9

Service Operations

Planning and Scheduling

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OverviewOverview

Introduction Scheduling Quasi-Manufacturing Service Operations Scheduling Customer-as-Participant Service

Operations Scheduling Customer-as-Product Service Operations Wrap-Up: What World-Class Companies Do

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IntroductionIntroduction

Services are operations with: Intangible outputs that ordinarily cannot be

inventoried Close customer contact Short lead times High labor costs relative to capital costs Subjectively determined quality

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IntroductionIntroduction

Facts about service businesses: Enormous diversity Service businesses can be any size Twice as many non-retail service businesses as

retail Technical training important due to significant

dependence on computers, automation and technology

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IntroductionIntroduction

Other Facts about service businesses: Service workers well paid relative to

manufacturing Need better planning, controlling, and

management to stay competitive

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Some of the Largest Service BusinessesSome of the Largest Service Businesses

Rank in the top 20 US Corporations: AT&T Wal-Mart Citigroup State Farm SBC Communications Sear, Roebuck & Company

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Spectrum of Service IndustriesSpectrum of Service Industries

Transportation Banking Retailing Health Care Entertainment

Insurance Real Estate Communications Utilities … and more

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No Clear Line BetweenManufacturing and Service Firms

No Clear Line BetweenManufacturing and Service Firms

Every business, whether manufacturing or service, has a mix of customer service aspects and production aspects in its operations

Manufacturing has much to learn from services that excel

Services have much to learn from manufacturers that excel

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Manufacturing and Service JobsManufacturing and Service Jobs

Percentage of US Jobs

1988 1998 2008*

Manufacturing Jobs 16.1% 13.4% 11.6%

Service Jobs66.2 70.8 73.9

* Projected

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Operations StrategiesOperations Strategies

Positioning strategies contain two elements: Type of service design

Standard or custom Amount of customer contact Mix of physical goods and intangible services

Type of production process Quasi-manufacturing Customer-as-participant Customer-as-product

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Types of Service OperationsTypes of Service Operations

Quasi-manufacturing Production occurs much as manufacturing Physical goods dominant over intangible services

Customer-as-participant High degree of customer involvement Physical goods may or may not be significant Service either standard or custom

Customer-as-product Service performed on customer... usually custom

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Scheduling Challenges in ServicesScheduling Challenges in Services

Planning and controlling day-to-day activities difficult due to:

Services produced and delivered by people Pattern of demand for services is non-uniform

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Non-Uniform DemandNon-Uniform Demand

Cannot inventory services in advance of high-demand periods, so businesses use following tactics:

Preemptive actions to make demand more uniform Off-peak incentives/discounts (telephone) Appointment schedules (dentist) Fixed schedules (airline)

Make operations more flexible so it is easier to vary capacity

Part-time personnel (supermarket) Subcontractors (postal service) In-house standby resources (fire department)

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Non-Uniform DemandNon-Uniform Demand

Additional tactics used by businesses: Anticipate demand and schedule employees during

each time period to meet demand Allow waiting lines to form

These two tactics will be covered in greater detail

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Scheduling Quasi-Manufacturing ServicesScheduling Quasi-Manufacturing Services

Product-Focused Operations Resemble product-focused production lines Customer demand is forecast and capacity

decisions made just as in manufacturing High volumes of standardized products Management focused on controlling production

costs, product quality, and delivery of physical goods

Example... McDonald’s back-room operation

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Scheduling Quasi-Manufacturing ServicesScheduling Quasi-Manufacturing Services

Process-Focused Operations Managed like job shops in manufacturing Input-output control important to balance capacity

between operations Gantt charts used to coordinate flows between

departments Sequence of jobs consider sequencing rules,

changeover costs, and flow times

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Work Shift SchedulingWork Shift Scheduling

Three difficulties in scheduling services: Demand variability Service time variability Availability of personnel when needed

Managers use two tactics: Use full-time employees exclusively Use some full-time employees as base and fill in

peak demand with part-time employees

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Example: Scheduling EmployeesExample: Scheduling Employees

The owner of a haircutting shop wants to convert from a drop-in system of customer arrivals to an appointment system.

Each customer requires an average of 30 minutes of a stylist’s time. The stylists are all full-time employees and can work any 4 consecutive days per week from 10 a.m. to 7 p.m. (with an hour off for lunch), Monday through Saturday.

On the next slide are: 1) average number of drop-in customers each day, and 2) estimated number of customer appointments each day.

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Example: Scheduling EmployeesExample: Scheduling Employees

Mon. Tue. Wed. Thu. Fri. Sat. Total

Drop-ins 40 30 10 20 30 60190

Appointments 32 32 32 32 32 32192

a) How many stylists are required to service 32 appointments in a day?

b) What is the minimum number of stylists required per week?

c) Use the work shift heuristic procedure to develop the stylists’ weekly work shift schedules.

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Example: Scheduling EmployeesExample: Scheduling Employees

Number of Stylists Required per Day

Number of customers per day

Number of work hours Number of customers per day per stylist served per hour per stylist

= 32/((8)(2)) = 2 stylists

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Example: Scheduling EmployeesExample: Scheduling Employees

Minimum Number of Stylists Required per Week?

Number of Customers per Week

Number of Customers per Stylist per Week

= 192/((8 hr/day)(4 days/week)(2 cust./hr/stylist))

= 192/64 = 3 stylists

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Example: Scheduling EmployeesExample: Scheduling Employees

Stylists’ Weekly Work Shift Schedules

StylistMon. Tue. Wed. Thu. Fri. Sat.

1 2 2 2 2 2 2

2 2 2 1 1 1 1

3 1 1 1 1 0 0

Note: Pair of days boxed represent days off.

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Scheduling Customer-as-Participant ServicesScheduling Customer-as-Participant Services

Must provide customer ease of use/access features.... lighting, walkways, etc.

Layouts must focus on merchandising and attractive display of products

Employee performance crucial to customer satisfaction

Waiting lines used extensively to level demand

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Waiting Lines in Service OperationsWaiting Lines in Service Operations

Waiting lines form because: Demand patterns are irregular or random. Service times vary among “customers”. Managers try to strike a balance between

efficiently utilizing resources and keeping customer satisfaction high.

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Waiting Line ExamplesWaiting Line Examples

Computer printing jobs waiting for printing Workers waiting to punch a time clock Customers in line at a drive-up window Drivers waiting to pay a highway toll Skiers waiting for a chair lift Airplanes waiting to take off

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Waiting Line AnalysisWaiting Line Analysis

Assists managers in determining: How many servers to use Likelihood a customer will have to wait Average time a customer will wait Average number of customers waiting Waiting line space needed Percentage of time all servers are idle

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Waiting Line TerminologyWaiting Line Terminology

Queue - a waiting line Channels - number of waiting lines in a queuing

system Service phases – number of steps in service process Arrival rate (l) - rate at which persons or things arrive

(in arrivals per unit of time) Service rate (m) - rate that arrivals are serviced (in

arrivals per unit of time)

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Waiting Line TerminologyWaiting Line Terminology

Queue discipline - rule that determines the order in which arrivals are serviced

Queue length – number of arrivals waiting for service Time in system – an arrival’s waiting time and service

time Utilization – degree to which any part of the service

system is occupied by an arrival

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Queuing System StructuresQueuing System Structures

Single Phase - Single Channel

Single Phase - Multichannel

S1S1

S1S1

S2S2

S3S3

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Queuing System StructuresQueuing System Structures

Multiphase - Single Channel

Multiphase - Multichannel

S12S12S11S11

S12S12S11S11

S22S22S21S21

S32S32S31S31

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Definitions of Queuing System VariablesDefinitions of Queuing System Variables

= average arrival rate

1/ = average time between arrivals

µ = average service rate for each server

1/µ = average service time

n1 = average number of arrivals waiting

nS = average number of arrivals in the system

t1 = average time arrivals wait

tS = average time arrivals are in the system

Pn = probability of exactly n arrivals in the system

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Model 1 Single channel Single phase Poisson arrival-rate distribution Poisson service-rate distribution Unlimited maximum queue length Examples:

Single-booth theatre ticket sales Single-scanner airport security station

Queuing ModelsQueuing Models

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Example: Queuing Model 1Example: Queuing Model 1

Jim Beam pulls stock from his warehouse shelves to fill customer orders. Customer orders arrive at a mean rate of 20 per hour. The arrival rate is Poisson distributed. Each order received by Jim requires an average of two minutes to pull. The service rate is Poisson distributed also.

Questions to follow ……

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Example: Queuing Model 1Example: Queuing Model 1

Service Rate Distribution

Question

What is Jim’s mean service rate per hour?

Answer

Since Jim can process an order in an average time of 2 minutes (= 2/60 hr.), then the mean service

rate, µ, equals 1/(mean service time), or 60/2 =

30/hr.

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Example: Queuing Model 1Example: Queuing Model 1

Average Time in the System

QuestionWhat is the average time an order must wait

from the time Jim receives the order until it is finished being processed (i.e. its turnaround time)?

AnswerWith = 20 per hour and = 30 per hour, the average time an order waits in the system is: tS = 1/(µ - ) = 1/(30 - 20)

= 1/10 hour or 6 minutes

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Example: Queuing Model 1Example: Queuing Model 1

Average Length of Queue

QuestionWhat is the average number of orders Jim has waiting to be processed?

AnswerThe average number of orders waiting in the queue is: n1 = 2/[µ(µ - )]

= (20)2/[(30)(30-20)] = 400/300 = 4/3 or 1.33 orders

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Example: Queuing Model 1Example: Queuing Model 1

Utilization Factor

QuestionWhat percentage of the time is Jim processing orders?

AnswerThe percentage of time Jim is processing

orders is equivalent to the utilization factor, /. Thus, the percentage of time he is processing orders is:

/ = 20/30 = 2/3 or 66.67%

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Queuing ModelsQueuing Models

Model 2 Single channel Single phase Poisson arrival-rate distribution Constant service rate Unlimited maximum queue length Examples:

Single-booth automatic car wash Coffee vending machine

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Example: Queuing Model 2Example: Queuing Model 2

The mechanical pony ride machine at the entrance to a very popular J-Mart store provides 2 minutes of riding for $.50. Children (accompanied of course!) wanting to ride the pony arrive according to a Poisson distribution with a mean rate of 15 per hour.

a) What fraction of the time is the pony idle?b) What is the average number of children waiting to

ride the pony?c) What is the average time a child waits for a ride?

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Example: Queuing Model 2Example: Queuing Model 2

Fraction of Time Pony is Idle

l = 15 per hour

m = 60/2 = 30 per hour

Utilization = l/m = 15/30 = .5

Idle fraction = 1 – Utilization = 1 - .5 = .5

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Example: Queuing Model 2Example: Queuing Model 2

Average Number of children Waiting for a Ride

Average Time a Child Waits for a Ride

or 1 minute

2 2

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λ (15)n = = .25 children

2μ(μ-λ) 2(30)(30 - 15)

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λ 15t = = .01667 hours

2μ(μ-λ) 2(30)(30 - 15)

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Queuing ModelsQueuing Models

Model 3 Single channel Single phase Poisson arrival-rate distribution Poisson service-rate distribution Limited maximum queue length Examples:

Auto repair shop with limited parking space Bank drive-thru with limited waiting lane

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Queuing ModelsQueuing Models

Model 4 Multiple channel Single phase Poisson arrival-rate distribution Poisson service-rate distribution Unlimited maximum queue length Examples:

Expressway exit with multiple toll booths Bank with multiple teller stations

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Scheduling Customer-as-Product ServicesScheduling Customer-as-Product Services

Wide range of complexity Every facet designed around the customer Highly trained, motivated, and effective workforce

critical to success Waiting-line analysis can be helpful in determining

staffing levels In more complex operations, simulation is a helpful

tool in scheduling resources

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Reasons for Simulating OperationsReasons for Simulating Operations

Experimentation with the real system is impossible, impractical, or uneconomical.

System is so complex that mathematical formulas cannot be developed.

Values of the system’s variables are not known with certainty.

Problem under consideration involves the passage of time and simulation could be faster

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Procedures of Computer SimulationProcedures of Computer Simulation

Define the problem. Develop and computer-program a model of problem.

Identify the variables and parameters. Specify the decision rules. Gather data and specify variables and parameters. Specify time-incrementing procedures. Specify summarizing procedures.

Process the simulation. Evaluate the results of the simulation. Recommend a course of action.

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Simulation ExampleSimulation Example

Whenever an international plane arrives at Lincoln airport the two customs inspectors on duty set up operations to process the passengers.

Incoming passengers must first have their passports and visas checked. This is handled by one inspector. The time required to check a passenger's passports and visas can be described by the probability distribution on the next slide.

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Simulation ExampleSimulation Example

Time Required to

Check a Passenger's

Passport and Visa Probability 20 seconds .20 40 seconds .40 60 seconds .30 80 seconds .10

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Simulation ExampleSimulation Example

After having their passports and visas checked, the passengers next proceed to the second customs official who does baggage inspections. Passengers form a single waiting line with the official inspecting baggage on a first come, first served basis.

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Simulation ExampleSimulation Example

The time required for baggage inspection has the following probability distribution:

Time Required For Baggage Inspection Probability

No Time .25 1 minute .60 2 minutes .10 3 minutes .05

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Simulation ExampleSimulation Example

Random Number Mapping

Time Required to Check a Passenger's Random Passport and Visa Probability Numbers

20 seconds .20 00 - 19

40 seconds .40 20 - 59

60 seconds .30 60 - 89

80 seconds .10 90 - 99

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Simulation ExampleSimulation Example

Random Number Mapping

Time Required For Random Baggage Inspection Probability Numbers

No Time .25 00 - 24

1 minute .60 25 - 84

2 minutes .10 85 - 94

3 minutes .05 95 - 99

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Simulation ExampleSimulation Example

Next-Event Simulation Records

For each passenger the following information must be recorded:

When his service begins at the passport control inspection

The length of time for this service When his service begins at the baggage inspection The length of time for this service

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Simulation ExampleSimulation Example

Time Relationships

Time a passenger begins service

by the passport inspector

= (Time the previous passenger

started passport service)

+ (Time of previous passenger's

passport service)

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Simulation ExampleSimulation Example

Time Relationships

Time a passenger begins service

by the baggage inspector

(If passenger does not wait for baggage inspection)

= (Time passenger completes service

with the passport control inspector)

(If the passenger does wait for baggage inspection)

= (Time previous passenger completes

service with the baggage inspector)

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Simulation ExampleSimulation Example

Time Relationships

Time a customer completes service

at the baggage inspector

= (Time customer begins service with

baggage inspector)

+ (Time required for baggage inspection)

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Simulation ExampleSimulation Example

A chartered plane from abroad lands at Lincoln Airport with 80 passengers. Simulate the processing of the first 10 passengers through customs.

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Simulation ExampleSimulation Example

Simulation Worksheet (partial)

Passport Control Baggage InspectionsPass. Time Ran. Serv. Time Time Ran. Serv. TimeNum. Beg. Num. Time End Beg. Num. Time End 1 0:00 93 1:20 1:20 1:20 13 0:00 1:20 2 1:20 63 1:00 2:20 2:20 08 0:00 2:20 3 2:20 26 :40 3:00 3:00 60 1:00 4:00 4 3:00 16 :20 3:20 4:00 13 0:00 4:00 5 3:20 21 :40 4:00 4:00 68 1:00 5:00

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Simulation ExampleSimulation Example

Simulation Worksheet (continued)

Passport Control Baggage InspectionsPass. Time Ran. Serv. Time Time Ran. Serv. TimeNum. Beg. Num. Time End Beg. Num. Time End 6 4:00 26 :40 4:40 5:00 40 1:00 6:00 7 4:40 70 1:00 5:40 6:00 40 1:00 7:00 8 5:40 55 :40 6:20 7:00 27 1:00 8:00 9 6:20 72 1:00 7:20 8:00 23 0:00 8:00 10 7:20 89 1:00 8:20 8:20 64 1:00 9:20

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Simulation ExampleSimulation Example

Explanation

For example, passenger 1 begins being served by the passport control inspector immediately. His service time is 1:20 (80 seconds) at which time he goes immediately to the baggage inspector who waves him through without inspection.

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Simulation ExampleSimulation Example

Explanation

Passenger 2 begins service with passport inspector 1:20 minutes (80 seconds) after arriving there (as this is when passenger 1 is finished) and requires 1:00 minute (60 seconds) for passport inspection. He is waved through baggage inspection as well.

This process continues in this manner.

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Simulation ExampleSimulation Example

Question

How long will it take for the first 10 passengers to clear customs?

Answer

Passenger 10 clears customs after 9 minutes and 20 seconds.

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Simulation ExampleSimulation Example

Question

What is the average length of time a customer waits before having his bags inspected after he clears passport control? How is this estimate biased?

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Simulation ExampleSimulation Example

Answer

For each passenger calculate his waiting time:

(Baggage Inspection Begins) - (Passport Control Ends) =0+0+0+40+0+20+20+40+40+0 = 120 seconds.

120/10 = 12 seconds per passenger.

This is a biased estimate because we assume that the simulation began with the system empty. Thus, the results tend to underestimate the average waiting time.

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Wrap-Up: World-Class PracticeWrap-Up: World-Class Practice

Successful companies have: Adapted advanced and well-known planning,

analyzing, and controlling approaches first developed in manufacturing

Recognized the unique properties of service operations and developed novel management approaches for these operations

Classify service operations into three types... quasi manufacturing, customer-as-participant, or customer-as-product...provides framework for analysis.

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Wrap-Up: World-Class PracticeWrap-Up: World-Class Practice

Factors that create satisfied customers Extrinsic quality of services The facilities...comfort, convenience, and

atmosphere The chemistry between customer and people in

service system...friendliness and courtesy Skill, competence, and professionalism of the

personnel The value of the service; cost relative to the

quantity of services received

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End of Chapter 9End of Chapter 9