Dr Ron Lembke How are Services Different Everyone is an expert on services What works well for one service provider doesnt necessarily carry over to another Quality of work is not quality of service ID: 756181
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Slide1
Service Processes
Operations Management
Dr. Ron
LembkeSlide2
How are Services Different?
Everyone is an expert on services
What works well for one service provider doesn’t necessarily carry over to another
Quality of work is not quality of service
“Service package” consists of tangible and intangible components
Services are experienced, goods are consumed
Mgmt of service involves mktg, personnel
Service encounters mail, phone, F2FSlide3
Degree of Customer Contact
More customer contact, harder to standardize and control
Customer influences:
Time of demand
Exact nature of service
Quality (or perceived quality) of serviceSlide4
3 Approaches
Which is Best?
Production Line
Self-Service
Personal attentionSlide5
What do People Want?
Amount of friendliness and helpfulness
Speed and convenience of delivery
Price of the service
Variety of services
Quality of tangible goods involved
Unique skills required to provide service
Level of customizationSlide6
Service-System Design Matrix
Mail contact
Face-to-face
loose specs
Face-to-face
tight specs
Phone
Contact
Face-to-face
total
customization
Buffered
core (none)
Permeable
system (some)
Reactive
system (much)
High
Low
High
Low
Degree of customer/server contact
Internet &
on-site
technology
Sales
Opportunity
Production
EfficiencySlide7
Applying Behavioral Science
The end is more important to the lasting impression (Colonoscopy)
Segment pleasure, but combine pain
Let the customer control the process
Follow norms & rituals
Compensation for failures: fix bad product, apologize for bad serviceSlide8
Restaurant Tipping
Normal Experiment
Introduce self
(Sun brunch)
15% 23%
Smiling
(alone in bar)
20% 48%
Waitress 28% 33%
Waiter
(upscale lunch)
21%
18%
“…staffing wait positions is among the most important tasks restaurant managers perform.”Slide9
Fail-Safing
“poka-yokes” – Japanese for “avoid mistakes”
Not possible to do things the wrong way
Indented trays for surgeons
ATMs beep so you don’t forget your card
Pagers at restaurants for when table ready
Airplane bathroom locks turn on lights
Height bars at amusement parksSlide10Slide11Slide12Slide13Slide14Slide15
How Much Capacity Do We Need?Slide16
Blueprinting
Fancy word for making a flow chart
“line of visibility” separates what customers can see from what they can’t
Flow chart “back office” and “front office” activities separately.Slide17
Capacity greater than Average
# customers arriving per hourSlide18
Queues
In England, they don’t ‘wait in line,’ they ‘wait on queue.’
So the study of lines is called queueing theory.Slide19
Cost-Effectiveness
How much money do we lose from people waiting in line for the copy machine?
Would that justify a new machine?
How much money do we lose from bailing out (balking)?Slide20
We are the problem
Customers arrive randomly.
Time between arrivals is called the “interarrival time”
Interarrival times often have the “memoryless property”:
On average, interarrival time is 60 sec.
the last person came in 30 sec. ago, expected time until next person: 60 sec.
5 minutes since last person: still 60 sec.
Variability in flow means excess capacity is needed Slide21
Memoryless Property
Interarrival time = time between arrivals
Memoryless property means it doesn’t matter how long you’ve been waiting.
If average wait is 5 min, and you’ve been there 10 min, expected time until bus comes = 5 min
Exponential Distribution
Probability time is t = Slide22
Poisson Distribution
Assumes interarrival times are exponential
Tells the probability of a given number of arrivals during some time period T.Slide23
Ce n'est pas les petits poissons.
Les poissons Les poissons
How I love les poissons
Love to chop And to serve little fish
First I cut off their heads
Then I pull out the bones
Ah mais oui Ca c'est toujours delish
Les poissons Les poissons
Hee hee hee Hah hah hah
With the cleaver I hack them in two
I pull out what's inside
And I serve it up fried
God, I love little fishes
Don't you? Slide24
Simeon Denis Poisson
"Researches on the probability of criminal and civil verdicts" 1837
looked at the form of the binomial distribution when the number of trials was large.
He derived the cumulative Poisson distribution as the limiting case of the binomial when the chance of success tend to zero.
Slide25
Binomial Distribution
The binomial distribution tells us the probability of having
x successes in
n trials, where
p is the probability of success in any given attempt.Slide26
Binomial Distribution
The probability of getting 8 tails in 10 coin flips is:Slide27
Poisson DistributionSlide28
POISSON(x,
mean
,
cumulative
)
X is the number of events.
Mean is the expected numeric value.
Cumulative is a logical value that determines the form of the probability distribution returned. If cumulative is TRUE, POISSON returns the cumulative Poisson probability that the number of random events occurring will be between zero and x inclusive; if FALSE, it returns the Poisson probability mass function that the number of events occurring will be exactly x.Slide29
Larger average, more normalSlide30
Queueing Theory Equations
Memoryless Assumptions:
Exponential arrival rate =
Avg. interarrival time = 1/
Exponential service rate =
Avg service time = 1/
Utilization =
= /Slide31
Avg. # in System
Lq = avg # in line =
Ls = avg # in system =
Prob. n in system=Slide32
Average Time
Wq = avg wait in line
Ws = avg time in systemSlide33
System Structure
The more comlicated the system, the harder it is to model:
Separate lines
Separate tellers, etc.Slide34
Now what?
Simulate!
Build a computer version of it, and try it out
Tweak any parameters you want
Change it as much as you want
Try it out with zero riskSlide35
Factors to Consider
Arrival patterns, arrival rate
Size of arrival units – 1,2,4 at a time?
Degree of patience
Length line grows to
Number of lines – 1 is best
Does anyone get priority?Slide36
Service Time Distribution
Deterministic – each person always takes 5 minutes
Random – low variability, most people take similar amounts of time
Random – high variability, large difference between slow & fast peopleSlide37
Which is better, one line or two?Slide38
Waiting Lines
Operations Management
Dr. Ron
LembkeSlide39
Everyone is just waitingSlide40
People Hate Lines
Nobody likes waiting in line
Entertain them, keep them occupied
Let them be productive: fill out deposit slips, etc. (Wells Fargo)
People hate cutters / budgers
Like to see that it is moving, see people being waited on
Tell them how long the wait will be (Space Mountain)Slide41
Retail Lines
Things you don’t need in easy reach
Candy
Seasonal, promotional items
People hate waiting in line, get bored easily, reach for magazine or book to look at while in line
MagazinesSlide42
Disney FastPass
Wait without standing around
Come back to ride at assigned time
Only hold one pass at a time
Ride other rides
Buy souvenirs
Do more rides per daySlide43
FastpassesSlide44
Some Lucky People Get TheseSlide45
In-Line Entertainment
Set up the story
Get more buy-in to ride
Plus, keep from boredomSlide46
Slow me down before going again
Create buzz, harvest email addressesSlide47
False Hope
Dumbo
Peter PanSlide48
What did we learn?
Human considerations very important in services
Queueing Theory can help with simple capacity decisions
Simulation needed for more complex ones
People hate lines, but hate uncertainty more
Keep them informed and amused