service processes
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Service Processes. Operations Management Dr. Ron Lembke. 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 - PowerPoint PPT PresentationTRANSCRIPT
Service Processes
Operations ManagementDr. Ron Lembke
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, F2F
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 service
3 Approaches Which is Best?
Production Line Self-Service Personal attention
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 customization
Service-System Design Matrix
Mail contact
Face-to-faceloose specs
Face-to-facetight specs
PhoneContact
Face-to-facetotal
customization
Buffered core (none)
Permeable system (some)
Reactivesystem (much)
High
LowHigh
Low
Degree of customer/server contact
Internet & on-site
technology
SalesOpportunity
ProductionEfficiency
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 service
Restaurant TippingNormal 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.”
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 parks
How Much Capacity Do We Need?
BlueprintingFancy word for making a flow chart“line of visibility” separates what customers
can see from what they can’tFlow chart “back office” and “front office”
activities separately.
Capacity greater than Average
0
0.05
0.1
0.15
0.2
0.25
0.3
0.35
9 10 11 12 1 2
Arrivals
Average
# customers arriving per hour
Queues In England, they don’t ‘wait in line,’ they
‘wait on queue.’ So the study of lines is called queueing
theory.
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)?
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
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 =
tetf )(
Poisson Distribution Assumes interarrival times are
exponential Tells the probability of a given number of
arrivals during some time period T.
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?
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.
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.
xnx ppxn
pnxb
1),,(
Binomial Distribution The probability of getting 8 tails in 10 coin
flips is:
b(8,10,0.5) 108
(0.5)8 1 0.5 10 8
10 *92 *1
* 0.0039062 *0.25 4.4%
Poisson Distribution
x
k
k
x
keCUMPOISSON
xePOISSON
0 !
!
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.
Larger average, more normal
Queueing Theory Equations Memoryless Assumptions:
Exponential arrival rate = • Avg. interarrival time = 1/
Exponential service rate = • Avg service time = 1/
Utilization = = /
Avg. # in System Lq = avg # in line =
Ls = avg # in system =
Prob. n in system=
Lq2
Ls
Lq
Pn 1
n
Average Time Wq = avg wait in line
Ws = avg time in system
WqLq
WsLs
System Structure The more comlicated the system, the
harder it is to model: Separate lines Separate tellers, etc.
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 risk
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?
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 people
Which is better, one line or two?
Waiting Lines
Operations ManagementDr. Ron Lembke
Everyone is just waiting
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)
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
Magazines
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 day
Fastpasses
Some Lucky People Get These
In-Line Entertainment
Set up the story Get more buy-in to ride Plus, keep from boredom
Slow me down before going again Create buzz, harvest email addresses
False HopeDumbo
Peter Pan
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