service processes operations management dr. ron tibben-lembke
TRANSCRIPT
Service Processes
Operations Management
Dr. Ron Tibben-Lembke
Nature of Services 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
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.”
Performance Priorities
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
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
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
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.
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
3 Approaches
Production Line Self-Service Personal attention
Degrees of personalization, Connection to customer Efficiency
Waiting Lines
Operations Management
Dr. Ron Tibben-Lembke
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
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
Make your own fun
Dumbo Ride
Queues
In England, they don’t ‘wait in line,’ they ‘wait on queue.’
So the study of lines is called queueing theory.
[It’s also the only English word I know with 5 vowels in a row.]
Cost-Effectiveness
How much money do we lose from people waiting in line for the copy machine?
Would that justify a new machine?
We are the problem Customers arrive randomly. Time between arrivals is called the “interarrival
time” Interarrival times have 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 λλ −=)(
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.
Capacity greater than Average
0
5
10
15
20
25
9 10 11 12 1 2
Arrivals
Average
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