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