service processes

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