Service Processes

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Service Processes

• Operations Management
• Dr. Ron Tibben-Lembke

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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
• 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

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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

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3 Approaches

• Which is Best?
• Production Line
• Self-Service
• Personal attention

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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

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Service-System Design Matrix
Degree of customer/server contact
Buffered
Permeable
Reactive
core (none)
system (some)
system (much)
High
Low
Face-to-face total customization
Face-to-face loose specs
Sales Opportunity
Production Efficiency
Face-to-face tight specs
Phone Contact
Internet on-site technology
Mail contact
High
Low
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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

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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.

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Fail-Safing

• poka-yokes Japanese for avoid mistakes
• Not possible to do things the wrong way
• Indented trays for surgeons
• ATMs beep so you dont forget your card
• Pagers at restaurants for when table ready
• Airplane bathroom locks turn on lights
• Height bars at amusement parks

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How Much Capacity Do We Need?
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Blueprinting

• Fancy word for making a flow chart
• line of visibility separates what customers can
  see from what they cant
• Flow chart back office and front office
  activities separately.

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Capacity greater than Average
customers arriving per hour
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Queues

• In England, they dont wait in line, they wait
  on queue.
• So the study of lines is called queueing theory.

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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)?

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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

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Memoryless Property

• Interarrival time time between arrivals
• Memoryless property means it doesnt matter how
  long youve been waiting.
• If average wait is 5 min, and youve been there
  10 min, expected time until bus comes 5 min
• Exponential Distribution
• Probability time is t

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Poisson Distribution

• Assumes interarrival times are exponential
• Tells the probability of a given number of
  arrivals during some time period T.

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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?

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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.

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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.

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Binomial Distribution

• The probability of getting 8 tails in 10 coin
  flips is

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Poisson Distribution
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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.

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Larger average, more normal
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Queueing Theory Equations

• Memoryless Assumptions
• Exponential arrival rate ?
• Avg. interarrival time 1/ ?
• Exponential service rate ?
• Avg service time 1/?
• Utilization ? ?/?

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Avg. in System

• Lq avg in line
• Ls avg in system
• Prob. n in system

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Average Time

• Wq avg wait in line
• Ws avg time in system

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System Structure

• The more comlicated the system, the harder it is
  to model
• Separate lines
• Separate tellers, etc.

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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

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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?

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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

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Which is better, one line or two?
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Waiting Lines

• Operations Management
• Dr. Ron Tibben-Lembke

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Everyone is just waiting
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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)

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Retail Lines
Magazines

• Things you dont 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

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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

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Fastpasses
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Some Lucky People Get These
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In-Line Entertainment

• Set up the story
• Get more buy-in to ride
• Plus, keep from boredom

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• Slow me down before going again
• Create buzz, harvest email addresses

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False Hope
Dumbo
Peter Pan
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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