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 NormalExperiment Introduce self (Sun brunch) 15%23% Smiling (alone in bar) 20%48% Waitress28%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-face loose specs Face-to-face tight specs Phone Contact Face-to-face total customization Buffered core (none) Permeable system (some) Reactive system (much) High Low High Low Degree of customer/server contact Internet & on-site technology Sales Opportunity Production Efficiency

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 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 =

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 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 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

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