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Service Processes Operations Management Dr. Ron Lembke.

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Presentation on theme: "Service Processes Operations Management Dr. Ron Lembke."— Presentation transcript:

1 Service Processes Operations Management Dr. Ron Lembke

2 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

3 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

4 3 Approaches Which is Best? Production Line Self-Service Personal attention

5 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

6 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

7 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

8 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.”

9 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






15 How Much Capacity Do We Need?

16 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.

17 Capacity greater than Average # customers arriving per hour

18 Queues In England, they don’t ‘wait in line,’ they ‘wait on queue.’ So the study of lines is called queueing theory.

19 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)?

20 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

21 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 =

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

23 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?

24 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.

25 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.

26 Binomial Distribution The probability of getting 8 tails in 10 coin flips is:

27 Poisson Distribution

28 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.

29 Larger average, more normal

30 Queueing Theory Equations Memoryless Assumptions: Exponential arrival rate = Avg. interarrival time = 1/ Exponential service rate =  Avg service time = 1/  Utilization =  = / 

31 Avg. # in System Lq = avg # in line = Ls = avg # in system = Prob. n in system=

32 Average Time Wq = avg wait in line Ws = avg time in system

33 System Structure The more comlicated the system, the harder it is to model: Separate lines Separate tellers, etc.

34 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

35 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?

36 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

37 Which is better, one line or two?

38 Waiting Lines Operations Management Dr. Ron Lembke

39 Everyone is just waiting

40 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)

41 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

42 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

43 Fastpasses

44 Some Lucky People Get These

45 In-Line Entertainment Set up the story Get more buy-in to ride Plus, keep from boredom

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

47 False Hope Dumbo Peter Pan

48 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

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