Week4

Chapter7 SERVICE PROCESSES 1. Understand the characteristics of service processes. 2. Analyze simple service systems. 3. Understand waiting line (queueing) analysis. 1. Service Package  Every service has a service package – the bundle of goods and services that is provided in some environment  The bundle consists of five features: 1) Supporting facility – the physical resources that must be in place before a service can be offered 2) Facilitating goods – the material purchased or consumed by the buyer or the items provided to the customer 3) Information – operations data or information that is provided by the customer to enable efficient and customized services 4) Explicit services – the benefits that are readily observable and which make up the essential features of the service 5) Implicit services – psychological benefits or other extrinsic features of the service (prestige, privacy, etc.) 2. Operational Classification of Services 3. Service Organization Design  Services cannot be stored in inventory  In services, capacity becomes the dominant issue  Too much capacity generates excessive costs  Insufficient capacity leads to lost customers  Seeking the assistance of marketing to influence demand  Waiting line models provide a powerful mathematical tool for analyzing many common service situations 4. Service-System Design Matrix

5. Customer Contact Characteristics 6. Virtual Services 7. Poka-Yokes  Poka-yokes - procedures that block the inevitable mistake from becoming a service defect (“avoid mistakes”)  Poka-yokes are common in factories  Many applications of poka-yokes to services  Warning methods (e.g. steps that lead to mistakes trigger a reminder)  Physical or visual contact methods (e.g. parts can only fit together in the correct way)  The Three T’s  Task to be done  Treatment accorded to the customer  Tangible features of the service facility 8. Blueprint: Automotive Service Operation 9. Fail-Safing an Automotive Service Operation  Web platform business – a company that creates value by enabling the exchange of information between two or more independent groups, usually consumers and providers of a service or product  eBay  YouTube  Airbnb  Non-platform Web businesses – Netflix HBO, Walmart.

14. Operations and SCM in Practice  Effective MANAGEMENT OF QUEUES DURING Boarding Can Increase Airline Bottom Line 15. Managing Queues  Understand customer expectations. It is important to know what is the customer's expectation of a reasonable wait time. For example, customers might be willing to wait longer in line in person than on the phone. In that case you have to ensure that phone waits are much shorter than physical waits. Expectations can also be affected by cultural backgrounds.  Segment the customers. If a group of customers needs something that can be done very quickly, give them a special line so that they do not have to wait for the slower customers.  Train your servers to be friendly. Greeting the customer by name or providing another form of special attention can go a long way toward overcoming the negative feeling of a long wait. Psychologists suggest that servers should be told when to invoke specific friendly actions, such as smiling when greeting customers, taking orders, and giving change (for example, in a convenience store).  Inform your customers of what to expect. This is especially important when the waiting time will be longer than normal. Tell customers why the waiting time is longer than usual and what you are doing to alleviate the wait. An important question for airlines is “What is the best way to board passengers?” Avoiding queues and getting passengers on a plane quickly can greatly affect an airline’s costs. Southwest says that if its boarding times increased by 10 minutes per flight, it would need 40 more planes at a cost of US$40 million each to run the same number of flights it runs now.

 Try to divert the customer’s attention when waiting. Providing music, a video, or some other form of entertainment may help distract the customers from the fact that they are waiting.  Encourage customers to come during slack periods. Inform customers of times when they usually would not have to wait; also tell them when the peak periods are —this may help smooth the load. 16. Queuing System Components 17. Queuing System Analysis ——Queuing Systems essentially consists of three major components 1) The source population – who are your customers?  Population size – finite or infinite?  How do customers arrive at the system?  Customer arrival rate – the expected number of customers that arrive during each period o Exponential o Poisson o Constant  Customer arrival characteristics o Arrival patterns (steady or seasonal) o Size or arrival rates (individuals or groups) o Degree of patience (will they wait?)  Service rate distribution o Exponential 2) Service Systems a) Waiting Lines  Line Length, Number of Lines, Queue Discipline (Priority Rules ) b) Servers and Service Rate (Time)  Service Rate Characteristics o Random; Exponential distribution, Constant rate; machine-controlled operations c) Service Line Structures  Single channel: single phase or multiphase  Multichannel: single phase or multiphase Mixed structures 3) Condition of Exiting Customers a) Low Probability of re-service b) High Probability of re-service 18. Waiting Line Model a) Customer Population Sources  The source of customers to the waiting line system, and it can be either infinite or finite .  Infinite population source: a large number of potential customers that it is always possible for one more

With a mean arrival rate of three per minute (λ), what is the probability of exactly five units will arrive within a one-minute period (n=5, T=1)?  Arrival Distributions – Exponential  The exponential distribution (also known as the negative exponential distribution) is the probability distribution that describes the time between events (arrivals) in a Poisson process  Describes the probability distribution of time between arrivals (inter-arrival times)  It has the infamous memoryless property o It does not matter when last arrival happened, the probability of a customer arriving in the next time unit is always the same  Queuing System Factors  Length of queue – how much waiting room space is available?  Number of lines – how many servers are working?  Line length  Number of lines  Queue discipline – priority rule or set of rules that determine the order of service for customers who are waiting in line

 Queue discipline – how do new arrivals enter the line? How do you decide which customer to serve next?  Service time distribution – what is the service rate and how much does it vary? (c) Priority Rule  Queue discipline (PRIORITY RULES) – how do new arrivals enter the line? How do you decide which customer to serve next?  Most common queue discipline is first come, first served (FCFS) .  Other disciplines assign priorities to the waiting units and then serve the unit with the highest priority first. (d) Service Line Structure  Service Line Structure – what does the process look like?  ¨ The service facility can be classified in terms of # of service channels and # of service phases.  Channels: # of parallel servers for serving customers  Phases: # of servers in sequence a customer must go through  In a college registration process, several department heads have to approve an individual student's semester course load. What is the queuing system line structure? A. Single channel, single phase B. Single channel, multiphase C. Multichannel, single phase D. Multichannel, multiphase E. None of the above  Buying food at a large food store with multiple checkout counters features which type of queuing system line structure? A. Single channel, single phase B. Single channel, multiphase C. Multichannel, single phase D. Multichannel, multiphase E. None of the above  Service Time Distribution Time the customer or unit spends with the server once the service has started

22.Waiting Line Model Notation λ – Arrival rate µ – Service rate 1 /µ – Average service time 1 /λ – Average time between arrivals ρ – Ratio of total arrival rate to service rate for a single server L q – Average number waiting in line L s – Average number in system W q – Average time waiting in line W s – Average total time in system n – Number of units in the system S – Number of identical service channels P n – Probability of exactly n units in system P w – Probability of waiting in line 23. 1) Model 1: Single Channel and Exponential Service Time 2) Model 2: Single Channel and Constant Service Time 3) Model 3: Multiple Channel and Exponential Service Time

Exhibit 7.12: Expected Number of People Waiting in Line (Lq) for various Values of S and λ/µ λ/µ S L q P 0 λ/µ S L q P 0 λ/µ S L q P 0 0.15 1 0.026 0.850 3 0.024 0.425 4 0.080 0.180 2 0.001 0.860 4 0.003 0.427 5 0.017 0.182 0.20 1 0.050 0.800 0.90 1 8.100 0.100 1.80 2 7.674 0.053 2 0.002 0.818 2 0.229 0.379 3 0.532 0.146 0.25 1 0.083 0.750 3 0.030 0.403 4 0.105 0.162 2 0.004 0.778 4 0.004 0.406 5 0.023 0.165 0.30 1 0.129 0.700 0.95 1 18.050 0.050 1.90 2 17.587 0.026 2 0.007 0.739 2 0.277 0.356 3 0.688 0.128 0.35 1 0.188 0.650 3 0.037 0.383 4 0.136 0.145 2 0.011 0.702 4 0.005 0.386 5 0.030 0.149 0.40 1 0.267 0.600 1.00 2 0.333 0.333 6 0.007 0.149 2 0.017 0.667 3 0.045 0.364 2.00 3 0.889 0.111 0.45 1 0.368 0.550 4 0.007 0.367 4 0.174 0.130 2 0.024 0.633 1.10 2 0.477 0.290 5 0.040 0.134 3 0.002 0.637 3 0.066 0.327 6 0.009 0.135 0.50 1 0.500 0.500 4 0.011 0.332 2.10 3 1.149 0.096 2 0.033 0.600 1.20 2 0.675 0.250 4 0.220 0.117 3 0.003 0.606 3 0.094 0.294 5 0.052 0.121 0.55 1 0.672 0.450 4 0.016 0.300 6 0.012 0.122 2 0.045 0.569 5 0.003 0.301 2.20 3 1.491 0.081 3 0.004 0.576 1.30 2 0.951 0.212 4 0.277 0.105 0.60 1 0.900 0.400 3 0.130 0.264 5 0.066 0.109 2 0.059 0.538 4 0.023 0.271 6 0.016 0.111 3 0.006 0.548 5 0.004 0.272 2.30 3 1.951 0.068 0.65 1 1.207 0.350 1.40 2 1.345 0.176 4 0.346 0.093 2 0.077 0.509 3 0.177 0.236 5 0.084 0.099 3 0.008 0.521 4 0.032 0.245 6 0.021 0.100 0.70 1 1.633 0.300 5 0.006 0.246 2.40 3 2.589 0.056 2 0.098 0.481 1.50 2 1.929 0.143 4 0.431 0.083 3 0.011 0.495 3 0.237 0.211 5 0.105 0.089 0.75 1 2.250 0.250 4 0.045 0.221 6 0.027 0.090 2 0.123 0.455 5 0.009 0.223 7 0.007 0.091 3 0.015 0.471 1.60 2 2.844 0.111 2.50 3 3.511 0.045 0.80 1 3.200 0.200 3 0.313 0.187 4 0.533 0.074 2 0.152 0.429 4 0.060 0.199 5 0.130 0.080 3 0.019 0.447 5 0.012 0.201 6 0.034 0.082 0.85 1 4.817 0.150 1.70 2 4.426 0.081 7 0.009 0.082 2 0.187 0.404 3 0.409 0.166 2.60 3 4.933 0.035 24. Waiting Line Model Equations Question Bowl If the service rate is 2 customers per hour, what is the “average service time” for this queuing situation? a. 40.00 minutes b. 0.5 hours c. 0.0667 hours d. 16% of an hour e. Can not be computed from data above If the arrival rate is 2 customers per hour, what is the “average time between arrivals” for this queuing situation? a. 30.00 minutes b. 0.6667 hours c. 0.0667 hours d. 16% of an hour e. Can not be computed from data above ——Waiting Line Model – Example 7.1  Western National Bank is considering opening a drive-through window. Management estimates that customers will arrive at a rate of 15 per hour and the teller staffing the window can serve customers at a rate of 1 every three minutes, or 20 per hour.  Management would like to know  Utilization rate of the teller  Average number in the waiting line  Average number in the drive-through system  Average time in line  Average time in the system, including service