Waiting Lines
module 10/MGT-530-Mod10-Discussion case and regulations.docx
MGT-530: Operation Management (Waiting Lines) Module 10
|
Regulations: |
|
· GRADING EXPECTATIONS : 1. I recommend that you use the grading rubric to shape your work product (Attached). 2. With respect to grading, The instructor really looks for 2 things: citations and substance . I encourage you to include cites and information from scholarly and/or peer-reviewed sources in addition to the course text (Stevenson, W. (2018). Operations management (13th ed.). New York, NY: McGraw-Hill Irwin. ISBN-13:9781259667473)
Otherwise, my potential for points is reduced. It indicates you have read the course materials and searched far and wide for theories, statistics, and facts to address the issue at hand. I encourage you to paraphrase these sources. Convert the content into your own words rather than using direct quotes. This improves the synthesis of information, and it makes the writing more closely resemble your own style.
In addition to scholarly citations, a substantive assignment is one that not only answers the question but advances the discussion. Please, do more than is expected. |
|
· The writer must apply APA style guidelines. |
|
· Support your submission with: 1. Course material concepts, principles, and theories from the textbook and Use it in the references (Stevenson, W. (2018). Operations management (13th ed.). New York, NY: McGraw-Hill Irwin. ISBN-13:9781259667473)
2. At least Three scholarly, peer-reviewed journal articles such as:
2.1 Insardi, A., & Oliveira Lorenzo, R. (2019). Measuring accessibility: A big data perspective on Uber service waiting times. RAE: Revista de Administração de Empresas, 59(6), 402–414. 2.2 Matta, R. (2019). Minimizing the total waiting time of intermediate products in a manufacturing process. International Transactions in Operational Research, 26(3), 1096. |
|
· Be 2 pages in length, does not include the title page, abstract, conclusion and required reference page, which are never a part of the minimum content requirements. |
Waiting Lines
Many businesses utilize waiting lines to manage customer service. For example, banks, amusement parks, supermarket checkouts, fast food restaurants, call centers, check-in counters at airports, emergency departments of hospitals, and so many more. In the course of your week, consider an experience you had that led to a temporary demand for service that exceeded capacity, for example, variation in service duration or arrival rates, which led to a waiting line.
Address the following requirements:
1. Explain common measures of system performance and which of the common measures may be most effective for the business where you experienced a waiting line.
2. Describe why that measure would be effective and develop a strategy to minimize the waiting line while minimizing cost.
3. Develop a calculation to aid the business in making their operational choices.
Thank you for your support
MGT
-
5
30
:
Operation Management
(
Waiting Lines
)
Module
10
Regulations
:
·
GRADING EXPECTATIONS
:
1.
I recommend that you use the grading rubric to shape your work product (
Attached
).
2.
With respect to
grading, The instructor really looks for 2 things:
citations
and substance
. I
encourage you to include cites and information from
scholarly and/or peer
-
reviewed sources in
addition to the course text
(
Stevenson
, W. (2018). Operations management (13th ed.). New
York, NY: McGraw
-
Hill Irwin. ISBN
-
13:9781259667473)
Otherwise
, my p
otential for points is reduced. It
indicates you have read the course materials
and searched far and wide for theories, statistics, and facts
to address the issue at hand.
I encourage
you to paraphrase these sources. Convert the content into your own word
s rather than using direct
quotes
. This improves the synthesis of information, and it makes the writing more closely resemble
your own style.
In addition to
scholarly citations, a substantive assignment is one that not only answers the
question but
advances the discussion. Please, do more than is expected.
·
The writer must apply
APA style guidelines
.
·
Support your submission with:
1.
Course material concepts, principles, and theories from the textbook and
Use
it in the
references
(Stevenson, W. (2018). Operations management (13th ed.). New York, NY:
McGraw
-
Hill Irwin. ISBN
-
13:9781259667473)
2.
At least
Three
scholarly, peer
-
reviewed journal articles such as:
2.1
Insardi, A., & Oliveira Lorenzo, R. (2019).
Measuring accessibility: A big data
perspective on Uber service waiting times
.
RAE: Revista de Administração de
Empresas,
59(6), 402
–
414.
2.2
Matta, R. (2019).
Minimizing the total waiting time of intermediate products in a
manufacturing process
.
International Transactions in Operational Research,
26(3), 1096
.
·
Be
2
pages
in length,
does not include the title
page, abstract, conclusion and
required
reference page,
which are never a part of the minimum content requirements.
MGT-530: Operation Management (Waiting Lines) Module 10
Regulations:
GRADING EXPECTATIONS :
1. I recommend that you use the grading rubric to shape your work product (Attached).
2. With respect to grading, The instructor really looks for 2 things: citations and substance. I
encourage you to include cites and information from scholarly and/or peer-reviewed sources in
addition to the course text (Stevenson, W. (2018). Operations management (13th ed.). New
York, NY: McGraw-Hill Irwin. ISBN-13:9781259667473)
Otherwise, my potential for points is reduced. It indicates you have read the course materials
and searched far and wide for theories, statistics, and facts to address the issue at hand. I encourage
you to paraphrase these sources. Convert the content into your own words rather than using direct
quotes. This improves the synthesis of information, and it makes the writing more closely resemble
your own style.
In addition to scholarly citations, a substantive assignment is one that not only answers the
question but advances the discussion. Please, do more than is expected.
The writer must apply APA style guidelines.
Support your submission with:
1. Course material concepts, principles, and theories from the textbook and Use it in the
references (Stevenson, W. (2018). Operations management (13th ed.). New York, NY:
McGraw-Hill Irwin. ISBN-13:9781259667473)
2. At least Three scholarly, peer-reviewed journal articles such as:
2.1 Insardi, A., & Oliveira Lorenzo, R. (2019). Measuring accessibility: A big data
perspective on Uber service waiting times. RAE: Revista de Administração de
Empresas, 59(6), 402–414.
2.2 Matta, R. (2019). Minimizing the total waiting time of intermediate products in a
manufacturing process. International Transactions in Operational Research, 26(3), 1096.
Be 2 pages in length, does not include the title page, abstract, conclusion and required
reference page, which are never a part of the minimum content requirements.
module 10/MGT530_Module10.pptx
Waiting Lines
Chapter 18
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
1
You should be able to:
LO 18.1 What imbalance does the existence of a waiting line reveal?
LO 18.2 What causes waiting lines to form, and why is it impossible to eliminate them completely?
LO 18.3 What metrics are used to help managers analyze waiting lines?
LO 18.4 What very important lesson does the constant service time model provide for managers?
LO 18.4 What are some psychological approaches to managing lines, and why might a manager want to use them?
Chapter 18: Learning Objectives
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
2
Waiting lines occur in all sorts of service systems
Wait time is non-value added
Wait time ranges from the acceptable to the emergent
Short waits in a drive-thru
Sitting in an airport waiting for a delayed flight
Waiting for emergency service personnel
Waiting time costs
Lower productivity
Reduced competitiveness
Wasted resources
Diminished quality of life
Waiting Lines
LO 18.1
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
3
Queuing theory
Mathematical approach to the analysis of waiting lines
Applicable to many environments
Call centers
Banks
Post offices
Restaurants
Theme parks
Telecommunications systems
Traffic management
Queuing Theory
LO 18.1
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
4
Why Is There Waiting?
Waiting lines tend to form even when a system is not fully loaded
Variability
Arrival and service rates are variable
Services cannot be completed ahead of time and stored for later use
LO 18.2
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
5
Why waiting lines cause concern:
The cost to provide waiting space
A possible loss of business when customers leave the line before being served or refuse to wait at all
A possible loss of goodwill
A possible reduction in customer satisfaction
Resulting congestion may disrupt other business operations and/or customers
Waiting Lines: Managerial Implications
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
6
Waiting Line Management
The goal of waiting line management is to minimize total costs:
Costs associated with customers waiting for service
Capacity cost
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
7
Waiting Line Characteristics
The basic characteristics of waiting lines
Population source
Number of servers (channels)
Arrival and service patterns
Queue discipline
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
8
Simple Queuing System
Calling population
Arrivals
Waiting
line
Exit
Service
System
Processing Order
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18-‹#›
9
Population Source
Infinite source
Customer arrivals are unrestricted
The number of potential customers greatly exceeds system capacity
Finite source
The number of potential customers is limited
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
10
Channels and Phases
Channel
A server in a service system
It is assumed that each channel can handle one customer at a time
Phases
The number of steps in a queuing system
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18-‹#›
11
Common Queuing Systems
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12
Arrival pattern
Most commonly used models assume the arrival rate can be described by the Poisson distribution
Arrivals per unit of time
Equivalently, interarrival times are assumed to follow the negative exponential distribution
The time between arrivals
Service pattern
Service times are frequently assumed to follow a negative exponential distribution
Arrival and Service Patterns
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
13
Poisson and Negative Exponential
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
14
Queue Discipline
Queue discipline
The order in which customers are processed
Most commonly encountered rule is that service is provided on a first-come, first-served (FCFS) basis
Non FCFS applications do not treat all customer waiting costs as the same
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
15
Managers typically consider five measures when evaluating waiting line performance:
The average number of customers waiting (in line or in the system)
The average time customers wait (in line or in the system)
System utilization
The implied cost of a given level of capacity and its related waiting line
The probability that an arrival will have to wait for service
Waiting Line Metrics
LO 18.3
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
16
Waiting Line Performance
The average number waiting in line and the average time customers wait in line increase exponentially as the system utilization increases
LO 18.3
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
17
Queuing Models: Infinite Source
Four basic infinite source models
All assume a Poisson arrival rate
Single server, exponential service time
Single server, constant service time
Multiple servers, exponential service time
Multiple priority service, exponential service time
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
18
Infinite-Source Symbols
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18-‹#›
19
System Utilization
Average number of customers being served
Basic Relationships
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18-‹#›
20
Basic Relationships (cont.)
Little’s Law
For a stable system the average number of customers in line or in the system is equal to the average customer arrival rate multiplied by the average time in the line or system
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
21
The average number of customers
Waiting in line for service:
In the system:
The average time customers are
Waiting in line for service
In the system
Basic Relationships (cont.)
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
22
M/M/1
Single Server, Exponential Service Time
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
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23
M/D/1
If a system can reduce variability, it can shorten waiting lines noticeably
For, example, by making service time constant, the average number of customers waiting in line can be cut in half
Average time customers spend waiting in line is also cut by half.
Similar improvements can be made by smoothing arrival rates (such as by use of appointments)
Single Server, Constant Service Time
LO 18.4
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
24
Multiple Servers (M/M/S)
Assumptions:
A Poisson arrival rate and exponential service time
Servers all work at the same average rate
Customers form a single waiting line (in order to maintain FCFS processing)
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
25
M/M/S
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
26
Cost Analysis
Service system design reflects the desire of management to balance the cost of capacity with the expected cost of customers waiting in the system
Optimal capacity is one that minimizes the sum of customer waiting costs and capacity or server costs
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
27
Total Cost Curve
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
28
An issue that often arises in service system design is how much space should be allocated for waiting lines
The approximate line length, Lmax, that will not be exceeded a specified percentage of the time can be determined using the following:
Maximum Line Length
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
29
Multiple priority model
Customers are processed according to some measure of importance
Customers are assigned to one of several priority classes according to some predetermined assignment method
Customers are then processed by class, highest class first
Within a class, customers are processed by FCFS
Exceptions occur only if a higher-priority customer arrives
That customer will be processed after the customer currently being processed
Multiple Priorities
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
30
Multiple–Server Priority Model
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
31
Appropriate for cases in which the calling population is limited to a relatively small number of potential calls
Arrival rates are required to be Poisson
Unlike the infinite-source models, the arrival rate is affected by the length of the waiting line
The arrival rate of customers decreases as the length of the line increases because there is a decreasing proportion of the population left to generate calls for service
Service times are required to be exponential
Finite-Source Model
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
32
Procedure:
Identify the values for
N, population size
M, the number of servers/channels
T, average service time
U, average time between calls for service
Compute the service factor, X=T/(T + U)
Locate the section of the finite-queuing tables for N
Using the value of X as the point of entry, find the values of D and F that correspond to M
Use the values of N, M, X, D, and F as needed to determine the values of the desired measures of system performance
Finite-Source Model (cont.)
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
33
Finite-Source Model (cont.)
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
34
Managers may be able to reduce waiting lines by actively managing one or more system constraints:
Fixed short-term constraints
Facility size
Number of servers
Short-term capacity options
Use temporary workers
Shift demand
Standardize the service
Look for a bottleneck
Constraint Management
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
35
Psychology of Waiting
If those waiting in line have nothing else to occupy their thoughts, they often tend to focus on the fact they are waiting in line
They will usually perceive the waiting time to be longer than the actual waiting time
Steps can be taken to make waiting more acceptable to customers
Occupy them while they wait
In-flight snack
Have them fill out forms while they wait
Make the waiting environment more comfortable
Provide customers information concerning their wait
LO 18.5
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
36
Operations Strategy
Managers must carefully weigh the costs and benefits of service system capacity alternatives
Options for reducing wait times:
Work to increase processing rates, instead of increasing the number of servers
Use new processing equipment and/or methods
Reduce processing time variability through standardization
Shift demand
Copyright ©2018 McGraw-Hill Higher Education. All rights reserved. No reproduction or distribution without the prior written consent of McGraw-Hill Education
18-‹#›
37
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