The document summarizes key concepts about waiting lines and queuing theory from Chapter 18. It discusses why waiting lines occur due to variability in arrival and service rates. Common metrics for analyzing waiting lines are described such as average number of customers waiting, average wait time, and system utilization. Different queuing models are presented including the single server exponential and constant service time models. The concept of population source, number of servers, arrival and service patterns, and queue discipline are defined.

1. Chapter 18
Waiting Lines
© McGraw-Hill Education. All rights reserved. Authorized only
for instructor use in the classroom. No reproduction or further
distribution permitted without the prior written consent of
McGraw-Hill Education.
1
Learning Objectives
You should be able to:
18.1 What imbalance does the existence of a waiting line
reveal?
18.2 What causes waiting lines to form, and why is it
impossible to eliminate them completely?
18.3 What metrics are used to help managers analyze waiting
lines?
18.4 What very important lesson does the constant service time
model provide for managers?
18.5 What are some psychological approaches to managing
lines, and why might a manager want to use them?
18-‹#›
© McGraw-Hill Education.
Learning Objective 18.1

2. Waiting Lines
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
18-‹#›
© McGraw-Hill Education.
Learning Objective 18.1
Queuing Theory
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
18-‹#›
© McGraw-Hill Education.

3. Learning Objective 18.2
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
18-‹#›
© McGraw-Hill Education.
Waiting Lines: Managerial Implications
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
18-‹#›
© McGraw-Hill Education.
Waiting Line Management
The goal of waiting line management is to minimize total costs:
Costs associated with customers waiting for service
Capacity cost

4. 18-‹#›
© McGraw-Hill Education.
Waiting Line Characteristics
The basic characteristics of waiting lines
Population source
Number of servers (channels)
Arrival and service patterns
Queue discipline
18-‹#›
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Simple Queuing System
18-‹#›
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Population Source (1 of 2)
Infinite source
Customer arrivals are unrestricted
The number of potential customers greatly exceeds system
capacity
18-‹#›
© McGraw-Hill Education.

5. Population Source (2 of 2)
Finite source
The number of potential customers is limited
18-‹#›
© McGraw-Hill Education.
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
18-‹#›
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Common Queuing Systems
18-‹#›
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Arrival and Service Patterns
Arrival pattern
Most commonly used models assume the arrival rate can be
described by the Poisson distribution
Arrivals per unit of time

6. 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
18-‹#›
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Poisson and Negative Exponential
18-‹#›
© McGraw-Hill Education.
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
18-‹#›
© McGraw-Hill Education.
Learning Objective 18.3
Waiting Line Metrics
Managers typically consider five measures when evaluating

7. 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
18-‹#›
© McGraw-Hill Education.
Learning Objective 18.3
Waiting Line Performance
The average number waiting in line and the average time
customers wait in line increase exponentially as the system
utilization increases
18-‹#›
© McGraw-Hill Education.
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

8. 18-‹#›
© McGraw-Hill Education.
Infinite-Source Symbols
18-‹#›
© McGraw-Hill Education.
Basic Relationships (1 of 3)
System Utilization
Average number of customers being served
18-‹#›
© McGraw-Hill Education.
Basic Relationships (2 of 3)
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
18-‹#›

9. © McGraw-Hill Education.
Basic Relationships (3 of 3)
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
18-‹#›
© McGraw-Hill Education.
Single Server, Exponential Service Time
M/M/1
18-‹#›
© McGraw-Hill Education.
Learning Objective 18.4
Single Server, Constant Service Time
M/D/1

10. 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)
18-‹#›
© McGraw-Hill Education.
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)
18-‹#›
© McGraw-Hill Education.
M/M/S
18-‹#›
© McGraw-Hill Education.

11. 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
18-‹#›
© McGraw-Hill Education.
Total Cost Curve
18-‹#›
© McGraw-Hill Education.
Maximum Line Length
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:
18-‹#›
© McGraw-Hill Education.
Multiple Priorities

12. 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
18-‹#›
© McGraw-Hill Education.
Multiple–Server Priority Model (1 of 3)
Performance Measure: System Utilization
Formula:
Formula Number: (18-15)
Performance Measure: Intermediate values(Lq from Table 18.4)
18-‹#›
© McGraw-Hill Education.
Multiple–Server Priority Model (2 of 3)
Performance Measure: Average waiting time in line for units in
kth priority class
Formula:

13. Formula Number: (18-18)
Performance Measure: Average time in the system for units in
the Kth priority class
Formula:
Formula Number: (18-19)
18-‹#›
© McGraw-Hill Education.
Multiple–Server Priority Model (3 of 3)
Performance Measure: Average number waiting in line for units
in the Kth priority class
Formula:
Formula Number: (18-20)
18-‹#›
© McGraw-Hill Education.
Finite-Source Model (1 of 5)
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

14. Service times are required to be exponential
18-‹#›
© McGraw-Hill Education.
Finite-Source Model (2 of 5)
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
18-‹#›
© McGraw-Hill Education.
Finite-Source Model (3 of 5)
Table 18.6 Finite-source queuing formulas and
notationPerformance MeasureFormulasNotation†Service
factor(18-21)D = Probability that a customer will have to wait
in lineAverage number waiting(18-22)F = Efficiency factor 1 –
Percentage waiting in lineAverage waiting time(18-23)H =
Average number of customers being servedAverage number
running
(18-24)J = Average number of customers not in line or in
service

15. 18-‹#›
© McGraw-Hill Education.
Finite-Source Model (4 of 5)Performance
MeasureFormulasNotation†Average number being
servedH=FNX(18-25)L = Average number of customers waiting
for serviceNumber in populationN=J+L+H(18-26)M = Number
of service channels
N = Number of potential customers
T = Average service time
U = Average time between customer service requirements per
customer
W = Average time customers wait in line
X = Service factor
18-‹#›
© McGraw-Hill Education.
Finite-Source Model (5 of 5)

16. 18-‹#›
© McGraw-Hill Education.
Constraint Management
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
18-‹#›
© McGraw-Hill Education.
Learning Objective 18.5
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

17. 18-‹#›
© McGraw-Hill Education.
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
18-‹#›
© McGraw-Hill Education.
End of Presentation
© McGraw-Hill Education. All rights reserved. Authorized only
for instructor use in the classroom. No reproduction or further
distribution permitted without the prior written consent of
McGraw-Hill Education.
18-‹#›
line
in
waiting
number

18. expected
maximum
The
(channels)
servers
of
number
The
system
the
in
units
of
y
probabilit
The
system
the
in
units

19. zero
of
y
probabilit
The
time
Service
system
the
in
spend
customers
time
average
The
line
in
wait
customers
time

20. average
The
n
utilizatio
system
The
served
being
customers
of
number
average
The
system
the
in
customer
of
number
average
The
service

21. for
waiting
customers
of
number
average
The
server
per
rate
Service
rate
arrival
Customer
=
=
=
=
=
=
=
=
=
=
=
=
=
max

22. n
0
s
q
s
q
L
M
n
P
P
μ
1
W
W
ρ
r
L
L
μ
λ
M
μ
λ
ρ
=
μ
λ
r
=
q
λW
q
L
s
λW

23. s
L
=
=
]
dependent.
[Model
q
L
r
L
L
q
s
+
=
λ
L
W
q
q
=
λ
L
μ
W
W
s
q
s
=
+
=
1

24. (
)
n
n
n
0
n
0
2
q
μ
λ
P
μ
λ
P
P
μ
λ
P
λ
μ
μ
λ
L
÷
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ö
ç
ç
è
æ
-
=
÷

25. ÷
ø
ö
ç
ç
è
æ
=
÷
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æ
-
=
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á
1
1
(
)
λ
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λ
L
2
q
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=
2
(

26. )
(
)
s
q
w
s
M
n
M
n
0
0
M
q
W
W
P
λ
M
μ
W
M
ν
λ
M
μ
λ
n
μ
λ
P
P
λ
M
μ

27. M
μ
λ
λμ
L
=
-
=
ú
ú
ú
ú
ú
û
ù
ê
ê
ê
ê
ê
ë
é
÷
ø
ö
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-
÷
÷
ø
ö
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28. æ
+
÷
÷
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ç
ç
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æ
=
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ç
è
æ
=
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=
å
1
1
!
!
!
1
1
1
0
2

29. (
)
ρ
L
K
ρ
K
ρ
K
L
q
max
-
-
=
=
1
percentage
specified
1
where
ln
ln
or
log
log
μ
λ
ρ
M
=
(
)
1
17)

30. -
(18
μ
λ
1
16)
-
(18
ρ
1
λ
1
c
=
-
=
-
=
å
=
0
k
c
k
q
B
M
B
L
A
Number
Formula
Formula
k
k

31. k
B
B
A
W
×
×
=
-
1
1
μ
1
+
=
k
W
W
k
k
k
W
λ
L
´
=
U
T
T
X
+
=
(
)
F

32. N
L
-
=
1
(
)
(
)
XF
F
T
L
N
U
T
L
W
-
=
-
+
=
1
(
)
X
NF
J
-
=
1
H
L
J
H

33. J
F
T
H
W
L
U
J
+
+
·
+
=
time
Average
|
|
:|
number
Average
Cycle
served
Being
Waiting
served
being
for
waiting
Not
4
4
4
4

34. 4
8
4
4
4
4
4
7
6
Chapter 17
Project Management
© McGraw-Hill Education. All rights reserved. Authorized only
for instructor use in the classroom. No reproduction or further
distribution permitted without the prior written consent of
McGraw-Hill Education.
1
Learning Objectives (1 of 2)
You should be able to:
17.1 Describe the project life cycle
17.2 Discuss the behavioral aspects of projects in terms of
project personnel and the project manager
17.3 Explain the nature and importance of a work breakdown
structure in project management
17.4 Name the six key decisions in project management
17.5 Give a general description of PERT/CPM techniques

35. 17-‹#›
© McGraw-Hill Education.
Learning Objective (2 of 2)
17.6 Construct simple network diagrams
17.7 Analyze networks with deterministic times
17.8 Analyze networks with probabilistic times
17.9 Describe activity ‘crashing’ and solve typical problems
17.10 Discuss the advantages of using PERT and potential
sources of error
17.11 Discuss the key steps in risk management
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.1
Projects
Projects
Unique, one-time operations designed to accomplish a specific
set of objectives in a limited time frame
Examples:
The Olympic Games
Producing a movie
Software development
Product development
ERP implementation
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.1

36. The Nature of Projects
Projects go through a series of stages – a life cycle
Projects bring together people with a diversity of knowledge
and skills, most of whom remain associated with the project for
less than its full life
Organizational structure affects how projects are managed
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.1
Project Life Cycle
Initiating
Planning
Executing
Monitoring and Controlling
Closing
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.2
Project Manager
The project manager is ultimately responsible for the success or
failure of the project
The project manager must effectively manage:
The work
The human resources
Communications
Quality
Time
Costs

37. 17-‹#›
© McGraw-Hill Education.
Learning Objective 17.2
The Project Management Triangle
Performance Objectives
Quality
Cost
Schedule
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.2
Behavioral Issues
Behavioral problems can be created or exacerbated by
Decentralized decision making
Stress of achieving project milestones on time and within
budget
Surprises
The team must be able to function as a unit
Interpersonal and coping skills are very important
Conflict resolution and negotiation can be an important part of a
project manager’s job
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.2

38. Avoiding Problems
Many problems can be avoided or mitigated by:
Effective team selection
Leadership
Motivation
Maintaining an environment of
Integrity
Trust
Professionalism
Being supportive of team efforts
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.2
Project Champion
Project champion
A person who promotes and supports a project
Usually resides within the organization
Facilitate the work of the project by ‘talking up’ the project to
other managers who might be asked to share resources with the
project team as well as employees who might be asked to wor k
on parts of the project
The project champion can be critical to the success of a project
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.3
Work Breakdown Structure (WBS)
WBS
A hierarchical listing of what must be done during a project

39. Establishes a logical framework for identifying the required
activities for the project
Identify the major elements of the project
Identify the major supporting activities for each of the major
elements
Break down each major supporting activity into a list of the
activities that will be needed to accomplish it
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.3
WBS
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.4
Project Management Decisions
Project success depends upon making key managerial deci sions
over a sequence of steps:
Deciding which projects to implement
Selecting the project manager
Selecting the project team
Planning and designing the project
Managing and controlling project resources
Deciding if and when a project should be terminated
17-‹#›

40. © McGraw-Hill Education.
Learning Objective 17.5
PERT and CPM
PERT (program evaluation and review technique) and CPM
(critical path method) are two techniques used to manage large-
scale projects
By using PERT or CPM Managers can obtain:
A graphical display of project activities
An indication of which activities are most critical to timely
project completion
An estimate of how long the project will take
An indication of how long any activity can be delayed without
delaying the project
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.6
Network Diagram (1 of 2)
Network diagram
Diagram of project activities that shows sequential relationships
by use of arrows and nodes
Activity on arrow (AOA)
Network diagram convention in which arrows designate
activities
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.6
Network Diagram (2 of 2)

41. Activity on node (AON)
Network convention in which nodes designate activities
Activities
Project steps that consume resources and/or time
Events
The starting and finishing of activities
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.6
Network Conventions
TABLE 17.2 Network conversions
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.7
Deterministic Time Estimates
Deterministic
Time estimates that are fairly certain
Probabilistic
Time estimates that allow for variation
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.7
Early Start, Early Finish (1 of 2)

42. Finding ES and EF involves a forward pass through the network
diagram
Early start (ES)
The earliest time an activity can start
Assumes all preceding activities start as early as possible
For nodes with one entering arrow
ES = EF of the entering arrow
For activities leaving nodes with multiple entering arrows
ES = the largest of the largest entering EF
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.7
Early Start, Early Finish (2 of 2)
Early finish (EF)
The earliest time an activity can finish
EF = ES + t
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.7
Late Start, Late Finish (1 of 2)
Finding LS and LF involves a backward pass through the
network diagram
Late Start (LS)
The latest time the activity can start and not delay the project
The latest starting time for each activity is equal to its latest
finishing time minus its expected duration:
LS = LF - t

43. 17-‹#›
© McGraw-Hill Education.
Learning Objective 17.7
Late Start, Late Finish (2 of 2)
Late Finish (LF)
The latest time the activity can finish and not delay the project
For nodes with one leaving arrow, LF for nodes entering that
node equals the LS of the leaving arrow
For nodes with multiple leaving arrows, LF for arrows entering
node equals the smallest of the leaving arrows
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.7
Slack and the Critical Path
Slack can be computed one of two ways:
Slack = LS – ES
Slack = LF – EF
Critical path
The critical path is indicated by the activities with zero slack
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.7
Using Slack Times
Knowledge of slack times provides managers with information
for planning allocation of scarce resources

44. Control efforts will be directed toward those activities that
might be most susceptible to delaying the project
Activity slack times are based on the assumption that all of the
activities on the same path will be started as early as possible
and not exceed their expected time
If two activities are on the same path and have the same slack,
this will be the total slack available to both
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
Probabilistic Time Estimates
The beta distribution is generally used to describe the inherent
variability in time estimates
The probabilistic approach involves three time estimates:
Optimistic time, (to)
The length of time required under optimal conditions
Pessimistic time, (tp)
The length of time required under the worst conditions
Most likely time, (tm)
The most probable length of time required
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
The Beta Distribution

45. 17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
Probabilistic Time Estimates (1 of 2)
The expected time, te ,for an activity is a weighted average of
the three time estimates:
The expected duration of a path is equal to the sum of the
expected times of the activities on that path:
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
Probabilistic Time Estimates (2 of 2)
The standard deviation of each activity’s time is estimated as
one-sixth of the difference between the pessimistic and
optimistic time estimates. The variance is the square of the
standard deviation:
Standard deviation of the expected time for the path
17-‹#›
© McGraw-Hill Education.

46. Learning Objective 17.8
Knowledge of Path Statistics
Knowledge of expected path times and their standard deviations
enables managers to compute probabilistic estimates about
project completion such as:
The probability that the project will be completed by a certain
time
The probability that the project will take longer than its
expected completion time
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
Path Probabilities
Calculating path probabilities involves the use of the normal
distribution
Although path activities are represented by the beta
distribution, the path distribution can be represented by a
normal distribution
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
Determining Path Probabilities

47. 17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
Project Completion Time
A project is not complete until all project activities are
complete
It is risky to only consider the critical path when assessing the
probability of completing a project within a specified time
To determine the probability of completing the project within a
particular time frame
Calculate the probability that each path in the project will be
completed within the specified time
Multiply these probabilities
The result is the probability that the project will be completed
within the specified time
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
Assumption: Independence
Independence
Assumption that path duration times are independent of each
other
Requires that
Activity times are independent
Each activity is on only one path
The assumption of independence is usually considered to be met
if only a few activities in a large project are on multiple paths

48. 17-‹#›
© McGraw-Hill Education.
Learning Objective 17.8
Simulation
When activity times cannot be assumed to be independent,
simulation is often used
Repeated sampling is used
Many passes are made through the project network
In each pass, a random value for each activity time is selected
based on the activity time’s probability distribution
After each pass, the project’s duration is determined
After a large number of passes, there are enough data points to
prepare a frequency distribution of the project duration
Probabilistic estimates of completion times are made based on
this frequency distribution
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.9
Time-Cost Trade-Offs
Activity time estimates are made for some given level of
resources
It may be possible to reduce the duration of a project by
injecting additional resources
Motivations:
To avoid late penalties
Monetary incentives
Free resources for use on other projects
17-‹#›

49. © McGraw-Hill Education.
Learning Objective 17.9
Time-Cost Trade-Offs: Crashing
Crashing
Shortening activity durations
Typically, involves the use of additional funds to support
additional personnel or more efficient equipment, and the
relaxing of some work specifications
The project duration may be shortened by increasing direct
expenses, thereby realizing savings in indirect project costs
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.9
Crashing Decisions
To make decisions concerning crashing requires information
about:
Regular time and crash time estimates for each activity
Regular cost and crash cost estimates for each activity
A list of activities that are on the critical path
Critical path activities are potential candidates for crashing
Crashing non-critical path activities would not have an impact
on overall project duration
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.9
Crashing: Procedure
General procedure:

50. Crash the project one period at a time
Crash the least expensive activity that is on the critical path
When there are multiple critical paths, find the sum of crashing
the least expensive activity on each critical path
If two or more critical paths share common activities, compare
the least expensive cost of crashing a common activity shared
by critical paths with the sum for the separate critical paths
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.9
Crashing Activities
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.10
PERT: Advantages
Among the most useful features of PERT:
It forces the manager to organize and quantify available
information and to identify where additional information is
needed
It provides the a graphic display of the project and its major
activities
It identifies
Activities that should be closely watched
Activities that have slack time

51. 17-‹#›
© McGraw-Hill Education.
Learning Objective 17.10
Sources of Error
Potential sources of error:
The project network may be incomplete
Precedence relationships may not be correctly expressed
Time estimates may be inaccurate
There may be a tendency to focus on critical path activities to
the exclusion of other important project activities
Major risk events may not be on the critical path
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.11
Risk Management (1 of 2)
Risks are an inherent part of project management
Risks relate to occurrence of events that have undesirable
consequences such as
Delays
Increased costs
Inability to meet technical specifications
17-‹#›
© McGraw-Hill Education.
Learning Objective 17.11
Risk Management (2 of 2)
Good risk management involves
Identifying as many risks as possible

52. Analyzing and assessing those risks
Working to minimize the probability of their occurrence
Establishing contingency plans and budgets for dealing with any
that do occur
17-‹#›
© McGraw-Hill Education.
End of Presentation
© McGraw-Hill Education. All rights reserved. Authorized only
for instructor use in the classroom. No reproduction or further
distribution permitted without the prior written consent of
McGraw-Hill Education.
17-‹#›
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TO: CEO
FROM: Michael Fleming, Operations Manager
DATE: May 4, 2020
SUBJECT: Project Management Plan

55. Background
Kibby and Strands is expanding due to increasing demand and
reputation for reliable services. The building next doors leasing
plan fell through, allowing Kibby and Strands to lease the
building. We will move our Receiving department to the new
building while expanding the production department to the old
receiving area.
Project Plan
Product planning refers to everything you do to set up your
project for success. This involves several steps in order to
create a steady flow of work from start to finish an optimize the
potential for downtime while the equipment is being moved, set
up or not currently producing products. These steps are:
1. Onboard important stakeholders and the project team
a. Ensuring everyone involved knows the plan and their
respective tasks.
2. End state goals
a. Defined goals that have been worked out by the project
manager to keep the team on track and guide the process.
3. Create tasks with a task list
a. Smaller delivers unique to each person that when complied
accomplish the end state goal.
4. Set priority
a. Setting a priority for each of the tasks a person is responsible
for.
b. Ensures one person knows what must be done before they can
start their task.
5. Create deadlines
a. By having a hard deadline for each task, a worker knows by
which time they must be complete
b. Adding in a margin for error between tasks will be ensure end
state goals are completed in a timely manner.
6. Set milestones

56. a. Milestone break chunks of work into smaller project phases
and easier to understand as a whole.
7. Assign work
a. Assigning team members to appropriate level of work for
their respective departments.
8. Project roadmap
a. Ensure the goals fit within the business strategy of the
organization.
9. Monitor and report
a. Continuing to check up on the progress and make shifts where
needed.
Network Diagram
Critical Path
A-B-E-G-H = 1+2+2+1 = 6 Days
A-C-G-H = 1+4+1 = 6 Days
A-D-F-G-H = 2+1+2+1 = 6 Days
Any of the activities would yield a completed time of 6 days
using the critical path method. The only difference is the
number of activities are passed through.
Reference
Stevenson, W. J. (2017). Operations Management (13th ed.).
McGraw-Hill Education.
Project Planning: Your Ultimate Guide
https://www.projectmanager.com/project-planning