Facility Planning Facility Planning and Design Used with permission: Dr. David Porter Data Management & Analysis Process Management Planning ApplicationAnalysis Reporting 2 Presentation Outline —Introduction to § Facilities Planning § Facilities Layout —Generating layout alternatives with § Systematic Layout Planning (SLP) § Computerized Relative Allocation of Facilities Technique (CRAFT) Data Management & Analysis Process Management Planning ApplicationAnalysis Reporting 3 Facilities Planning — Facilities planning determines how an activity’s tangible fixed assets best support achieving the activity's objectives — Facilities Planning Viewpoints § Civil Engineering § Electrical/Mechanical Engineering § Architectural § Construction Management/Contractor § Real Estate § Urban Planning § Industrial Engineering (IE) Data Management & Analysis Process Management Planning ApplicationAnalysis Reporting 4 IE Viewpoint of Facilities Planning — Industrial Engineers focus on § Requirements § Resource allocation, and § Efficient use of resources — Facilities are the integration of many lower level systems § Space requirements with respect to flow and operations control § Personnel & Equipment Requirements § System design/layout with respect to flow and operations control § The use of information systems and technology to increase effectiveness § Movement within a facility and between facilities (i.e., location) Data Management & Analysis Process Management Planning ApplicationAnalysis Reporting 5 Example of a Manufacturing Facility Data Management & Analysis Process Management Planning ApplicationAnalysis Reporting 6 From an IE Viewpoint — Why is the equipment in this facility located as shown? — Why are they arranged as shown? — Why are there so many duplicated items? — Why is the facility so large or small? — How many people will be working in the facility? — Does this design meet requirements? — etc. Data Management & Analysis Process Management Planning ApplicationAnalysis Reporting 7 IE Approaches — Industrial Engineers develop models to understand, design and validate systems § Procedures • e.g., Systematic Layout Planning (SLP) § Analytical models • e.g., machine fraction equations, queuing models § Analytical layout models/software § Computer simulations Data Management & Analysis Process Management Planning ApplicationAnalysis Reporting 8 Elements of Facilities Planning Facilities Planning Facilities Location Facilities Design Facilities Systems Production System Design Layout Design Handling/Storage Systems Design Data Management & Analysis Process Management Planning ApplicationAnalysis Reporting 9 Facilities Layout — Facilities layout is a design activity and as such there is often a lot of art (i.e., experience) and application-specific knowledge that must be utilized when developing a layout § Grocery store layout vs. department store lay ...

1. Facility Planning
Facility Planning and Design
Used with permission: Dr. David Porter
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
2
Presentation Outline
—Introduction to
§ Facilities Planning
§ Facilities Layout
—Generating layout alternatives with
§ Systematic Layout Planning (SLP)
§ Computerized Relative Allocation of Facilities Technique
(CRAFT)

2. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
3
Facilities Planning
— Facilities planning determines how an activity’s tangible
fixed assets
best support achieving the activity's objectives
— Facilities Planning Viewpoints
§ Civil Engineering
§ Electrical/Mechanical Engineering
§ Architectural
§ Construction Management/Contractor
§ Real Estate
§ Urban Planning
§ Industrial Engineering (IE)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

3. Reporting
4
IE Viewpoint of Facilities Planning
— Industrial Engineers focus on
§ Requirements
§ Resource allocation, and
§ Efficient use of resources
— Facilities are the integration of many lower level systems
§ Space requirements with respect to flow and operations
control
§ Personnel & Equipment Requirements
§ System design/layout with respect to flow and operations
control
§ The use of information systems and technology to increase
effectiveness
§ Movement within a facility and between facilities (i.e.,
location)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
5

4. Example of a Manufacturing Facility
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
6
From an IE Viewpoint
— Why is the equipment in this facility located as shown?
— Why are they arranged as shown?
— Why are there so many duplicated items?
— Why is the facility so large or small?
— How many people will be working in the facility?
— Does this design meet requirements?
— etc.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting

5. 7
IE Approaches
— Industrial Engineers develop models to understand, design
and
validate systems
§ Procedures
• e.g., Systematic Layout Planning (SLP)
§ Analytical models
• e.g., machine fraction equations, queuing models
§ Analytical layout models/software
§ Computer simulations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
8
Elements of Facilities Planning
Facilities
Planning
Facilities

6. Location
Facilities
Design
Facilities
Systems
Production
System
Design
Layout
Design
Handling/Storage
Systems
Design
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9
Facilities Layout
— Facilities layout is a design activity and as such there is
often a lot of

7. art (i.e., experience) and application-specific knowledge that
must be
utilized when developing a layout
§ Grocery store layout vs. department store layout
§ Layout of an engineering complex
§ Layout of an educational/research building
§ Layout of plants that produce different products
• Vehicle vs. computers
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
10
Facilities Layout
— There are no recipes for the layout
— In reality, politics and other organizational considerations
will place
constraints on layouts
— All material presented related to layout design is decision
support
Data Management &

8. Analysis Process Management
Planning
ApplicationAnalysis
Reporting
11
Facilities Layout
— Information needed to design a layout
1. What are the “blocks” or departments that constitute units or
areas
within a layout?
2. Building/facility footprint
• Exits/entrances/docks, etc.
• Columns, ceiling height, location of utilities.
• Other?
3. Flow measurement/Adjacency measurement
4. Space requirements
• Departments
§ Workstations, aisles, storage, meeting rooms, etc.
• Central storage
• Administration
• Etc.
Data Management &

9. Analysis Process Management
Planning
ApplicationAnalysis
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12
Units or Areas within a Layout
— Normally this is given as input data
§ Often will follow organizational and/or production system
structure
— Examples
§ Engineering complexes – Various product development
departments are given
§ Batch production system – Sheet metal press lines
• Various press line sizes are given
— Support functions must be included
§ Storage, admin./engineering offices, IT support, cafeterias,
lockers, restrooms,
conference rooms, etc.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

10. Reporting
13
Building Footprint
— Often also given as input data
— Items of particular concern that affect flow in a facility
§ Location of shipping/receiving docks
§ Location of entrances/exits
§ Columns
§ Ceiling heights
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
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14
Facilities Layout
— Measuring flow
§ Quantitative
• Appropriate when large volumes of material/people move
between departments
§ Qualitative

11. • How important is adjacency to two departments?
• Often applied to the layout of office environments
— Other types of flow?
§ Sound
§ RF signals
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
15
Quantitative Flow Measurement
— Captured in a From-To chart
— Units are the number of trips of equal ease of movement per
time
unit
§ e.g., moving a large die is much more effort than moving a
small bin of parts
To
From Stores Milling Turning Press Plate Assembly Warehouse
Stores X 12 6 9 1 4
Milling X 7 2
Turning 3 X 4
Press X 3 1 1

12. Plate 3 1 X 4 3
Assembly 1 X 7
Warehouse X
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
16
Qualitative Flow Measurement
— Captured in what is called an Activity Relationship Chart
— The criticality of department adjacency is captured on the
following
scale:
§ A – Absolutely necessary
§ E – Especially important
§ I – Important
§ O – Ordinary closeness OK
§ U – Unimportant
§ X – Undesirable
— Assessed through interviews and meetings
Data Management &

13. Analysis Process Management
Planning
ApplicationAnalysis
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17
Example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
18
Generating Layout Alternatives
— Objective
§ Develop a good or optimal block layout
— Block layout
§ A two dimensional top down view arrangement of departments
in a facility
§ Departments are represented as rectangles (or shapes
constructed from rectangles)

14. with the relative area of the department captured by the size of
the rectangle
Office Fab Paint
Stores Assembly
Maint
Sup
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
19
Generating Layout Alternatives
— Procedures
§ Construction procedures
• “Greenfield” layout à the layout of a new facility
§ Improvement procedures
• Changes/ improvements to existing facilities
Data Management &
Analysis Process Management

15. Planning
ApplicationAnalysis
Reporting
20
Systematic Layout Planning - SLP
— A qualitative method
1. Use a from-to or flow-between chart and/or an activity
relationship chart, and
space requirements to create a relationship diagram
2. Next, use the relationship diagram to create a space
relationship diagram
3. The space relationship diagram is used to generate layout
alternatives in the
form of block layouts
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
21

16. SLP - Example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
22
SLP – In Class Example
— Four depts. are to be located on a 600’ x 1000’ bldg.
The expected personnel traffic flows and area
requirements for departments are shown in the tables
below
a. Develop a block layout design using SLP
Dept. A B C D
A 0 250 25 240
B 125 0 400 335
C 100 0 0 225
D 125 285 175 0
Dept. Dimen.
A 200’x200’

17. B 400’x400’
C 600’x600’
D 200’x200’
From-To Chart
Fr
om
To
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
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23
SLP – In Class Example
1) Construct a Flow-Between Chart
2) Rank the department pairs in order of greatest two-way flow
Data Management &

18. Analysis Process Management
Planning
ApplicationAnalysis
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24
SLP – In Class Example
3) Create an Activity-Relationship diagram
4) Create a Space-Relationship diagram
Data Management &
Analysis Process Management
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ApplicationAnalysis
Reporting
25
SLP – In Class Example
4) Generate layout options
Dept. Dimen.
A 200’x200’
B 400’x400’

19. C 600’x600’
D 200’x200’
1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
1 2 3 4 5 6 7 8 9 10
1
2
3
4
5
6
Data Management &
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26
SLP – In Class Example
4) Generate layout options

20. Dept. Dimen.
A 200’x200’
B 400’x400’
C 600’x600’
D 200’x200’
1 2 3 4 5 6 7 8 9 10
1 C C C C C C D D A A
2 C C C C C C D D A A
3 C C C C C C B B B B
4 C C C C C C B B B B
5 C C C C C C B B B B
6 C C C C C C B B B B
1 2 3 4 5 6 7 8 9 10
1 C C C C C C C C C D
2 C C C C C C C C C D
3 C C C C C C C C C D
4 C C C C C C C C C D
5 A A B B B B B B B B
6 A A B B B B B B B B
Data Management &
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ApplicationAnalysis
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27
Computer-Aided Layout

21. — Computer-aided layout supports layout design
§ Generate many layout alternatives in a short time
§ Helps conduct what if and sensitivity analysis
§ Modeling the problem helps understand the system
perspective
— Commercial software
§ Most algorithms have not yet been commercialized although
they are available as
research code and used by consultants
§ Educational software is available
• We will use one package
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
28
Computer-Aided Layout
— Inputs
1. From-To chart and/or
2. Activity relationship chart
§ You may have the flexibility to map the A,E,I,O,U,X scale to

22. different
numerical scales
§ Can change results by changing the scaling
§ Can have negative values for relationships (e.g., X)
3. Usually, the building footprint
§ May be restricted to a rectangular footprint
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
29
Computer-Aided Layout
— Layout development criteria
§ When constructing a layout, an algorithm implemented on a
computer needs
specific criteria to compare alternative layouts
§ These criteria may differ depending on the form of input data
characterizing
flow/relationships
§ The criteria must be computable (i.e., quantitative) and is
referred to as the

23. objective function of the layout problem
• From-To chart as input
Ø Distance-based objective
• Activity relationship chart as input
Ø Adjacency-based objective
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
30
Specific Computerized Layout Algorithms
— CRAFT – Computerized Relative Allocation of Facilities
Technique
§ Inputs
• From-To chart
• Cost matrix
• Initial layout
§ Objective
• Distance based
§ Department representation
• Discrete grids
• No shape restrictions

24. Data Management &
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ApplicationAnalysis
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31
Specific Computerized Layout Algorithms
— CRAFT automatically implements a modified pairwise
interchange method
§ Many details must be addressed
— CRAFT Algorithm
1. Start with an initial layout with all departments made up of
individual square grids (Note: each grid represents the same
amount of space)
2. Estimate the best two-way department exchange assuming
department centroids exchange exactly
§ Departments i and j exchange
Ø New centroid i = centroid j
Ø New centroid j = centroid i
§ Only consider exchanging adjacent departments

25. Data Management &
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Specific Computerized Layout Algorithms
— CRAFT Algorithm (cont’d)
3. Execute the exchange if the estimated cost of the best
exchange
in (2) is lower than the best cost found so far
§ The actual result of the exchange is problem-dependent
4. If the estimated cost of the best exchange in (2) is higher
than
the best cost found so far, stop
§ Else, go to 1
Data Management &
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Planning
ApplicationAnalysis
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33

26. CRAFT Example
— Consider four departments labeled A, B, C and D. Each
department is represented by a 1 x 1 square. The following
data are given:
— Assuming department A is fixed, compute the actual cost of
this layout using the computerized version of CRAFT
A B C D
A -- 6 0 3
B -- 5 0
C -- 0
D --
A B C D
A -- 2 0 3
B 2 -- 1 0
C 0 1 -- 0
D 3 0 0 --
A B
C D
Initial layout
Flow-Between Matrix Unit Cost Matrix
Commemorative Speech Assignment
Specific Purpose:
To pay tribute to an object or invention which has made a
difference in your life and/or the life of others (no electronics
such as includes TV, cell phone, computer, TiVo, etc.)
Length:

27. 3-4 minutes (thirty-second grace period either way).
1. Start with a narrative or quotation (see bartleby.com for
example) to elevate the person, occasion, subject, etc.
(attention-getter)
2. Explain why you are honoring this (credibility) and want to
pay a tribute (reveal topic). Formulate one simple sentence to
express why your subject deserves to be honored (your thesis!)
3. Continue using the format of an informative speech, but pay
attention to LANGUAGE, make sure it is elevated and
inspiring.
3. Consider the type of ceremony where this speech will take
place. Is it a formal and ceremonial gathering? What does your
audience think about the subject? Why are they there?
4. Refer to the particular contributions, achievements or merits
of the honoree. Give concrete examples. This can be a list or a
narrative (story).
5. Consider your honoree in light of the values and beliefs of
your audience. Why are these contributions, this way of
thinking valuable to the audience? Why does the audience need
to value these same traits?
6. Can you come up with a brief story, an illustration, a quote, a
creative and organized visual aid, and/or figurative language?
Can you reveal any not well known factors or achievement?
How does your subject benefit society?
7. Praise the special characteristics or virtues. Tell the public an
insight story, but only mention the positive sides.
8. What are the major specific characteristics or virtues your
honoree possesses or caused you to possess? Courage,
compassion, kindness, great defender of family values, wisdom,
energy, charity, etc?

28. 9. End your speech with a short quotation, or refer back to the
original quotation or story, reminding the audience why your
subject is truly worthy of being honored.
10. REMEMBER: Be honest and sincere (focusing on the
positive), use expressive and elevated language, but do not over
exaggerate.
Organization:
· A written preparation outline is required for this speech and
should include all relevant content and sources. Follow the
model of an INFORMATIVE SPEECH.
· A speaker's outline/notecard is recommended when delivering
the speech. (For an example see pages 218-224 in your text.) It
should be DELIVERED, not READ.
Sources:
· At leastone outside source must be mentioned in the speech
and referenced properly (APA style) in outline.
Visual Aid: A visual aid is required. It may be a Power Point
presentation, but the object is best.
Self-Assessment: video critique is required
Use the ALT & F1 keys together to move through the document
Save this file with a unique name to your storage device.
Commemorative Speech Outline

29. Name:
Date: November 26, 2016Topic reviewed: _____
Topic: Commemorative Speech
Purpose: MACROBUTTON FormFieldOptions To
Commemorate
Specific purpose: To commemorate my BDU’s (Battle Dress
Uniform)
Thesis: My uniform has had the biggest impact on my life as a
person and my life after the military.
Introduction:
I. A lot of people look at this uniform and see just that,
something that a soldier, or an airman wears. It doesn’t have
many distinguishing marks besides a nametape, and stripe
insignia. However, I see an amount of miles traveled that would
circle the earth six times. I see the holidays spent away from
home. I see the groups of strangers that became family.
II. My uniform carries it’s own authority, it’s own memories,
and even though I no longer wear it, will grant me opportunities
such as this one the rest of my life.
III. I spent six years and thousands of hours and miles in this
uniform, and never regret a single minute of it.
IV. I want to share with you the reasons why this is more than
just a uniform, it’s part of me and part of my life. It’s part of
my character, and part of my memory. It too has it’s own
personality that we formed together. It saw a boy turned into a
man, and earned stripes just like he had.
Body:

30. I. This uniform gave a young kid purpose, direction, and life
experience.
A. After I graduated high school, I was like many other kids. I
was searching. I was as a spotlight, searching for the escapee
that once I had found, I wouldn’t release. Joining the Air
Force ended that search.
B. The uniform taught me financial responsibility, pride in
having a strong self-image, and the responsibility it takes to
wake up every day and do something because it’s the right
thing to do. It also taught me the history of many before me
that paid the ultimate price so the ones after them could
carry on their legacy.
C. I have met more people and seen more places than most
twenty nine year olds. If I hadn’t have had this uniform, I
know
for a fact that I would not have seen everything that I
have.
My uniform has carried on with me like a candle that refuses to
burn away, and it continues to burn well after I have left the
military.
II. Many aspects of my life are affected today because of my
uniform.
A. My wife and I live in a house that I bought after being
approved for a VA loan. For veterans, the VA is the sleepless
advocate.

31. B. I’m in this class today working toward a better future for
myself and for my family because I spent time in a uniform. I
receive a benefit called the Montgomery G.I. bill that pays for
my school and my expenses.
C. Every year Americans, and their allies the world over, stop
on November eleventh and pay tribute to the uniform and
those who wear or have worn it. On that day I am humbled
to know that so many people, if only for a small grain in the
sand of time, remember what the uniform means.
Conclusion:
I. Once my uniform became part of my attire, I soon found out
that it also becomes part of your life, and you become part of a
culture that many before you have formed. The uniform ushers
you into a club that has no membership card, or monthly dues.
The club goes by only a single name, “veteran”.
II. Sir Robert Baden-Powell was quoted as saying “The uniform
makes for brotherhood, since when universally adopted it covers
up all differences of class and country.” Even though a uniform
may get dirty or worn out, even though it’s colors may fade and
need to be replaced, the uniform itself lives on. You see men
and women don’t make uniforms, uniforms make men and
women.
Thank you for your time today, and I appreciate your attention.
References:
Hint: To help remember how to properly list your references go
to http://www.sinclair.edu/facilities/library/research/index.cfm
Thinkexist.com. (Sir Robert Baden-Powell, founder of the boy
scouts)
Audience analysis information:

32. In the space below discuss how you have tailored your speech
for this particular audience and situation. Include specific
demographic, environmental and audience expectation
information you considered important to this speech.
My audience includes… People who may have family members
who are in or who have been in the military, or another
profession that wears a uniform.
My environment involves… being in a formal setting, possibly
behind a podium and being required to give the most polished
speech yet for our class.
Audience expectations for this speech include … my sharing
what my uniform means in descriptive language, and sharing
some of the values that has.
Choices I've made include…personal things I’ve learned, usage
of simile and personification. Trying to describe a uniform as an
idea or a value as opposed to just an object.
Copyright © 2012
Processes, Variation, & Measurement
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting

33. Process
Definition:
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Processes and Performance Measurement
"You can't control what you don't measure".
(Deming, W.E. Out of the Crisis. Cambridge, MA: MIT,
1986. )
Without measurement there is no way to know how a
process is performing; therefore there is no way to
improve it. By measuring the voice of the customer and
the voice of the process, gaps can be identified between
the two. This information gives us direction in our
improvement efforts as we begin closing the gap.
Data Management &
Analysis Process Management

34. Planning
ApplicationAnalysis
Reporting
The Deming Cycle for Process
Improvement Plan
Do
Study (or Check)
Act Plan
Do Study
Act
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Seven Tools of Quality Control
Scatter Diagram
Histogram
Pareto Chart
Flowchart

35. Cause and Effect Diagram
Run (trend) chart
Control Chart
Pareto Diagram Practice
Defective Items # Percent
Defective
O-rings missing 16
Improper torque 25
Loose connections 193
Fitting burrs 47
Cracked connectors 131
Total 412
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Process Mapping (Flowcharts)

36. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Common flow chart symbols
Activity Delay
Transportation Decision
Inspection
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Process Mapping Practice
Customer enters grocery order and a computer-generated order
sheet is
generated

37. Order sheet is taken to the warehouse
Order sheet is given to warehouse supervisor
Supervisor separates orders according to work area
Order forms taken to work areas
Picker separates out order forms
Produce picker fills each order and places on conveyor to dairy
Dairy picker fills each order and place on conveyor to meat
aisle
Meat picker fills each order and sends to shipping
Shipping inspects each order
Shipping loads each order onto cart based on route
Order ships
Cause/ Effect Diagrams aka Ishikawa Diagrams
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Sources of Variation
Data Management &
Analysis Process Management

38. Planning
ApplicationAnalysis
Reporting
Ishikawa diagrams
Fishbone diagram example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Fishbone diagram how-to’s (1)
1. Clearly define the effect or symptom for which the causes
must be
identified.
2. Place the effect or symptom being explored at the right,
enclosed in a
box.
3. Draw the central spine as a thick line pointing to it from the
left.

39. 4. Brainstorm to identify the "major categories" of possible
causes using
the 6 “sources
of variation”
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Fishbone diagram how-to’s (2)
5. Place each major category in a box and connect it to the
central spine.
6. Within each major category, ask "Why does this happen?
Why does this
condition exist?"
7. Continue to add clauses to each branch until the fishbone is
completed.
8. Once all the bones have been completed, identify the likely,
actionable
root cause.

40. Process Control
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Measures of location
Average and mean
Median
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Variation
Definition:

41. Random variation:
Nonrandom variation:
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Deming on Variation
“If I had to reduce my message to management to just a few
words, I’d
say it all had to do with variation”
Deming (1982)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting

42. Measures of variation
Range
Variance
Standard deviation
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Inspecting in Quality
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting

43. Prevention
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Designed Experiments
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Run Charts
Cereal Box Weight on 10/31/06
3
3.5
4

44. 4.5
5
5.5
6
0 5 10 15 20 25
Time (hour)
W
ei
gh
t (
gr
am
s)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Control Charts

45. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Control Charts
Different types of control charts are used depending on the type
of data you
have
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Control Chart Example
0.49
0.495
0.5

46. 0.505
0.51
0.515
0.52
0.525
0.53
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39
Other Process Improvement Tools
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Interrelationship Digraph
When should I use this technique?

47. Why should I use this technique?
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Interrelationship Digraph
Arrange the ideas
Look for relationships
Determine the direction of influence
Tally the arrows
Identify the Driver & Outcome
Draw final Interrelationship Diagraph
Affinity Diagram Practice
Create an AD for “What are
the issues related to
performing well on
engineering exams?”
Two groups of 9, develop 20
– 30 cards
Place on white board

48. arranged in logical groups
Develop a summary card for
each grouping
Spending enough time
reviewing class notes
Force Field Diagram Practice
Create an Force Field Diagram to
identify those factors that
support and work against
students spending more time
studying for their exams.
Two groups of 9, develop at least
5 forces driving towards the
idea situation and 5 that keep
you from the ideal situation
Draw final force field on white
board using a “T” to separate
+ Driving Forces Restraining Forces -
Study group pressure
Roommate wants to
go out for dinner
Data Management &

49. Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Class Plan
• Data Management & Analysis
• Histograms
• Scatter Plots
• Regression
1
Data Analysis in Excel
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics

50. Measures of central tendency
• Mean (average): Most popular measure of
central tendency
Xbar = sum (from I to n) of Xi
divided by n
Where
xi = Observation number i
n = Total number of
observations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of central tendency
• Median: Middle observation within a data set
when the observations are arranged in increasing
order
If number of values (n) in data set is odd, then the median is the
middle observation

51. If number of values (n) in data set is even then median = (x n/2
+
xn/2 +1)/2
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of central tendency
• Mode: Value that occurs more often than any of
the others in a data set
Does not always exist
Example: Scores from a test
Is not necessarily unique, i.e. a data set can
have more than one mode
= 2 modes è Bimodal
> 2 modes è Multimodal
Applicable to both quantitative and qualitative data
Particularly useful in marketing and inventory considerations

52. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of dispersion
• Range :Difference between the largest and
smallest values in a data set
Xlargest - Xsmallest
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of dispersion
• Variance: Measures how a set of measurements
fluctuate relative to the mean of the data set:

53. S2 = sum (x – xbar)2 /(n-1)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of dispersion
Standard deviation: What is the problem with
the variance? It has different units of measurement
(e.g., cm2)
To return data to its original units; Standard
deviation = square root of variance
Graphical Analysis
Examples
Data Management &
Analysis Process Management

54. Planning
ApplicationAnalysis
Reporting
Bar or Column Graph
Displays frequency of observations that fall into nominal
categories
Color distribution for a random package of M&Ms
0
5
10
15
20
25
brown red yellow green orange blue
Color
Q
ty
Data Management &
Analysis Process Management

55. Planning
ApplicationAnalysis
Reporting
Line Chart
Shows trends in data at equal intervals
0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
M
ax
S
ke
w
A

56. ve
ra
ge
M
ax
P
itc
h
A
ve
ra
ge
C
on
tr
ol
le
d
S
ca
n
F

57. re
eh
an
d
S
ca
n
B
rig
ht
L
ig
ht
N
or
m
al
L
ig
ht
Lo
w

58. L
ig
ht
Performance Category
S
ca
n
Ti
m
e
(S
ec
on
ds
)
CCD1 CCD2 LR LCCD CMOS
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

59. Reporting
Graphical methods
Acceptable graph
EDC Warehouse
Test Results for Read Time
ALL SYSTEMS
0.64
0.20
0.52
0.81
0.66
N/A
1.46
0.88
0
1
2
1 2 3 4 5 6 7 8
RFID System
R

60. ea
d
Ti
m
e
(s
ec
s/
re
ad
)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Graphical methods
Better graph
EDC Warehouse
Test Results for Read Time
ALL SYSTEMS

61. 0.88
1.46
N/A
0.66
0.81
0.52
0.20
0.64
0
2
A B C D E F G H
RFID System
R
ea
d
Ti
m
e
(s
ec

62. s/
re
ad
)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Graphical Analysis Details
Always label axis with titles and units
Always use chart titles
Use scales that are appropriate to the range of data being
plotted
Use legends only when they add value
Use both points and lines on line graphs only if it is
appropriate – don’t use if the data is discrete
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

63. Reporting
Histograms
Histograms are pictorial representations of the distribution of
a measured quantity or of counted items.
It is a quick tool to use to display the average and the amount
of variation present.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Histogram example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
The Pareto principle

64. Dr. Joseph Juran (of total quality management fame)
formulated the Pareto Principle after expanding on the
work of Wilfredo Pareto, a nineteenth century economist
and sociologist.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Pareto example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Example
In the manufacturer of integrated circuits (IC),
many different features are patterned onto the
silicon wafer. To make sure that the devices work
properly, these features must be specific sizes.

65. Process engineers will measure various feature
sizes across the product. This data set contains
feature size measurements for multiple cassettes,
multiple wafers, and multiple locations on each
wafer.
Graph the data using a scatter plot
Complete a histogram of the data.
Complete a Pareto diagram
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Fitting Lines to Paired Data
• Engineers frequently collect paired data in order
to understand
• Relationships between paired data is often
developed graphically
• Mathematical relationships between paired data
can provide additional insight
• Regression analysis is a mathematical analysis
technique used to determine something about the
relationship between random variables.

66. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis
Regression models are used primarily for the purpose of
prediction
Regression models typically involve
A dependent or response variable
Represented as à y
One or more independent or explanatory variables
Represented as à x1, x2, …,xn
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis

67. X
Y
X
Y
X
Y
X
Y
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis Model
SIMPLE LINEAR REGRESSION MODEL
However, both β0 and β1 are population parameters
εi à Represents the random error in Y for each observation
i that occurs

68. Yi = β0 + β1Xi + εi
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis Model
Since we will be working with samples,
the previous model becomes
Where
b0 = Y intercept (estimate of β0)
Value of Y when X = 0
b1 = Slope (estimate of β1)
Expected change in Y per unit change in X
Yi = Predicted (estimated) value of Y
Yi = b0 + b1Xi
^
^
Data Management &
Analysis Process Management

69. Planning
ApplicationAnalysis
Reporting
Regression Analysis Model
What happened with the error term?
Unfortunately, it is not gone. We still have errors in the
estimated values
iii ŶYe −=
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis
X
Y
0

70. 0
Positive Straight-Line Relationship
e1
e2
e3
e4
e5
Yi = b0 + b1Xi
b0
xΔ
yΔ
b1
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Least Squares Method

71. Mathematical technique that determines
the values of b0 and b1
It does so by minimizing the following
expression
∑
=
n
1i
2
ieMin
( ) ( )[ ]
2n
1i
i10i
2n
1i
ii
n
1i
2
i XbbYŶYeMin ∑∑∑
===
+−=−=

72. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Assessing Fit
How do we know how good a regression
model is?
Coefficient of determination à r2 where a
value close to 1 suggests a good fit
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Method 1
When all you need is the slope and intercept of a best fit line,
you can use Excel functions (SLOPE and INTERCEPT) to
determine these values. You can also use RSQ to find the
coefficient of determination (R2)

73. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Performing a linear regression in Excel is very easy. Once the
data have been graphed, regression can be done very simply.
Just because it is easy, does not mean that a linear regression
always makes sense. Graph the data first and always inspect
the “quality” of the fit.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
When regression is done with the trend line feature of Excel,
the fitted curve is automatically added to the graph.

74. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Method 2
The process of performing a linear regression for a slope and
intercept requires the computation of various sums using both
the independent (x) values and dependant (y) values in the
data set being analyzed.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Method 2
You can calculate the slope b1 and intercept
b0 with formulas, but Excel will do this for
you.

75. When trying to find the best fit, always start
with a linear fit (unless it is obvious that
won’t work), then try exponential and
polynomial fits if you think you can get a
better fit.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Method 3
There is an add-in under tools (regression)
that can provide you all the details
resulting from a linear regression.
It is easy to use, but interpreting the results
requires some understanding of regression
terminology

76. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Regression in EXCEL
Pumpkin experiment
Regression Statistics
Multiple R 0.968
R Square 0.937
Adjusted R Square 0.934
Standard Error 3.260
Observations 23.000
ANOVA
df SS MS F Significance F
Regression 1 3334.239 3334.239 313.650 0.000
Residual 21 223.239 10.630
Total 22 3557.478
Coefficients Standard Error t Stat P-value Lower 95% Upper
95%
Intercept 33.293 1.372 24.272 0.000 30.440 36.145
Circumference 0.011 0.001 17.710 0.000 0.010 0.013
r2
SSE

77. SSTb1
b0
IE/MFGE 285: Week 2
Production Planning and Control
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Measuring Processes
Little’s Law:
37
I = Inventory or “line length”
T = Throughput or flow time
R = Flow rate into process
I = T x R
Data Management &

78. Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Measuring Processes
Capacity:
maximum rate of output of a process
Process capacity =
minimum (capacity of resource 1, capacity of resource 2,
capacity of resource 3, ….)
þ The throughput, or the number of units a facility can hold,
receive,
store, or produce in a period of time
þ Determines fixed costs
þ Determines if demand will be satisfied
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting

79. Measuring Processes
Bottleneck:
Capacity of the most constrained (smallest capacity) resource
39
Flow rate = minimum (supply, demand, capacity)
Week 2 Lecture 3, Cont’d
Types of Production Systems
40
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Process Focus
projects, job shops
(machine, print,
carpentry)

80. Standard Register
Repetitive
(autos, motorcycles)
Harley Davidson
Product Focus
(commercial
baked goods, steel,
glass)
Nucor Steel
High Variety
one or few units
per run, high
variety
(allows
customization)
Changes in
Modules
modest runs,
standardized
modules
Changes in
Attributes (such as
grade, quality, size,
thickness, etc.)
long runs only
Mass Customization
Dell Computer Co.

81. Poor Strategy
(Both fixed and
variable costs are
high)
Low
Volume
Repetitive
Process
High
Volume
Volume
41
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Many
inputs

82. High
variety
of
outputs
Print Shop
Process Focus
42
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
þ Facilities often organized as assembly
lines
þ Characterized by modules with parts
and assemblies made previously
þ Modules may be combined for many
output options
þ Less flexibility than process-focused

83. facilities but more efficient
Repetitive Focus
43
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Raw
materials
and
module
inputs
Modules
combined
for many
output
options
Few
modules

84. Automobile Assembly Line
Repetitive Focus
44
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
þ Facilities are organized by product
þ High volume but low variety of
products
þ Long, continuous production runs
enable efficient processes
þ Typically high fixed cost but low
variable cost
þ Generally less skilled labor
Product Focus
45

85. Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Many
inputs
Output
variation
in size,
shape, and
packaging
Bottling Plant
Product Focus
46
Adopted from Heizer and Render (2007) Operations
Management

86. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
þ The rapid, low-cost production of
goods and service to satisfy
increasingly unique customer desires
þ Combines the flexibility of a process
focus with the efficiency of a product
focus
Mass Customization
47
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Modular

87. techniques
Mass Customization
Effective
scheduling
techniques
Rapid
throughput
techniques
Repetitive Focus
Modular design
Flexible equipment
Process-Focused
High variety, low volume
Low utilization (5% to 25%)
General-purpose equipment
Product-Focused
Low variety, high volume
High utilization (70% to 90%)
Specialized equipment
Mass Customization
48
Adopted from Heizer and Render (2007) Operations
Management

88. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Process
Focus
(Low volume,
high variety)
Repetitive
Focus
(Modular)
Product Focus
(High-volume,
low-variety)
Mass
Customization
(High-volume,
high-variety)
Small
quantity, large
variety of
products

89. Long runs,
standardized
product made
from modules
Large
quantity, small
variety of
products
Large
quantity, large
variety of
products
General
purpose
equipment
Special
equipment
aids in use of
assembly line
Special
purpose
equipment
Rapid
changeover
on flexible
equipment
Comparison
49

90. Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Process
Focus
(Low volume,
high variety)
Repetitive
Focus
(Modular)
Product Focus
(High-volume,
low-variety)
Mass
Customization
(High-volume,
high-variety)
Operators are

91. broadly
skilled
Employees are
modestly
trained
Operators are
less broadly
skilled
Flexible
operators are
trained for the
necessary
customization
Many job
instructions
as each job
changes
Repetition
reduces
training and
changes in job
instructions
Few work
orders and job
instructions
because jobs
standardized
Custom
orders require

92. many job
instructions
Comparison
50
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Identify or Define:
Capacity Planning
51
þ Capacity
þ Design capacity
þ Effective capacity
þ Utilization
Adopted from Heizer and Render (2007) Operations
Management

93. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Capacity Definition
52
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Modify capacity Use capacity
Intermediate-
range
planning
Subcontract Add personnel
Add equipment Build or use inventory

94. Add shifts
Short-range
planning
Schedule jobs
Schedule personnel
Allocate machinery*
Long-range
planning
Add facilities
Add long lead time equipment *
* Limited options exist
Planning and Capacity
53
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Types of Capacity

95. 54
Adopted from Heizer and Render (2007) Operations
Management
þ Design capacity is the maximum theoretical
output of a system
þ Normally expressed as a rate
þ Effective capacity is the capacity a firm
expects to achieve given current operating
constraints
þ Often lower than design capacity
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Utilization is the percent of design capacity
achieved
Efficiency is the percent of effective capacity
achieved
Utilization =
Efficiency =

96. Utilization and Efficiency
55
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Adopted from Heizer and Render (2007) Operations
Management
Example
Data Management &
Analysis Process Management
Planning

97. ApplicationAnalysis
Reporting
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Example
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%

98. Example
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Example
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management

99. Planning
ApplicationAnalysis
Reporting
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
Example
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour

100. Bakery operates 7 days/week, 3 - 8 hour shifts
Design capacity = (7 x 3 x 8) x (1,200) = 201,600 rolls
Utilization = 148,000/201,600 = 73.4%
Efficiency = 148,000/175,000 = 84.6%
Example
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts
Efficiency = 84.6%
Efficiency of new line = 75%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 rolls
Example

101. Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Actual production last week = 148,000 rolls
Effective capacity = 175,000 rolls
Design capacity = 1,200 rolls per hour
Bakery operates 7 days/week, 3 - 8 hour shifts
Efficiency = 84.6%
Efficiency of new line = 75%
Expected Output = (Effective Capacity)(Efficiency)
= (175,000)(.75) = 131,250 rolls
Example
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management

102. Planning
ApplicationAnalysis
Reporting
1. Making staffing changes
2. Adjusting equipment and processes
þ Purchasing additional machinery
þ Selling or leasing out existing equipment
3. Improving methods to increase
throughput
4. Redesigning the product to facilitate
more throughput
Matching Demand and
Capacity
64
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting

103. (a) Leading demand with
incremental expansion
D
em
an
d
Expected
demand
New
capacity
(b) Leading demand with
one-step expansion
D
em
an
d
New
capacity
Expected
demand
(d) Attempts to have an average
capacity with incremental
expansion
D

104. em
an
d
New
capacity Expected
demand
(c) Capacity lags demand with
incremental expansion
D
em
an
d
New
capacity
Expected
demand
Capacity Expansion
Adopted from Heizer and Render (2007) Operations
Management
Data Management &
Analysis Process Management
Planning

105. ApplicationAnalysis
Reporting
þ Specifies the order in which jobs should
be performed at work centers
þ Priority rules are used to dispatch or
sequence jobs
þ FIFO: First in, first out
þ SPT: Shortest processing time
þ EDD: Earliest due date
þ LPT: Longest processing time
Adopted from Heizer and Render (2007) Operations
Management
Sequencing jobs
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Job
Job Work
(Processing) Time
(Days)

106. Job Due
Date
(Days)
A 6 8
B 2 6
C 8 18
D 3 15
E 9 23
Apply the four popular sequencing rules to
these five jobs
Adopted from Heizer and Render (2007) Operations
Management
Sequencing Example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Job
Sequence
Job Work
(Processing)

107. Time
Flow
Time
Job Due
Date
Job
Lateness
A 6 6 8 0
B 2 8 6 2
C 8 16 18 0
D 3 19 15 4
E 9 28 23 5
28 77 11
FIFO: Sequence A-B-C-D-E
Adopted from Heizer and Render (2007) Operations
Management
FIFO Example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

108. Reporting
Job
Sequence
Job Work
(Processing)
Time
Flow
Time
Job Due
Date
Job
Lateness
A 6 6 8 0
B 2 8 6 2
C 8 16 18 0
D 3 19 15 4
E 9 28 23 5
28 77 11
FCFS: Sequence A-B-C-D-E
Average completion time = = 77/5
= 15.4 days
Total flow time
Number of jobs
Utilization = = 28/77 = 36.4%

109. Total job work time
Total flow time
Average number of
jobs in the system = = 77/28 = 2.75
jobs
Total flow time
Total job work time
Average job lateness = = 11/5 = 2.2
days
Total late days
Number of jobs
Adopted from Heizer and Render (2007) Operations
Management
FIFO Example Calculations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Job
Sequence
Job Work

110. (Processing)
Time
Flow
Time
Job Due
Date
Job
Lateness
B 2 2 6 0
D 3 5 15 0
A 6 11 8 3
C 8 19 18 1
E 9 28 23 5
28 65 9
SPT: Sequence B-D-A-C-E
Adopted from Heizer and Render (2007) Operations
Management
SPT Example
Data Management &
Analysis Process Management
Planning

111. ApplicationAnalysis
Reporting
Job
Sequence
Job Work
(Processing)
Time
Flow
Time
Job Due
Date
Job
Lateness
B 2 2 6 0
D 3 5 15 0
A 6 11 8 3
C 8 19 18 1
E 9 28 23 5
28 65 9
Average completion time = = 65/5 =
13 days
Total flow time
Number of jobs
Utilization = = 28/65 = 43.1%

112. Total job work time
Total flow time
Average number of
jobs in the system = = 65/28 = 2.32
jobs
Total flow time
Total job work time
Average job lateness = = 9/5 = 1.8 days
Total late days
Number of jobs
Adopted from Heizer and Render (2007) Operations
Management
SPT Calculations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Job
Sequence
Job Work
(Processing)

113. Time
Flow
Time
Job Due
Date
Job
Lateness
B 2 2 6 0
A 6 8 8 0
D 3 11 15 0
C 8 19 18 1
E 9 28 23 5
28 68 6
EDD: Sequence B-A-D-C-E
Adopted from Heizer and Render (2007) Operations
Management
EDD Example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

114. Reporting
Job
Sequence
Job Work
(Processing)
Time
Flow
Time
Job Due
Date
Job
Lateness
B 2 2 6 0
A 6 8 8 0
D 3 11 15 0
C 8 19 18 1
E 9 28 23 5
28 68 6
Average completion time = = 68/5 =
13.6 days
Total flow time
Number of jobs
Utilization = = 28/68 = 41.2%
Total job work time

115. Total flow time
Average number of
jobs in the system = = 68/28 = 2.43
jobs
Total flow time
Total job work time
Average job lateness = = 6/5 = 1.2 days
Total late days
Number of jobs
Adopted from Heizer and Render (2007) Operations
Management
EDD Calculations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Job
Sequence
Job Work
(Processing)

116. Time
Flow
Time
Job Due
Date
Job
Lateness
E 9 9 23 0
C 8 17 18 0
A 6 23 8 15
D 3 26 15 11
B 2 28 6 22
28 103 48
LPT: Sequence E-C-A-D-B
Adopted from Heizer and Render (2007) Operations
Management
LPT Example
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

117. Reporting
Job
Sequence
Job Work
(Processing)
Time
Flow
Time
Job Due
Date
Job
Lateness
E 9 9 23 0
C 8 17 18 0
A 6 23 8 15
D 3 26 15 11
B 2 28 6 22
28 103 48
Average completion time = = 103/5 =
20.6 days
Total flow time
Number of jobs
Utilization = = 28/103 = 27.2%
Total job work time

118. Total flow time
Average number of
jobs in the system = = 103/28 = 3.68
jobs
Total flow time
Total job work time
Average job lateness = = 48/5 = 9.6
days
Total late days
Number of jobs
Adopted from Heizer and Render (2007) Operations
Management
LPT Calculations
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Rule
Average
Completion
Time (Days)

119. Utilization
(%)
Average Number
of Jobs in
System
Average
Lateness
(Days)
FIFO 15.4 36.4 2.75 2.2
SPT 13.0 43.1 2.32 1.8
EDD 13.6 41.2 2.43 1.2
LPT 20.6 27.2 3.68 9.6
Adopted from Heizer and Render (2007) Operations
Management
Adopted from Heizer and Render (2007) Operations
Management
Summary of Results
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

120. Reporting
þ No one sequencing rule excels on all criteria
þ SPT does well on minimizing flow time and
number of jobs in the system
þ But SPT moves long jobs to the end which may
result in dissatisfied customers
þ FIFO does not do especially well (or poorly) on
any criteria but is perceived as fair by customers
þ EDD minimizes lateness
Adopted from Heizer and Render (2007) Operations
Management
Comparison of Sequencing
Rules
IE/MFGE 285: Week 2
Introduction & Syllabus Review; Introduction to IME
Data Management &
Analysis
Process Management
Planning

121. ApplicationAnalysis
Reporting
Class Plan
• Data Management & Analysis
• Histograms
• Scatter Plots
• Regression
1
Data Analysis in Excel
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics

122. Measures of central tendency
• Mean (average): Most popular measure of
central tendency
Xbar = sum (from I to n) of Xi
divided by n
Where
xi = Observation number i
n = Total number of
observations
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of central tendency
• Median: Middle observation within a data set
when the observations are arranged in increasing
order
If number of values (n) in data set is odd, then the median is the
middle observation

123. If number of values (n) in data set is even then median = (x n/2
+
xn/2 +1)/2
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of central tendency
• Mode: Value that occurs more often than any of
the others in a data set
Does not always exist
Example: Scores from a test
Is not necessarily unique, i.e. a data set can
have more than one mode
= 2 modes è Bimodal
> 2 modes è Multimodal
Applicable to both quantitative and qualitative data
Particularly useful in marketing and inventory considerations

124. Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of dispersion
• Range :Difference between the largest and
smallest values in a data set
Xlargest - Xsmallest
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis

125. Reporting
Descriptive Statistics
Measures of dispersion
• Variance: Measures how a set of measurements
fluctuate relative to the mean of the data set:
S2 = sum (x – xbar)2 /(n-1)
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Descriptive Statistics
Measures of dispersion
Standard deviation: What is the problem with
the variance? It has different units of measurement
(e.g., cm2)
To return data to its original units; Standard
deviation = square root of variance

126. Graphical Analysis
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
In-class Example
Strength testing of materials often involves a
tensile test in which a sample of the material
is held between two mandrels and increasing
force (stress) is applied. A stress-strain curve
is generated to provide information about a
particular material. Strain is the amount of
elongation of the sample divided by the
original sample length.
Data Management &
Analysis
Process Management

127. Planning
ApplicationAnalysis
Reporting
Data Analysis Example
Stress Strain
(Mpa) (mm/mm)
0.000 0.000
5.380 0.003
10.760 0.006
16.140 0.009
21.520 0.012
25.110 0.014
30.490 0.017
33.340 0.020
44.790 0.035
52.290 0.052
57.080 0.079
59.790 0.124
60.100 0.167
59.580 0.212
57.500 0.264
55.420 0.300
The stress-strain data taken from a soft,
ductile material tested in this way is
tabulated to the left.
Graph this data – Strain is the independent
(x) and Stress is the dependent (y) variable.

128. Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Graphical methods
Quantitative vs. Qualitative data
Quantitative data (numerical data)
Cost of a computer (continuous)
Number of production defects (discrete)
Weight of a person (continuous)
Parts produced this month (discrete)
Temperature of etch bath (continuous)
Graphical tools
Line charts
Histograms
Scatter charts
Data Management &
Analysis

129. Process Management
Planning
ApplicationAnalysis
Reporting
Quantitative vs. Qualitative data
Quantitative data (categorical and attribute)
Type of equipment (Manual, automated, semi-automated)
Operator (Tom, Nina, Jose)
Graphical tools
Bar charts
Pie charts
Pareto charts
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting

130. Getting Started
Classify data
Quantitative vs. Qualitative
Continuous or discrete (quantitative)
Chose the right graphical tool
Chose axes and scales to provide best “view” of data
Label graphs to eliminate ambiguity
Graphical Analysis
Examples
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Bar or Column Graph
Displays frequency of observations that fall into nominal
categories
Color distribution for a random package of M&Ms

131. 0
5
10
15
20
25
brown red yellow green orange blue
Color
Q
ty
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Line Chart
Shows trends in data at equal intervals

132. 0
0.5
1
1.5
2
2.5
3
3.5
4
4.5
M
ax
S
ke
w
A
ve
ra
ge
M
ax
P

133. itc
h
A
ve
ra
ge
C
on
tr
ol
le
d
S
ca
n
F
re
eh
an
d
S
ca
n

134. B
rig
ht
L
ig
ht
N
or
m
al
L
ig
ht
Lo
w
L
ig
ht
Performance Category
S
ca

135. n
T
im
e
(S
ec
o
n
d
s)
CCD1 CCD2 LR LCCD CMOS
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Graphical methods
Acceptable graph

136. EDC Warehouse
Test Results for Read Time
ALL SYSTEMS
0.64
0.20
0.52
0.81
0.66
N/A
1.46
0.88
0
1
2
1 2 3 4 5 6 7 8
RFID System
R
e
a
d
T

137. im
e
(s
e
cs
/r
e
a
d
)
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Graphical methods
Better graph
EDC Warehouse
Test Results for Read Time

138. ALL SYSTEMS
0.88
1.46
N/A
0.66
0.81
0.52
0.20
0.64
0
2
A B C D E F G H
RFID System
R
ea
d
Ti
m
e
(s

139. ec
s/
re
ad
)
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Graphical Analysis Details
Always label axis with titles and units
Always use chart titles
Use scales that are appropriate to the range of data being
plotted
Use legends only when they add value
Use both points and lines on line graphs only if it is
appropriate – don’t use if the data is discrete
Data Management &
Analysis

140. Process Management
Planning
ApplicationAnalysis
Reporting
Histograms
Histograms are pictorial representations of the distribution of
a measured quantity or of counted items.
It is a quick tool to use to display the average and the amount
of variation present.
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Histogram example
Data Management &
Analysis

141. Process Management
Planning
ApplicationAnalysis
Reporting
The Pareto principle
Dr. Joseph Juran (of total quality management fame)
formulated the Pareto Principle after expanding on the
work of Wilfredo Pareto, a nineteenth century economist
and sociologist.
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Pareto example
Data Management &
Analysis

142. Process Management
Planning
ApplicationAnalysis
Reporting
Example
In the manufacturer of integrated circuits (IC),
many different features are patterned onto the
silicon wafer. To make sure that the devices work
properly, these features must be specific sizes.
Process engineers will measure various feature
sizes across the product. This data set contains
feature size measurements for multiple cassettes,
multiple wafers, and multiple locations on each
wafer.
Graph the data using a scatter plot
Complete a histogram of the data.
Complete a Pareto diagram
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting

143. Fitting Lines to Paired Data
• Engineers frequently collect paired data in order
to understand
• Relationships between paired data is often
developed graphically
• Mathematical relationships between paired data
can provide additional insight
• Regression analysis is a mathematical analysis
technique used to determine something about the
relationship between random variables.
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis
Regression models are used primarily for the purpose of
prediction
Regression models typically involve
A dependent or response variable

144. Represented as à y
One or more independent or explanatory variables
Represented as à x1, x2, …,xn
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis
X
Y
X
Y
X
Y
X
Y

145. Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis Model
SIMPLE LINEAR REGRESSION MODEL
However, both β0 and β1 are population parameters
εi à Represents the random error in Y for each observation
i that occurs
Yi = β0 + β1Xi + εi
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis

146. Reporting
Regression Analysis Model
Since we will be working with samples,
the previous model becomes
Where
b0 = Y intercept (estimate of β0)
Value of Y when X = 0
b1 = Slope (estimate of β1)
Expected change in Y per unit change in X
Yi = Predicted (estimated) value of Y
Yi = b0 + b1Xi
^
^
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis Model

147. What happened with the error term?
Unfortunately, it is not gone. We still have errors in the
estimated values
iii ŶYe −=
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Regression Analysis
X
Y
0
0
Positive Straight-Line Relationship
e1

148. e2
e3
e4
e5
Yi = b0 + b1Xi
b0
xΔ
yΔ
b1
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Least Squares Method
Mathematical technique that determines
the values of b0 and b1
It does so by minimizing the following
expression

149. ∑
=
n
1i
2
ieMin
( ) ( )[ ]
2n
1i
i10i
2n
1i
ii
n
1i
2
i XbbYŶYeMin ∑∑∑
===
+−=−=
Data Management &
Analysis

150. Process Management
Planning
ApplicationAnalysis
Reporting
Assessing Fit
How do we know how good a regression
model is?
Coefficient of determination à r2 where a
value close to 1 suggests a good fit
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Method 1
When all you need is the slope and intercept of a best fit line,
you can use Excel functions (SLOPE and INTERCEPT) to
determine these values. You can also use RSQ to find the
coefficient of determination (R2)

151. Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Performing a linear regression in Excel is very easy. Once the
data have been graphed, regression can be done very simply.
Just because it is easy, does not mean that a linear regression
always makes sense. Graph the data first and always inspect
the “quality” of the fit.
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel

152. When regression is done with the trend line feature of Excel,
the fitted curve is automatically added to the graph.
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Method 2
The process of performing a linear regression for a slope and
intercept requires the computation of various sums using both
the independent (x) values and dependant (y) values in the
data set being analyzed.
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis

153. Reporting
Linear Regression in Excel
Method 2
You can calculate the slope b1 and intercept
b0 with formulas, but Excel will do this for
you.
When trying to find the best fit, always start
with a linear fit (unless it is obvious that
won’t work), then try exponential and
polynomial fits if you think you can get a
better fit.
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Linear Regression in Excel
Method 3
There is an add-in under tools (regression)

154. that can provide you all the details
resulting from a linear regression.
It is easy to use, but interpreting the results
requires some understanding of regression
terminology
Data Management &
Analysis
Process Management
Planning
ApplicationAnalysis
Reporting
Regression in EXCEL
Pumpkin experiment
Regression Statistics
Multiple R 0.968
R Square 0.937
Adjusted R Square 0.934
Standard Error 3.260
Observations 23.000
ANOVA
df SS MS F Significance F

155. Regression 1 3334.239 3334.239 313.650 0.000
Residual 21 223.239 10.630
Total 22 3557.478
Coefficients Standard Error t Stat P-value Lower 95% Upper
95%
Intercept 33.293 1.372 24.272 0.000 30.440 36.145
Circumference 0.011 0.001 17.710 0.000 0.010 0.013
r2
SSE
SSTb1
b0
IE/MFGE 285: Week 1
Introduction & Syllabus Review; Introduction to IME
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Course Plan

156. • Introduction and Basics
• Data Management & Analysis
• Process Management
• Application
1
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Process Improvement Group
Dr. Eseonu (Pronounced Eh-son-u)
• Ph.D. Systems & Engineering Management,
Texas Tech University
• MSc. Engineering Management, University of
Minnesota-Duluth
• BASc. Mechanical Engineering, University of
Ottawa Office: Rogers 406
Phone: 541-737-0024
Email: [email protected]
Office Hours:

157. Tues &Thurs 11am – 12:30pm
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
PIGroup Overview
Improving process resilience through
Lean process improvement
Change management (conceptual change)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Rationale
• 70% Change project failure rate (Judge, Thoresen, Pucik, &

158. Welbourne, 1999; Mazur, McCreery, & Rothenberg, 2012)
• Continuous improvement for quadruple aim, profit
(Hammer and Champy, 1993)
• Organizational vs individual level of
analysis (Judge, Thoresen, Pucik, & Welbourne, 1999; Mazur,
McCreery, &
Rothenberg, 2012)
4
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Teaching Assistants
• Mr. Ayush Aryal
• Office: Rogers 304 (till 9/30); Merryfield 107B
thereafter
• Office Hours: MW 11am – 1pm; F 9am – 11am
• Mr. Ben Tankus
• Ms. Natalie Avalos
5

159. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Course Details
6
• Canvas Site
• You are responsible for material, dates, etc
posted on Canvas
• Assigned readings – before lecture
• Mutual learning
• Professionalism and courteousness
• Purchase texts and Littlefield Subscription
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting

160. Course assignments
4 homework assignments that relate to 1 or more course
activities (50 points each)
2 take-home exams (100 points)
Team design project (100 points)
Participation points – quizzes, assigned activities, etc (50
points)
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Due Dates
Assignments and due dates posted to Canvas
All assignments due at the posted time.
Late assignments: Zero credit once I start talking.
8
Data Management &
Analysis Process Management
Planning

161. ApplicationAnalysis
Reporting
Grading Questions
Any questions or concerns about the grading of specific work
must be brought to the attention of the instructor within one
week of when the graded work is returned.
9
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9/22/16
10
Miscellaneous items
• Bring laptops or tablets to class
• Bring Memory Jogger to class
• Most class sessions will be interactive, so come
prepared to participate.
• Ask questions, share opinions, share experiences.

162. This course is designed to help you learn more about
the IME discipline, I need your help to make sure we
are successful in this.
What IEs and MfgEs do?
Making and doing things optimally.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Making processes and products safer, faster, easier,
and less expensive and make work more rewarding.
How?
Improved business practices
Improved customer service
Improved product quality
Improved efficiency
Improved processes from worker’s perspective
Produce more products quickly
Better designed products
Reducing costs associated with new technologies

163. What do IE’s and MfgE’s do?
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Industrial Engineers and Manufacturing
Engineers work on
Project Management
Manufacturing, Production
and Distribution
Quality Measurement
and Improvement
Ergonomics /
Human Factors
Strategic
Planning
Data Management &

164. Analysis Process Management
Planning
ApplicationAnalysis
Reporting
http://www.iienet2.org/media/disney/flowplayer.htm
Industrial Engineers at Work
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9/22/16
15
Definitions Related to IME
engineering; the application of science and mathematics by
which
the properties of matter and the sources of energy in nature are
made
useful to people in structures, machines, products, systems, and
processes.

165. industrial engineering; engineer that deals with the design,
improvement, and installation of integrated systems of people,
materials, equipment, and energy.
manufacture; to make into a product suitable for use; to make
from raw materials by hand or by machinery; to produce
according to
an organized plan and with division of labor
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Terms associated with IE and MfgE
Cycle time
Optimal cost
Productivity and efficiency
Profitable
Quality and reliability
Safe
System optimization
Data Management &
Analysis Process Management
Planning

166. ApplicationAnalysis
Reporting
IE/MfgE Work
• Production line performance
• Workplace layout and design
• Efficiency of baggage claim systems
• Scheduling of equipment
• Scheduling of flights
• Quality control
• Inventory control/ordering points
• Distribution and routing
• Organizational performance
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Where do Industrial and Manufacturing Engineers
Work?
Industrial Engineers are the people who strive for a more
efficient, effective, money-saving company. For this reason
they are demanded in a wide-range of businesses.
- Freightliner - Adidas
- Hewlett-Packard - Starbucks

167. - UPS - INTEL
- Tektronix - Boeing
- Motorola - IBM
18
Where are the Jobs?
Industrial Engineers are the people who strive
for a more efficient, effective, money-saving
company. For this reason they are demanded in
a wide-range of businesses.
- Freightliner - Adidas
- Hewlett-Packard - Starbucks
- UPS - INTEL
- Tektronix - Boeing
- Motorola - IBM
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9/22/16
20

168. A Brief History of IME
• 1832 Charles W. Babbage
• Late 1800 -- Henry R. Towne stressed the
economic aspect of an engineer’s job.
• Henry L. Gantt was interested in selection of
workers and their training.
• Fredrick Winslow Taylor ("scientific
management”)
• The Gilbreth family
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9/22/16
21
Degrees at OSU
BS, MS and PhD in Industrial Engineering
BS in Industrial Engineering with a business engineering
option; MS with Engineering Management option
BS, MS, PhD in Manufacturing

169. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
22
Areas of Specialization
Simulation
Operations research
Human factors/ergonomics
Information systems
Process engineering/control
Healthcare
Facilities/storage design
Management systems
Engineering economics
Humanitarian Engineering
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting

170. 9/22/16
23
Professional Societies
Institute of Industrial Engineers (IIE)
Formed in 1948 as AIIE
Proceeded by The Society of IE (1920)
Society of Manufacturing Engineers (SME)
Formed in 1932
Both societies have student memberships
and student organizations here on campus
Tools to get us started
Brainstorming; Affinity Diagrams; 5-Why’s
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Brainstorming
Creatively and efficiently generate a high volume of ideas
Process free of criticism and judgment
It is OK to seek clarification

171. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
Affinity Diagram
Way to organize and summarize a large number of ideas into
natural groupings
To understand the essence of a problem which can lead to
breakthrough solutions
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
5-Whys
Tendency for individuals and groups to only develop a
superficial understanding of a problem.
The five whys is a question asking method used to explore the
cause/effect relationships underlying a particular problem.

172. Operating Room Example
Physical system
This system is governed
by the laws of physics!
Apply “Traditional”
Engineering Analysis:
• Mechanics/Dynamics
• Thermodynamics
• Materials Science
• Chemistry
• Electrical engineering
Organizational system (People and Processes)
The operation of this system is governed by
human behavior, process “laws”, and technology!
Apply IE Analysis:
• Simulation
• Operations research
• Statistics/Quality control
• Production and inventory theory
• Work measurement/improvement
• Human factors/ergonomics
• Organizational design
• Economic analysis
• Individual and team behaviors

173. Data Management &
Analysis Process Management
Planning
ApplicationAnalysis
Reporting
9/22/16
29
In-Class Exercise
* Operating Room (OR) Change-Over Process
* Develop a list of at least 10 system design related issues that
an IE/
MfgE would have to consider when designing an OR and in
designing
the procedures for a change-over. Use brainstorming, affinity
diagrams
and 5-whys to help in this exercise.
Data Management &
Analysis Process Management
Planning
ApplicationAnalysis

174. Reporting
Tuesday’s class
Complete assigned readings (GR and MJ)
Bring Laptop to class