IENG 471 Lecture 15: Systematic Layout Planning
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Transcript IENG 471 Lecture 15: Systematic Layout Planning
IENG 471 - Lecture 15
Layout Planning –
Systematic Layout Planning & Intro to
Mathematical Layout Improvement
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Warehousing Terms - Review
SKU – Stock Keeping Unit
Product in (packaged) form for warehouse operations.
Value-Added
A modification to the product to obtain business
(a product enhancement from the customer’s perspective or an
enhancement to the customer’s experience in getting the item).
Cross-Docking
Transforming incoming product to outgoing product without
moving the product to production or storage.
Slotting
Selecting the location of SKUs in the storage zones. Goal is to
optimize (reduce) pick times across all SKUs within a zone.
Forward Pick Area
An area housing fast-moving/frequently-picked items between
the shipping and storage areas for quick order fulfillment.
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Layout Alternatives - Strategies
Fixed Position Layout
(Difficult-to-move Products)
Process Layout
(Job Shop)
Product Layout
(Mass Production Line)
Group Technology Layout
(Product Family)
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Layout Alternatives: Fixed Pos.
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Layout Alternatives: Process
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Layout Alternatives: Product
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Layout Alternatives: GT / Family
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How to get from data to design?
Product, Process &
Schedule Data:
BOM
Routing/Assembly Chrt
Operations Process
Chart
Precedence Diagram
Scrap/Reject Rates
Equipment Fractions
Material Handling
Unit Loads
Storage Systems
Space Data:
Group Technology
From – To Chart
Relationship Chart
Dept Footprint & Aisle
Space
Personnel Space
Parking Lot
Restroom/Locker room
Food Prep/Cafeteria
ADA Compliance
Order Data Profile
Efficiencies
Transportation Systems
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Flow, Activity &
Multiple Analysis Profiles
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Muther: Systematic Layout Plan
SLP
Benefit is methodical
consideration of issues
Can work the process
manually or with computer
aides
“Roadmap” for the process is
good for communication
Adds the following stages:
Analysis
Search
Evaluation
Engineering Design Process!
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Relationship Chart - Qualitative
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Converting Closeness to Affinity
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From – To Chart Example
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From – To Chart to Flow
Review: flow volume in chart
Above diagonal is forward flow
Below diagonal is back-track flow
Combine both flows to represent
volume of interactions, then Pareto!
Qualitative Flow
Quantitative Flow
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Converting Quantitative Flow to Affinity
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Converting Both to Final Affinity
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Review: Conversion Steps
Convert Flows to Affinities
Qualitative converts directly to A E I O U X
Quantitative converts to A E I O U X via Pareto analysis of flow
volume
Combine Flow Affinities Numerically
A = 4, E = 3, I = 2, O = 1, U = 0, X = negative value
Quantitative flow may be multiplied by a weighting factor
Sum Quantitative & Qualitative
Convert to Final Affinities
Pareto analysis of numeric affinities to get A E I O U X
Add: Check Final Affinities for Political Correctness
Communication feedback to involved parties
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Converting Flow to Affinity
Strength of
relationship is
shown graphically
Number of lines
similar to rubber
bands holding depts
together
Spring symbol to
push X relations
apart
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Converting Flow to Affinity
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Converting Flow to Affinity
Lay the
Affinity
Diagram
over a site
plan to get
better idea
of layout
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Improvement: Size of Departments
Some experts suggest modification:
Use circles instead of flow symbols
Scale circles to equate with the estimated
size of the departments
Use rectangular, sized blocks instead of
circles – improves input to computer layout
methods
Computer packages are still being
developed …
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Layout Models –
Mathematical Objective Functions
Mathematical models can be constructed to measure a design,
and help to quantify when it has been improved
Like many mathematical
models of physical systems, part of the “art” is
knowing what assumptions are made in a model, and when these
assumptions are “reasonably met”
The “best” models are not always the most complex – in fact many
“comprehensive” mathematical models become intractable or take too
long for computation when scaled up to a “realistically–sized” problem
Frequently, meeting the data collection (and verification) requirements
for many mathematical problems is very difficult
However, as the cost of automated data collection and storage drops,
and has computational power increases (hardware speeds and parallel
programming techniques improve), both mathematical models and
simulations become more attractive – more tools for the toolbox!
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Layout Models –
Mathematical Objective Functions
Assume we have these variables defined for n departments:
i is an index to the “FROM” department in a pair of departments
j is an index to the “TO” department in a related pair
Thus i and j could be the row/column indices for a From/To Chart
fij is the unit load FLOW from the i
th
to the j th department
Thus fij is the cell entry in the From/To Chart (matrix)
cij is the COST to transport a unit load from the i
dij is the travel DISTANCE from the i
aij is the ADJACENCY of the i
th
th
th
to the j th dept
to the j th department
and j th department pair, which is
defined to be:
and j th departments share a common edge (border) – or
0 if the departments have no common edge or only touch at a point
1 if the i
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Layout Models –
Mathematical Objective Functions
Minimize the transportation cost:
n
n
z fij c ij dij
min
i1 j1
Maximize the flow-weighted adjacency of departments:
n
n
y fij aij
max
i1 j1
Evaluate flow weighted layout efficiency (relative measure):
n
x
n
f a
i1 j1
n
n
ij
f
i 1 j1
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ij
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Example –
Mathematical Objective Function
Assume the From/To matrix (below)
From\To
A
A
B
I
200
C
B
C
D
E
E
300
E
350
A
500
F
I
250
X
-20
U
10
I
175
O
100
D
E
F
… and the department layout(s) (below):
A
A
A
C
C
A
A
A
C
C
B
B
F
F
C
B
B
F
F
C
B
B
D
D
E
B
B
D
D
E
E
E
E
E
E
E
E
E
A
A
A
C
C
A
A
A
C
C
B B B
B
F F D
F F D
C C E
B
B
D
D
E
B
E
E
E
E
B
E
E
E
E
A A B B
A A B B
A A D D
D D
F F F F
B
C
C
C
E
B
C
C
C
E
B
E
E
E
E
B
E
E
E
E
then the Flow-Weighted Adjacency score(s) would be:
n
max
n
y fij aij
i1 j1
200(1)+250(1)+300(1)+500(1)–20(1)+350(0)+10(1)+175(1)+100(0) = 1415
200(1)+250(1)+300(1)+500(1)–20(0)+350(0)+10(1)+175(1)+100(0) = 1435
200(1)+250(0)+300(1)+500(1)–20(0)+350(1)+10(0)+175(1)+100(1) = 1625
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Criticisms and Resources
Frequently, improvements in the simpler mathematical objective
functions result in long, “snake-y” department shapes
Not always physically possible
Adjusting the objective function to penalize snake-y results in
more complex objective functions
Data representations become more complex, too – and can
increase computation time disproportionately
The simple, transportation cost function assumes we move
from/to the center “point” of the departments
Isn’t really accurate for real departments (especially large sized)
Becomes even less true when the departments get more snake-y
Text Chapter 10 presents more mathematical models–try some!
MIL Lab computers have some software available
The software tends to be research prototypes, but can be fun to try!
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Questions & Issues
Class time is for project (after Exam II)
Review & HW solutions TODAY.
Exam II scheduled for 07 NOV.
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