OMSAN LOJİSTİK
Session 1 – Morning
Defining the Problem
• Context
- Supply Chain, Distribution Network
- Single Warehouse Operations
• Logistics Activity Profiling
Session 3 – Morning
Process and Technology
Alternatives
• Small Item Picking
- Order Picking Methods
- Forward Pick, Slotting, and
Replenishment
•
Mechanized Picking Systems
- Zone Picking
Logistics Activity
Profiling
Session 4 – Afternoon
- Batch Picking
Session 2 – Afternoon
Process and Technology
Alternatives
• Pallet Storage
• Case Picking
- Manual
- Mechanized
-2-
Day 1
Putting it all Together
• Developing the integrated design
- Sizing the Functions
- Defining Overall Facility Flow
- Orienting Shipping and Receiving
- Planning Aisle Patterns
• Integrating Human Activity
Day 2
Warehousing—Do You Know?
Logistics Activity Profiling
• What is profiling?
• Why do we do it?
(Hint: this is a trick question—it’s what
we’re going to spend the next hour
exploring!!)
-3-
Logistics Activity Profiling
Definition
Profiling is an umbrella term used to define analysis you perform on
your company’s key inventory data*, such as:
• Inventory physical characteristics
• Orders/order history
• SKU-level movement history
In this session we’ll explore:
• Motivations—why profile?
• Inventory, orders, and history—what to do with them and why it’s
important
• Creating an activity database (overview)
* The official TPG term for this activity is “wallerin’ around in the data.”
-4-
Logistics Activity Profiling
Motivations—Why Do It?
•
•
•
•
•
Gain insight into how processes might be designed
Evaluate alternative operating methods
Identify automation/mechanization opportunities
Define inventory positioning rules (slotting)
Increase space utilization and throughput capacity in an existing
facility
• Consolidate distribution centers
• Improve order fulfillment efficiency
• Spotlight changes in inventory activity . . .
– End-of-life-cycle seasonal goods
– Changes from fast to slow movers, and vice versa
– Dead dogs
Logistics Activity Profiling
Which to Discuss First? The “Chicken-and-Egg” Problem
Orders
SKUs
History
Dimensions
Patterns/Trends
Lines
Cube
Pack/Storage
Configurations
Weight
Single
Multiple
Pallets
Cases
Broken Cases
-6-
Handling
Reqt’s
Logistics Activity Profiling
Which to Discuss First? The “Chicken-and-Egg” Problem
Orders
History
SKUs have dimensions
from which you derive
cubic volumePatterns/Trends
and track
weight . . .
Lines
Cube
SKUs
Dimensions
Pack/Storage
Configurations
Weight
Single
Multiple
Pallets
Cases
Broken Cases
-7-
Handling
Reqt’s
Logistics Activity Profiling
Which to Discuss First? The “Chicken-and-Egg” Problem
Orders
History
Pack and storage
configurations that tell
you something
about
Patterns/Trends
how they are stored
Lines
Cube
and picked . . .
SKUs
Dimensions
Pack/Storage
Configurations
Weight
Single
Multiple
Pallets
Cases
Broken Cases
-8-
Handling
Reqt’s
Logistics Activity Profiling
Which to Discuss First? The “Chicken-and-Egg” Problem
Orders
History
SKUs
And handling
Dimensions
requirements may
Patterns/Trends
affect pack/storage
Lines
configurations and how Cube
Pack/Storage
Configurations
orders can be
Weight
sequenced and picked.
Single
Handling
Conversely,
order
Pallets
Reqt’s
patterns
Multiplemay dictate
Cases
pack and storage
Broken Cases
configurations.
-9-
Logistics Activity Profiling
Which to Discuss First? The “Chicken-and-Egg” Problem
Orders
History
SKUs
Dimensions
Patterns/Trends
Lines
Cube
Pack/Storage
Configurations
Weight
Single
Multiple
- 10 -
Orders are made up of
lines; sometimes
Cases only
one, sometimes
Broken Cases
hundreds . . .
Pallets
Handling
Reqt’s
Logistics Activity Profiling
Which to Discuss First? The “Chicken-and-Egg” Problem
Orders
History
The lines contain
SKUs, which have
cubic volume Patterns/Trends
and
weight and can come in
Lines
Cube
a variety of pack and
handling configurations Weight
...
Single
Multiple
SKUs
Dimensions
Pack/Storage
Configurations
Pallets
Cases
Broken Cases
- 11 -
Handling
Reqt’s
Logistics Activity Profiling
Which to Discuss First? The “Chicken-and-Egg” Problem
Orders
History
And any and all of the
SKU characteristics
Patterns/Trends
may vary dramatically
Lines
Cube
by order type!
SKUs
Dimensions
Pack/Storage
Configurations
Weight
Single
Multiple
Pallets
Cases
Broken Cases
- 12 -
Handling
Reqt’s
Logistics Activity Profiling
Which to Discuss First? The “Chicken-and-Egg” Problem
Orders
History
SKUs
Dimensions
Patterns/
Trends
Lines
Cube
Pack/Storage
Configurations
Weight
Historical data
Single provides the
foundation upon
Multiple
which profiling
analysis is done
- 13 -
Pallets
Cases
Broken Cases
Handling
Reqt’s
Logistics Activity Profiling
Inventory Characteristics
• Physical
– Weight, cube and dimensions of each product
– Material handling requirements, e.g,:
• Full pallets (standard pallets, clamp loads)
• Roll stock (paper)
• Drums
• Cold (perishables, wine)
• Frozen (meats, ice cream)
• Population
– Total SKU counts
– Family/Group SKU counts
• Anticipated movement profile
– Stock/repeatable item
- 14 – Seasonal or “one-time use” goods
Logistics Activity Profiling
Order Data—Three Views
•
•
•
Identify demand patterns at the aggregate level:
– Daily
– Weekly
– Monthly
– Quarterly
– Annually
Identify and model order/line characteristics
– Single-line orders
– Multi-line orders
– Pallet, case, and unit combinations
Identify inventory-specific order characteristics:
– For all SKUs
– For individual SKUs
– For groups/families of SKUs
- 15 -
Logistics Activity Profiling
Aggregate Order Demand
Data
Shipping cutoffs, system
download windows, and a host
of other events can cause
spikes and dips in daily order
activity
Activity
Time of Day
Replenishment schedules to
stores or regional facilities,
customer “habits,” etc. can drive
weekly patterns
Activity
M TU W TH FR SA SU
Day of Week
- 16 -
Logistics Activity Profiling
Aggregate Order Demand
Data
Analysis may show some clear
indications of order variability
during certain days or weeks of
the month . . .
Activity
1 2 3 4 5 6 7 8 9 10 11 12 13 14
Day of Month
. . . and show further trends
at the quarterly level
Activity
JAN
FEB
Q1
- 17 -
MAR
Logistics Activity Profiling
Aggregate Order Demand Data
Activity
The annual view can be an
excellent indicator of seasonal
peaks, especially when tracked
over several years.
JAN FEB MAR
APR MAY
JUN JUL AUG
Months of the Year
- 18 -
SEP OCT NOV DEC
Logistics Activity Profiling
Aggregate Order Data—Uses and Risks
. . . while negotiating
3rd party support for
the October peak
Activity
The shape of the curve and its
repeatability might cause you to
design a facility to handle your
June peak volume . . .
JAN FEB MAR
APR MAY
JUN JUL AUG
Months of the Year
- 19 -
SEP OCT NOV DEC
Logistics Activity Profiling
Aggregate Order Data—Uses and Risks
Be very careful when making a key, high-cost decision based on this form of
analysis—the data could be skewed by one-time or rare events. Know your
business—know your data!!
This peak could be . . .
• Regular, repeatable, and expected
• Caused by an acquisition/expansion
• The result of your having the nation’s
only remaining supply of pet rocks in a
sudden, unexpected nostalgia craze!
AUG
SEP
ar
- 20 -
OCT NOV DEC
Logistics Activity Profiling
Order/Line Characteristics—Shows Distributions
80,00%
70,05%
70,00%
30.000
60,00%
25.000
50,00%
20.000
40,00%
15.000
10.000
5.000
0
30,00%
% of Orders
Number of Orders
% of Orders
20,00%
7,48%
10,00%
5,76%3,08% 3,72%3,02%3,26%1,83%
1,80%
0,00%
Number of Orders
30.000
60,00%
Cubic Ft. Ranges
Cube Per Order
25.000
50,00%
20.000
40,00%
15.000
10.000
30,00%
22,20%
5.000
20,00%
6,19%
10,00%
2,93%
3,44%
4,64%
2,95%
2,20%
11-15
Lines
16-25
Lines
26-50
Lines
51-100
Lines
101+
Lines
0
0,00%
1 Line 2-5 Lines
6-10
Lines
Number of Lines
- 21 -
Lines Per Order
% of Orders
55,59%
Number of Orders
Number of Orders
35.000
% of Orders
Logistics Activity Profiling
Order/Line Characteristics—Shows Pick Type Makeup
15%
Mixed
% Orders
20%
% Lines
10%
Full Pallet
30%
75%
Loose Carton
50%
0% 10 20 30 40 50 60 70 80
% % % % % % % %
Pallet/Case
- 22 -
Logistics Activity Profiling
Order/Line Characteristics—Shows Pick Type Makeup
% Orders
10%
% Pick Lines
Mixed
20%
30%
Full Case Only
25%
60%
Broken Case Only
55%
0%
10
%
20
%
30
%
40
%
50
%
60
%
Full Case/Broken Case
- 23 -
Logistics Activity Profiling
Order/Line Characteristics—Shows Relationships
Order
Lines
1
2
3
4
5
6
7
8
9
10+
Total %
0-.5 CbFt
37.55%
12.48%
3.55%
0.28%
0.15%
0.06%
0.03%
0.02%
0.01%
0.02%
54.15%
.5-1
CbFt
8.19%
3.34%
0.86%
0.27%
0.15%
0.07%
0.04%
0.02%
0.01%
0.01%
12.97%
1-1.5
CbFt
3.29%
1.49%
0.70%
0.34%
0.21%
0.11%
0.06%
0.02%
0.01%
0.02%
6.26%
1.5-2
CbFt
2.08%
0.94%
0.59%
0.31%
0.20%
0.15%
0.06%
0.03%
0.01%
0.01%
4.38%
2-2.5
CbFt
1.13%
0.51%
0.37%
0.25%
0.16%
0.09%
0.05%
0.01%
0.01%
0.02%
2.58%
2.5-3
CbFt
Total %
0.95%
53.19%
0.34%
19.10%
0.28%
6.36%
0.19%
1.63%
0.12%
0.99%
0.08%
0.55%
0.03%
0.28%
0.02%
0.12%
0.01%
0.06%
0.02%
0.10%
2.04%
82.39%
Logistics Activity Profiling
Order/Line Characteristics—Shows
Daily Activity Patterns
Customers In Dispatch Sequence
Product
A1
B1
11-231
11-324
11-367
11-508
11-612
11-798
11-856
25
22
18
14
12
9
6
21-176
21-234
21-338
21-387
21-465
21-559
21-673
21-789
100
89
78
65
62
45
39
35
26-123
26-175
26-237
26-279
26-362
26-491
26-568
26-654
C1
30
24
22
16
11
10
5
A2
35
32
26
24
19
15
12
B2
21
21
14
12
9
7
5
D1
40
37
26
22
17
15
11
D2
D3
F1
39
34
28
23
18
12
12
20
15
14
13
10
9
7
27
23
14
13
11
6
3
144
122
94
87
79
67
55
54
98
84
81
64
58
47
42
33
105
96
92
84
80
78
58
43
130
110
97
85
75
66
53
50
Logistics Activity Profiling
SKU Characteristics
Enables you to look at SKU-level behavior . . .
• For all SKUs
• For individual SKUs
• For groups/families of SKUs
And react to a number of potential outcomes:
• Demand patterns
• ABC classification
• Grouping opportunities
- 26 -
Logistics Activity Profiling
Identify Demand Patterns
Declining
Inclining
Consistent
Seasonal
Irregular
Logistics Activity Profiling
ABC Classification
Three words . . .
Pareto, Pareto, Pareto!!
“separating the critical few from the trivial many”
The Pareto diagram is named after Vilfredo Pareto, a
19th century Italian economist who postulated that a
large share of wealth is owned by a small percentage of
the population1. This basic principle,often referred to as
the “80-20” rule, translates well into a huge variety of
applications.
Vilfredo Pareto 1848-1923
Why does this matter? Mathematical/statistical modeling
and applications developed from Pareto’s principle can
drive huge benefits to logistics and distribution
operations. If the industry has not yet erected a
monument to Pareto, it should!!
- 28 -
1source:
http://www.ecom.unimelb.edu.au/ecowww/rdixon/pareto.html
Logistics Activity Profiling
Typical Observations
• 20% of SKUs will generate 80% of activity, as measured in a variety
of ways:
– Cube movement
– Line count
– Dollar value
– Staffing levels
• Of these populations, the “outliers” will have an even greater impact
...
– 5% of SKUs often generate 50%-60% of activity
– 50% to 60% of SKUs often generate only 5% of activity
- 29 -
Logistics Activity Profiling
ABC Classification—Analysis Example
Traditional ABC Pallet Pick Stats
Pallet
Rank
A
B
C
SKUS
45
97
649
791
Pallets
15,655
2,978
981
19,614
% Spread by
Pallet Volume
80%
15%
5%
Traditional ABC Layer Pick Stats
Layer
Rank
A
B
C
SKUS
139
177
475
791
% Spread by
Layers
Picks
Layers/Pick Layer Volume
13,949
3,537
3.9
80%
2,628
624
4.2
15%
873
206
4.2
5%
17,450
4,367
Traditional ABC Case Pick Stats
Case
- 30 -
Rank
A
B
C
SKUS
201
303
287
791
% Spread by
Case Volume
Cases
Picks
Cases/Pick
54,516
10,322
5.3
80%
10,037
2,377
4.2
15%
3,749
830
4.5
5%
68,302
13,529
Logistics Activity Profiling
ABC Classification—Application Example
19,614
18,633
20,000
15,655
“C” Items:
• 649 SKUs
• Last 5% of movement
• Double-deep rack
15,000
Total
Pallet
Activity
“B” Items:
• 97 SKUs
• Next 15% of movement
• With “A” items=95% of movement
• Block stack 4 -7 deep
10,000
“A” Items:
• 45 SKUs
•Top 80% of movement
• Fast-turn lanes at dock
• Block stack 7-9 deep
5,000
0
0
100
45
200
300
400
500
600
142
800
791
Cumulative SKU Count
- 31 -
700
Logistics Activity Profiling
ABC Classification—Application Example
20,000
17,450
16,577
13,949
Total
Layer
Activity
15,000
“C” Items:
• 475 SKUs, 873 layers, 206 picks
• Last 5% of movement
• Double-deep rack
• Pick to pick area with DD reach truck
“B” Items:
• 177 SKUs, 2,628 layers, 624 picks
• Next 15% of movement
• With “A” items=95% of movement
• Push-back rack
• Clamp truck accessible
10,000
5,000
“A” Items:
• 139 SKUs, 13,949 layers, 3,537 picks
• Top 80% of movement
• 4-deep flow-through lanes
• Clamp truck accessible
0
0
100
200
139
- 32 -
300
400
500
600
316
Cumulative SKU Count
700
800
791
Logistics Activity Profiling
ABC Classification—Application Example (Traditional ABC)
68,302
64,553
70,000
60,000
54,516
Total
Case
Activity
“C” Items:
• 287 SKUs, 3,749 cases, 830 picks
• Last 5% of movement
• Double-deep rack rear locations or
single-deep/turret truck access
• Pick to pick area
50,000
40,000
“B” Items:
• 303 SKUs, 10,037 cases, 2,377 picks
• Next 15% of movement
• With “A” items=95% of movement
• Double-deep rack rear locations or
single-deep/turret truck access
• Pick to pick area
30,000
20,000
“A” Items:
• 201 SKUs, 54,516 cases, 10,322 picks
• Top 80% of movement
• 4-deep flow-through lanes
• Hand picked onto pallet jack or similar device
10,000
0
0
100
200
201
- 33 -
300
400
500
600
504
Cumulative SKU Count
700
800
791
Logistics Activity Profiling
ABC Classification—Application Example (Expanded ABC)
68,302
64,553
70,000
60,000
54,516
47,776
Total
Case
Activity
“C” Items:
• 287 SKUs, 3,749 cases, 830 picks
• Last 5% of movement
• Double-deep rack rear locations or
single-deep/turret truck access
• Pick to pick area
50,000
“A3” Items:
• 71 SKUs, 6,740 cases, 1,382 picks
• Next 10% of movement
• All “A” items = 80% of movement
• Storage?
• Picking?
40,000
34,044
30,000
“A2” Items:
• 58 SKUs, 13,732 cases, 2,690 picks
• Next 20% of movement
• With “A1” items=70% of movement
• Storage?
• Picking?
“A1” Items:
• 72 SKUs, 34,044 cases, 6,250 picks
• Top 50% of movement
• 4-deep flow-through lanes
• Hand picked onto pallet jack or similar device
20,000
10,000
0
0
100
72
- 34 -
200
130
300
400
“B” Items:
• 303 SKUs, 10,037 cases, 2,377 picks
• Next 15% of movement
• With “A” items=95% of movement
• Double-deep rack rear locations or
single-deep/turret truck access
• Pick to pick area
500
600
201
700
800
791
Cumulative SKU Count
Logistics Activity Profiling
Grouping Opportunities
Item
Number
Item
Number
Pair
Frequency
189-2-4
189-2-1
58
493-2-1
493-2-8
45
007-3-3
007-3-2
36
119-2-1
119-2-7
30
999-1-8
999-1-6
22
207-4-2
207-4-4
15
662-1-9
662-1-1
12
339-7-4
879-2-8
9
112-3-8
112-3-4
6
One form of grouping SKUs is
“pair frequency,” in which you
assess how often two (or more)
SKUs are ordered together.
These SKUs might be
candidates to slot next to one
another.
Logistics Activity Profiling
Grouping Opportunities (cont.)
Other grouping opportunities include . . .
• Store-specific
• Aisle specific
• Color/size/style
• Oversize/heavy
• Sortable/non-sortable
• Others?
Logistics Activity Profiling
Creating a Database
INV.
MASTER
Inventory Snapshots
Average Inventory Levels
- 37 -
ORDER
MASTER
Order Header
Order Detail (Lines)
ITEM
MASTER
Items Classification
Item Weight
Cases Per Pallet
$Value
Item Cube
Logistics Activity Profiling
Creating a Database (cont.)
HISTORICAL TRANSACTION
DATABASE
DAILY TRANSACTION
DATABASE
(CURRENT MONTH)
(CURRENT AND PREVIOUS YEARS)
Static
Archive
Current
MS
Access,
other db
- 38 -
1 YEAR OF ORDERS
(6 MONTHS IF NOT SEASONAL)
INVENTORY SNAPSHOTS
Logistics Activity Profiling
Creating Reports
Current
Archive
Static
MS
Access,
other db
}
Consolidate and
Calculate
MS Excel, Other Reporting Tool
Rank
1
2
3
4
- 39 -
Item
355
138SA
353
SW95A
Number
Total
% Of
Of Order Quantity Total
Lines Ordered Volume
1895
1820
1734
1669
8971
7238
6630
5266
0.5742%
0.4633%
0.4244%
0.3371%
Cumulativ # Pick Daily Pick
e Volume Days Frequency
0.574%
1.038%
1.462%
1.799%
57
57
57
57
33.25
31.93
30.42
29.28
}
Analyze
(sort/rank)
and Present
Logistics Activity Profiling
Session Wrap Up—Profiling Pays!!
•
•
•
•
•
Identify and operationally leverage behavior of . .
– Orders
– SKUs
– Activity
Spot and quickly react to trends . . . predict the future
Make informed design decisions
but remember . . .
Be cautious about spending too much time on the analysis and getting mired in the
data
Data is all about yesterday . . .
Know your business . . . Know your data!!
Wallerin’ in the data stimulates
creative thinking. Keep your eyes open and your
mind prepared for doing something different!!