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1. ADVANCED ANALYTICS APPLICATIONS FOR
EFFICIENT WAREHOUSING AND PACKAGING
Oct 2019
DHL Supply Chain – Operations Science & Analytics

2. 2
PRESENTER INFO
George Guo
Sr. Data Scientist
DHL Supply Chain
Manjeet Singh
Research Director
DHL Supply Chain

3. 3
Applied Analytics in Warehousing

4. 4
Introduction
Rising consumer expectations with regard to order delivery due to eComm growth.

5. 5
E-commerce Warehouse
Traditional Warehouse E-Commerce Warehouse

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Picking Costs
 Picking is the most labor intensive and costly activity in warehouse operations.
Warehouse Operation Cost

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Sub-optimal picking leads to
Labor Utilization Throughput
Order Cycle
Time

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Robots are an effective solution

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How Robots work
“Follow me” Bot Swarm Bot

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But secret sauce is not the hardware
Software &
Algorithms

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Lets see how these software work
Software &
Algorithms

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How to Optimize Picking using Software
 To improve the picking speed in the warehouse, 3 main aspects need optimization.
Slotting Allocation Tasking
Where to put the items
Where to pick the items
(if stored in multiple locations)
Which orders to pick together

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Solution to Optimize Slotting
WCSA (Weighted Completeness Slotting Algorithm)

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Current State – Slotting
 ABC Slotting based on velocity
 Color ∝ % times ordered together
 Color Intensity ∝ # of orders

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Velocity Slotting vs WCSA
 Velocity slotting – puts fast movers randomly together.
 WCSA - considers SKU correlation to maximize complete orders in each aisle.

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WCSA Test Results
 Testing of WCSA with sample data set(500 orders, 215 SKUs) from an American cosmetic brand.
- 8%
33,995
31,290
28,595
Random Slotting Velocity Slotting WCSA Slotting
Travel Distance Comparison for Different
Slotting Strategies
Travel Distance(ft)
- 15%

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Solution to Optimize Allocation
OAA (Order Allocation Algorithm)

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Current State – Allocation
 For warehouses where SKUs are stored in multiple locations.
 First Location Rule: WMS’ assigns the first available location for each SKU as its picking location
Order # 1
SKU A
SKU B
SKU C
SKU D
Available zones by SKU
2 3 5 7 11
1 4 7 8 10 12
4 5 7 8 9
7 9 11 12 2 1 4 7
 4zones
First available
zone

Zones needed to fulfill order 1
using First Location Rule:

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OAA
Order # 1
SKU A
SKU B
SKU C
SKU D
Available zones by SKU
2 3 5 7 11
1 4 7 8 10 12
4 5 7 8 9
7 9 11 12 7 7 7 7
 1zone
Zones needed to fulfill order 1
using OAA:
2 1 4 7
 4zones
Zones needed to fulfill order 1
using First Location Rule:
 OAA allocates best pick location per SKU for each order to minimize travel.
 Allocate the SKUs to the zone that cover the most number of SKUs.
 Loop

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Solution to Optimize Tasking
OGA (Order Grouping Algorithm)

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Current State – Tasking
Aisle1 Aisle2 Aisle3 Aisle4 Aisle5 Aisle6
O
1
O
3
O
4
O
5
O
7
O
8
O
9
O
10
O
12
O
15
O
16
O
1
O
2
O
2
O
3
O
4
O
5
O
6
O
6
O
7
O
8
O
9
O
10
O
11
O
11
O
12
O
13
O
13
O
15
O
16
O
1
O
1
O
1
O
1
O
2
O
2
O
2
O
3
O
3
O
2
O
3
O
3
O
5
O
5
O
5
O
5
O
4
O
4
O
4
O
4
O
6
O
6
O
6
O
6
Aisle1 Aisle2 Aisle3 Aisle4 Aisle5 Aisle6 SKUs for orders O1 to O16 shown in figure on left
SKUs/Order = 2
FCFS Pick Assignment
 Orders assigned = 1,2,3,4,5,6
 Number of Aisles visited = 6

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Aisle1 Aisle2 Aisle3 Aisle4 Aisle5 Aisle6
O
1
O
3
O
4
O
5
O
7
O
8
O
9
O
10
O
12
O
15
O
16
O
1
O
2
O
2
O
3
O
4
O
5
O
6
O
6
O
7
O
8
O
9
O
10
O
11
O
11
O
12
O
13
O
13
O
15
O
16
O
1
O
1
O
1
O
1
O
2
O
2
O
2
O
15
O
9
O
2
O
15
O
9
O
7
O
7
O
11
O
11
O
7
O
7
O
9
O
9
O
11
O
11
O
15
O
15
Aisle1 Aisle2 SKUs for orders O1 to O16 shown in figure on left
SKUs/Order = 2
Optimal Pick Assignment
 Orders assigned = 1,2,7,9,11,15
 Number of Aisles visited = 2
OGM

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Impact of OAA + OGA
 5% fewer stops
Current State Zone 1-12
Future State Zone 1-12
 32% less aisles visited  34% total travel distance total

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Schematic
Warehouse Operation Intelligent System
Warehouse Management
System

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Architecture

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Implementation 1
25%
Productivity
improvement
Chicago, IL AEM Sector
• 70K SKUs with wide range in sizes
• IBM I series warehouse management system
• IDEA drives savings by automating manual waving process
and sorting orders by zone and equipment type
IDEA’s clustering algorithm significant reduced travel across
330K ft2 warehouse

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Implementation 2
35%
Productivity
improvement
• 55K SKUs, wide range in sizes
• Orders are processed via forklifts and order pickers
• Infor Smart Office W3 warehouse management system
• IDEA drives savings by automating manual waving process and
sorting orders by zone, equipment type and priority
Edmonton, CA AEM Sector
IDEA groups assignments for heavy items together
(explained later in document)

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Conclusion
• WCSA reduces pick path by putting correlated SKUs together so that each aisle can fulfill as many
complete order as possible.
• WCSA reduces pick path by 9% comparing with velocity slotting.
• OAA minimizes travel distance by reducing the number of zones/areas each order needs to visit.
• OGA reduces pick path by clustering orders whose items are located nearby.
• OGA + OAA, reduces 34% travel distance comparing with FCFS + First-Location-Rule.

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Applied Analytics in Packaging

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Background Introduction
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63.6
74.4
2014 2015 2016 2017
Parcel Volume Growth in Billion
* https://www.pitneybowes.com/us/shipping-index.html
 eCommerce growth YOY >15% YoY
 Surpass 100 Billion parcels in 2020, with 279 Billion dollars in revenue.*

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Empty Cartons

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Dimensional Weight Calculation
• In 2015 FedEx and UPS expanded dimensional ratings to all packages.
• Shipping cost based on max of weight and size

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Empty Space is Costly
Size: 18” X 9” X 9”
Utilization Rate: 18%
Cost: $ 22
Size: 6” X 6” X 6”
Utilization Rate: 91%
Cost: $ 6.5

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Growth in Size and Complexity Makes the Situation Worse

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Current Solution
Make-to-order packaging machines
• May not keep up with increasing demand
• Increases operational complexity
• High cost solution
• Not a fit for all businesses
Add more carton sizes
• Low cost solution
• Does not guarantee savings
without careful selection in
absence of advanced analytics

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Store many pre-made cartons
• Warehouse typically stores around 25 carton sizes
• Selecting the right carton set needs complex
analytics.
• No easily accessible software
• The carton set based on operators’ experience
A Ecommerce warehouse packing operation

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Carton Optimization
* In 08/2018 DHL filed a patent in the US under (application # 16111847), under the title of “SHIPPING CARTON OPTIMIZATION SYSTEM AND METHOD”
Step 1
Cube each order based on dimension
of individual items
Step 2
Create feasible candidate carton sizes
based on order profile
Step 3
Determine optimal cartons and
conduct sensitivity analysis
Step 4
Update current carton set (Current carton
C1-C6 replaced by carton A, B, I and J)

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Modified Cubing Algorithm
Original LAFF*(Largest Area First Fit)
*Gurbuz M Z, Akyokus S, Emiroglu I, et al. An Efficient Algorithm for 3D Rectangular Box Packing, 2009[J]. Applied Automatic Systems: Proceedings of Selected AAS,
2009: 131-134.
Utilization Rate = 63.9%
MLAFF(Modified LAFF) algorithm: ~18% improvement
Utilization Rate = 82.4%

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Site Implementation: Savings
Equal to
current 15
Saves
$2.1MM
Make to Order
Saves $10.8MM

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Detailed Results
• Savings using optimal 15 cartons ≈ $2.1MM.
• Savings using make to order packaging ≈
$10.8MM.
• Current 11 cartons were also chosen.
• Number of medium sized cartons increased.
• Reduce the amount of corrugate materials,
void fillers and carbon emission.
• Perform carton optimization periodically to
ensure savings

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Continuous Cost Oriented
Challenges:
• Order profile may change during the year
• New SKUs
• Customer preference
• New carton manufacturing takes time
Solutions:
• Continuous cost oriented
• Order profile monitoring
• Volume prediction
• Make to order packaging machine

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Continuous Cost Oriented
Jan Feb Mar Apr May Jun Jul Aug Sep Oct
Data collected for analysis Data validated for cost savings
Jan Feb Mar Apr May Jun Jul Aug Sep Oct
Jan Feb Mar Apr May Jun
Feb Mar Apr May Jun Jul
Mar Apr May Jun Jul Aug
Apr May Jun Jul Aug Sep
May Jun Jul Aug Sep Oct
Cost Savings
3.98%
Cost Savings
4.69%
$ 260,000
Additional Savings

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Merging with End to End Packaging Solution
1.Carton Optimization Analysis 3.Pack Station Design
4.Carton Selection System 6.Packaging Machine Selection5.Set Packaging Rules
Machine Learning, Data Analytics,
Operations Research
Identify when to separate orders Identify appropriate make to order
machine
Pack station replenishment and
MRP planning
if WMS does not provide cubing logic
2.Carton Procurement
Maintain the stock of different
size of cartons

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Conclusion
• Parcel packaging is an important issue, especially in this eComm era.
• Carton optimization tool helps warehouses reduce parcel shipping cost by using advanced
cubing algorithm, machine learning and optimization algorithm.
• Comprehensive parcel packaging solution requires the collaboration between smart software
and appropriate hardware(make-to-order packaging machine).

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Thank you
Q & A