M Starovasnik Practice Director Distribution Engineering Design Distribution Center Design and Integration Case Study Apparel Manufacturer Overview Peach State Overview Process High Points ID: 623809
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Dean M. StarovasnikPractice Director, Distribution Engineering Design
Distribution Center
Design and Integration
Case Study:
Apparel ManufacturerSlide3
Overview
Peach State OverviewProcess High Points
Case Study
Discussion
This session will provide an overview of an objective design methodology and an example case study where this process was used.
Though “Discussion” is listed last, questions or
comments throughout the session are welcome and encouraged.
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Overview
Peach State OverviewProcess High Points
Case Study
Discussion
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Top 25 with Network TotalsPeach State
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Peach State Spotlight
Founded 1975, Headquartered - Atlanta, GA
Regional team members throughout the USA
Deep expertise in supply chain network optimization, distribution facility design, operational excellence, labor management, material handling and storage systems engineering and integration, automated systems, robotics, and systems maintenance
Over 800 projects completed and over 530 clients served
An Associated Company
One
of the largest Integrated Supply Chain Solutions providers in North America
450+ team members
$180+ million annual revenue
Member of the Raymond/Toyota Family Slide6
Global
Supply Chain Consulting &
Engineering
Material
Handling & Storage Systems
Logistics engineering design – network logistics
analysis, modeling &
strategy
Distribution
e
ngineering
d
esign – alternative analysis, greenfield & retrofit DC design / facility layout, order fulfillment methodologies & design, business case & metrics development
Operational
e
xcellence – labor
management programs – Six Sigma / Lean continuous improvementVendor selection – 3PL evaluations, and WMS & WCS requirement definition
Project & construction management services
Integrated material handling systems – engineering, simulations, procurement, & implementation
High-speed sortation, automated order fulfillment, automated palletizing, AS / RS, AGVs & LGVs
Rack, shelving and mezzanine systems design, engineering and installation
Material handling
s
ystems
s
pare
parts – sales & inventory management Flexible service & maintenance programs – MHE certified technicians, system tune-ups & maintenance trainingEquipment sales
Services
6
Consulting
Integration
Customer Service
& SupportSlide7
Clients
Healthcare / CPG / Parts
Food & Beverage
Retail / E-commerceSlide8
Overview
Peach State OverviewProcess High Points
Case Study
Discussion
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Process Overview
Where do we start?Operational Review
Data Collection
Data Analysis
Profiling
Select an Order Fulfillment Methodology (OFM)
Based on order, customer and SKU profilesMinimize handling, maximize service levelHow big? & How fast?
Forward pick? Which tools?
Numbers of slots, facings, locations
Sortation parameters and requirements.
Connect the dots
To begin, a summary of the overall process will help visualize the destination. This will help in understanding the path to get there.
Keeping this process in mind while examining each of the individual steps will help keep the forest in view while looking at each tree.
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Data-Based Design Process
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A design methodology based on historical
data
projected into
future design
requirements requires a range of data sources.
Collect
Data
Analyze
Data
Construct
Profiles
Develop
Parameters
Model
Scenarios
Define
Requirements
Assumptions
:
SKU Base
Handling Unit Type
Cartons Shipped
Pick Face Days Supply
Design Requirements
:Order Fulfillment MethodologyMHE Throughput RatesPick ZonesStorage MediaDesign Parameters:Planning HorizonGrowth Rates Inventory TurnsShip Window
Hourly Surges
NetworkSlide11
Profiling – Input to the OFM Decision
OrderProfiles
Handling Unit
Profiles
SKU
Profiles
ORDER
FULFILLMENT
METHODOLOGIES
Broken
Case
OFMs
Full
Case
OFMs
Primary Manual vs. Automated Considerations
:
Throughput requirements (hourly volumes)
Labor requirements (amount, cost, availability)
Service requirements (accuracy, service levels, costs of non-conformance)
Per ship method (parcel vs. truck)
Per order distributions
Per carton distributions
Order completion
Single line percentage
Per day &
hr distributionsFull Case %Broken Case
%
Full Pallet %
Mixed Orders %Special handling
ABC (Pareto) Distribution
Full Case, Broken Case, Full Pallet Volumes
Cube movement
Identifying the correct OFM’s for each portion of the operation is the first step in developing the facility design.
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OFM Matrix
Storerooms
Garages
Cart Batch Pick
OP to Pallet
SKU
Pick & Sort
Zone Pick & Sort/
Consol
Dynamic Zone
Pick & Pass
Automated Picking
Volume
Complexity
Product to Order
Order to Product
Automation
Two primary factors in determining the appropriate order fulfillment methodologies (OFM) are facility volume and order profile.
Cube/Order
(pallet, carton, tote)
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Broken Case OFMs
Discrete (Single)
Order Pick
Batch
(Cluster)
Order Pick
Pick &Pass
SKU Pick & Marry
Dynamic Zone
Pick To Tote
Bulk
Pick &
Re-Pick
Pick
To
Put
Pick &Sort(Tilt-tray)Auto.
Pick(A Frame)
Complexity (Automation & Technology)
Precise order cube cannot be pre-determined
Re-handling/VAS at packing
Precise order cube can be pre-determined
Order ship ready at point of pick
Low order
complete % within
pick zones
High order complete % within pick zonesLow Lines/order
Low Cube/order
Small footprint (path)
Frequent order releaseWMS capable>1
fit on pick vehicle?
Med-high volumes
Med Cube/order
Limited SKUs complete orders
Med-high Lines/order
Low number of customer-order sort points per wave
High hourly volumes
Sturdy/ durable products
Very high hourly volumes
Sturdy/ durable products
Uniform/ standard product shapes & sizes
Limited WMS
Large number of SKUs needed to complete orders
Order Picking
SKU Picking
Low lines/order
Opportunity to batch
many
orders
High SKU commonality across orders
Enhancements
:
RF Voice
PTL RFID
Low volumes
Small footprint (travel path)
High Lines/order
Large Cube/order
Limited WMS
Pick To Carton
Sequential
(Static) Zone
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Full Case MethodologiesSingleOrder Pick
To Pallet
Multi
Order Pick
To Pallet
SKU
Pick & Sort DownstreamPick to
Pallet & Sort
Zone pick
& drop to induct
point
Pick to Belt
Med-high volume
Most applicable for Parcel
Small footprint
Random storage
Very high hourly volumesSmall # SKUs represent high % volumeLimited WMSLarge number of SKUs needed to complete orders
Adequate sort & staging space
Low volumes
Most applicable for large, truck (LTL) orders
Small order size
Pick vehicle has capacity for >1 order
Automation Considerations
:
Throughput requirements (peak hourly volumes)
Labor requirements (amount, cost, availability) –current & projected
Service requirements (accuracy, service levels, costs of non-conformance)Dock doors available/requiredStaging space available/required
Full Case OFMs
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Complexity (Automation & Technology)
Order Picking
SKU PickingSlide15
Overview
Peach State OverviewProcess High Points
Case Study
Discussion
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Project Overview
Data Analysis & Requirements DefinitionFacility Design
Case StudySlide17
Project Overview
Multi-channel operationDLM (Agents)
Retail (owned and department stores)
Export
Current processes and methods robust
Manhattan Associates WMS
Engaged, capable IEs on staffOwnership focus on supply chain
Combination vertically integrated and outsourced product mix
Primarily local manufacturing
Basics present continuously
“Complimentary” products purchased off-shore
Real estate availability limited
Urban environment
Proximity to production facilities
Labor availability
This client has a very successful, family owned, international business based in South America but had outgrown their current, 20+ year old distribution center.
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Case Study
Project Overview
Data Analysis & Requirements Definition
Facility DesignSlide19
Data Validation - Inventory
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The following exhibit demonstrates the quantity summary of the on-hand inventory composition across a range of parameters.
Units Per SKU Inventory Summary
Cases Per SKU Inventory SummarySlide20
Requirements - Inventory
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The following exhibit demonstrates anticipated inventory levels in cartons and pallets stored based on a similar turn through the design window.Slide21
Data Analysis - Outbound
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Outbound data has been analyzed across a number of different characteristics. The following data represents the different daily outbound volumes from the line data provided. Slide22
Data Analysis - Outbound
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Outbound Daily Characteristics
Average daily “orders (shipments) are at 6,585 orders per day
Unique shipment ID =
OrderNumber+LoadNumber+ShipVia
Average daily lines are at 71,487 lines per day
Average daily units are at 122,078 units per day
The daily outbound graph shows little seasonality and the peak-to-average ratio for lines is 1.25
Data includes Saturday & Sunday
activity
This outbound data was looked at in terms of daily volumes of units, lines and orders as well as in handling units of outbound cartons.Slide23
Data Analysis - Outbound
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In the perspective of Campaign, the following exhibit demonstrates the outbound volumes by each Campaign from 7/23/13 to 7/22/2014.Slide24
Requirements - Outbound
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Also provided was the business projections from the Client team members. This data was used to forecast the resulting annual volumes through the design period.Slide25
Requirements - Outbound
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The annual outbound unit volumes are demonstrated below by channel.
Annual outbound unitsSlide26
Requirements - Outbound
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From the annual numbers we determine the average and peak day unit volumes.
Average day – expected units
Peak day – expected unitsSlide27
Requirements - Outbound
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From the annual numbers we determine the average and peak day line volumes.
Average day – expected lines
Peak day – expected linesSlide28
Requirements - Outbound
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From the annual numbers we determine the average and peak day order volumes.
Average day – expected orders
Peak day – expected ordersSlide29
Requirements - Outbound
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The annual outbound carton volumes are demonstrated below by channel.
Annual outbound cartonsSlide30
Data Analysis - SKU
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The ratio of outbound lines to SKU suggests high commonality, ranging from 1 to 22 lines per SKU; average roughly 12 lines per SKU.Slide31
Data Analysis - SKU
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The following exhibit demonstrates the product distribution across product status and the line volume associated to each grouping.Slide32
Data Analysis – SKU
32
The following exhibit demonstrates the ABC analysis across a number of different variables. A items represent top 80% of lines shipped, B items next 15%, C items next 4% and D items bottom 1%; N items had a current quantity on-hand but no outbound history.Slide33
Data Analysis - SKU
33
We also took a look at the SKU breakdown across the Campaigns. The following demonstrates the make-up of the Campaigns across the ABC analysis.Slide34
Data Analysis - SKU
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Approached by Campaign, the following exhibits demonstrate the ABC SKU movement by Campaign. The average per campaign is roughly 3,100 SKUs.Slide35
Case Study
Project Overview
Data Analysis & Requirements Definition
Facility DesignSlide36
Process Definition
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The process combinations that will be analyzed includes the following areas for comparison.
Pick-n-Pass (non-automated) picking with in-line label & seal and destination sortation – This serves as the baseline of the current design and subsequent comparison
Pick-n-Pass
(automated
) picking with in-line label & seal and destination
sortation
Cart batch picking with in-line label & seal
and destination sortation
Further comparison will include either a manual sort or an automated sort outbound.
These areas will be scaled by the anticipate volumes and the capital cost, labor and space impacts will be compared to find the best applications for the outbound process.Slide37
Process Definition
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Similar ConceptsSlide38
Process Definition
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First the layouts for the associated methods are created. The first exhibit below is the floor level of the manual Pick-N-Pass option.Slide39
Process Definition
39
This exhibit represents the second level of the manual Pick-N-Pass option.Slide40
Process Definition
40
This exhibit represents the
floor
level of the
automated
Pick-N-Pass option.Slide41
Process Definition
41
This exhibit represents the
second
level of the automated Pick-N-Pass option.Slide42
Process Definition
42
This exhibit represents the floor level of the
cart batch option
.Slide43
Process Definition
43
This exhibit represents the second level of the
cart batch
option.Slide44
Process Definition
44
New ConceptsSlide45
Process Definition
45
The cost estimates below represent the equipment costs of the baseline design including manual pick-n-pass picking.Slide46
Process Definition
46
The resulting labor impacts of the three picking process options and the two outbound processing options are calculated in the exhibit below.Slide47
Recommendations
47
To determine the most cost effective solution we look at the cost differences among the options. The following exhibits show picking cost impacts.Slide48
Recommendations
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Converting the previous page into a cash-flow comparisons, we find the following results.
This shows that option 1 is not only competitive with option 3 for least combined initial cost, but is also the long term least cost option.Slide49
Final Design
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The below provides a look at the final facility design.
Shipping
Receiving
Pick Module
Reserve Storage
Reserve Storage
Reserve Storage
P
acking VAS
ExportSlide50
Final Design
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Recommendations
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The features of the recommended layout are the following
This design meets all volumetric requirements through the year 2021
Adding the third level of picking will add capacity beyond the 2024 design window
Labor reductions are targeted to already congested areas of receiving, put-away and replenishment
Fluid loading will help keep dock space clearer during daily processingSlide52
Overview
Peach State OverviewProcess High Points
Case Study
Discussion
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Questions?
M.I.T
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Contact Information
E-mail: dstar@peachstate.comWeb: www.peachstate.comPhone: 678-327-2013
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