2. Design and Integration
Distribution Center
Design and Integration
Case Study:
Apparel Manufacturer
Dean M. Starovasnik
Practice Director, Distribution Engineering Design

3. Overview Peach State Overview Process High Points Case Study
This session will provide an overview of an objective design methodology and an example case study where this process was used.
Peach State Overview
Process High Points
Case Study
Discussion
Though “Discussion” is listed last, questions or comments throughout the session are welcome and encouraged.

4. Overview
Peach State Overview
Process High Points
Case Study
Discussion

5. Top 25 with Network Totals
Peach State Spotlight
Peach State
Top 25 with Network Totals
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

6. Services Consulting Integration Material Handling & Storage Systems
Customer Service
& Support
Global Supply Chain Consulting & Engineering
Logistics engineering design – network logistics analysis, modeling & strategy
Distribution engineering design – alternative analysis, greenfield & retrofit DC design / facility layout, order fulfillment methodologies & design, business case & metrics development
Operational excellence – labor management programs – Six Sigma / Lean continuous improvement
Vendor 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 systems spare parts – sales & inventory management
Flexible service & maintenance programs – MHE certified technicians, system tune-ups & maintenance training
Equipment sales

7. Healthcare / CPG / Parts
Clients
Healthcare / CPG / Parts
Food & Beverage
Retail / E-commerce

8. Overview
Peach State Overview
Process High Points
Case Study
Discussion

9. Process Overview
To begin, a summary of the overall process will help visualize the destination. This will help in understanding the path to get there.
Where do we start?
Operational Review
Data Collection
Data Analysis
Profiling
Select an Order Fulfillment Methodology (OFM)
Based on order, customer and SKU profiles
Minimize handling, maximize service level
How big? & How fast?
Forward pick? Which tools?
Numbers of slots, facings, locations
Sortation parameters and requirements.
Connect the dots
Keeping this process in mind while examining each of the individual steps will help keep the forest in view while looking at each tree.

10. Data-Based Design Process
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
Design Parameters:
Planning Horizon
Growth Rates
Inventory Turns
Ship Window
Hourly Surges
Assumptions:
SKU Base
Handling Unit Type
Cartons Shipped
Pick Face Days Supply
Network
Design Requirements:
Order Fulfillment Methodology
MHE Throughput Rates
Pick Zones
Storage Media

11. Profiling – Input to the OFM Decision
Identifying the correct OFM’s for each portion of the operation is the first step in developing the facility design.
Order
Profiles
Handling Unit
Profiles
SKU
Profiles
Full Case %
Broken Case %
Full Pallet %
Mixed Orders %
Special handling
Per ship method (parcel vs. truck)
Per order distributions
Per carton distributions
Order completion
Single line percentage
Per day & hr distributions
ABC (Pareto) Distribution
Full Case, Broken Case, Full Pallet Volumes
Cube movement
ORDER
FULFILLMENT
METHODOLOGIES
Primary Manual vs. Automated Considerations:
Throughput requirements (hourly volumes)
Labor requirements (amount, cost, availability)
Service requirements (accuracy, service levels, costs of non-conformance)
Broken
Case
OFMs
Full
Case
OFMs

12. OFM Matrix Volume Automation Cube/Order Complexity
Two primary factors in determining the appropriate order fulfillment methodologies (OFM) are facility volume and order profile.
Product to Order
Order to Product
SKU Pick & Sort
Zone Pick & Sort/Consol
Dynamic Zone
Pick & Pass
Automated Picking
Volume
Automation
Storerooms
Garages
Cart Batch Pick
OP to Pallet
Complexity
Cube/Order
(pallet, carton, tote)

13. Complexity (Automation & Technology)
Broken Case OFMs
Complexity (Automation & Technology)
Order Picking
SKU Picking
Discrete
(Single)
Order Pick
Batch
(Cluster)
Order Pick
Pick &
Pass
SKU Pick & Marry
Low lines/order
Opportunity to batch many orders
High SKU commonality across orders
Low volumes
Small footprint (travel path)
High Lines/order
Large Cube/order
Limited WMS
Low Lines/order
Low Cube/order
Small footprint (path)
Frequent order release
WMS capable
>1 fit on pick vehicle?
Med-high volumes
Med Cube/order
Limited SKUs complete orders
Med-high Lines/order
Bulk
Pick &
Re-Pick
Pick To
Put
Pick
&
Sort
(Tilt-tray)
Auto.
Pick
(A Frame)
Pick To Tote
Pick To Carton
Limited WMS
Large number of SKUs needed to complete orders
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
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
Sequential
(Static) Zone
Dynamic Zone
Enhancements:
RF Voice
PTL RFID
Low order complete % within pick zones
High order complete % within pick zones

14. Full Case Methodologies
Full Case OFMs
Full Case Methodologies
Complexity (Automation & Technology)
Order Picking
SKU Picking
Single
Order Pick
To Pallet
Multi
Order Pick
To Pallet
SKU
Pick & Sort
Downstream
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/required
Staging space available/required
Pick to
Pallet & Sort
Zone pick
& drop to induct
point
Pick to Belt
Limited WMS
Large number of SKUs needed to complete orders
Adequate sort & staging space
Med-high volume
Most applicable for Parcel
Small footprint
Random storage
Very high hourly volumes
Small # SKUs represent high % volume

15. Overview
Peach State Overview
Process High Points
Case Study
Discussion

16. Case Study
Project Overview
Data Analysis & Requirements Definition
Facility Design

17. Project Overview
This client has a very successful, family owned, international business based in South America but had outgrown their current, 20+ year old distribution center.
Multi-channel operation
DLM (Agents)
Retail (owned and department stores)
Export
Current processes and methods robust
Manhattan Associates WMS
Engaged, capable IEs on staff
Ownership 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

18. Case Study
Project Overview
Data Analysis & Requirements Definition
Facility Design

19. Data Validation - Inventory
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 Summary

20. Requirements - Inventory
The following exhibit demonstrates anticipated inventory levels in cartons and pallets stored based on a similar turn through the design window.

21. Data Analysis - Outbound
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.
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

22. Data Analysis - Outbound
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.
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
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

23. Data Analysis - Outbound
In the perspective of Campaign, the following exhibit demonstrates the outbound volumes by each Campaign from 7/23/13 to 7/22/2014.
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

24. Requirements - Outbound
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.
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

25. Requirements - Outbound
The annual outbound unit volumes are demonstrated below by channel.
Annual outbound units
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

26. Requirements - Outbound
From the annual numbers we determine the average and peak day unit volumes.
Average day – expected units
Peak day – expected units
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

27. Requirements - Outbound
From the annual numbers we determine the average and peak day line volumes.
Average day – expected lines
Peak day – expected lines
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

28. Requirements - Outbound
From the annual numbers we determine the average and peak day order volumes.
Average day – expected orders
Peak day – expected orders
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

29. Requirements - Outbound
The annual outbound carton volumes are demonstrated below by channel.
Annual outbound cartons
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

30. Data Analysis - SKU
The ratio of outbound lines to SKU suggests high commonality, ranging from 1 to 22 lines per SKU; average roughly 12 lines per SKU.
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

31. Data Analysis - SKU
The following exhibit demonstrates the product distribution across product status and the line volume associated to each grouping.
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

32. Data Analysis – SKU
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.

33. Data Analysis - SKU
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.
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

34. Data Analysis - SKU
Approached by Campaign, the following exhibits demonstrate the ABC SKU movement by Campaign. The average per campaign is roughly 3,100 SKUs.
Unique shipment ID = OrderNumber+LoadNumber+ShipVia

35. Case Study
Project Overview
Data Analysis & Requirements Definition
Facility Design

36. Process Definition
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.

37. Process Definition
Similar Concepts

38. Process Definition
First the layouts for the associated methods are created. The first exhibit below is the floor level of the manual Pick-N-Pass option.

39. Process Definition
This exhibit represents the second level of the manual Pick-N-Pass option.

40. Process Definition
This exhibit represents the floor level of the automated Pick-N-Pass option.

41. Process Definition
This exhibit represents the second level of the automated Pick-N-Pass option.

42. Process Definition
This exhibit represents the floor level of the cart batch option.

43. Process Definition
This exhibit represents the second level of the cart batch option.

44. Process Definition
New Concepts

45. Process Definition
The cost estimates below represent the equipment costs of the baseline design including manual pick-n-pass picking.

46. Process Definition
The resulting labor impacts of the three picking process options and the two outbound processing options are calculated in the exhibit below.

47. Recommendations
To determine the most cost effective solution we look at the cost differences among the options. The following exhibits show picking cost impacts.

48. Recommendations
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.

49. Final Design The below provides a look at the final facility design.
Export
Reserve Storage
Shipping
Receiving
Pick Module
Packing VAS
Reserve Storage
Reserve Storage

50. Final Design

51. Recommendations
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 processing

52. Overview Peach State Overview Process High Points Case Study
Discussion 52

53. Questions?
M.I.T 53

54. E-mail: dstar@peachstate.com
Contact Information
Web:
Phone: 54