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Dean - PPT Presentation

M Starovasnik Practice Director Distribution Engineering Design Distribution Center Design and Integration Case Study Apparel Manufacturer Overview Peach State Overview Process High Points ID: 623808

pick amp design order amp pick order design outbound data requirements process sku high volumes analysis lines definition case exhibit picking day

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Slide1
Slide2

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.

3Slide4

Overview

Peach State OverviewProcess High Points

Case Study

Discussion

4Slide5

Top 25 with Network TotalsPeach State

5

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

8Slide9

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.

9Slide10

Data-Based Design Process

10

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.

11Slide12

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)

12Slide13

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

13Slide14

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

14

Complexity (Automation & Technology)

Order Picking

SKU PickingSlide15

Overview

Peach State OverviewProcess High Points

Case Study

Discussion

15Slide16

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.

17Slide18

Case Study

Project Overview

Data Analysis & Requirements Definition

Facility DesignSlide19

Data Validation - Inventory

19

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

20

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

21

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

22

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

23

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

24

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

25

The annual outbound unit volumes are demonstrated below by channel.

Annual outbound unitsSlide26

Requirements - Outbound

26

From the annual numbers we determine the average and peak day unit volumes.

Average day – expected units

Peak day – expected unitsSlide27

Requirements - Outbound

27

From the annual numbers we determine the average and peak day line volumes.

Average day – expected lines

Peak day – expected linesSlide28

Requirements - Outbound

28

From the annual numbers we determine the average and peak day order volumes.

Average day – expected orders

Peak day – expected ordersSlide29

Requirements - Outbound

29

The annual outbound carton volumes are demonstrated below by channel.

Annual outbound cartonsSlide30

Data Analysis - SKU

30

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

31

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

34

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

36

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

37

Similar ConceptsSlide38

Process Definition

38

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

48

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

49

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

50Slide51

Recommendations

51

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

52Slide53

Questions?

M.I.T

53Slide54

Contact Information

E-mail: dstar@peachstate.comWeb: www.peachstate.comPhone: 678-327-2013

54Slide55