Dean

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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 Download Presentation

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Dean




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Dean M. StarovasnikPractice Director, Distribution Engineering Design

Distribution Center

Design and Integration

Case Study:

Apparel Manufacturer

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Overview

Peach State OverviewProcess High PointsCase 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 PointsCase Study Discussion

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Top 25 with Network Totals

Peach State

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Peach State Spotlight

Founded 1975, Headquartered - Atlanta, GA

Regional team members throughout the USADeep 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 maintenanceOver 800 projects completed and over 530 clients servedAn Associated CompanyOne of the largest Integrated Supply Chain Solutions providers in North America 450+ team members $180+ million annual revenueMember of the Raymond/Toyota Family

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Global

Supply Chain Consulting &

Engineering

Material Handling & Storage Systems

Logistics engineering design – network logistics

analysis, modeling & strategyDistribution engineering design – alternative analysis, greenfield & retrofit DC design / facility layout, order fulfillment methodologies & design, business case & metrics developmentOperational excellence – 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 & LGVsRack, 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 trainingEquipment sales

Services

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Consulting

Integration

Customer Service

& Support

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Clients

Healthcare / CPG / Parts

Food & Beverage

Retail / E-commerce

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Overview

Peach State OverviewProcess High PointsCase Study Discussion

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Process Overview

Where do we start?Operational ReviewData CollectionData AnalysisProfilingSelect 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, locationsSortation 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 BaseHandling Unit TypeCartons ShippedPick Face Days Supply

Design Requirements:Order Fulfillment MethodologyMHE Throughput RatesPick ZonesStorage Media

Design Parameters:Planning HorizonGrowth Rates Inventory TurnsShip WindowHourly Surges

Network

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Profiling – Input to the OFM Decision

OrderProfiles

Handling UnitProfiles

SKUProfiles

ORDERFULFILLMENTMETHODOLOGIES

BrokenCaseOFMs

FullCaseOFMs

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 completionSingle line percentagePer day & hr distributions

Full Case %Broken Case %Full Pallet %Mixed Orders %Special handling

ABC (Pareto) DistributionFull Case, Broken Case, Full Pallet VolumesCube 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 PickOP to Pallet

SKU Pick & SortZone Pick & Sort/Consol

Dynamic Zone Pick & PassAutomated 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

BulkPick &Re-Pick

Pick ToPut

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-determinedOrder ship ready at point of pick

Low order complete % within pick zones

High order complete % within pick zones

Low Lines/order

Low Cube/orderSmall footprint (path)Frequent order releaseWMS capable>1 fit on pick vehicle?

Med-high volumes

Med Cube/orderLimited SKUs complete ordersMed-high Lines/order

Low number of customer-order sort points per wave

High hourly volumes

Sturdy/ durable products

Very high hourly volumesSturdy/ durable productsUniform/ standard product shapes & sizes

Limited WMSLarge 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 VoicePTL RFID

Low volumesSmall footprint (travel path)High Lines/orderLarge Cube/orderLimited WMS

Pick To Carton

Sequential(Static) Zone

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Full Case Methodologies

SingleOrder PickTo Pallet

MultiOrder PickTo Pallet

SKUPick & Sort Downstream

Pick toPallet & Sort

Zone pick& drop to inductpoint

Pick to Belt

Med-high volumeMost applicable for ParcelSmall footprintRandom storage

Very high hourly volumesSmall # SKUs represent high % volume

Limited WMSLarge number of SKUs needed to complete ordersAdequate 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 & projectedService 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 Picking

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Overview

Peach State OverviewProcess High PointsCase Study Discussion

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Project OverviewData Analysis & Requirements DefinitionFacility Design

Case Study

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Project Overview

Multi-channel operationDLM (Agents)Retail (owned and department stores)ExportCurrent processes and methods robustManhattan Associates WMSEngaged, capable IEs on staffOwnership focus on supply chainCombination vertically integrated and outsourced product mixPrimarily local manufacturingBasics present continuously“Complimentary” products purchased off-shoreReal estate availability limitedUrban environmentProximity to production facilitiesLabor 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 Design

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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 Summary

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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.

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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.

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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+ShipViaAverage daily lines are at 71,487 lines per dayAverage daily units are at 122,078 units per dayThe daily outbound graph shows little seasonality and the peak-to-average ratio for lines is 1.25Data 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.

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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.

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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.

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Requirements - Outbound

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The annual outbound unit volumes are demonstrated below by channel.

Annual outbound units

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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 units

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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 lines

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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 orders

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Requirements - Outbound

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The annual outbound carton volumes are demonstrated below by channel.

Annual outbound cartons

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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.

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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.

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Data Analysis – SKU

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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.

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Data Analysis - SKU

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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.

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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.

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Case Study

Project Overview

Data Analysis & Requirements Definition

Facility Design

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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.

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Process Definition

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Similar Concepts

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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.

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Process Definition

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

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Process Definition

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This exhibit represents the

floor

level of the automated Pick-N-Pass option.

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Process Definition

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This exhibit represents the

second

level of the automated Pick-N-Pass option.

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Process Definition

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This exhibit represents the floor level of the

cart batch option

.

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Process Definition

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This exhibit represents the second level of the

cart batch

option.

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Process Definition

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New Concepts

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Process Definition

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

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Process Definition

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

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Recommendations

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

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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.

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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

Export

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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 windowLabor reductions are targeted to already congested areas of receiving, put-away and replenishmentFluid loading will help keep dock space clearer during daily processing

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Overview

Peach State OverviewProcess High PointsCase 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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