© 2011 Peach State Integrated Technologies. Contents are confidential. All rights reserved....

© 2011 Peach State Integrated Technologies. Contents are confidential. All rights reserved.

P E A C H S T A T E

Distribution Center

Design and Integration

Case Study – Health & Beauty Aid Manufacturer

By:Dean M. Starovasnik

Practice Director, Distribution Engineering Design

Peach State Integrated Technology

Presented to:Warehouse & Distribution ScienceISyE 6202Georgia Institute of Technology


P E A C H S T A T E

Overview

3

Peach State Overview

Process High Points

Case Study Data Analysis Results Design Requirements Financial Review Site Photos

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.


P E A C H S T A T E

Peach State Overview


Corporate Credentials

5

Headquarters in Atlanta, GA.

Regional offices throughout North America.

Over 34 years of experience engineering and integrating supply chain logistics, distribution, and material handling solutions on a national and global scale.

Results Oriented, Performance Driven, Team Based Culture.

Deep expertise in Supply Chain Strategy, Distribution/Manufacturing Design and Engineering, Site Operational Optimization and Labor Standards, Material Handling Systems Integration, and Customer Support.


Industry – Thought Leadership

6

Council of Supply Chain Management Professionals (CSCMP) Track Chair 2004 and 2006

Conference Presentationso ProMato Retail Leaders Industry Association

(RILA)o National Conference on Operations &

Fulfillment (NCOF)o HK Systems Annual Material

Handling and Logistics Conference

Peach State Speakers’ Bureau

Numerous White Paper Publications

Material Handling Equipment Distributors Association (MHEDA) – Member, Past Board Member, President 2003

The National Logistics & Distribution Conference (NLDC) Founder and Producer

Georgia Tech Supply Chain and Logistics Institute – Lecture Presenter since 2000

DC Velocity – Editorial Board Member

Frequent contributor to major trade journal articles


Core Services

7

Focused on Results – “From Strategy to Reality”

Global Consulting & Engineering

Facility Design & Engineering

Material Handling Solutions

Customer Service and

Support

• Logistics network strategy and design

• Strategic distribution master planning

• Rationalizing for outsourcing/3PL

• Labor Management and Operational Excellence

• Facility Designer Toolsettm determines the appropriate mix of people, space, equipment, and systems.

• Solution development focused on delivering a rapid ROI.

• Detailed engineering, bid management, procurement, and implementation of integrated material handling systems

• High-speed sortation, automated order fulfillment, AS/RS, AGV/LGVs, and palletization.

• Material handling systems spare parts to keep your facility running.

• Service and maintenance programs that are tailored to ensure maximum ‘uptime’ and performance.


Major Clients

8

Serving the ‘Best of the Best’…

Healthcare & Pharmaceutic

als

Food & Beverage

PartsDistribution

ConsumerProducts/

Retail

Manufacturing

http://www.abbott.com/index.cfm

http://www.kraft.com/index.html

http://www.gracobaby.com/


P E A C H S T A T E

Facility Design Process


Process Overview

10

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

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 view while examining each of the individual steps will help keep the forest in view while looking at each tree.


Data Analysis Methodology

11

Our analysis methodology transformed historical data into future design requirements:

Collect Data

Collect Data

Analyze Data

Analyze Data

Construct Profiles

Construct Profiles

Develop ParametersDevelop

Parameters

Model ScenariosModel

Scenarios

Define Requirements

Define Requirements

Assumptions:• SKU Base

• Handling Unit Type• Cartons Shipped• Pick Face Days

Supply

Design Requirements:

• Order Fulfillment Methodology• MHE Throughput Rates

• Pick Zones• Storage Media

Design Parameters:• Planning Horizon

• Growth Rates• Inventory Turns• Ship Window• Hourly SurgesN

etwork


Facility Design Profiles

12

Planning & Design Issue

Key Focus Primary Data Source

Profiles

Storage Sizing Right storage media & corresponding facings for reserve slots

Item, Location & Inventory Data

• ABC inventory distribution• Handling unit (pallets, cube, cases, etc.) inventory profile

Order Fulfillment Methodologies

Effective strategies for picking & packing (e.g., zone pick & sort opportunities?)

Order Files • Per order distributions (lines, units, cartons, cube, etc.)• Per carton distributions• Order mix/completion distribution• Handling unit profile (broken/full case, full pallet, mixed)

Material Handling System Throughput & Capacity

Peak hourly volumes to be processed

Order Files • Daily activity profile (orders, lines, full cases, split cartons, total boxes)

• Hourly activity distribution (particularly with respect to order drop & cutoff times)

Warehouse Zone & Facing Requirements

Right storage media & corresponding facings for primary slots

Order, Item & Location Files

• ABC activity (Pareto) profile• Cube movement distribution• Storage zone profiles (SKUs, volumes, etc. by special

requirements - drug, cooler, etc.)

Profiles of different data elements help to address the variety of questions that must be answered in the facility design effort.

Actual development of most profiles involves generating each “day’s” activity, statistically analyzing them then developing the distributions.


Profiling – Input to the OFM Decision

OrderProfiles

OrderProfiles

Handling UnitProfiles

Handling UnitProfiles

SKUProfiles

SKUProfiles

BrokenCaseOFMs

BrokenCaseOFMs

FullCaseOFMs

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 completion•Single line percentage•Per day & hr distributions

• Full Case Pct• Broken Case

Pct• Full Pallet Pct• Mixed Orders

Pct• Special handling• Lot control• Hazmat• Refrig/Freezer

•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.

13

ORDERFULFILLMENT

METHODOLOGIES

ORDERFULFILLMENT

METHODOLOGIES


OFM Matrix

14

Storerooms Garages

Cart Batch PickOP to Pallet

SKU Pick/SortPick to AutoPak

Dynamic Zone Pick & Pass

Automated Picking

Vol

ume

Line/Order

Product to Order Order to ProductA

utom

atio

n

Two primary factors in determining the appropriate order fulfillment methodologies (OFM) are facility volume and order profile.

Cube/

Order

(med

ia)


•Low Lines/order•Low Cube/order•Small travel path•Frequent order release•WMS capable•Can fit >1 order on pick vehicle

Broken Case Methodologies

15

Discrete (Single)

Order Pick

Discrete (Single)

Order Pick

Batch(Cluster)

Order Pick

Batch(Cluster)

Order PickPick &Pass

Pick &Pass

SKU Pick & MarrySKU Pick & Marry

Sequential(Static) Zone


Pick To TotePick To Tote

BulkPick &

Re-Pick

BulkPick &

Re-Pick

Pick ToPut

Pick ToPut

Pick &

Sort(Tilt-tray)

Pick &

Sort(Tilt-tray)

Auto.Pick

(A Frame)

Auto.Pick

(A Frame)

Complexity (Automation & Technology)

Pick To CartonPick To Carton

•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 completion pct within pick zones

•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 & release many

orders• High SKU commonality across orders

Enhancements:RFVoicePTL RFID

•Low volumes•Small footprint (travel path)

•High Lines/order•Large Cube/order•Limited WMS

Pick &Pass

Pick &Pass

Pick To CartonPick To Carton


Sequential(Static) Zone Dynamic ZoneDynamic Zone


Full Case Methodologies

16

SingleOrder PickTo Pallet

SingleOrder PickTo Pallet

MultiOrder PickTo Pallet

MultiOrder PickTo Pallet

SKUPick & Sort Downstream


Pick toPallet & Sort

Pick toPallet & Sort

Zone pick& drop to induct

point

Zone pick& drop to induct

pointPick to BeltPick to Belt

•Med-high volume•Most applicable for Parcel•Small footprint•Random storage

•Very high hourly volumes

•Small # SKUs represent high % volume

•Limited WMS•Large number of SKUs needed to complete orders

•Adequate sort & staging space

Order Picking SKU Picking

•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 BeltPick to Belt

Complexity (Automation & Technology)


P E A C H S T A T E

Case Study


Project Overview

18

Growing through the recession (20%) Recently purchased by a private equity

firm

High profile, luxury product identity

Persistent demand from existing customers

New customers gained through Internet and QVC

Originally in two fulfillment facilities Both space constrained

Retail & QVC fulfilled in one facility

Internet fulfilled (from same SKU base) at HQ

Spec building selected prior to design Size and door count validated immediately

Sufficient for 2015 and beyond

Some expansion capability available

The design project we are reviewing proceeded through implementation. The client is philosophy, a high end skin care cream manufacturer.

They were moving very fast, with aggressive growth projections and desired a rapid evidence of return on investment.


P E A C H S T A T E

Data Analysis Results

19


Outbound Profiles – Daily Activity

20

The below statistics help to illustrate the activity levels of the combined business, Retail DSDC and Internet channels.

Parameter Orders/day Lines/day Units/day CubicFeet/day Weight/day SKUs/dayAverage 934 3,875 23,748 502 14,931 328 95th Percentile 2,240 9,485 77,391 1,420 43,814 454 Max 3,882 14,611 135,662 2,802 87,689 473 Peak to Avg 2.40 2.45 3.26 2.83 2.93 1.39

Parameter Orders/day Lines/day Units/day CubicFeet/day Weight/day SKUs/dayAverage 894 3,282 3,430 71 1,794 308 95th Percentile 2,227 8,197 8,922 190 5,024 417 Max 3,815 14,160 14,721 374 8,503 460 Peak to Avg 2.49 2.50 2.60 2.70 2.80 1.36

Parameter Orders/day Lines/day Units/day CubicFeet/day Weight/day SKUs/dayAverage 87 765 10,278 226 6,765 103 95th Percentile 254 2,145 33,098 801 22,907 178 Max 482 3,214 91,685 1,459 50,182 218 Peak to Avg 2.93 2.80 3.22 3.54 3.39 1.72

Combined

Retail DSDC

Internet


Outbound Profiles – Order Statistics

21

The below statistics help to illustrate the nature of the orders across the combined business, Retail DSDC and Internet channels.

Combined

Retail DSDC

Internet

Parameter Lines/Order Units/Order Cubic/Order Weight/Order Units/LineAverage 4.7 52.8 1.0 30.3 8.6 95th Percentile 8.1 176.4 2.9 85.7 22.4 Max 37 2,251 34 1,043 172 Peak to Avg 1.73 3.34 2.85 2.83 2.60

Parameter Lines/Order Units/Order Cubic/Order Weight/Order Units/LineAverage 3.6 3.8 0.1 2.0 1.1 95th Percentile 4.4 4.6 0.1 3.0 1.1 Max 8 8 1 8 8 Peak to Avg 1.20 1.22 1.38 1.50 1.05

Parameter Lines/Order Units/Order Cubic/Order Weight/Order Units/LineAverage 11.1 277.8 6.5 200.9 24.9 95th Percentile 24.8 927.2 19.6 592.8 120.2 Max 56 7,372 304 9,288 413 Peak to Avg 2.23 3.34 3.01 2.95 4.82


Outbound Profiles - Throughput

22

The daily throughput profile reveals considerable seasonality, peaking in October & November.

0

20,000

40,000

60,000

80,000

100,000

120,000

140,000

Apr

-09

May

-09

Jun-

09

Jul-0

9

Aug

-09

Sep-

09

Oct

-09

Nov

-09

Dec

-09

Total (No QVC) Daily Outbound Units Shipped

Units 95th Percentile Average


Outbound Profiles - Internet

23

The daily throughput profile reveals considerable seasonality, peaking in November.

0

2,000

4,000

6,000

8,000

10,000

12,000

14,000

16,000

Apr

-09

May

-09

Jun-

09

Jul-0

9

Aug

-09

Sep-

09

Oct

-09

Nov

-09

Dec

-09

Internet Daily Outbound Units Shipped

Units Average Percentile


Outbound Profiles – Retail DSDC

24

The daily throughput profile reveals some seasonality, peaking in September & October.

0

10,000

20,000

30,000

40,000

50,000

60,000

70,000

80,000

90,000

100,000

Apr

-09

May

-09

Jun-

09

Jul-0

9

Aug

-09

Sep-

09

Oct

-09

Nov

-09

Dec

-09

Daily Retail (DSDC) Outbound Units Shipped

Units Average Percentile


Outbound Profiles – Lines Per Order

25

Lines per order profiles were developed for Retail DSDC and Internet orders.

21%

32%

19%18%

9%

1% 0%0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

5%

10%

15%

20%

25%

30%

35%

1 2-5 6-10 11-20 21-50 51-100 >100Cu

m P

ct

Pct

Retail - DSDC Lines per Order

Pct Orders Pct Lines Cumm Pct Orders

16%

69%

15%

0% 0% 0% 0%0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

10%

20%

30%

40%

50%

60%

70%

80%

1 2-5 6-10 11-20 21-50 51-100 >100

Cum

Pct

Pct

Internet Lines per Order


Average 8.8 Average 3.7


0%5%

12%18%

27%

17% 20%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

1 2-5 6-10 11-20 21-50 51-100 >100Cu

m P

ct

Pct

Retail DSDC Units per Order

Pct Orders Pct Units Cumm Pct Orders

14%

68%

16%

1% 0% 0% 0%0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

10%

20%

30%

40%

50%

60%

70%

80%

1 2-5 6-10 11-20 21-50 51-100 >100

Cum

Pct

Pct

Internet Units per Order

Pct Orders Pct Units Cumm Pct Orders

Outbound Profiles – Units Per Order

26

Unit per order profiles were developed for Retail DSDC and Internet orders.



5%2%

9%

24%22%

35%

3%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0-0.0025 0.0025-0.005 0.005-0.01 0.01-0.03 0.03-0.05 0.05-0.1 >0.1

Cum

Pct

Pct

Internet Cubic Feet per Order


23%

14%

20%

12%

24%

5% 3%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

0-0.25 0.25-0.5 0.5-1 1-1.5 1.5-5 5-10 >10Cu

m P

ct

Pct

Retail DSDC Cubic Feet per Order


Outbound Profiles – Cube Per Order

27

Cube per order profiles were developed for Retail DSDC and Internet orders.



43%

19% 18%

13%

4%2% 1%

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0%

5%

10%

15%

20%

25%

30%

35%

40%

45%

50%

1 2 3-4 5-10 11-20 21-50 >50Cu

m P

ct

Pct

Retail DSDC Cartons per Order

Pct Orders Pct Cartons Cumm Pct Orders

100%

0% 0% 0% 0% 0% 0%100%

100%

100%

100%

100%

100%

100%

0%

20%

40%

60%

80%

100%

120%

0-1 2 3 4 5 6-15 >15

Cum

Pct

Pct

Internet Cartons per Order

Pct Orders Pct Cartons Cumm Pct Orders

Outbound Profiles – Cartons Per Order

28

Cartons per order profiles were developed for Retail DSDC and Internet orders.



P E A C H S T A T E

SKU Data Review

29


Pareto Profile - Lines

30

A Pareto profile helps illustrate the concentration (or lack thereof) of activity within a particular range of products. The below shows the variation in line activity across the SKU base for Retail, Internet and Combined orders.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Retail Pareto by Lines

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Internet Pareto by Lines

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 91%

Combined Pareto by Lines


Pareto Profile - Units

31

The below shows the variation in unit activity across the SKU base for Retail, Internet and Combined orders.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 91%

Combined Pareto by Units

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Retail Pareto by Units

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Internet Pareto by Units


0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 91%

Combined Pareto by CF

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Retail Pareto by CF

Pareto Profile - Cube

32

The below shows the variation in cubic velocity across the SKU base for Retail, Internet and Combined orders.

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0% 10% 20% 30% 40% 50% 60% 70% 80% 90%

Internet Pareto by CF


Total Active SKUs by Month

33

The total SKUs active in a month at peak is over 800 SKUs. This compares to a baseline of ~1,370 total SKUs with activity across the timeframe analyzed.


P E A C H S T A T E

OFM - Zone Pick & Consolidate

34


Outbound Profiles – Full Case vs. Broken Case

35

To determine how orders “are” fulfilled, full case and broken case volumes were calculated for both Retail DSDC and Internet orders, first in lines.

-

100,000

200,000

300,000

400,000

500,000

600,000

700,000

800,000

All Channels Retail Internet Ulta Sephora

Lines

Both

Broken Case Only

Full Case Only

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

All Channels Retail Internet Ulta Sephora

All Retail Internet Ulta SephoraTotal 771,105 150,695 620,410 2,533 5,915 Full Case Only 52,824 42,197 10,627 956 1,279 Broken Case Only 682,098 100,143 581,955 937 1,346 Both 8,065 7,949 116 615 3,187

All Retail Internet Ulta SephoraFull Case Only 6.85% 28.0% 1.71% 37.7% 21.6%Broken Case Only 88.46% 66.5% 93.80% 37.0% 22.8%Both 1.05% 5.3% 0.02% 24.3% 53.9%

Lines - Quantity

Lines - %


Outbound Profiles – Full Case vs. Broken Case

36

To determine how orders “are” fulfilled, full case and broken case volumes were calculated for both Retail DSDC and Internet orders, next in units.

All Retail Internet Ulta SephoraTotal 4,725,811 4,077,473 648,338 256,854 1,908,940 Full Case Only 3,263,052 3,249,327 11,504 221,555 1,778,472 Broken Case Only 1,456,572 828,025 628,547 35,298 130,465

All Retail Internet Ulta SephoraFull Case Only 69.0% 79.7% 2.1% 86.3% 93.2%Broken Case Only 30.8% 20.3% 97.9% 13.7% 6.8%

Units - Quantity

Units - %-

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

5,000,000

All Retail Internet Ulta Sephora

Units

Broken Case Only

Full Case Only

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%



OFM Rationale & Criteria

37

Multi-channel order fulfillment in common areas provides considerable benefits:

Improved utilization of labor throughout year Increased opportunity to use shipping sortation automation

Common shipping area increases flexibility due to variations in channel seasonality

Handling full case separate from piece pick allows for proper slotting of the SKU by cubic velocity in that UOM while reducing the walk time.

Performing all piece picks in a common module consolidates repack operations in one location for enhanced process control and efficiency.

Consolidation can be error prone and also increase non-value added handling. A shipping sorter assist with palletization of LTL and fluid load of

parcel carriers will address both issues.

Repack replenishment can also be supported by “picking” the required replenishment cases, and then delivering to the repack module, either via

conveyor or, after palletizing by SKU, by vehicle.

The benefit of zone pick & consolidate OFM is a reduction in non-value added labor and an improvement in quality & cycle time.


P E A C H S T A T E

Full Case OFM – Pick to Label

39


Full Case Pick to Label Rationale

Retail orders require a large portion of their volume in full case quantities (80%).

Creating a full case pick zone using a pick to label approach will eliminate

the non-value added handling of repacking all case quantities into

repack containers.

Pick to label addresses the issue with small cases while retaining efficient picking. Cases 3” tall or less will be

handled as piece picks.

Repack replenishment can also be supported by “picking” the required replenishment cases, palletizing by SKU and then delivering to the repack module.

The benefit of a pick to label OFM is the elimination of non-value added repack activity while improving quality with verification of cases at the shipping sorter.

-

500,000

1,000,000

1,500,000

2,000,000

2,500,000

3,000,000

3,500,000

4,000,000

4,500,000

5,000,000


Units

Broken Case Only

Full Case Only

40


SKUs 285 96.6% of case volumePallet Flow Bays 143 2 SKUs/bayConveyor Length 590 8.25 Bay widthConnecting Conv. 50 Total Conv. 640

Pallet Flow $100,600Conveyor $304,200MHE Total $404,800

Access to Sorter

Pick To Belt – MHE Capital

The Pick to Belt concept is quite simple and economical. The below budgetary estimate illustrates the expected capital needed to implement this capability.

41


P E A C H S T A T E

Broken Case OFM – Zone Pick & Pass

43


ZPP – Rationale

Cartons requiring units from multiple zones must be manually moved from zone to zone increasing walk time by pickers.

Pickers remaining in their zones while conveyor moves the cartons from zone to zone will eliminate the non-value walk

time.

By separating full case volumes from broken case, the pick faces in the ZPP can be reduced to minimize pick travel paths.

Appropriate configuration of powered and gravity conveyors can assist with the passing required to complete cartons.

The expected benefit of a pick and pass order fulfillment methodology is the elimination/reduction of non-value added labor.

44


Broken Case Pick Module

A broken case pick module with pallet & carton flow and static shelving provides flexibility and efficient order fulfillment across both Retail and Internet channels.

45


P E A C H S T A T E

47

Design Requirements


Growth Projections

The different fulfillment channels contribute varying amounts to the overall corporate growth of Client.

Year 2009 2010 2011 2012 2013 2014 2015Total Revenue $150.0 $188.0 $238.0 $300.0 $330.0 $363.0 $399.3Internet Volume 10.9% $16.4 $24.9 $40.4 $60.0 $63.6 $67.4 $71.5Retail Volume 71.3% $107.0 $91.1 $121.1 $159.0 $183.0 $209.7 $239.3QVC Volume 17.7% $67.5 $72.0 $76.5 $81.0 $83.4 $85.9 $88.5Internet Vol % 10.9% 14.0% 17.0% 20.0% 19.3% 18.6% 17.9%Retail Vol % 71.3% 48.4% 50.9% 53.0% 55.4% 57.8% 59.9%QVC Vol % 17.7% 38.3% 32.1% 27.0% 25.3% 23.7% 22.2%Growth 25% 27% 26% 10% 10% 10%QVC Growth Rate 3% 3% 3% 3% 3% 3% 3%Multiplier 1.00 1.25 1.59 2.00 2.20 2.42 2.66 Turns 4.00 4.17 4.33 4.50 4.67 4.83 5.00 Turns Change Mult 0.96 0.96 0.96 0.96 0.97 0.97

48


Storage Requirements

Storage requirements were based on increases in shipping volumes for all three channels. Baseline storage for 2012 was calculated from inventory data for retail and internet volumes and historical requirements for Primary Location storage.

Note that the driving factor is kitting storage. The growth associated with this area does not overcome the improvement in turns until 2012.

Category 2009 2010 2011 2012 2013 2014 2015 Max UtilQVC/Internet 1,697.7 1,762.0 2,236.5 2,169.5 1,796.0 1,793.5 1,790.5 2,236.5 85%KA Components 7,000.0 7,168.0 7,323.1 7,466.7 7,416.0 7,375.1 7,343.1 7,466.7 CapacityTotal 8,697.7 8,930.0 9,559.6 9,636.2 9,212.0 9,168.6 9,133.6 9,636.2 11,337

Pallet Inventory

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Pick Module Sizing

Each SKU was assessed for its broken case volume, Internet and Retail. These volumes were then assigned to pick media. Replenishment was assumed to be every four days on average for a slot classification.

A “slice” is one bay wide, includes both sides and all levels of the module. The number of slices determines the overall length of the module.

MediaSKUs 2010 2011 2012 2013 2014 2015

Faces/ SKU

Face/ Bay

2012 2015

PF 57 63 69 76 84 92 101 1 2 40 54 CF - 3 27 30 33 36 40 44 48 3 40CF - 2 69 76 84 92 101 111 122 2 40CF - 1 113 124 136 150 165 182 200 1 40 12 16 Shelf - 3 69 76 84 92 101 111 122 3 120Shelf - 2 125 138 152 167 184 202 222 2 120Shelf - 1 907 998 1,098 1,208 1,329 1,462 1,608 1 120 16 22 Total SKUs 1367 1,505 1,656 1,821 2,004 2,204 2,423 Total Bays 68 92

SKU Growth 10% 10% 10% 10% 10% 10% Bays/Slice 4 6 Slices 17 15

Bays

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P E A C H S T A T E

Conceptual Design

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Facility Overview

While the overall concept has remained unchanged, some elements were modified to meet requirements and improve design.

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Full Case Pick Lines

The below two lanes with shelving in 3 bays at the downstream end support over 99.2% of the full case units, 73% of those in the pallet flow bays.

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Material Flow

Key

Inbound

Internal

Outbound

54


Reserve Storage

56


Broken Case Pick Module

There are currently 18 “slices” in the module. However, as the below illustrates, there will be some losses due to stairs, trash chutes, etc.

Note: Each slice increases capital by about $20K.

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Packing

The packing configuration below allows for future expansion, consumable material staging and operator egress.

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Shipping

MHE Capacity 2009 2010 2011 2012 Peak 2013 2014 2015 Max Peak P2AFull Case Throughput 1,052 1,318 1,669 2,103 5,760 2,314 2,545 2,799 2,799 7,667 2.74 BC Throughput 1,074 1,346 1,704 2,148 5,882 2,599 2,859 2,859 2,859 7,829 2.74 Total Cases/Day 2,126 2,664 3,373 4,251 11,643 4,912 5,404 5,658 5,658 15,496 CPM - 1 shift, 85% 7.9 9.9 12.5 15.7 28.7 18.2 20.0 21.0 21 38.3

The shipping sorter supports a peak throughput of less than 40 cpm. Technology of this type can manage approximately twice that, if necessary.

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P E A C H S T A T E

Budget & Staffing

60


The below reflects some budgeting allowances but could serve as a GMP.

Detailed Budget

Category Investment DescriptionsReserve Rack $417,100 6,500 new pallet positions, 6,000 used relocatedPick Module $375,900 18 slices, 2/3 pallet flow, 1/6 carton flow, 1/6 shelvingPack Stations $7,000 4 pack stations, 1 singles pack station, 1 QA capable stationFull Case Conveyor $110,300 2 pick aisles plus merges, including 3 shelf bays & flow lanesPick Module Conveyor $197,500 two levels power, gravity outriggers, gatesSpiral $48,000 second to first level, poweredSorter Conveyor $232,500 recirc, accumulation, merge, pack station conveyor, lanesSorter $215,000 11 diverts (6 reused), 9 LTL, 1 parcel, 1 NR/RejectWCS $140,000 WMS interface, sorter, accumulation controlTotal $1,743,300

Note: ~1,500 pallet positions are lost above the used rack. To cube out this area completely would increase capital by ~$225K with re-used beams).

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The modifications to the order fulfillment method recommended will affect only the outbound personnel. Below is the conservative staffing estimate.

Outbound Staffing

Rates Daily Activity 2009 2010 2011 2012 Peak 2013 2014 2015 Max Peak P2A UnitsShipping (cpd) 2,126 2,664 3,373 4,251 11,643 4,912 5,404 5,658 5,658 15,496 2.74 cases

24 Palletization 1,052 1,318 1,669 2,103 5,760 2,430 2,674 2,799 2,799 7,667 2.74 cases150 cph 1.0 1.5 1.5 2.0 4.0 2.5 2.5 2.5 2.5 5.0 Pltzr FTEs

20 Fluid Load 1,074 1,346 1,704 2,148 5,882 2,482 2,730 2,859 2,859 7,829 2.74 cases180 cph 1.0 1.0 1.5 2.0 3.0 2.0 2.0 2.5 2.5 4.5 Loaders FTEs

60 Packing 1,074 1,346 1,704 2,148 5,882 2,482 2,730 2,859 2,859 7,829 2.74 cartons60 cph 2.5 3.5 4.0 5.5 9.5 6.0 6.5 7.0 7.0 13.0 Packers FTEs36 Broken Case Picking 2,634 3,301 4,179 5,268 12,447 5,795 6,374 7,012 7,012 16,567 2.36 lines

100 lph 4.0 5.0 6.0 7.5 12.0 8.5 9.5 10.5 10.5 16.0 Pickers FTEs14 Full Case Picking 1,052 1,318 1,669 2,103 5,760 2,314 2,545 2,799 2,799 7,667 2.74 cases

250 cph 0.5 1.0 1.0 1.0 2.5 1.5 1.5 1.5 1.5 3.0 Pickers FTEs36 Broken Case Replen 1,074 1,346 1,704 2,148 5,882 2,482 2,730 2,859 2,859 7,829 2.74 cases

100 cph 1.5 2.0 2.5 3.0 6.0 3.5 4.0 4.0 4.0 7.5 PJ FTEsTotal FTEs (avg) 11.0 14.0 17.0 21.0 35.0 24.0 26.0 28.0 28.0 49.0 Peak FTEs

Note: Peak values reflect operating for 1.5 shifts for peak.

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P E A C H S T A T E

Project Implementation

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This schedule reflects a first pass at the phases of the implementation. It reflects decommissioning the existing facility as well as relocating equipment to the new DC.

Implementation Schedule

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Questions?

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P E A C H S T A T E

CONTACT INFORMATION

E-mail: [email protected]

Web: www.peachstate.com

Phone: 678-327-2013

Date post:	29-Mar-2015
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