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Presented by:

Dean M. Starovasnik

Reading the Tea Leaves: How Big Data Modeling Can Prepare

Your Facility for Handling Omni-

Channel Fulfillment

Sponsored by:

P E A C H S T A T E

© 2014 Peach State Integrated Technologies. Contents are confidential. All rights reserved.

Overview

Data-based Design Process

Case Study

Agenda


Overview

3

Evolution of DC Design

Rules of Thumb

Mathematical Tools

Big Data Modeling

Selecting an Order Fulfillment Methodology (OFM)

Profiles should drive this critical decision

o Order profiles

o SKU profiles

o Activity (daily, hourly) profiles

Often more than one

Objective: minimize handling, maximize service level

“The Recipe”

Two questions: “How big?” & “How fast?”

Numbers of pallets, slots, facings, locations, doors, etc.

Throughput parameters and requirements.

First of all, some initial thoughts before we get too far into this.


“Big Data” Definition

4

Big Data in DC design

Full disclosure

Variety of sources

Future projections

Sources of data

Demand

o Order entry system

o ERP

o WMS

Product – Item Master (major sticking point)

o ERP/MRP

o WMS

Transportation

o TMS

o 3rd Party

Synchronization of data is critical

While definitions vary, Big Data is normally thought to include disparate, diverse data in the realm of terabytes and petabytes or trillions and quadrillions of bytes.


Facility Design Profiles

6

Planning &

Design Issue

Key Focus Primary Data

Source

Profiles

Order

Fulfillment

Methodologies

Effective strategies for

picking & packing

(e.g., zone pick & sort

opportunities?)

Order & Item

Master 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)

Storage Sizing Right storage media &

corresponding facings

for reserve slots

Location,

Inventory &

Item Master

• ABC inventory distribution (Pareto)

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

Warehouse

Zone & Facing

Requirements

Right storage media &

corresponding facings

for primary slots

Order, Item

Master &

Location Files

• ABC activity (Pareto) profile

• Cube movement distribution

• Storage zone profiles (SKUs, volumes, etc. by special

requirements - drug, cooler, etc.)

Material

Handling

Throughput &

Capacity

Peak hourly volumes

to be processed

Order, Ship &

Item Master

Files

• Daily activity profile (orders, lines, full cases, split cartons,

total boxes)

• Hourly activity distribution (particularly with respect to

order drop & cutoff times)

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

The collection of these profiles represents a historical model of the operation.

The next step is to “grow” this model into the future across all parameters.


“Growing” Profiles

7

Volume Sales projections

o NOT from the VP sales (sandbagging)

o CEO/COO are the best bet

Mergers & Acquisitions

Demographic demand baseline

SKU Historical behavior

o New product initiatives

o Market requirements

o Obsolescence (or lack of it)

Mergers & Acquisitions (again) o Cannibalization

o Brand management

Order Mix Market behavior

Historical trends

The rear view mirror only helps you identify whose catching up on you.

The historical models now need to be projected into the future.


Profiling – Input to the OFM Decision

8

Order

Profiles

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 o Per order distributions

o Per carton distributions

• Order completion

• Single line percentage

• Per day & hour distributions

• Full Case %

• Broken Case %

• Full Pallet %

• Mixed Orders %

• Special handling o Lot control

o Hazmat

o Refr/Freezer

• ABC (Pareto) Distribution

• Full Case, Broken Case,

Full Pallet Volumes

• Cube movement

Once the future state has been developed, identifying the correct OFM’s for each portion of the operation is the first step in developing the facility design.


Case Study

Project Overview

Data Analysis Activity Profiles

SKU Profiles

Container Profiles

Requirements Definition

Facility Design


Project Overview

11

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 DTC and home shopping

Was in two fulfillment facilities

Both space constrained

Retail & home shopping fulfilled in one facility

DTC fulfilled (from same SKU base) at HQ

Spec building selected prior to completion of design

Size and door count validated immediately

Sufficient for five years and beyond

Some expansion capability available

The design project we are reviewing is actually in the midst of an ownership transition at present. Therefore, the client identity will remain hidden.

Customer was moving very fast, had an aggressive growth

strategy and desired rapid evidence of return on investment.


Case Study

Project Overview


SKU Profiles

Container Profiles


Facility Design


Outbound Profiles – Daily Activity

13

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

Parameter Orders/day Lines/day Units/day CubicFeet/day Weight/day SKUs/day

Average 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


Average 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


Average 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

DTC


Outbound Profiles – Order Statistics

14

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

Combined

Retail DSDC

DTC

Parameter Lines/Order Units/Order Cubic/Order Weight/Order Units/Line

Average 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


Average 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


Average 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

15

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

Ap

r-0

9

May

-09

Jun

-09

Jul-

09

Au

g-0

9

Sep

-09

Oct

-09

No

v-0

9

De

c-0

9

Total (No QVC) Daily Outbound Units Shipped

Units 95th Percentile Average


Outbound Profiles - DTC

16

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

Ap

r-0

9

May

-09

Jun

-09

Jul-

09

Au

g-0

9

Sep

-09

Oct

-09

No

v-0

9

De

c-0

9

Internet Daily Outbound Units Shipped

Units Average Percentile


Outbound Profiles – Retail DSDC

17

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

Ap

r-0

9

May

-09

Jun

-09

Jul-

09

Au

g-0

9

Sep

-09

Oct

-09

No

v-0

9

De

c-0

9

Daily Retail (DSDC) Outbound Units Shipped

Units Average Percentile


Outbound Profiles – Lines Per Order

18

Lines per order profiles were developed for Retail DSDC and DTC 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 >100C

um

Pct

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

Cu

m P

ct

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 >100C

um

Pct

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

Cu

m P

ct

Pct

Internet Units per Order

Pct Orders Pct Units Cumm Pct Orders

Outbound Profiles – Units Per Order

19

Unit per order profiles were developed for Retail DSDC and DTC 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

Cu

m P

ct

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 >10C

um

Pct

Pct

Retail DSDC Cubic Feet per Order


Outbound Profiles – Cube Per Order

20

Cube per order profiles were developed for Retail DSDC and DTC 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 >50C

um

Pct

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

Cu

m P

ct

Pct

Internet Cartons per Order

Pct Orders Pct Cartons Cumm Pct Orders

Outbound Profiles – Cartons Per Order

21

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



Case Study

Project Overview


SKU Profiles

Container Profiles


Facility Design


Pareto Profile - Lines

23

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, DTC 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

24

The below shows the variation in unit activity across the SKU base for Retail, DTC 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


Total Active SKUs by Month

25

As the below graph shows, the total number of all SKUs active in a month grows into the peak period to just over 800 SKUs. This compares to the baseline of ~1,370 total SKUs with any activity in the Apr – Dec ‘09 window that was analyzed.


Case Study

Project Overview


SKU Profiles

Container Profiles


Facility Design


Outbound Profiles – Full Case vs. Broken Case

27

To determine how orders “are” fulfilled, full case and broken case volumes were calculated for both Retail DSDC and DTC 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 Sephora

Total 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


Full 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

28

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


Total 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


Full 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


Units

Broken Case Only

Full Case Only

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%



Case Study

Project Overview


SKU Profiles

Container Profiles


Facility Design

Table of Contents


Growth Projections

30

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

Year 2009 2010 2011 2012 2013 2014 2015

Total Revenue $150.0 $188.0 $238.0 $300.0 $330.0 $363.0 $399.3

Internet Volume 10.9% $16.4 $24.9 $40.4 $60.0 $63.6 $67.4 $71.5

Retail Volume 71.3% $107.0 $91.1 $121.1 $159.0 $183.0 $209.7 $239.3

QVC Volume 17.7% $67.5 $72.0 $76.5 $81.0 $83.4 $85.9 $88.5

Internet 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

Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6


Storage Requirements

31

Storage requirements were calculated based on increases in shipping volumes for all three channels. Baseline storage for Year 0 was calculated from inventory data for retail and DTC volumes as well as historical location requirements for the Primary Location storage.

Note that the driving factor is kitting storage. The growth associated

with this area does not overcome the improvement in turns past Year 3.

Category 2009 2010 2011 2012 2013 2014 2015 Max Util

QVC/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 Capacity

Total 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

Year 0 Year 1 Year 2 Year 3 Year 4 Year 5 Year 6


Pick Module Sizing

32

Each SKU was assessed for its broken case volume flow, both DTC 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/

Bay2012 2015

PF 57 63 69 76 84 92 101 1 2 40 54

CF - 3 27 30 33 36 40 44 48 3 40

CF - 2 69 76 84 92 101 111 122 2 40

CF - 1 113 124 136 150 165 182 200 1 40 12 16

Shelf - 3 69 76 84 92 101 111 122 3 120

Shelf - 2 125 138 152 167 184 202 222 2 120

Shelf - 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

Year 1 Year 2 Year 3 Year 4 Year 5 Year 6


Case Study

Project Overview


SKU Profiles

Container Profiles


Facility Design

Table of Contents


Facility Overview

34


Material Flow

35

Key

Inbound

Internal

Outbound


Reserve Storage

36


OFM Rationale & Criteria

Multi-channel order fulfillment in common areas provides numerous 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 pulls separately from piece picks allows for proper slotting of the

SKU by cubic velocity in that UOM while reducing the walk time for piece picks.

Performing all piece picks in a common module consolidates repack operations in

one location for enhanced process control and efficiency.

Consolidation normally creates considerable opportunity for error while increasing

non-value added handling. Using a shipping sorter to assist with palletization of

LTL and fluid load of parcel carriers will reduce non-value added handling and

improve quality.

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 expected benefit of the zone pick & consolidate order fulfillment methodology is a reduction in non-value added labor and an improvement in quality & cycle time.

37


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.

A pick and pass OFM for less than case picking provides a reduction in of non-value added labor, specifically walking.

38


Broken Case Pick Module

39

A pick module comprised of pallet flow, carton flow and static shelving pick faces provides the flexibility and efficient order fulfillment for broken case demand across both Retail and DTC channels.


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 expected benefit of a pick to label full case area is an elimination of non-value added repack activity while improving quality through automated verification of all 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


Full Case Pick Lines

41

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.


SKUs 285 96.6% of case volume

Pallet Flow Bays 143 2 SKUs/bay

Conveyor Length 590 8.25 Bay width

Connecting Conv. 50

Total Conv. 640

Pallet Flow $100,600

Conveyor $304,200

MHE Total $404,800

Access to Sorter

Pick To Belt – MHE Capital

42

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


Shipping

43

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


By developing an accurate MHE budget, the client was able to determine the overall return on investment of this consolidation step.

Detailed Budget

44

Category Investment Descriptions

Reserve Rack $417,100 6,500 new pallet positions, 6,000 used relocated

Pick Module $375,900 18 slices, 2/3 pallet flow, 1/6 carton flow, 1/6 shelving

Pack Stations $7,000 4 pack stations, 1 singles pack station, 1 QA capable station

Full Case Conveyor $110,300 2 pick aisles plus merges, including 3 shelf bays & flow lanes

Pick Module Conveyor $197,500 two levels power, gravity outriggers, gates

Spiral $48,000 second to first level, powered

Sorter Conveyor $232,500 recirc, accumulation, merge, pack station conveyor, lanes

Sorter $215,000 11 diverts (6 reused), 9 LTL, 1 parcel, 1 NR/Reject

WCS $140,000 WMS interface, sorter, accumulation control

Total $1,743,300


The modifications to the order fulfillment method we have recommended will affect only the outbound personnel. Below lays out our conservative estimate for that staff.

Outbound Staffing

45

Rates Daily Activity 2009 2010 2011 2012 Peak 2013 2014 2015 Max Peak P2A Units

Shipping (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 cases

150 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 cases

180 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 cartons

60 cph 2.5 3.5 4.0 5.5 9.5 6.0 6.5 7.0 7.0 13.0 Packers FTEs

36 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 FTEs

14 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 FTEs

36 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 FTEs

Total 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.


For More Information:

Speaker email: [email protected]

Website: www.peachstate.com

Or visit MODEX 2014 Booth #4329

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speaker’s email address/home page, the Exhibiting Member Company’s name,

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