WAREHOUSE PERFORMANCE ASSESSMENT & BENCHMARKING

(1)

The On-Floor Seminar Series Welcomes You to:

SESSION 208

WAREHOUSE PERFORMANCE

ASSESSMENT

&

BENCHMARKING

(2)

SINGLE

-

_-

FACTOR

_FACTOR

PRODUCTIVITY METRIC

COMPARISONS CAN BE

RISKY, BUT YOU’VE

GOT TO START

SOMEWHERE!

PREMISE

(3)

Order Fill Rates

Order Cycle Times

Lines and Orders/Hour

Errors

Inventory Accuracy

Damage

Cost/Order

Cost as % of Sales

Days on Hand

Order Fill Rates

Order Cycle Times

Lines and Orders/Hour

Errors

Inventory Accuracy

Damage

Cost/Order

Cost as % of Sales

Days on Hand

THE PROCESS BEGINS WITH

BENCHMARKING

THE PROCESS BEGINS WITH

BENCHMARKING

(4)

MEASURE ORDER

FULFILLMENT PERFORMANCE

MEASURE ORDER

FULFILLMENT PERFORMANCE

On-Time Delivery

_{Total Orders Shipped}Orders On-Time

%

% $

Orders Filled Complete

Total Orders Shipped

Order Fill Rate

%

% $

Error-Free Orders Total Orders Shipped

Order Accuracy

%

% $

Error-Free Lines Total Lines Shipped

Line Accuracy

%

% $

Actual Ship Date Minus Customer Order Date

Order Cycle Time

Hrs

Hrs $

Perfect Deliveries

Total Orders Shipped

Perfect Order

Completion

%

% $

(5)

AUDIT INVENTORY

MANAGEMENT PERFORMANCE

AUDIT INVENTORY

MANAGEMENT PERFORMANCE

Inventory Accuracy

Actual Qty per SKU

System Reported Qty

%

$

Total Damage $$$

Inventory Value (Cost)

Damaged Inventory

%

$

Avg. Occupied Sq. Ft. Total Storage Capacity

Storage Utilization

_%

%

$

Total Dock to Stock Hrs Total Receipts

Dock to Stock Time

Hrs

$

Receipt Entry Time

-Physical Receipt Time

Inventory Visibility

Hrs

$

MEASURE

CALCULATION

TODAY FUTURE VALUE

Avg. Month Inventory $ Avg. Daily Sales/Month

(6)

Orders per Hour

Orders Picked/Packed Total Whse Labor Hrs

Total Warehouse Cost Total Orders Shipped

Lines per Hour

Items per Hour

Cost per Order

Cost as % of Sales

Ord/Hr

$/Order

%

$

Ord/Hr

Lines/Hr

Items/Hr

Lines/Hr

Lines Picked/Packed

Total Whse Labor Hrs Items Picked/Packed Total Whse Labor Hrs

Total Warehouse Cost Total Revenue

$/Order

MEASURE

CALCULATION

TODAY FUTURE VALUE

ASSESS WAREHOUSE

PRODUCTIVITY

ASSESS WAREHOUSE

PRODUCTIVITY

(7)

MEASURE

CALCULATION

TODAY FUTURE VALUE

On-Time Deliveries

On-Time Deliveries

Total Deliveries

%

$

Damage

Shipment Damage $

%

% $

Total Shipment $

Frt. Bill Accuracy

Billing Error $

%

% $

Total Transport Costs

Assessorials

Assessorial Costs

$

Total Transport Cost

%

Demurrage

Demurrage Costs

%

$

Total Transport Cost

Missed Appointments Total Appointments

Appointments

%

$

EVALUATE TRANSPORTATION

PERFORMANCE

EVALUATE TRANSPORTATION

PERFORMANCE

(8)

MATCH OPPORTUNITIES TO SOLUTIONS

TMS W/LMS MH ADC SCV AOM F’cast/Plan

•

Total Logistics Costs Total Revenue COST % OF SALES

•

Total Logistics Costs Total Orders

COST PER ORDER

•

Lines Picked & Packed

Total Labor Hours

LINES / HOUR

•

Orders Picked & Packed Total Labor Hours

ORDERS / HOUR

•

Receipt Data Entry -Time of Physical Receipt

VISIBILITY

•

Average Dock-To-Stock Hours per Receipt

DOCK-TO-STOCK

•

Avg. Inventory Sq. Ft. Storage Capacity Sq. Ft. STORAGE USAGE

•

Avg. Inventory Value Avg. Daily Sales

DAYS ON HAND

•

Total Damage $ Total Inventory $ DAMAGE

•

Ship Date - (minus) Customer Order Date

CYCLE TIME

•

Error-Free Lines

Total Lines Shipped

LINE ACCURACY

•

Error-Free Orders Total Orders Shipped

ORDER ACCURACY

•

Orders Filled Complete Total Orders Shipped

ORDER FILL RATE

•

Orders On-Time Total Orders Shipped

ON-TIME DELIVERY

ENABLING TECHNOLOGY & SYSTEMS

METRICS

(9)

$1.8 Million

Probable Cost

$2.4 Million

Annual Savings

See above

2.7%

3.1%

Total Warehouse Costs / Total Revenue

Cost % of Sales

See above

$3.62

4.26

Total Warehouse Costs / Total Orders

Cost per Order

See above

54/Hr

40/Hr

Total Lines Picked / Total Whse. Labor Hrs

Lines per Hour

$864,000

20/Hr

15/Hr

Orders Picked & Packed / Total Whse. Labor Hrs

Orders per Hour

$100,000

85%

78%

Avg. Inventory Sq. Ft. / Storage Capacity Sq. Ft.

Storage Utilization

$1 Million

42 Days

50 Days

Avg. Inventory Value ($) / Average Daily Sales $

Days on Hand

$100,000

.50%

.75%

Total Damage $$$ / Total Inventory Value

Damaged Inventory

See above

99%

96%

Actual Quantity by SKU/ Reported Qty. by SKU

Inventory Accuracy

$100,000

8 Hrs

12 Hrs

Actual Ship Date (minus) Customer Order Date

Order Cycle Time

See above

98%

92%

Errorless Orders / Total Orders Shipped

Order Accuracy

$250,000

95%

87%

Total Orders On Time / Total Orders Shipped

On-Time Delivery

Value

Target

Current

Calculation

Measure

QUANTIFY PERFORMANCE IMPROVEMENT POTENTIAL

(10)

Logistics measures must be “in harmony with

a company's overall business strategy”. If

Amazon.com drove its logistics activities with

measures focused solely on reducing

delivery costs, it would cripple its ability to

serve customers. (Smart managers) are

fusing logistics plan(s) with their business

strategies, ensuring that what is measured in

the field is valued at the top of the

organization”.

Logistics measures must be “in harmony with

a company's overall business strategy”. If

Amazon.com drove its logistics activities with

measures focused solely on reducing

delivery costs, it would cripple its ability to

serve customers. (Smart managers) are

fusing logistics plan(s) with their business

strategies, ensuring that what is measured in

the field is valued at the top of the

organization”.

KEEPING METRICS IN PERSPECTIVE

(11)

Is it possible to assess the

“system” performance of a

warehouse, and compare

system performance

across warehouses, or

across time periods?

Is it possible to assess the

“system” performance of a

warehouse, and compare

system performance

across warehouses, or

across time periods?

THE GEORGIA TECH CHALLENGE

(12)

WHAT TO DO WITH ALL THOSE SINGLE

FACTOR PRODUCTIVITY METRICS?

(13)

What we need is a

“handicapping” system

for warehouse

performance

What we need is a

“handicapping” system

for warehouse

performance

(14)

ONE

PERFORMANCE

INDEX

ONE

PERFORMANCE

INDEX

DATA ENVELOPMENT ANALYSIS

Resources

Activities

_Activities

Services

_Services

Total Staffing

Lines Shipped

_{Lines Shipped}

Equipment

“Replacement”

Cost

Equipment

“Replacement”

Cost

Storage Function

Warehouse area

(15)

Resource/Input

Production/Output

For One Input, One Output

PRODUCTION FUNCTION THEORY

(16)

SYSTEM EFFICIENCY CONCEPT

Resource/Input

Production/Output

O O B B A A

System efficiency

of warehouse B is

the ratio

OA

OB

System efficiency

of warehouse B is

the ratio

OA

OB

(17)

DEA is a mathematical

technique that does this

same kind of analysis,

but with multiple inputs

and multiple outputs.

DEA is a mathematical

technique that does this

same kind of analysis,

but with multiple inputs

and multiple outputs.

DATA ENVELOPMENT ANALYSIS

(18)

Html

documents

Database

Solver

At your site

Georgia Tech Server

Over the Internet

WEB

(19)

(20)

INVESTMENT COSTS

(21)

OUTPUT CALCULATOR

(22)

YOUR RESULTS

(23)

Results to Date

(24)

OVER 150 QUALIFIED USERS

(25)

USER PROFILE

Retail

30%

Wholesale

22%

Manufacturing

33%

Distribution

15%

(26)

OUTPUT SEGMENTATION

• Broken Case: 49

• Full Case:

32 • Pallet: 13

• Mix: 65

• Total: 159

• Broken Case: 49

• Full Case:

32 • Pallet: 13

• Mix: 65

• Total: 159

(27)

Input Efficiency Compared Within

(49/49)

Input Efficiency Compared Within

(49/49)

BROKEN CASE

10

20

20 Frequency

Frequency

0

0.1

0.2

0.1 _0.2

0.3 _0.3

0.4 _0.4

0.5 _0.5

0.6 _0.6

0.7 _0.7

0.8 _0.8

0.9 _0.9

1.0 _1.0

(28)

Lines/Labor hour(Broken Case)

0

5

10

15

20

25

0.0

0.0 _10.0

_10.0

_20.0

_30.0

_40.0

_50.0

_60.0

_70.0

_80.0

_90.0

100.0

100.0 More

More

Frequency

Ave = 17 SD = 27 Ave = 17 SD = 27

Pick Rates

BROKEN CASE PICKING

(29)

0.1

0.2 _0.2

0.3 _0.3

0.4 _0.4

0.5 _0.5

0.6 _0.6

0.7 _0.7

0.8 _0.8

0.9 _0.9

1.0 _1.0

0

1

2

3

4

5

6

7

8

8 Frequency

Frequency

Input Efficiency Compared Within

(32/32)

Input Efficiency Compared Within

(32/32)

FULL CASE

(30)

FULL CASE PICKING

Lines/Labor Hour ( Full Case)

0

10

20

30

0.00

0.00 _10.00

_10.00

_20.00

_30.00

_40.00

_50.00

_60.00

_70.00

_80.00

_90.00

100.00

100.00 More

More

Frequency

Ave = 14

SD = 27.7

Ave = 14

SD = 27.7

(31)

Input Efficiency Compared within

(13/13)

Input Efficiency Compared within

(13/13)

PALLET

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0

5

10

10 Frequency

Frequency

(32)

PALLET PICKING

Lines/Labor Hour ( Pallet)

0

2

4

6

8

10

0.0

0.0 _10.0

_10.0

_20.0

_30.0

_40.0

_50.0

100.0

150.0 _150.0

200.0 _200.0

More

Frequency

Ave = 25 SD = 27.7 Ave = 25 SD = 27.7

(33)

0.15 0.15 0.25_0.25 0.35_0.35 0.45_0.45 0.55_0.55 0.65_0.65 0.75_0.75 0.85_0.85 0.95_0.95 1.05_1.05

0

10

20

20 Frequency

Frequency

Input Efficiency Compared Within

(65/65)

Input Efficiency Compared Within

(65/65)

MIXED

(34)

MIXED PICKING

Lines/Labor Hour (Mixed)

0

10

20

30

40

50

60

0.00

0.00 _20.00

_20.00

_40.00

_60.00

_80.00

100.00

100.00 Frequency

Frequency

Ave = 10.6

SD = 23

Ave = 10.6

SD = 23

More

(35)

Bigger is not always better, at

least with regard to equipment

and labor. There is, however,

some evidence that more

warehouse space leads to

better system efficiency.

Bigger is not always better, at

least with regard to equipment

and labor. There is, however,

some evidence that more

warehouse space leads to

better system efficiency.

RESULTS & CONCLUSIONS

(36)

RESULTS & CONCLUSIONS

Labor hours was not found to be a significant

factor, by itself, in predicting system

efficiency. However, the interaction of labor

with investment was found to be significant in

the sense that labor hours mitigates the effect

of investment (in other words, though high

investment warehouses tended to be less

efficient than low investment warehouses, the

differences becomes less prominent the

higher the labor hours).

Labor hours

was not found to be a significant

factor, by itself, in predicting system

efficiency. However, the interaction of labor

with investment was found to be significant in

the sense that

labor hours

mitigates the effect

of investment (in other words, though high

investment warehouses tended to be

less

efficient than low investment warehouses, the

differences becomes

less

prominent the

(37)

The interaction of investment

and space was found to be

significant. This means that

high investment warehouses

are even less efficient if they

are also large.

The interaction of investment

and space was found to be

significant. This means that

high investment warehouses

are even less efficient if they

are also large.

RESULTS & CONCLUSIONS

(38)

No matter how we segment the data,

a very large proportion of

warehouses are operating at or

below 50% system efficiency. While

this may reflect industry or business

differences, it still represents a very

significant opportunity for

improvement.

No matter how we segment the data,

a very large proportion of

warehouses are operating at or

below 50% system efficiency. While

this may reflect industry or business

differences, it still represents a very

significant opportunity for

improvement.

RESULTS & CONCLUSIONS

(39)

The opportunity for improvement

seems largest for the segment of

warehouses doing predominantly full

case picking. In that segment, a

smaller proportion of the warehouses

are "efficient' than in any other

segment, and a larger proportion are

operating below 50% efficiency.

The opportunity for improvement

seems largest for the segment of

warehouses doing predominantly full

case picking. In that segment, a

smaller proportion of the warehouses

are "efficient' than in any other

segment, and a larger proportion are

operating below 50% efficiency.

RESULTS & CONCLUSIONS

(40)

WHERE DO WE GO FROM HERE?

(41)

• Enhance the basic input/output model

• Enhance the ability to benchmark for

technology, practice, & requirements

• Enhance the basic input/output model

• Enhance the ability to benchmark for

technology, practice, & requirements

MANY OPPORTUNITIES TO IMPROVE

THE BENCHMARKING TOOL

(42)

VERSION 2.0 METRICS

Inputs

• Space

• Capital

• Labor

• Inventory

– # of skus

– turns

Inputs

• Space

• Capital

• Labor

• Inventory

– # of skus

– turns

Outputs

• Inbound

– total replenishment orders

received

• Fulfillment

– total lines picked, by type

– total orders shipped

Outputs

• Inbound

– total replenishment orders

received

• Fulfillment

– total lines picked, by type

– total orders shipped

(43)

“Marker”

Analysis

“Marker”

Analysis

VERSION 2.0

(44)

MARKER ANALYSIS

Performance “Marker” Attribute

DEA Performance Score

(45)

MARKER ANALYSIS

Performance “Marker” Practice

DEA Performance Score

(46)

VERSION 2.0 MARKERS

• Industry

• Total # SKUs

• SKU turnover

• Pick seasonality

• Pick variability

• Planning lead time

• Value adding activities

• Cube/order

• Weight/order

• Space utilization

• Industry

• Total # SKUs

• SKU turnover

• Pick seasonality

• Pick variability

• Planning lead time

• Value adding activities

• Cube/order

• Weight/order

• Space utilization

• Response time

• Rush orders

• Multi-floor?

• Total # of suppliers

• WMS?

• Compliant shipping?

• Velocity-based slotting?

• Pick-to-light?

• RF dispatching?

• other...

• Response time

• Rush orders

• Multi-floor?

• Total # of suppliers

• WMS?

• Compliant shipping?

• Velocity-based slotting?

• Pick-to-light?

• RF dispatching?

• other...

(47)

On-Line at:

www.isye.gatech.edu/ideas

On-Line at:

www.isye.gatech.edu/ideas

Participate in the study, learn about your

own system performance, and work

with us to improve the practice of

warehousing.

Participate in the study, learn about your

own system performance, and work

with us to improve the practice of

warehousing.

(48)

GETTING STARTED

Thanks for coming

Questions?

WAREHOUSE PERFORMANCE ASSESSMENT & BENCHMARKING

The On-Floor Seminar Series Welcomes You to:

SESSION 208