SPrIng - WERC

Spring 2012
WAREHOUSING EDUCATION AND RESEARCH COUNCIL
THE ASSOCIATION FOR LOGISTICS PROFESSIONALS
Watch
A Periodic Assessment
of Industry Trends
Provided by and for the Warehousing Professional
Findings of a survey of benchmarking measures among WERC members and DC Velocity readers.
DC Measures 2012
by
Karl B. Manrodt, PhD
Professor
Georgia Southern University
Kate L. Vitasek
Managing Partner
Supply Chain Visions
Joseph M. Tillman
Senior Researcher
Supply Chain Visions
Senior Management Interest in Performance Measures
We are continually pleased with the interest in the metrics
survey. In part, we believe that it is due to senior executives’
interest in how well their supply chain is performing relative
to everyone else. One of the annual questions asks about the
level of interest in measures on the part of senior management.
The results show that almost 96% of respondents say their
executives’ interest in performance is not waning.
Over the past few years, we have noticed senior
management’s interest in measures hit a plateau. Growth
in the number of senior executives reported as having an
increasing interest in measures slowly declined starting
with the 2008 study. This year is the first time in four years
the percentage of senior executives reported an increased
interest in measures (Figure 1). We believe this increase can
be attributed to a couple of factors. First, as the economy
improves it will become necessary to better understand if
goals are being met. > pg. 2
About the Study
2012 is the ninth year of the Warehouse Education and
Research Council (WERC) and DC Velocity’s annual
warehouse benchmarking study, “DC Measures.”
As in previous years, the study results and analysis are
complied and presented by our partners at Georgia
Southern University and Supply Chain Visions, two widely
respected organizations in the area of performance
management and measurement.
The heart of this study is to help practitioners gain a
better understanding of key distribution metrics and
report how performance has changed over time. Over
the years the study has focused in on various themes; for
instance, highlighting the importance of the perfect order,
tracking significant changes in measures, comparing
bad warehouses to good warehouses and a Seven Step
process for Benchmarking. This year we want to challenge
practitioners to rethink their performance scorecards.
Rather than focus only on operational performance and
view supply chains solely as purely logical or mechanical
systems, they should start measuring both the hard and
soft sides of performance if they want to truly gauge their
supply chain success.
This survey was launched via an email invitation to WERC
members and DC Velocity readers in early January
2012. Survey participants are asked to report their actual
levels of performance for 2011. The study captures 44 key
operational metrics that are close to the heart of most
distribution center professionals. The measures have been
grouped into 5 balanced sets —customer, operational,
financial, capacity/quality and employee —plus the
additional sets related to perfect order and cash-to-cash
cycle measurement.
Warehousing education and research council • 1100 Jorie blvd, ste 170, Oak Brook, IL 60523-4413
P | 630.990.0001 F | 630.990.0256 E | wercoffice@werc.org W | www.werc.org

Figure 1. Interest in measures on part of
senior management for 2012
Decreasing 4.2%
Increasing
59.2%
Staying the
Same
36.6%
Figure 3. Respondents by Industry
Other 3.5%
Transportation
Service Provider 1.4%
Third-party
Warehouse
25.2%
Utilities/Government
1.4%
Food Distribution
2.1% Wholesale
Distribution
7.7%
Retail
16.1%
Manufacturing
35%
Second, as warehouses and DCs prepare for growth, or for the
next economic crisis, their scorecards will need to go beyond
operational metrics. Developing relationships with your suppliers
is quickly becoming an important aspect for warehouses and DCs
to succeed in this rapidly changing economic environment. In
order to capture a true picture of success, facilities will need to
begin measuring the softer side of performance.
Respondents Represent Diverse Industries, Operations
and Firm Sizes
The number of responses was relatively large for this year
(in excess of 225 for 2012). However, it did not match last
year’s usable responses. In order to increase the predictive
power of the benchmarks, 2012 results were combined with
2011 results. Analysis was completed and verified that the
results were statistically similar. Therefore, over the two-year
period, 802 individuals responded to the survey. The largest
group of respondents reported their title as Manager (50.7%),
while Director (27.5%) and Senior VP (14.1%) were the second
Figure 2. Respondent Titles
CEO/President 3.5%
Manager
50.7%
Other 4.2%
Senior VP
14.1%
Director
27.5%
Life Sciences –
Medical Devices
4.2%
and third largest groups. Executives represent 3.5%. Figure 2
provides a breakdown of the survey respondents’ titles.
In addition to understanding who participated in the study,
we reviewed five unique demographic areas, including:
1. Type of industry
2. Type of operation
3. Type of customer
4. Business strategy
5. Size of company
Demographics by Industry Type
Figure 3 provides a breakdown of the various business
segments that participated in the study. As with previous
years, the manufacturing industry segment remains the largest
demographic base for the study. However, a continued increase
in participation by third party warehouses is good news as this
reflects how diverse the industry has become.
Because the manufacturing segment is so large, a further
breakdown and explanation of the types of industries falling
under the manufacturing segment is shown in Table 1.
Table 1. Manufacturing Industry Breakdown
Business Segment
Manufacturing
Life Sciences –
Pharmaceuticals 3.5%
Further Industry
Breakdown
Percent
Consumer Products 19.6%
High Technology 4.2%
Automotive 1.4%
General 9.8%
2
®
Watch
SPRING 2012

Table 2. Respondents by DC Operation
Metric % of Total % Cases vs. Pallet
Broken Case Picking 36.9%
Full Case Picking 33.4%
Partial Pallet Picking 11.9%
Full Pallet Picking 17.8%
70.3%
29.7%
Demographics by DC Operation
In addition to industry, respondents were asked how goods
moved through their DC. As shown in Table 2, the majority of
facilities (70.3%) are picking cases rather than pallets.
In calculating percentages for the type of work performed,
we only used responses where majority of the respondents’
activity was in one of the four classifications.
Compared to 2011, respondents categorized as primarily a pallet
picking operation continued to decline this year. Most of the
decrease was from respondents categorized as a full pallet
picking operation. Over the past four years respondents primarily
operating a DC utilizing full case picking have increased each
year. While case picking is at its highest level in four years (70.3%)
it is not as large as the 72.7% reported back in 2008.
These operational fluctuations did not impact a critical
distribution metric: inventory turns. Inventory turns increased
from 2011 to 2012 for median and best-in-class. Inventory turns
at the median increased from 10.1 turns in 2011 to 10.4 turns.
Best-in-class increased from 20.3 in 2011 to 26.3 in 2012.
Type of Customer Served
Another important consideration is the location of the company
within the supply chain. We were curious to learn if companies
that are upstream suppliers used a similar set of measures to that
of their customers or their customers’ customers. Respondents
were asked to classify who their primary customers were in the
supply chain (Figure 4).
As seen in previous studies, the majority of respondents reported
that they were either at or near the end of the supply chain. This
year is no exception in that 50% of respondents reported their
customers were either an end consumer or a retail firm.
Business Strategy
The fourth demographic area is business strategy and it is
another area that some suggest could impact measures in this
year’s survey. Do different strategies place a higher emphasis
on some measures and not on others And at what level in the
organization can these differences be seen or noticed
To answer these questions, we asked respondents to indicate
the overall business strategy for their business unit or division
with respect to cost leadership, customer service, innovation or
simply being all things to all people (Figure 5).
Figure 4. Respondents by Type of Customer
Manufacturer 16.9%
Figure 5. Respondents by business strategy
Cost Leadership 12%
End
Consumer
19%
Retail Firm
31%
Distributor/
Wholesaler
33.1%
Mix: Be All
Things to
All People
40.1% Customer
Service
38.7%
Product/Market
Innovation 9.2%
Watch
SPRING 2012
3

Once again respondents report their desire to “be everything to
everybody.” Since 2007, when a shift from Customer Service to the
Mix strategy was first mentioned, respondents have consistently
reported the Mix strategy, “be all things to all people,” as the
number one strategy for DCs and warehouses. However, the Mix
strategy did decrease slightly from 41.9% in 2011.
Which strategy did respondents move away from Product/
Market Innovation slightly lost ground, but overall there are no
definitive movements from one strategy to another. For example,
cost leadership and customer service slightly increased from
11.4% and 37.2% in 2011. We believe these slight changes are
related to warehouses and DCs fine tuning their strategies
during a disruptive economic cycle.
This development will be interesting to watch. We believe once
the economy stabilizes and begins to improve more respondents
will revert course and focus their attention back to “being all
things to all people.”
Respondents were also asked about their operational
management strategy with respect to outsourcing. Respondents
were asked whether their global, domestic and regional
operations were managed internally or by a third party (Table 3).
companies reporting annual sales between $100 million and
$1 billion represent 39.3% of the respondents. As seen in our
previous studies, we continue to have a good representation
of the industry.
Because we continue to have a good representation by
company size, we can compare performance and determine
whether larger size companies perform better than smaller
companies on various metrics.
Table 3. How DCs Are Managed
Who Provided Responses
Percent
Solely 3PL Results 14.7%
Mix of Both 3PL and Internal Results 14.1%
Solely Internal Results 71.2%
Demographics by Company Size
Each year respondents indicate the relative size of their company
by using annual sales. The purpose of this question is to help
determine what effect size has on the kinds and number of
metrics used, in addition to creating additional benchmarks
based on size (Figure 6).
Location, Location, Location
For the past few years we have had continuing interest in the
benchmarking study from respondents operating outside of the
North American continent.
This naturally leads us to want to make comparisons based on
location as well. In our second attempt to identify those countries
where we have respondents, we noticed an increasing interest
from respondents outside North America. As more warehouses
and DC s outside of North America continue to report their findings,
we will be able to provide statistically viable benchmarks based on
location. This year just over 90% of the respondents reported North
America as their location, while the remaining 9.6% respondents
are from countries outside of North America (Figure 7).”
Figure 6. Respondents by Company Size
Figure 7. Your Warehouse’s location
> $1 BILLION
$100 MILLION – $1 BILLION
< $100 MILLION
31.1%
39.3%
29.6%
EEA 1.4% Oceania 1.4%
Asia 4.8%
Middle East/Africa
0.7% Europe – Not EEA 0.7%
0% 10% 20% 30% 40%
Once again, we are pleased to see that companies of all
sizes are participating in the study supporting our philosophy
that companies should benchmark and focus on their
performance regardless of their size. Companies with annual
sales less than $100 million comprised 29.6% of our total
respondents, while participants having greater than $1 billion
in annual sales comprise 31.1% of the respondents. Those
North America
90.4%
4
®
Watch
SPRING 2012

Answering the Big Question
It is a question we hear all of the time. “Our industry is unique.”
“We’re different.” “We’re special.” “Your metrics don’t apply
to us because…”
The list goes on, but here is the quick answer. In the majority
of cases, when it comes to DC performance, we don’t see
statistically significant differences between firms based on any
of the demographics listed above. Quantitative performance
is quantitative performance. Are there differences No doubt.
These differences are primarily qualitative in nature. This is
why we’ve stressed using both quantitative and qualitative
benchmarking.
Some of you may disagree. If so, here is some good news.
You can benchmark your performance on each of the metrics
online at www.werc.org and compare yourself to each of
the demographics listed above. In other words, if you’d like to
compare yourself to other firms with less than one hundred
million dollars in sales, you can do so. You can also compare
yourself to the entire set of findings as well.
Interpreting the Benchmarking Results
A primary objective for this study is to provide a benchmark of
key measures by industry and type of business and to see how
these benchmarks are changing (if at all) over time.
As in previous benchmark studies, we primarily looked at
two benchmarks: median performance and best practice
performance. We chose the median as it is not easily swayed
by outliers. The benchmarking data is reported using a “quintile”
format which presents the data on a five-point maturity scale
that reflects where the respondents are situated with respect
to the journey toward “best practice.”
It gives readers an improved tool for judging their own
performance and what constitutes best practice. To be
considered best practice, the level of performance would
have to fall within the top 20% of all respondents.
How Good is the Data
Given that the group of respondents are members of a premier
warehousing and distribution association and/or readers of a
leading distribution magazine, the benchmarks may be better
than the general population of DCs. This organization and
publication tend to attract high performers who are continually
improving their operations.
It is also important that you compare your performance with an
appropriate set of partners. While not as important for comparing
overall service level and customer oriented key performance
indicators, it is especially true when comparing productivity and
cost type metrics. Ideally this would be a firm which is similar in
size and in the same or similar business segment. For this reason,
WERC provides a detailed benchmarking report that segments the
benchmarking data by industry, company size, strategy, operations
and type of customer.
“A primary objective for this study is to
provide a benchmark of key measures
by industry and type of business.”
Watch
SPRING 2012
5

What This Year’s Data Said
Table 4 provides a summary of all of the metrics from this year’s study presented in seven columns that are aimed to shed light
about how companies are performing.
• Column 1: Metric
This represents the metrics that is being examined (metric definitions can be found on pages 12-15).
• Columns 2 – 6: Quintile Rankings
These columns split all data responses into five equally divided groups. Each quintile ranking indicates 20% of the responses,
with the five groups representing:
> Major Opportunity: Represents the lowest 20% of responses.
> Disadvantage: Represents responses in the 20-40th percentile.
> Typical: Represents responses in the 40-60th percentile.
• Column 7: Median
This indicates the actual median performance of all respondents.
> Advantage: Represents responses the 60-80th percentile.
> Best Practice: Represents top 20% of all responses.
Table 4. Quintile Performance Classifications for Metrics
Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7
Customer Metrics* Major Opportunity Disadvantage Typical Advantage Best-in-class Median
On-time Shipments Less than 95.7% >= 95.7 and < 98% >= 98 and < 99.1% >= 99.1 and < 99.8% >= 99.8% 98.5%
Total Order Cycle Time Greater than 72 Hours >= 48 and < 72 >= 22.9 and < 48 >= 5.4 and < 22.9 < 5.4 Hours 33.5 Hours
Internal Order Cycle Time Greater than 36 Hours >= 24 and < 36 >= 8 and < 24 >= 3 and < 8 < 3 Hours 13 Hours
Perfect Order Completion Index Less than 83.6% >= 83.6 and < 94.8% >= 94.8 and < 97.3% >= 97.3 and < 99% >= 99% 95.3%
Lost Sales (Percent of SKUs Stocked Out) Greater than 5.7% >= 2 and < 5.6% >= 1 and < 2% >= 0.29 and < 1% < 0.29% 2%
Backorders as a Percent of Total Orders Greater than 7.4% >= 2.2 and < 7.4% >= 1 and < 2.24% >= 0.2 and < 1% < 0.2% 1.9%
Backorders as a Percent of Total Lines Greater than 5% >= 2 and < 5% >= 1 and < 2% >= 0.2 and < 1% < 0.2% 1.5%
Backorders as a Percent of Total Dollars/Units Greater than 9.2% >= 2.3 and < 9.2% >= 1 and < 2.3 >= 0.2 and < 1 < 0.2% 1.8%
Operations Metrics Major Opportunity Disadvantage Typical Advantage Best-in-class Median
Inbound Metrics
Dock-to-Stock Cycle Time, in Hours Greater than 18.1 Hours >= 8 and < 18.1 >= 4 and < 8 >= 2 and < 4 < 2 Hours 6 Hours
Suppliers Orders Received per Hour Less than 1 per Hour >= 1 and < 2 >= 2 and < 4.7 >= 4.7 and < 10
>= 10
per Hour
3.7 per Hour
Lines Received and Put Away per Hour
Less than 5 Lines
>= 48 Lines per 15 Lines
>= 5 and < 13.2 >= 13.2 and < 20 >= 20 and < 48
per Hour
Hour per Hour
Percent of Supplier Orders Received with
Correct Documents
Less than 90% >= 90 and < 95% >= 95 and < 98% >= 98 and < 99% >= 99% 96%
Percent of Supplier Orders Received
Damage Free
Less than 95% >= 95 and < 98% >= 98 and < 98.5%
>= 98.5 and <
99.04%
>= 99.04% 98%
On-time Receipts from Supplier Less than 85% >= 85 and < 91.8% >= 91.8 and < 95% >= 95 and < 98% >= 98% 94%
Outbound Metrics
Fill Rate – Line Less than 95% >= 95 and < 98% >= 98 and < 99% >= 99 and < 99.8% >= 99.8% 98.5%
Order Fill Rate Less than 90.3% >= 90.3 and < 97% >= 97 and < 99% >= 99 and < 99.8% >= 99.8% 98%
Lines Picked and Shipped per Hour
Less than 14 Lines
>= 81 Lines
>= 14 and < 25 >= 25 and < 43 >= 43 and < 81
per Hour
per Hour
29.9 per Hour
Orders Picked and Shipped per Hour
Less than 2 Orders
>= 29.4 Orders
>= 2 and < 5 >= 5 and < 12 >= 12 and < 29.4
per Hour
per Hour
7.5 per Hour
Cases Picked and Shipped per Hour
Less than 30.9 Cases
>= 255 Cases
>= 30.9 and < 65 >= 65 and < 140 >= 140 and < 255
per Hour
per Hour
99.4 per Hour
Pallets Picked and Shipped per Hour
Less than 7.5 Pallets
>= 27.2 Pallets
>= 7.5 and < 14.3 >= 14.3 and < 20 >= 20 and < 27.2
per Hour
per Hour
19 per Hour
On-time Ready to Ship Less than 95.4% >= 95.4 and < 98% >= 98 and < 99% >= 99 and < 99.8% >= 99.8% 99%
6
®
Watch
SPRING 2012

Table 4. Quintile Performance Classifications for Metrics, CONT.
Column 1 Column 2 Column 3 Column 4 Column 5 Column 6 Column 7
Financial Metrics Major Opportunity Disadvantage Typical Advantage Best-in-class Median
Distribution Costs as a Percent of Sales Greater than 8.9% >= 4.7 and < 8.9% >= 2.9 and < 4.7 >= 1.6 and < 2.98 < 1.6% 3.7%
Distribution Costs as a Percentage of COGS Greater than 15% >= 8.5 and < 15% >= 4.6 and < 8.5 >= 1.8 and < 4.6 < 1.8% 7%
Distribution Costs per Unit Shipped Greater than $10 >= $2.5 and < $10 >= $0.83 and < $2.5 >= $0.3 and < $0.83 < $0.3 $1.32
Days on Hand Raw Materials Greater than 61.2 Days >= 41.4 and < 61.2 >= 28.4 and < 41.4 >= 14 and < 28.4 < 14 Days 30 Days
Days on Hand Finished Goods Inventory Greater than 78 Days >= 45 and < 78 >= 30 and < 45 >= 15 and < 30 < 15 Days 37.4 Days
Inventory Shrinkage as a Percent of
Total Inventory
Greater than 1.8% >= 0.7 and < 1.8% >= 0.2 and < 0.7 >= 0.1 and < 0.2 < 0.1% 0.35%
Capacity/Quality Metrics Major Opportunity Disadvantage Typical Advantage Best-in-class Median
Average Warehouse Capacity Used** Less than 73.2% >= 73.2 and < 80% >= 80 and < 85% >= 85 and < 91.2% >= 91.2% 84.9%
Peak Warehouse Capacity Used** Less than 88.6% >= 88.6 and < 94% >= 94 and < 98% >= 98 and < 100% >= 100% 95%
Honeycomb Percent Less than 15% >= 15 and < 43.8% >= 43.8 and < 70% >= 70 and < 85% >= 85% 50%
Inventory Count Accuracy by Dollars/Units Less than 93.8% >= 93.8 and < 98% >= 98 and < 99.3% >= 99.3 and < 99.8% >= 99.8% 99%
Inventory Count Accuracy by Location Less than 93.4% >= 93.4 and < 97.5% >= 97.5 and < 99.1% >= 99.1 and < 99.8% >= 99.8% 98.8%
Order Picking Accuracy (Percent by Order) Less than 98.3% >= 98.3 and < 99.1% >= 99.1 and < 99.7% >= 99.7 and < 99.9% >= 99.9% 99.5%
Material Handling Damage Greater than 1.7% >= 0.5 and < 1.7% >= 0.12 and < 0.5% >= 0.08 and < 0.12% < 0.08% 0.2%
Equipment/Forklifts Capacity Used Less than 62.6% >= 62.6 and < 75.2% >= 75.2 and < 88.3% >= 88.3 and < 95.8% >= 95.8% 80%
Employee Metrics Major Opportunity Disadvantage Typical Advantage Best-in-class Median
Annual Workforce Turnover Greater than 15.1% >= 8.7 and < 15.1% >= 4.2 and < 8.7% >= 1 and < 4.2% < 1% 5%
Productive Hours to Total Hours Less than 74.7% >= 74.7 and < 82.9% >= 82.9 and < 89.1% >= 89.1 and < 92% >= 92% 85%
Perfect Order Metrics Major Opportunity Disadvantage Typical Advantage Best-in-class Median
Percent of Orders with On-time Delivery Less than 90% >= 90 and < 95.6% >= 95.6 and < 98% >= 98 and < 99.03% >= 99.03% 97.1%
Percent of Orders Shipped Complete Less than 89.7% >= 89.7 and < 96% >= 96 and < 98.5% >= 98.5 and < 99.6% >= 99.6% 98%
Percent of Orders Shipped Damage
Free (Outbound)
Percent of Orders Sent with
Correct Documentation
Less than 97.8% >= 97.8 and < 99% >= 99 and < 99.6% >= 99.6 and < 99.9% >= 99.9% 99.5%
Less than 95.9% >= 95.9 and < 99% >= 99 and < 99.7% >= 99.7 and < 100% >= 100% 99.3%
Cash-to-Cash Metrics Major Opportunity Disadvantage Typical Advantage Best-in-class Median
Inventory Days of Supply Greater than 100.4 Days >= 50.7 and < 100.4 >= 32.8 and < 50.7 >= 21.2 and < 32.8 < 21.2 Days 37.4 Days
Average Days Payable Greater than 60.7 Days >= 45.04 and < 60.7 >= 30 and < 45.04 >= 23.5 and < 30 < 23.5 Days 35 Days
Average Days of Sales Outstanding Greater than 61.3 Days >= 45 and < 61.3 >= 27.1 and < 45 >= 13.9 and < 27.1 < 13.9 Days 35 Days
Which Metrics Really Matter
Each year we identify the Top 12 most popular measures based
on the number of respondents to report results for each metric.
Table 5 shows the Top 12 most popular metrics used and how
that has changed since the 2010 study.
There is strong agreement among DCs and warehouses in
regards to which metrics are critical to measuring performance.
Survey participants still favor the basic metrics they’ve been
using since the beginning of the study.
The only change was in the order of the Top 12 for this year.
This year’s most frequently employed metrics were on-time
shipments, order picking accuracy and average warehouse
capacity used suggesting that veracity, velocity and volume
remain the top concerns for warehouses and DCs.
In terms of performance, the most interesting metric to
watch over the next couple of years will be annual workforce
turnover. While overall performance at the median improved
by 37%, from 8% in 2011 to 5% this year, we believe this is
an indication of the uncertainty that plagues the economy in
terms of employment in 2011 (Figure 8). The labor market is
still lagging other indicators. Our thought is the performance
for annual workforce turnover will “decrease” for median
and best-in-class companies once more people become
comfortable with the current direction of the economy.
Until then, those employees will continue to stay put. As the
economic engine heats up, we expect to see these numbers
return to 2007 levels, when Median performance was 14%.
** Note: Additional customer metrics can be found under Perfect Order Metrics Section.
** Note: Average and Peak Warehouse Capacity does not always reflect best practices. Due to the calculations for quintiles, we have continually reported that best-in-class is above 90%.
A high average warehouse capacity is not beneficial; studies have shown that an average warehouse capacity between 80 and 85% allows the warehouse to respond to shifts in demand.
** Legend: > greater than; >= greater than or equal to; < less than
Watch
SPRING 2012
7

Table 5. Top 12 Most Popular Measures Used
Metric/Metric Category 2011 Rank 2010 Rank
Figure 8. Annual Workforce Turnover
1. On-time Shipments – Customer 1 1
2. Order Picking Accuracy – Quality 3 2
3. Average Warehouse Capacity
Used – Capacity
4. Dock-to-Stock Cycle Time, in
Hours – Inbound Operations
5. Internal Order Cycle Time –
Customer
2 4
5 6
6 10
Performance (Percentage)
15%
12%
9%
6%
3%
Median
Best-in-class
6. Total Order Cycle Time –
Customer
7 –
0%
2005 2006
2007 2008 2009 2010 2011 2012
7. Peak Warehouse Capacity Used –
Capacity
8. Lines Picked and Shipped per
Hour – Outbound Operations
9. Annual Workforce Turnover –
Employee
10. Fill Rate – Line –
Outbound Operations
11. Lines Received and Put Away per
Hour – Inbound Operations
12. Percent of supplier orders
received damage free –
Inbound Operations
4 9
8 11
12 8
11 3
9 –
10 –
Of interest this year for several metrics is the gap in
performance between median and best-in-class performers.
We are finding that for several metrics the gap is not widening
for the top performers. For example, order picking accuracy,
on-time shipments and fill rate – line have not seen a lot of
movement in terms of performance, i.e., performance has
plateaued at the top end. See Tables 6, 7 and 8 below.
However, performers at the bottom end (major opportunity
performers) are getting better. The normal distribution is
getting narrower, where you still have a large number of the
respondents centered around the median and growing taller
(if you think about a bell shaped curve), but the tail ends
(particularly major opportunity performers) are moving closer
to the median performers.
Table 6. Performance for 2012
Major Opportunity Typical Best-in-class Median
On-time Shipments Less than 95.7% >= 98 and < 99.1% >= 99.8% 98.5%
Order Picking Accuracy Less than 98.3% >= 99.1 and < 99.7% >= 99.9% 99.5%
Fill Rate – Line Less than 95% >= 98 and < 99% >= 99.8% 98%
Table 7. Performance for 2010
Major Opportunity Typical Best-in-class Median
On-time Shipments Less than 95% >= 98 and < 99% >= 99.84% 98.5%
Order Picking Accuracy Less than 98.04% >= 99.12 and < 99.69% >= 99.9% 99.5%
Fill Rate – Line Less than 94.8% >= 97.92 and < 98.88% >= 99.8% 98.5%
Table 8. Performance for 2008
Major Opportunity Typical Best-in-class Median
On-time Shipments Less than 94% >= 97.84 and < 99% >= 99.8% 98.1%
Order Picking Accuracy Less than 98% >= 97.5 and < 98.6% >= 99.9% 99.3%
Fill Rate – Line Less than 94.34% >= 99 and < 99.54% >= 99.7% 98%
8
®
Watch
SPRING 2012

It is interesting to note that the balanced use of measures
continued to show this year. Clearly DCs and warehouses are
taking a forward looking approach by focusing in on customers
and processes rather than financial metrics, which tend to be
lagging indicators of performance.
The Dilemma of Defining
“I heard what you said…But what do you mean”
This dilemma of defining words is nowhere more critical or
often misunderstood than when it comes to on-time delivery.
A supplier knows the customer wants the package on-time,
but what does that really mean To make it more interesting,
each customer can have a different perspective on the term.
The percentage of customers that use each of the different
definitions has changed since we last looked at defining on-time in
2008. We asked respondents what percentage of their customers
used a particular definition of on-time. In other words how many
different definitions of on-time delivery were being used
Table 9. Number of definitions of on-time
Number of Definitions
% of Respondents Using
1 43%
2-3 36.2%
4-5 10.2%
6 or More 10.2%
We found that a majority of respondents still have to deal
with multiple definitions of on-time (Table 9). Over 20% had four
or more definitions of on-time delivery. The difficulty in this is to
communicate clearly with employees as to how the metric is
defined and operationally execute on each expectation.
Unfortunately the majority of respondents, 51.1%, have a very
narrow definition of on-time to meet. Table 10 shows the various
definitions that managers must meet by percentage of their
customers using that definition. The change from 2008, where
over 55% of respondents only had to deliver on the requested
or agreed upon day, is quite stark compared to today. It appears
as though customers have further refined their definitions.
Another factor to consider is the Lean and JIT movements to
reduce excess inventory. Several of the other definitions given
considered on-time to be “right before I need more materials.”
Table 10. Varied definitions of on-time
Metric
% Using
On or Before Appointment Time 15.7%
+ 15 Minutes from the Appointment Time 11.7%
+/- 30 Minutes from the Appointment Time 11.7%
=/- 1 Hour from the Appointment Time 12%
- 1 Hour / + 0 Hours of the Appointment Time 6.6%
On the Requested Day 20.2%
On the Agreed Upon Day 17.1%
Other Definition 5.1%
Softer Side of Performance
Since the inception of this study, our focus has been on the
operational side of performance. The number of labels licked
and boxes kicked per hour, the percent of orders sent damage
free; you name the measure and if it’s important to the industry –
we have the metric. To an extent this is the standard approach,
over several decades, of performance management: Specific,
Measurable, Actionable, Relevant and Timely or “SMART.”
Then in the early 1990’s two Harvard Business professors turned
the world of performance upside down with the balanced
scorecard. In order to understand how we are performing
as a company, we need to look at our performance from all
sides: financial, customer, growth and learning, and internal
processes. Unfortunately, many companies continue to look
at “hard” operational measures to determine performance for
these categories. Over the last several years, some academics
and consulting firms have been conducting field-based research
and found a few companies are starting to augment traditional
“hard” operational measures with an evaluation of the “softer”
side of business performance.
Watch
SPRING 2012
9

Soft considerations include assessing the Relationship Quality,
such as communication and collaboration approaches and trust
in the relationship; and Organization Culture, such as leadership
orientation, innovation and team focus.
Communication
Approaches
Relationship Quality
(Performance)
Collaboration
Approaches
Trust
Leadership
Orientation
Organization Culture
(Suitability)
Innovation
Team Focus
Why measure the softer side of business
Most supply chain managers are accustomed to measuring
operational performance, and they typically have an affinity for
and are skilled in this type of analysis. Today it is easier than
ever to measure the operational aspects of a business, thanks to
the availability of free or inexpensive operational benchmarking
data through organizations like the Warehousing Education and
Research Council (WERC). The advent of automated scorecards,
dashboards, financial analyses and reporting tools has also
made it easier to accurately measure operational performance.
Operational analysis is a top priority in today’s business
environment, which has driven companies to manage by the
numbers and emphasize bottom-line results. And when the
going gets tough, organizations focusing solely on bottomline
results tend to revert to what they know best. In the
case of performance measurement, the tendency is to place
“Today it is easier than ever to measure
the operational aspects of a business.”
10
®
Watch
SPRING 2012

additional emphasis on improved operational efficiency at
both the organizational and individual levels. As a result, many
organizations are reducing the number of customer-facing
personnel they employ, slighting customer service and imposing
greater pressure and unfavorable terms on vendors.
Without solid relationships to back them up, however, the
operational components of a business are unlikely to be
successful. Simply put, the interpersonal skills that promote
and nurture strong relationships—the ability to communicate
well, interact effectively with others, make mutually beneficial
decisions, solve problems jointly and collaborate—can have
as direct an impact on supply chain performance as do the
operational aspects of a business.
To develop a true picture of supply chain success, then,
organizations must measure both the “what” (operational)
and the “how” (interpersonal) aspects of performance. The
importance of these soft skills dictates a need for a new
generation of business metrics and scorecards able to gauge
both sides of performance.
Which “Soft” Skills Should You Measure
We want to advocate for the soft side of supplier and customer
relationships because we believe companies that don’t formally
measure the overall health of their trading partner relationships
are myopic.
This is particularly important in the supply chain arena. The
effectiveness of a supply chain relationship with customers
Soft Metrics
Communication Approaches
Collaboration Approaches
Trust
Leadership Approaches
Innovation
Team Focus
Table 11. Soft Metrics
Concepts
Communication frequency and effectiveness
People management including clarity of
direction and respect
Conflict management and collaboration styles
Decision making and problem solving
Delegation and governance
Cooperation, openness and behavior
Trustworthiness, ethics and integrity
Culture, values and equality of stakeholders
Leadership styles and accountability of actions
Creativity and alignment of strategic direction
Flexibility, agility and speed of response
Improvement and change management
Balanced risk and rewards
Goal setting and shared vision
Peer-to-peer cross-functional teams
Value (reward) and recognition
Win-win orientation
“Interpersonal relationships directly impact such factors
as purchase/repurchase decisions, sharing critical
information, collaborative education (educating each
other on critical aspects of the product, strategy, or
service) and the degree of strategic partnership.”
and suppliers depends heavily upon non-operational elements
of performance: employees’ ability to communicate well,
personally interface with counterparts at other companies,
make mutually beneficial decisions, solve problems jointly,
and collaborate. Interpersonal relationships directly impact
such factors as purchase/repurchase decisions, sharing critical
information, collaborative education (educating each other
on critical aspects of the product, strategy, or service) and
the degree of strategic partnership. Sacrificing any of these
elements for short-term gain can often have negative, long-term
consequences on supply chain relationships, which is likely
to have severe consequences for a company as a whole.
If you are not yet convinced that the softer side of business
performance should be measured alongside operational
metrics, then consider the following highlights from research
conducted by the University of Tennessee. Their results show
how some companies are benefiting from measuring both sides
of performance.
Soft on Metrics
Field-based research conducted by the University of Tennessee
(UT) on performance-focused outsourcing agreements shows
that the “soft stuff” involved in building long-term supplier
relationships genuinely matters. UT researchers have studied
highly successful outsourcing relationships across all industries
and functions being outsourced. They have seen a clear trend
toward companies reducing the number of operational metrics
and increasing the number of soft metrics.
In one outsourcing relationship the UT teams studied, the
company and the service provider went so far as to remove all
of the task-level service agreements from their contract. Instead,
they relied on just three, high-level “critical success factors” to
gauge operational performance. The contract also spelled out
nine additional “soft” metrics, including such hard to measure
items as “flexibility” and “responsiveness.”
Watch
SPRING 2012
11

The most unusual metric in the relationship addressed what the
companies termed “discretionary governance.” This was an
index derived from surveying the top 40 people involved in the
outsourcing relationship, ranging from those with direct control
to major stakeholders in the affected business units. The goal
of the discretionary governance index was to gauge the overall
“happiness” of the relationship. The companies referred to the
index as “discretionary governance” because the parties had
agreed to tie a full 20 percent of the service provider’s fees to the
index—a rarity in a world where most companies only want to
make payments based on bullet-proof, quantifiable metrics. UT
researchers came to call the discretionary governance metric
the “happy factor” because the happier the company, the bigger
the payout for the service provider.
It may sound far-fetched, but the company named this
particular supplier “Supplier of the Year” two years in a row—
out of 80,000 suppliers. The supplier confirms that focusing on
the softer side of supply chain performance has helped it to build
deep and meaningful relationships, which are driving overall
business success. n
Metric Definitions
One ongoing goal of this study is to help practitioners link key
measures to various demographics to help companies better
compare themselves to organization similar to their own.
As pointed out in the study, there is often a lack of consensus—
or sometimes, understanding—of what the metrics actually mean.
Over the past five years we have been told that companies
have adopted these definitions and calculations across their
organizations in an attempt to develop a consistent approach
to reporting performance at each location. This has been one
aspect of the study that is most rewarding. Use of an agreed upon
standard and definition will go a long way in assisting firms to
understand and compare internal performance.
Definitions for the key operational metrics are provided here—
grouped into categories to help you interpret the metrics in
the report—as well as to provide a common understanding
for benchmarking. n
Customer Metrics Definition Calculation
On-time Shipments
Total Order Cycle Time
Internal Order Cycle Time
Perfect Order Index
Lost Sales
(Percentage SKUs Stocked Out)
Backorders as a Percentage of
Total Orders
and/or
Backorders as a Percentage of
Total Lines
and/or
Backorders as a Percentage of
Total Dollars / Units
The percentage of orders shipped at the planned time (shipped means off
the dock and in transit to its final destination).
NOTE: The time to ship may be defined by the customer, or it may be
determined by the shipper in order to accommodate an on-time delivery.
The average end-to-end time between order placement by the customer
and order receipt by the customer.
The average internal time between when the order was received from the
customer and order shipment by the supplier.
NOTE: Order shipment is defined as off of the dock, onto the shipping
conveyance and ready for transit.
A compilation score which measures the result of each of the 4 major
components of a perfect order:
• Delivered On-time
• Shipped Complete
• Shipped Damage Free • Correct Documentation
An important risk indicator: the percentage of sales lost due to stock outs.
The portion of total orders that are held and shipped late due to lack of
availability of stock. Can be measured by lines or by PO, by units or by
dollar value.
Number of order shipped on-time /
Total number of orders shipped
Excluding non-working days: sum of
(Time order received by customer – Time
order placed) / Total number of orders shipped
Excluding non-working days: sum of
(Time order shipment – Time order received
from the customer) / Number of orders shipped
The Perfect Order Index (POI) is established
by multiplying each component of the perfect
order to one another. For example, if a company
is experiencing a measure of 95% across all 4
metrics of the perfect order (on-time, complete,
damage free and correct documentation), the
resulting perfect order index would be 81.4%
Dollar sales that were lost (i.e., they did not
become backorders) / Total sales
Number of orders held and not shipped /
Total number of orders
Number of order lines held and not shipped /
Total number of order lines
Number of order dollars or units held and not
shipped / Total number of order dollars or units
Operations Metrics Definition Calculation
Inbound Metrics
Dock-to-Stock Cycle
Time, in Hours
The dock-to-stock cycle time equals the time (typically measured in hours)
required to put away goods. The cycle time begins when goods arrive from
the supplier and ends when those goods are put away in the warehouse and
recorded into the inventory management system.
For a given time period: sum of the cycle time
in hours for all supplier receipts / Total number
of supplier receipts
12
®
Watch
SPRING 2012

Supplier Orders Received
per Hour
Lines Received and
put Away per Hour
Measures the productivity of receiving operations in supplier orders
processed per person hour.
Measures the productivity of receiving operations in lines processed
and put away per person hour.
Total supplier orders processed in receiving / Total
person hours worked in the receiving operation
Total lines received and put away / Total person
hours worked in the receiving operation
Percent of Supplier Orders
Received With Correct
Documentation
Percent of Supplier Orders
Received Damage Free
On-time Receipts
From Supplier
Fill Rate – Line
Order Fill Rate
Lines Picked and Shipped
per Person Hour
and/or
Orders Picked and Shipped
per Person Hour
and/or
Cases Picked and Shipped
per Person Hour
and/or
Pallets Picked and Shipped
per Person Hour
The number of orders that are processed with complete and correct
documentation as a percentage of total orders.
Documentation includes packing slips, case and pallet labeling,
certifications, ASN, carrier documents or other documents as required
by the purchase order.
The number of orders that are processed damage free as a percentage
of total orders.
Percent of orders received from a supplier on the date requested.
Outbound Metrics
Measures percent of orders lines filled according to customer request.
NOTE: A single customer order line can request multiple shipments.
In this case each shipment would be tracked as a separate request.
Measures percent of orders filled according to customer request.
NOTE: A single customer order can request multiple shipments.
In this case each shipment would be tracked as a separate request.
Measures the productivity of picking and shipping operations
in lines per person hour.
Measures the productivity of picking and shipping operations
in orders per person hour.
Measures the productivity of picking and shipping operations
in cases per person hour.
Measures the productivity of picking and shipping operations
in pallets per person hour.
Number of supplier orders that are processed with
complete and correct documents / Total supplier
orders processed in the measurement period
Number of supplier orders that are processed
damage free / Total supplier orders processed in
the measurement period
Number of supplier orders received on-time /
Total number of orders received
Percentage of orders lines filled to customer
request / Total number of order lines filled
Number of orders filled to customer request /
Total number of orders filled
For a given time period:
Total order lines picked and shipped / Total hours
worked in the picking and shipping operation
Total orders picked / Total hours worked in the
picking and shipping operation
Number of cases picked and shipped / Total hours
worked in the picking and shipping operation
Number of pallets picked and shipped / Total hours
worked in the picking and shipping operation
On-time Ready to Ship
The percentage of orders ready for shipment at the planned time.
NOTE: “Ready for shipment” typically means that packaging and
shipping documents are completed and ready for pickup.
Number of orders ready for shipment on-time /
Number of total orders shipped
Financial Metrics Definition Calculation
Distribution Cost as a
Percent of Sales
The cost to run distribution relative to total sales. Activities included in the
operate warehousing process are: management activities, track inventory
deployment, receive, inspect and store inbound deliveries, track product
availability, pick, pack and ship product for delivery, track inventory
accuracy, track third-party logistics storage and shipping performance.
Total distribution costs / Total sales
Distribution Costs as a
Percent of COGS
The cost to run distribution relative to COGS. Activities included as part
of total distribution operating costs are: management activities, track
inventory deployment, receive, inspect and store inbound deliveries, track
product availability, pick, pack and ship product for delivery, track inventory
accuracy, track third-party logistics storage and shipping performance.
Total distribution costs / Total COGS
(based on corporate income statement)
Distribution Cost
per Unit Shipped
The cost to run distribution relative to the units shipped through distribution.
Distribution costs include: management activities, track inventory
deployment, receive, inspect and store inbound deliveries, track product
availability, pick, pack and ship product for delivery, track inventory
accuracy, track third-party logistics storage and shipping performance.
Total cost of operating distribution /
Total units shipped
Watch
SPRING 2012
13

Inventory Shrinkage as a
Percent of Total Inventory
The amount of breakage, pilferage and deterioration of all inventories relative
to total inventory. Usually stated in terms of value, not units.
Sum (value of breakage, pilferage, deterioration
to all inventory) / Total value of all inventory
Days on Hand –
Raw Materials
The number of productive days before raw material supply is consumed.
Gross raw material inventory value / Average daily
value of raw material usage
Days on Hand –
Finished Goods Inventory
Average sales days of finished goods inventory on hand in plants
and warehouses.
Average finished goods inventory value ($) /
Average daily sales $ per month
Capacity & Quality Metrics Definition Calculation
Average Warehouse
Capacity Used
The average amount of warehouse capacity used over a specific amount
of time (month to month or yearly).
Average capacity used / Average
capacity available
Peak Warehouse
Capacity Used
The amount of warehouse capacity used during designated peak seasons.
Peak capacity used / Capacity available
Honeycomb Percentage
Measures how well actual cube utilization within the warehouse is
managed. Especially important where slots may be only partially full.
An example would be if 1 unit is in a location, and it has room for 10,
the utilization for that slot/bin location is 10%.
Actual cube utilization / Total warehouse
cube positions available
Inventory Count Accuracy
(by Units/Dollars)
and/or
Inventory Count Accuracy
(Percent by Location)
Measures the accuracy (by location and units) of the physical inventory
compared to the reported inventory: If the warehouse management system
indicates that 10 units of part number XYZ are in slot B0029, the inventory
count accuracy indicates how frequently one can go to that location and
find that the physical count matches the system’s.
1 - (The sum of the absolute variance in units
or dollars / The sum of the total inventory in units
or dollars)
1 - (The sum of the number of locations containing
an error / The total number of locations counted)
Order Picking Accuracy
This measures the accuracy of the orders picking process where errors
may be caught prior to shipment such as during packaging.
Orders picked correctly / Total orders picked
Material Handling Damage
Measures the value of material damaged from handling/storage
as a percentage of COGS.
The value of material damaged from
handling/storage / COGS
Equipment/Forklift
Capacity Used
The amount of up time logged for equipment/forklifts.
Total amount of time equipment is used /
Total amount of planned available time for use
Employee Metrics
Definition
Annual Workforce Turnover
The rate at which permanent employees are replaced (excludes casual
or seasonal labor).
Number of NEW employees at the beginning
of the period / Total number of employees at the
beginning of the previous period
Productive Hours
to Total Hours
Measures employee productivity against total hours (includes all
hours—indirect and direct).
Hours charged to specific activities, tasks
or projects / Total hours worked
14
®
Watch
SPRING 2012

Perfect Order Metric Definition Calculation
Percent of Orders with
On-time Delivery
The percentage of orders that arrive at their final destination at the
agreed upon-time.
NOTE: There are many definitions of “on-time,” and that the “time” may
be a specific hour or day, or a window of time. “Agreed upon” means that
the customer and shipper have agreed to the delivery time as a general
commitment or as a part of the purchase order or contract.
Number of orders delivered on-time /
Total number of orders shipped
Shipped Complete
per Customer Order
Measures the percentage of orders which shipped completely, meaning
that all line/units ship with the order per agreement between the customer
and shipper.
Number of orders shipped with all lines and units /
Total number of orders shipped
Shipped Damage Free
(Outbound)
This measures the percentage of customer orders shipped in good and
usable condition.
NOTE: Orders damaged in transit are not considered here.
Number of orders shipped damage free /
Number of total orders shipped
Correct Documentation
(ASN, Invoice, etc.)
The percent of total orders for which the customers received an accurate
invoice and other required documents including ASNs, etc.
Number of orders with correct documentation /
Number of total orders
Cash-To-Cash Metrics Definition Calculation
Inventory Days of Supply
Average Days Payable
Average Days Sales
Outstanding
Measure of quantity of inventory-on-hand, in relation to number of days for
usage which will be covered. Total gross value of inventory at standard cost
before reserves for excess and obsolescence. Only includes inventory on
company books, future liabilities should not be included.
Measure of the length of time required to pay suppliers; key element in
cash-to-cash cycle time.
The amount of time required to convert receivables to cash. To even out
seasonality, this includes a rolling monthly average of AR (this is also
known as “average collection period”).
Current (or period ending) total inventory value /
(Total annual COGS / 365)
Average daily payables / (Total annual COGS / 365)
Average 5 month AR / (Total annual sales / 365)
Watch
SPRING 2012
15

WAREHOUSING EDUCATION AND RESEARCH COUNCIL
THE ASSOCIATION FOR LOGISTICS PROFESSIONALS
1100 Jorie Boulevard, suite 170
Oak brook, Illinois 60523-4413
Research Team
Joseph M. Tillman is a Senior Researcher at Supply Chain
Visions. Joe began his work on the study while an MBA
student at Georgia Southern University. He is a blogger for
DCVelocity.com on Young Professionals in Logistics. He has
held positions with Walmart and Union Pacific prior to joining
Supply Chain Visions.
Karl B. Manrodt, PhD, Professor, Georgia Southern University,
is also the Co-Director of the Southern Center for Logistics
and Intermodal Transportation. Recognized as a 2004
Rainmaker by DC Velocity, his research has appeared in
leading academic and practitioner journals. Dr. Manrodt is
a recognized speaker, and is one of the authors of Keeping
Score: Measuring the Business Value of Logistics in the
Supply Chain. In addition Dr. Manrodt blogs for DCVelocity.com
on metrics and benchmarking.
Kate L. Vitasek, MBA, is the founder and Managing Partner
of Supply Chain Visions, a small consulting practice that
specializes in supply chain strategy and education. Kate
has been widely published, and she currently blogs for
DCVelocity.com on Bad Warehouses. She was recognized
as a Rainmaker by DC Velocity in 2004. She is a well-known
speaker for industry groups and universities and currently
teaches seminars for WERC. n
WERCwatch findings reflect what WERC members are experiencing and predicting
within their U.S. distribution networks. There is no presumption that the findings
are representative of an entire industry sector, product category or type of firm.
WERCwatch is published by WERC, 1100 Jorie Blvd., Suite 170, Oak Brook, IL 60523-4413.
©2012, Warehousing Education and Research Council. All rights reserved.
Our Observations
Overall, it is encouraging to see an increase and continual
support of measurement as a key practice in most organizations
by senior executives. Today’s demands require organizations to
adopt a balanced approach between the hard and soft sides of
performance measurement. How we get results is as important
to supply chain effectiveness as what we achieve.
As performance measurement moves beyond operational
metrics to include interpersonal performance, companies
should emphasize both equally. As we have demonstrated,
effective interpersonal behaviors lead to stronger relationships,
which lead to better operational performance. Organizations
that fail to consider the interpersonal component eventually
find that this failure has serious consequences for their
operational performance. Because of this, the new generation
of business metrics and scorecards must reflect this thinking
if they are to capture the full picture of supply chain success.
What does the future hold Overall, we believe that the bestin-class
facilities will continue to lead the way to continued
performance, especially on the softer side. This march to
excellence will not be inexpensive nor without effort. All of the
low hanging opportunities have been identified and corrected.
The last push towards excellence will not only benefit his or
her firm, but will increase everyone’s understanding of what
is possible. n
Acknowledgments
WERC thanks the sponsoring
company who helped make
this report possible:
warehousing education and research council • 1100 Jorie blvd, ste 170, Oak Brook, IL 60523-4413
P | 630.990.0001 F | 630.990.0256 E | wercoffice@werc.org W | www.werc.org