2013 AASA Pulse: Supplier KPI Benchmarks AASA Pulse: Supplier KPI Benchmarks 2013 1

2013 AASA Pulse: Supplier KPI Benchmarks

AASA Pulse:Supplier KPI Benchmarks

2013

1

2013 AASA Pulse: Supplier KPI Benchmarks 2

Contents

Page

Executive Summary 3

How to Use this Study 8

Returns 11

Fill Rates 18

Financial 34

Operations 44

Marketing and Forecasting 51

Results Segmented by Company Size 61

Results Segmented by Product Category 71

Appendix A: Profile of Respondents and Full Responses to Selected Question

81

Appendix B: Methodology 87


Executive Summary


Executive Summary (1/4)

Metric Key Findings Page

Returns

85% of respondents indicate that 2.5% or less of warranty returns are related to actual quality issues

14

Stock adjustment rose again to worrisome levels, raising concern about this practice across the industry

15

Fill Rates

Differentiation through high fill rates is becoming more difficult as 74% of respondents had 94% or better fill rate by unit volume

19

75% had order turnaround of three days or less 25

Although on average only a small amount of business is done through vendor direct programs, this activity has been increasing steadily for the past 3 years; in addition, 26% of respondents had 10% or more of their sales through vendor direction

26-27


Executive Summary (2/4)

Metric Key Findings Page

Financial Price changes decreased, though gross margin mean and median were able to hold steady

35, 41

e-TailingRespondents estimate that on average 3.4% of their sales ultimately are sold by channel partners through e-tailing

31

Payment Terms

Payment terms with retailers averaged 177 days for respondents in 2012, with 24% at average terms of 291 – 360 days

36

Payment terms with WDs/Distributors average was much less than retailers, averaging 86 days with 2/3 of respondents reporting terms between 46 to 90 days

37


Executive Summary (3/4) Metric Key Findings Page

Operations

The percent of sales manufactured in-house increased slightly to an average of 63.6% in 2012, though there are widely divergent models on this point

45

Manufacturing in low cost countries averaged 36% for 2012, up 10 percentage points from 2011

47

Average days on hand of inventory reported by respondents was 63 days

48

Marketing

Overall marketing spend as a percent of revenue averaged 3% for 2012

52

Customer specific marketing spend on average was typically the highest area of marketing spend

53

Forecasting

90% of respondents reported access to POS and forecasting data from channel partners; however only 34% use it “Always” or “Frequently”

57

For most of respondents POS data is very useful; however, a subset finds limited value in the data

59-60


Executive Summary (4/4) Metric Key Findings Page

Results Segmented

by Company

Size

For larger firms there is a more significant portion of their business done via vendor direct

65

Smaller sized firms have played the largest part in the e-tailing space

66

Larger firms experienced the greatest price increase from 2011

67

Results Segmented by Product Category

Chemicals had the highest fill rates. The other product segments were slightly below Chemicals, but still all roughly achieved the target 95% fill rate.

74

Respondents estimate that e-tailing penetration is the highest in the under car product segment. Companies active in under car also participate the most in the vendor direct programs.

76

Chemicals sell the most directly through e-tailing 76

Engine and General maintenance have the longest payment terms

78


How to Use this Study


How to Use this StudyResults can be an invaluable resource in your business planning

What is the AASA Pulse?

• A benchmarking survey conducted on an annual basis

• Available exclusively to AASA members• Covers such key benchmarks as returns,

payment terms, order metrics and key financial metrics

• Developed with the assistance and input of the AASA Marketing Executives Council (MEC)

• Analysis specifically targeted at aftermarket manufacturers. It is the only research of its kind available

• The AASA Pulse is absolutely anonymous and answers are kept strictly confidential

• Detailed results are shared only with survey participants

What to use the data for:

• Use the AASA Pulse as a vital tool to assist you in your business and strategic planning

– Allows you to benchmark against industry standards and best practices

• The data can help you determine what you should target for improvement

• And help identify where you are a benchmark

– And therefore allow you to focus your improvement on other areas and metrics

• Industry benchmarks to help in discussions with your Board and investors


Understanding Box PlotsBox plots are used throughout the report; please use the following chart to reach a better

understanding of how to read and interpret a box plot

Range of responses

Average or mean of responses

The line between the blue boxes is the median or the midpoint of all the responses. 0%

2%

4%

6%

8%

10%

12%

14%

16%

18%

20%

Lower Quartile Upper Quartile MeanSource: 2013 AASA Pulse: KPI Benchmarks Survey

25%50%

75%

100% of responses

4th Quartile

3rd Quartile or Upper Quartile

2nd Quartile or Lower Quartile

1st Quartile


Returns


Warranty Returns (1/2)While warranty made up 1% or less of sales for 47% of respondents , 38% had 1-5% of

sales as warranty and 15% had greater than 5%

47%

38%

15%


Warranty Returns (2/2)While warranty returns averaged 2.6% of sales, the range of responses was very large

Based on this feedback, the

aftermarket has roughly $3.5

billion dollars of parts warranty returns a year.

Note: at retail sales level

Source: AASA/AAIA Joint Channel Forecast Model


Warranty Returns and Quality Issues85% of respondents indicate that 2.5% or less of returns are related to actual quality

issues

If the industry could reduce warranty returns by half, the

industry could save ~$1.8 billion which would go straight to the bottom line of suppliers and

channel partners. If the industry could reduce returns just to

quality issues the industry would have $3.4 billion in addition profit.

Source: AASA/AAIA Joint Channel Forecast Model


Stock and Obsolescence Returns (1/2)Stock and obsolescence returns clustered around 2-3%, with a wide range of results


Stock and Obsolescence Returns (2/2)Stock adjustment rose in 2012, raising concern about this practice across the industry

In an era of “big data”, advanced analytics and supply chain

integration, should the industry be

experiencing this level of annual stock and

obsolescence returns?

Note: ~1 out of 10 respondents had >5% returns in this category


Discrepancy ReturnsAverage discrepancy return continues to decrease, with 56% experiencing 0.05% or less

discrepancy returns

YearAverage

Discrepancy Return

2009 0.53%

2010 0.31%

2011 0.29%

2012 0.24%


Fill Rates


Fill Rates by Unit Volume (1/2)Differentiation through high fill rates is becoming more difficult as 74% of respondents

had 94% or better fill rate by unit volume


Fill Rates by Unit Volume (2/2)Fill rate by unit volume continues to increase with both the mean and median of

respondents falling above the desired target of 95%


Fill Rate by Dollar Value (1/2)Fill rate by dollar value averaged 95.4% for respondents with 25% falling between 96.1%

- 98%

Mean = 95.4%


Fill Rate by Dollar Value (2/2)Average fill rate by dollar value continue to increase from the low seen in 2010; the 95%

fill rate goal in the industry is increasingly becoming standard


Fill Rate by Line Item (1/2)Fill rate by line item averaged 94.9%, slightly below the typical 95% goal in the

automotive aftermarket

Mean = 94.9%


Fill Rate by Line Item (2/2)Fill rate by line item also increased in 2012 capping a three year upward trend


Order TurnaroundAverage order turnaround for respondents averaged slightly above 3 days, though 75%

had order turnaround of three days or less

Year

Average Order

Turnaround (in days)

2009 5.7

2010 2.0

2011 2.8

2012 3.2

75%


Vendor Direct (1/2)Business models diverge: while 26% sell more than 10% through vendor direct, 39%

report business through vendor direct programs is less than 1%

26%

39%


Vendor Direct (2/2)Although on average only a small amount of business is done through vendor direct

programs, this involvement has been increasing steadily for the past 3 years


Overnight Shipping (1/2)The majority of respondents (59%) indicate that less than 1% of their business was done

with overnight shipping, though the range of responses is wide


Overnight Shipping (2/2)Surprisingly, median use of direct shipping has been declining though the average remains

fairly high (2.5% of business) due to outliers who utilize a lot of overnight shipping


e-Tailing Through Direct SaleMajority of respondents (76%) sell virtually nothing directly in the e-tailing channel, but

that means 26% are selling direct online 1% or more of sales

10% sell 3% or more through direct e-tailing, indicating that the direct e-

commerce sales model is being explored by some suppliers


e-Tailing by Channel PartnersRespondents estimate that on average 3.4% of their sales ultimately are sold through

e-tailing

Mean = 3.4%


Drop Shipments and Cross Docking (1/2)Business requiring drop shipments/cross docking averaged 8.5% for 2012

Mean = 8.5%


Drop Shipments and Cross Docking (2/2)In contrast to the previous three year decline, there was a significant spike in business

done by drop shipments or cross docking in 2012

While median remains lower than 2009-2010, average

has increased as drop ship/cross

docking increased significantly among

a subset of suppliers


Financial


PriceAftermarket price changes declined in 2012 versus 2011, averaging +1.8%; 56% of

respondents increased prices while 18% saw declines

YearAverage

Aftermarket Price Change

2011 +2.4%

2012 +1.8%

56%

18%


Terms with RetailersPayment terms with retailers averaged 177 days for respondents in 2012; 24% had

average terms of 291 – 360 days

Mean = 177


Terms with WDs/DistributorsPayment terms with WDs/Distributors were much less than retailers, averaging 86 days

with 2/3 of respondents reporting terms between 46 to 90 days

Mean = 86


SG&A expenses in 2012General selling and sales administration (SG&A) expenses as a percent of aftermarket

sales averaged 12% across respondents

Mean = 12.0%


SG&A expenses over timeAverage SG&A has crept up over the last 3 years, though the approximate quartile range

of 6% to 15% has remained steady


Gross Margin in 2012Weighted average gross margin across all respondents was 32.6%; the range was large

with 26% at 25% or lower GM and 29% above 40% GM

Mean = 32.6%

29%

26%


Gross Margin over timeWeighted gross margin quartile range expanded and increased in 2012; mean increased

slightly (31.7% to 32.6%) although the median remained on par with 2011


Aftermarket Research and Development Spending in 2012Respondent’s companies’ estimated average spend on R&D was 3% for 2012, although

over a fifth spend less than 1%

Mean = 3%


Aftermarket Research and Development Spending over TimeR&D spend continues to increase for aftermarket suppliers


Operations


In-House ManufacturingThe percent of sales manufactured in-house increased to an average of 63.6% in 2012;

nearly half of respondents manufacture more than 80% of their goods in-house

YearAverage

Percent of In-House Sales

2011 55.1%

2012 63.6%


Manufacturing in the U.S.On average, respondents reported that 47% of aftermarket sales were manufactured in

the US, about the same level as the previous year

Year

Average Percent of Sales

Manufactured in US

2011 48%

2012 47%

Will the reshoring US “manufacturing renaissance” be seen in the future in the aftermarket?


Manufacturing in Low Cost CountriesManufacturing in low cost countries averaged 36% for 2012, up 10 percentage

points from 2011

Year

Average Percent of Sales

Manufactured in Low Cost Countries

2011 26%

2012 36%


Inventory Days on HandAverage days on hand of inventory reported by respondents was 63 days, with nearly a

third reporting “Greater than 90 days”

Mean = 63 days


Outbound Freight Costs3.1 – 5% was the most common range for freight costs as a percent of SG&A


Inventory CostInventory costs averaged 19% of cost of goods sold; inventory was 20% or more of

COGS for 35% of respondents

Mean = 19%

35%


Marketing and Forecasting


Marketing SpendOverall marketing spend as a percent of revenue averaged 3% for 2012, though 62%

spent 3% or less

Mean = 3%

62%


Marketing Spend – Customer SpecificCustomer specific marketing spend on average was the highest area of marketing

spend, with nearly 30% of the overall marketing budget

Mean = 30%


Marketing Spend – AdvertisingOver half of respondents reported marketing spend for advertising comprised less than

20% of their overall marketing budget; mean ad spending was 21%

Mean = 21%

57%


Marketing Spend – Trade FairsMarketing spend for trade fairs averaged 21% of the overall budget for respondents

Mean = 21%


Marketing Spend – OtherWhile the previous three categories comprise most marketing spend, 32% of

respondents spend 20% or greater of their marketing budget on other promotional tools

Mean = 19%

Note: Means of the four categories do not add up to 100 percent as not all respondents added up to 100 percent.

32%


POS and Forecasting Data90% of respondents reported access to POS and forecasting data from channel

partners; however only 34% use it “Always” or “Frequently”

Are suppliers fully leveraging the

data available to them?


Value of POS data (1/3)Nearly two thirds of respondents report that the POS data is “Useful” or “Very useful” in

improving their inventory levels and line fill


Use of POS data (2/3)For most respondents, POS data is very useful; data is used as a benchmark for

performance and forecasts guide suppliers in projecting growth

“Guiding light in terms of true product performance in store… We leverage this in combination of our outbound shipments to truly

understand demand so we can be responsive to customer needs as well as proactive if we see issues with our key accounts

who provide this.”

“Helps determine potential demand activity for upcoming periods. Provides a picture of the "health" of the market when

consolidated with other customers' performance. Allows collaboration with customers on determining market strategies

in given markets.”“To understand trends and to understand forecasting. To help

plan for advertising budget.”

Throughout your organization, how are you using POS/forecasting data provided by customers?

For full results, see Appendix

“Inventory modeling - we just started it, but it is very valuable.”


Use of POS data (3/3)However, a subset of respondents finds limited value in POS data

“Do not use.”

“Limited use”

“Rarely used”

Throughout your organization, how are you using POS/forecasting data provided by customers?

For full results, see Appendix

“The forecast error is approximately 50% and this applies to our customers as well. (Therefore ½ of what was made does not sell through

for that period, simultaneously ½ of the actual demand is made in an expedite mode or via the good fortune of having raw based on the

“minimum buy quantities” required by the supply-chain.”

“Unfortunately, we do not have a lot of additional information provided to us at this time.”

“We cannot as it is terribly inaccurate.”


Results Segmented by Company Size


Methodology and How to Use

Methodology

• To obtain further insight into the data, we segmented results for select questions by the aftermarket revenue size of the respondent.

• We grouped respondents into three size segments:

• Note: Respondents who did not answer this question were not included in the company size segmentation

Annual US Aftermarket Revenue

Number of Respondents

<=$150 million 25

$151-$500 million 20

>$500 million 11

How to Use

1. This analysis can provide insight on advantages/disadvantages of size for different metrics.

– In other words, are smaller or larger companies more efficient or effective on a given metric?

2. This analysis can also be used to benchmark your company against your closer peers in terms of relative size.

– The issues and performance of smaller and larger companies can vary considerably; this analysis helps you understand better how your performance benchmarks against enterprises that are similar to you in scale.


Returns by Company SizeRespondent Revenue Size

Metric <=$150 M $151-500M >$500M

Warranty Returns

Lower Quartile 0% 0.5% 0.5%

Median 0.5% 1.8% 1.1%

Upper Quartile 3.5% 4.5% 3.1%

Mean (Avg.) 1.9% 3.4% 2.5%

Quality Issues as a percent of Warranty Returns

Lower Quartile 1.3% 1.3% 1.3%

Median 1.3% 1.3% 1.3%


Mean (Avg.) 2.6% 3.1% 1.3%

Stock Adjustment and Obsolescence Returns


Median 1.5% 2.5% 1.5%


Mean (Avg.) 2.4% 2.5% 2.4%

Mid-size firms had the largest

warranty returns

Majority of respondents,

despite the size of the company,

indicated a small amount of actual

quality issues contributing to

warranty returns

Stock adjustments were relatively the

same across company size


Fill Rates by Company SizeRespondent Revenue Size

Metric <=$150 M $151-500M >$500M

Fill rate as a % of unit volume

Lower Quartile 95% 95% 95%

Median 95% 95% 95%

Upper Quartile 97% 97% 97%

Mean (Avg.) 95.4% 95.4% 95.3%

Fill rate as a % of dollar value


Median 97% 96% 95%

Upper Quartile 98.5% 97% 98.5%

Mean (Avg.) 96% 95.4% 94.7%

Fill rate as a % of line items filled


Median 95% 95% 93%

Upper Quartile 98.5% 97.4% 95%

Mean (Avg.) 95.6% 94.6% 93.8%

Fill rate as a % of unit volume was

consistent across firm size

Fill rate as a % of dollar value was

highest for smaller firms

Fill rate as a % of line items filled was lowest with

larger firms


Shipping Metrics by Company SizeRespondent Revenue Size

Metric <=$150 M $151-500M >$500M

Order turnaround (days)

Lower Quartile 1.8 1.8 2

Median 2 2 2

Upper Quartile 4 4 3

Mean (Avg.) 2.7 2.7 2.5

Vendor direct (% of business)


Median 1.5% 4% 8.8%

Upper Quartile 3% 11.3% 13.8%

Mean (Avg.) 8.1% 8.2% 10.3%

Overnight shipping (% of business)


Median 0.2% 1% 1%


Mean (Avg.) 2.1% 3.6% 1%

Order turnaround for larger firms is

slightly lower

For larger firms there is a more

significant portion of their business done

via vendor direct

Medium sized firms have the most overnight shipping as a

percent of their business


e-Tailing by Company SizeRespondent Revenue Size

Metric <=$150 M $151-500M >$500M

e-Tailing through Direct Sales


Median 0.2% 0.2% 0.5%


Mean (Avg.) 2.8% 0.2% 0.7%

e-Tailing through Channel Partners


Median 1.2% 2.2% 2.2%

Upper Quartile 5.3% 4% 3.5%

Mean (Avg.) 3.5% 3.8% 2.6%

Smaller sized firms have sold

the most directly in the e-tailing

space


Financial Metrics by Company Size (1/2)Respondent Revenue Size

Metric <=$150 M $151-500M >$500M

Gross Margin (Weight Average, Aftermarket)

Lower Quartile 31.5% 24% 24.3%

Median 34% 33% 27.5%


Mean (Avg.) 34.4% 32.2% 29.8%

Price Change Year-over-Year

Lower Quartile No change -0.4% +0.5%

Median +0.5% +0.5% +1.5%

Upper Quartile +2.3% +1.9% +1.5%

Mean (Avg.) +0.9% +0.4% +1.1%

R&D (Aftermarket, % Sales)


Median 2.5% 2.3% 1.8%


Mean (Avg.) 4% 2.3% 1.7%

Smaller firms had the highest gross

margins

Larger firms experienced the greatest price increase from

2011

Smaller firms spend the most on R&D as percent of

sales


Financial Metrics by Company Size (2/2)Respondent Revenue Size

Metric <=$150 M $151-500M >$500M

SG&A (percent of aftermarket sales)

Lower Quartile 4.5% 7% 8%

Median 5.5% 11% 10%

Upper Quartile 11.5% 15% 18%

Mean (Avg.) 9.8% 12.1% 12.7%

Payment Terms to Retailers (in days)

Lower Quartile 68 158 68

Median 113 258 258

Upper Quartile 158 326 258

Mean (Avg.) 133.7 231.3 183.6

Inventory Turns

Lower Quartile 3.5 3.7 3.5

Median 9.5 5.5 4

Upper Quartile 11 11 5.5

Mean (Avg.) 7.7 6.9 5.5

Smaller firms had the lowest SG&A

Smaller firms had the smallest

payment terms to retailers with large and medium firms having a median response of 258

days

Smaller firms had the largest amount of inventory turns


Manufacturing Strategies by Company SizeRespondent Revenue Size

Metric <=$150 M $151-500M >$500M

% of Sales Manufactured In-House


Median 65% 75% 85%


Mean (Avg.) 57.6% 68.6% 66.7%

% of Sales Manufactured in US


Median 40% 45% 55%


Mean (Avg.) 47.4% 43.2% 57.5%

% of Sales Manufactured in Low Cost Countries

Lower Quartile 2.5% 11.3% 15%

Median 25% 35% 25%


Mean (Avg.) 34.6% 35% 34.4%

Larger and medium sized

firms manufacture nearly 2/3 of sales

in-house on average

Larger firms manufacture in the

US more than small and medium

firms

Medium firms manufacture in

low cost countries slightly more than

others


Marketing Spend by Company SizeRespondent Revenue Size

Metric <=$150 M $151-500M >$500M

Marketing Spend – Advertising (% of overall marketing spend)


Median 15% 25% 25%


Mean (Avg.) 18.5% 26.9% 21.7%

Marketing Spend – Trade Fairs (% of overall marketing spend)


Median 20% 15% 5%

Upper Quartile 27.5% 30% 25%

Mean (Avg.) 24.2% 19% 18.3%

Marketing Spend – Customer Specific (% of overall marketing spend)


Median 25% 25% 25%


Mean (Avg.) 28.5% 30.3% 28.8%

Medium firms spend the most on

advertising as a marketing tool

Smaller firms tend to spend the most of their marketing budget on trade

fairs

Customer specific spending is the

same regardless of firm size


Results Segmented by Product Category


Methodology

• To obtain further insight into the data, we segmented results for select questions by company product category.

• Respondents provided the key product categories they were active in:

• Respondents were able to choose multiple product categories. Their responses were included in each applicable category. Therefore the number of responses in the sum of the categories noted above will exceed the total number of survey responses.

Methodology and How to Use

Annual US Aftermarket Revenue

Number of Respondents

Engine 29

Under Car 21

General Maintenance Parts 20

Chemicals / Paint / Lubricant 8

How to Use

1. This analysis can provide insight on strength / weakness of different product categories.

– In other words, are different product segments more efficient or effective on a given metric?

2. This analysis can also be used to benchmark your company against your closer peers in terms of product segment.

– The issues and performance of companies in different product segments can vary considerably; this analysis helps you understand better how your performance benchmarks against enterprises that are roughly similar to you in product category.


Returns by Product SegmentRespondent by Product Segment

Metric Engine Under Car General Maintenance Parts

Chemicals / Paint / Lubricant

Warranty Returns

Lower Quartile 0.5% 0.5% 0.5% -

Median 2.5% 1.8% 1.8% 0%

Upper Quartile 3.5% 3.8% 2.5% -

Mean (Avg.) 2.7% 2.6% 2.9% 1%

Quality Issues as Percent of Warranty Returns


Median 1.3% 1.3% 1.3% 1.3%


Mean (Avg.) 2.2% 4% 3.5% 1.8%

Stock Adjustment and Obsolescence Returns


Median 2.5% 2.5% 2.5% 0.1%

Upper Quartile 4.5% 3.5% 4% -

Mean (Avg.) 3.1% 2.7% 3% 1%

Chemicals had the lowest median of warranty returns;

engine the highest

Amount of quality issues was the

same regardless of product segment

Chemicals had the lowest amount of stock adjustments for 2012; returns were similar for other categories

Note: Chemicals product category does not include the lower or upper quartile to ensure confidentiality of those respondents.


Fill Rates by Product Segment

Chemicals had the highest fill rate for unit volume, dollar

and line items filled. The other

product segments were slightly

below Chemicals, but still all roughly

achieved the target 95% fill

rate.

Respondent by Product Segment



Fill rate as a % of unit volume

Lower Quartile 93.5% 92.5% 93% -

Median 95% 95% 95% 97%

Upper Quartile 97% 97.3% 97% -

Mean (Avg.) 94.6% 94.7% 94.8% 96.5%

Fill rate as a % of dollar value

Lower Quartile 93% 91% 93% -

Median 95% 95% 95% 98.2%

Upper Quartile 97% 97.3% 97% -

Mean (Avg.) 94.7% 94.2% 94.4% 97.7%

Fill rate as a % of line items filled

Lower Quartile 93% 92.5% 93% -

Median 95% 94% 95% 98.3%

Upper Quartile 97% 95.5% 97.9% -

Mean (Avg.) 94.3% 93.8% 94.6% 95.9%



Shipping Metrics by Product Segment

Order turnaround is lowest for engine and

general maintenance

parts; highest for Chemicals

Under car participates the most in vendor direct programs

All product segments have

very little business done via overnight

shipping




Order turnaround (days)

Lower Quartile 2 1.8 2 -

Median 2 2.5 2 3

Upper Quartile 3 3.3 3 -

Mean (Avg.) 2.6 3 2.4 3.1

Vendor direct (% of business)


Median 2.5% 5% 1% 3%

Upper Quartile 12.5% 12.5% 11.4% -

Mean (Avg.) 8% 9.2% 6.3% 26.9%

Overnight shipping (% of business)


Median 1.2% 0.7% 1% 0.2%


Mean (Avg.) 4.5% 2.3% 5% 0.3%



e-Tailing by Product Segment

Chemicals sell the most direct

through e-tailing

The under car product segment

estimates e-tailing has the highest

share of segment sales.




e-Tailing through Direct Sales


Median 0.2% 0.2% 0.2% 1.2%


Mean (Avg.) 1.6% 1.8% 2.2% 1.2%

e-Tailing through Channel Partners


Median 2.2% 3.5% 2.5% 1.2%

Upper Quartile 3.5% 6% 4.3% -

Mean (Avg.) 4% 4.6% 4.2% 2.1%



Financial Metrics by Product Segment

General Maintenance

spends the most on aftermarket

R&D as a percent of sales

Under Car has a slightly higher median SG&A

compared to other segments




R&D (Aftermarket, % Sales)


Median 2% 1.8% 2.3% 1.8%


Mean (Avg.) 3.2% 2.4% 3% 2.6%

SG&A (percent of aftermarket sales)

Lower Quartile 5.5% 7% 5.5% -

Median 9.5% 10.8% 9.5% 9.5%

Upper Quartile 15% 23% 17.3% -

Mean (Avg.) 11.8% 14% 12.9% 15.9%



Payment Terms by Product Segment

Engine and General Service have the longest payment terms of

the product segments to

retailers

Payment terms to WDs/Distributors

has the same median across

segments, though engine has some

outliers




Payment Terms to Retailers (in days)

Lower Quartile 158 68 113 -

Median 258 158 258 113

Upper Quartile 326 258 326 -

Mean (Avg.) 221.7 161.7 201.8 149.9

Payment Terms to WDs/Distributors (in days)

Lower Quartile 68 68 68 -

Median 68 68 68 68

Upper Quartile 113 68 68 -

Mean (Avg.) 95.3 81.5 78.5 75.5



Manufacturing Strategies by Product Segment

General Maintenance and

Chemicals manufacture the most in-house at median, though the story is the opposite at the mean; some

players in these segments have high levels of outsourcing

Chemicals manufacture

significantly more in the US than other segments

More than a third of parts (non-Chemicals)

manufacturing is in low cost countries




% of Sales Manufactured In-House


Median 65% 65% 75% 75%

Upper Quartile 85% 85% 85% -

Mean (Avg.) 62.3% 56.8% 52.6% 49.6%

% of Sales Manufactured in US


Median 35% 25% 45% 95%


Mean (Avg.) 44.2% 41.1% 43.4% 83.6%

% of Sales Manufactured in Low Cost Countries


Median 35% 35% 35% 0.3%


Mean (Avg.) 37.6% 33% 35% 11.9%



Marketing Spend by Product Segment

Chemicals spend the most of their

advertising budget on advertising

(many players in this segment have branded products)

Spend on trade fairs is roughly the

same across product segments

Chemicals and maintenance parts

spend more of their marketing

spend on customer specific

initiatives




Marketing Spend – Advertising (% of overall marketing spend)


Median 15% 15% 10% 45%


Mean (Avg.) 20.2% 20.6% 15% 28.6%

Marketing Spend – Trade Fairs (% of overall marketing spend)


Median 15% 15% 15% 15%


Mean (Avg.) 21.8% 17.9% 15.6% 16.4%

Marketing Spend – Customer Specific (% of overall marketing spend)


Median 25% 25% 35% 45%


Mean (Avg.) 26.4% 32.1% 37.5% 42.1%



Appendix A

Profile of Respondents

Full Responses to Selected Question


Size of Respondents


Number of Employees of Respondents


Product Segments of Respondents

Note: Total does not add up to 100% due to respondents ability to select multiple categories


Throughout your organization, how are you using POS/forecasting data provided by customers? (1/2)

“Compare POS data to trends”“Comparing customer generated forecasts to our own internal forecasts”“Component orders”“Demand planning, assortment recommendations, SKU sales and return trends”“Do not use.”“Forecasting. Inventory levels.”“Guiding light in terms of true product performance in store… We leverage this in combination of our outbound shipments to truly understand demand so we can be responsive to customer needs as well as proactive if we see issues with our key accounts who provide this.”“Helps determine potential demand activity for upcoming periods. Provides a picture of the "health" of the market when consolidated with other customers' performance. Allows collaboration with customers on determining market strategies in given markets.”“Identify specific regional / customer trends for new numbers.”“In our sales and operational planning process.”“Input into internal forecast systems”“Inventory modeling - we just started it, but it is very valuable.”

“Limited to sales forecast. Not used consistently for supply chain activities.”“Limited use”“Logistics team is using POS data for one major customer to track new part sales at store level to adjust SKU level forecast and planning to improve fill rates an reduce inventory.”“Mainly used for SKUs on the front end of the sales trend to monitor the ramp up in sales, as well as large changes in trend with mature or declining SKUs.”“Our customers forecasts are vital to our production scheduling and inventory planning to insure high order fill and control inventory costs.”“POS data very useful. Customer forecasts not very accurate, used on exception basis. We review forecasts with the customer when there are large discrepancies to our internal forecast.”“Purchasing / Forecasting; Product Management; Customer Category Management”“Rarely used”“Sales & Operations budgets, PC&L, Supply chain”“Sales forecasting”


Throughout your organization, how are you using POS/forecasting data provided by customers? (2/2)

“The forecast error is approximately 50% and this applies to our customers as well. (Therefore ½ of what was made does not sell through for that period, simultaneously ½ of the actual demand is made in an expedite mode or via the good fortune of having raw based on the “minimum buy quantities” required by the supply-chain.”“To confirm internal information”“To determine inventory levels”“To improve our fill-rate”“To optimize inventory and order fill”“To order long lead time items and establish inventory levels”“To understand trends and to understand forecasting. To help plan for advertising budget.”“Unfortunately, we do not have a lot of additional information provided to us at this time.”“Use the data to bounce up against current forecast by customer to look for flyers/numbers that are trending significantly upward or downward.”“Used in product rationalization and forecasting”“Used in SIOP process.”

“Using both historical POS with running POS provides much more accurate forecasting capabilities. Comparing both also helps to identify growing and declining trends of specific parts or categories.”“Using it to predict future sales trends; assessing individual customers' share gains/losses.”“We cannot as it is terribly inaccurate.”“We have a dedicated analyst that uses the information to coordinate inventory plans.”“We try to understand customer forecast values and compare to our historic and forecast data to optimize materials management”


Appendix B

Methodology


AASA Pulse methodology notes

• The AASA Supplier Pulse report is an annual benchmarking survey covering key automotive aftermarket supplier performance indicators (KPIs).

• The purpose of the Pulse is to provide members with general information on high-level benchmarking of sector performance. The information and opinions contained in this report are for general information purposes only.

• Participation is only available to AASA supplier members. Full reports are provided only to survey participants and AASA Board members.

• Surveys are sent to senior executives and member representatives at all AASA members. There were 60 responses to this year’s survey.

• The survey covers 2012 data (calendar or fiscal year, depending on respondent). Answers were provided by respondents based on actual data if available or best estimates. Most questions were multiple choice, so numbers used in the report represent the midpoint of a selected multiple choice answer.

• The survey is anonymous and answers are kept strictly confidential. Therefore, only aggregated results will be reported and individual responses will not be released and will be destroyed after results are compiled.


Contact Information

AASA | Automotive Aftermarket Suppliers Association

10 Laboratory Drive | Research Triangle Park | NC | 27709 | USA

www.aftermarketsuppliers.org

Paul McCarthyVice President

Industry Analysis, Planning and Member ServicesOffice: +1 919.406.8812 | Mobile: +1 248.914.2567

Email: [email protected]

Bailey L. W. OvermanAnalyst/Coordinator

Industry Analysis and Member ServicesOffice: +1 919.406.8823

Email: [email protected]

Documents

2013 AASA Pulse: Supplier KPI Benchmarks AASA Pulse: Supplier KPI Benchmarks 2013 1