chemical supply chain

0

Presentation Clearing the Fog Robert Peels

The Lehman WaveInvestigating what is happening in the Financial Crisis

Robert Peels, April 2010

Logichem

1


2007

2008

2009

Observations in Quarter 4, 2008Observations in Quarter 4, 2008

Sales went down very deepMedia, Banks, Cars, Stock marketsConsumer confidenceCommodity prices

Normal retail sales levels.GDP forecast -/- few %

Construction continuing

2


Hypothesis in December 2008Hypothesis in December 2008

The strong dip in demand for industrial products in Q4 is caused by de-stocking, triggered by bankruptcy Lehman brothers mid September.

If a value chain is 250 days long and de-stocking is 12%, a whole month is taken out of the chain, thus causing a 30% dip during three months.

Resin Paint Parts OEM Retail

3


CheckingChecking

• Telephone interviews of 50 value chain partners (Jan 09).

• Quarterly reports companies.

• Survey Newspaper under 500 companies (Oct 09).

• US Census info.

4


Retail vs Manufacturing (US Census Bureau)

-30

-25

-20

-15

-10

-5

0

5

10

jan

mar

may ju

l

sep

nov

jan

mar

may ju

l

Jan 2008 - July 2009

de

lta

% v

s 2

00

7

retail manufacturers

In the USA the dip for manufacturing is much deeper than for retail

Normal

paint

parts

oemEnd-market

resin-30

-20

-8

-15

A telephone survey under 50 value chain partners in January was the first confirmation that cumulative de-

stocking caused the dip

Philipswithout seasonal effect

-50%

-40%

-30%

-20%

-10%

0%

10%

20%

Q1'07 Q1'08 Q1'09(d

elt

a v

s 2

00

6)/

Q1

08

Philips sales to retail Supplier sales to Philips

Turnover development of 125 companies depends on their

distance from the end-market

(Newspaper survey)

-45

-40

-35

-30

-25

-20

-15

-10

-5

0

pre-crisis Dip Now

Ch

an

ge

wit

h p

re-c

ris

is %

1 step

2 step

2 step

4 step

5


ACTIVE

DE-STOCKING

RE-ACTIVE

RE-ACTIVE

resin

6


The “Lehman Wave”

7



8

Pre

senta

tion C

learin

g t

he F

og R

obert

Peels

































































































































































9

Presentation Clearing the Fog Robert Peels9

Retail Data US Census

80

85

90

95

100

105

110

jan-

08ap

r-08

jul-0

8ok

t-08

jan-

09ap

r-09

jul-0

9ok

t-09

Retail Shipments

Retail Inventory

Inventory/Shipments

Manufacturing Data US Census

80

85

90

95

100

105

110

115

120

125

jan-

08ap

r-08

jul-0

8ok

t-08

jan-

09ap

r-09

jul-0

9ok

t-09

Manufacturing Shipments

Manufacturing Inventory

Inventory/Shipments


Quantifying via ModelingQuantifying via Modeling

11


One of the echelons in value chainOne of the echelons in value chain

.

12


Dynamic modelingDynamic modeling

.


Input= end market curveOutput = sales curves

13


Demand according to echelon

105

100

95

90

0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300

Time (Week)

per

cen

tag

e sa

les

vs

20

07

DSM demand

Paint producer demand

Coater demand

OEM demand

End market demand

14


Demand according to echelon

105

70

35

0

0 15 30 45 60 75 90 105 120 135 150 165 180 195 210 225 240 255 270 285 300

Time (Week)

per

centa

ge

sale

s vs

2007

DSM demand

Paint producer demand

Coater demand

OEM demand

End market demand

15


Sales and Stocks

60,00

70,00

80,00

90,00

100,00

110,00

120,00

jan-07 jun-07 nov-07 apr-08 sep-08 feb-09 jul-09 dec-09 mei-10 sep-10 feb-11 jul-11

langzaam dalende consumentenvraag Stocks in 4e echelon

Sales in 4e echelon Total Stocks

End market demand

Sales in 4th echelon

Stocks in 4th echelon

Total Stocks

16


Automobile indexes (2007=100%)

80,00%

85,00%

90,00%

95,00%

100,00%

105,00%

110,00%

jan-0

5apr-0

5ju

l-05

okt-0

5ja

n-06

apr-0

6ju

l-06

okt-0

6ja

n-07

apr-07

jul-0

7ok

t-07

jan-

08apr-0

8ju

l-08

okt-0

8ja

n-09

apr-0

9ju

l-09

okt-0

9

Month

Ind

ex

New car registration Motor vehicle turnover index

Household goods retail trade index (2007=100%)

84,00%

86,00%

88,00%

90,00%

92,00%

94,00%

96,00%

98,00%

100,00%

102,00%

104,00%

jan-

05ap

r-05

jul-0

5ok

t-05

jan-

06ap

r-06

jul-0

6ok

t-06

jan-

07ap

r-07

jul-0

7ok

t-07

jan-

08ap

r-08

jul-0

8ok

t-08

jan-

09ap

r-09

jul-0

9

Month

Ind

ex

Retail food turnover index (2007=100%)

90,00%

95,00%

100,00%

105,00%

110,00%

jan-05

ap r-05

jul-05

okt-05

jan-06

apr-06

jul-06

okt-06

jan-07

apr-07

jul-07

okt-07

jan-08

ap r-08

jul-08

okt-08

jan-09

apr-09

jul-09

Month

Ind

ex

Production Index US (2005=100%)

40,00

50,00

60,00

70,00

80,00

90,00

100,00

110,00

120,00ja n -0 5ap r-0

5ju l-0

5ok t -0

5ja n -0 6apr -0

6ju l-0

6o k t-0

6ja n -0 7ap r-0

7ju l-0

7ok t-0

7ja n -0 8apr -0

8ju l-0

8ok t -0

8ja n -0 9apr -0

9ju l-0

9o k t-0

9

Month

Ind

ex

Food

Paper

Glass

Automotive

Metal

Construction

17


Segment 4, volume/month(3 March 2010)

1500

2000

2500

3000

3500

40000

10

20

30

40

50

60

70

80

91

01

11

20

10

20

30

40

50

60

70

80

91

01

11

20

10

20

30

40

50

60

70

80

91

01

11

20

10

20

30

40

50

60

70

80

91

01

11

20

10

2

2006 2007 2008 2009 2010

100%

50%

Segment 4, volume/month(3 March 2010)

1500

2000

2500

3000

3500

40000

10

20

30

40

50

60

70

80

91

01

11

20

10

20

30

40

50

60

70

80

91

01

11

20

10

20

30

40

50

60

70

80

91

01

11

20

10

20

30

40

50

60

70

80

91

01

11

20

10

2

2006 2007 2008 2009 2010

100%

50%

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Segment 4, seasonality corrected

1500

2000

2500

3000

3500

4000

01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02

2006 2007 2008 2009 2010

100%

50%

Segment 4, seasonality corrected

1500

2000

2500

3000

3500

4000

01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02

2006 2007 2008 2009 2010

100%

50%

19


Segment 4, without seasonality

2006 2007 2008 2009 2010 2011

Seas Corr Model 3mma & seas corr

MS gain

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Segment 4, with seasonality(2 April 2010)

01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10 11 12 01 02 03 04 05 06 07 08 09 10

2006 2007 2008 2009 2010 2011

Model seasoned Actual

50%

100%

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Sales Forecasting

HistoricCustomer info

Model Pipeline

S&OPStrategy

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DoDo’’s and Dons and Don’’ts for companies in a Lehman Wavets for companies in a Lehman Wave

Don’t:

• Overreact

• Close factories

• Cut innovation

• Fire crucial people

• Stop marketing communication

Do:

• Flexibilize

• Communicate over the chain

• Communicate internally

• Make plans based on scenarios

• Cut cost

• Continue M&A

23


ConclusionsConclusions

Macro A lot is to be learnt and understood about these phenomena at the macro-economic level; Little research is available; Poor quality or absence of inventory statistics; Governments should collect and publish detailed stock data. Economic planning systems should take stocks into account.

Stock Moves can have a strong influence on world economy. They help explain why some industries have wider fluctuations in the economic cycle. At echelon 4 de-stocking can be >75% of the dip. For Europe we estimate the Lehman Wave effect to be at least 1,5% of GDP in 2009.

Markets Each company should know its supply chain and end markets. It should follow downstream branch organizations and statistical market info.

Model can help distinguish between real market growth and stock piling. It can give an early warning if market share changes. The model predicts a second, more shallow dip in 2010. The timing of peaks and dips was predicted very accurate. It can help companies translate end-market growth and stock changes into sales forecasts.

24


Thank you

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Education

oem d elta

resinpaintparts oem

normal retail sales

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value chain partners

strong dip

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