Chemical Supply Chain

0

The Lehman Wave
Investigating what is happening in the Financial Crisis

Robert Peels, April 2010
Logichem

Presentation Clearing the Fog Robert Peels

Observations in Quarter 4, 2008 1

Normal retail sales levels.
GDP forecast -/- few %
Construction continuing
2007

2008

2009
Sales went down very deep
Media, Banks, Cars, Stock
markets
Consumer confidence
Commodity prices


Hypothesis in December 2008 2

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

Resin Paint Parts OEM Retail
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.


Checking 3

• Telephone interviews of 50 value chain partners (Jan 09).
• Quarterly reports companies.
• Survey Newspaper under 500 companies (Oct 09).
• US Census info.


Retail vs Manufacturing
(US Census Bureau)
Normal 10 4
5
End-market 0
-8 oem

d elta% vs 2007
-5

v
ju l

ju l
ja n

ja n
p
ar

ar
ay

ay
no
se
m

m
m

m
-15 parts -10
-15
-20 paint
-20
-30 resin -25
-30
Jan 2008 - July 2009
retail manufacturers

A telephone survey under 50 value chain partners in In the USA the dip for manufacturing is much deeper than
JanuaryTurnover development of 125 companies depends on their
was the first confirmation that cumulative de- for retail Philips
stocking caused the dip from the end-market
distance without seasonal effect
(Newspaper survey)
20%
0
pre-crisis Dip Now
-5 10%

( d e lta v s 2 0 0 6 )/Q 1 0 8
-10 0%
C h a n g e w ith p re -c ris is %

Q1'07 Q1'08 Q1'09
-15
-10%
-20 1 step
2 step -20%
-25 2 step
4 step -30%
-30

-35 -40%

-40 -50%
-45 Philips sales to retail Supplier sales to Philips

5

RE-ACTIVE

ACTIVE
DE-STOCKING

re
sin RE-ACTIVE


6

The “Lehman Wave”


7




8
Resin Resin Paint Parts OEM
Paint Parts OEM Retail Retail
Resin Resin Paint Parts OEM Retail
Paint Parts OEM Retail
Resin Paint Parts OEM Resin Parts Retail
Resin Paint Parts Retail Paint OEM Retail
Resin Paint OEM Parts OEM Retail
Resin Resin Paint Parts OEM Retail
Paint Parts OEM Retail
Resin Paint Parts OEM Resin PartsRetail



Retail Data US Census Manufacturing Data US Census
110 125 9
120
105
115

100 110

105
95 100

95
90
90

85 85

80
80

8

9
08

8

09

9
08

09
l-0

l-0
t-0

t-0
n-

n-
r-

r-
ju

ju
ok

ok
ap

ap
ja

ja
8

9
8

9
08

8
09

9
r-0

r-0
t- 0

t- 0
l-0

l-0
n-

n-
ju

ju
ap

ap
ok

ok
ja

ja

Retail Shipments Manufacturing Shipments
Retail Inventory Manufacturing Inventory
Inventory/Shipments Inventory/Shipments 9

Quantifying via Modeling


One of the echelons in value chain 11

.


Dynamic modeling 12

.


Output = sales curves Input= end market curve


Demand according to echelon
105
13

100
percentage sales vs 2007

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)
DSM demand OEM demand
Paint producer demand End market demand
Coater demand


Demand according to echelon
105
14

70
percentage sales vs 2007

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)
DSM demand OEM demand
Paint producer demand End market demand
Coater demand


Sales and Stocks 15
120,00

110,00

100,00

90,00

80,00

70,00

60,00
jan-07 jun-07 nov-07 apr-08 sep-08 feb-09 jul-09 dec-09 mei-10 sep-10 feb-11 jul-11
End market demand
langzaam dalende consumentenvraag Stocks in 4th echelon
Stocks in 4e echelon

Sales in 4th echelon
Sales in 4e echelon Total Stocks
Total Stocks

In d e x Index

ja n

90,00%
95,00%
100,00%
105,00%
110,00%
80,00%
85,00%
90,00%
95,00%
100,00%
105,00%
110,00%
jan
-0 -0
5 5
ap ap
r -0 r-0
5 5
jul ju
-0 l-0
5
ok 5 ok
t- 0 t- 0
5
jan 5 ja n
-0 -0
6
ap 6 ap
r -0 r-0
6 6
j ul
jul
-0 -0
6
ok 6 ok
t- 0
t- 0 6
jan 6 ja n
-0
-0 7
ap 7 ap

New car registration
r-0
r -0 7
7

j ul
jul -0
-0 7

Month

Month
ok 7 ok
t- 0
t- 0 7
ja n
jan 7 -0
-0 8
Automobile indexes (2007=100%)

ap
ap 8 r-0
8

Retail food turnover index (2007=100%)
r -0
8 j ul
jul -0
-0 8
ok
ok 8 t- 0
8
Motor vehicle turnover index

t- 0
ja n
jan 8 -0
-0 9
ap
ap 9 r-0
r -0 9
9 j ul
jul -0
-0 9
9 ok
t- 0
9

In d e x Index

40,00
50,00
60,00
70,00
80,00
90,00
100,00
110,00
120,00

ja n
ja
84,00%
86,00%
88,00%
90,00%
92,00%
94,00%
96,00%
98,00%
100,00%
102,00%
104,00%

n-
05
a p r -0 5
-0 ap
j u l- 5 r-0
5
okt 05 ju
- l-0
5
ja n 0 5 ok
t- 0
5
ja
a p r -0 6
-0 n-
06
j u l- 6 ap
r-0
6
okt 06
- ju
ja n 0 6 l-0
6
ok
a p r -0 7 t- 0
-0 6
ja
j u l- 7 n-
07
Month
okt 07 ap
- r-0
ja n 0 7 7
- ju
l-0
Month

apr 0 8 7
-0 ok
t- 0
j u l- 8 7
ja
Production Index US (2005=100%)

n-
okt 08 08
- ap
ja n 0 8 r-0
8
a p r -0 9 ju
-0 l-0
8
Household goods retail trade index (2007=100%)

j u l- 9 ok
t- 0
8
ja
okt 09
-0 9 n-
09
ap
r-0
9
ju
l-0
16

Food

9
Metal
Glass
Paper

Automotive

Construction

Segment 4, volume/month 17
(3 March 2010)
4000

3500

100%
3000

2500

2000

50%
1500
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


Segment 4, seasonality corrected
18

4000

3500

100%
3000

2500

2000

50%
1500
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


Segment 4, without seasonality 19

MS gain

2006 2007 2008 2009 2010 2011

Seas Corr Model 3mma & seas corr

Segment 4, with seasonality
(2 April 2010) 20

100%

50%
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

21

Customer info Historic

Sales S&OP
Forecasting Strategy

Model Pipeline


Do’s and Don’ts for companies in a Lehman Wave 22

Don’t: Do:
• Overreact • Flexibilize
• Close factories • Communicate over the chain
• Cut innovation • Communicate internally
• Fire crucial people • Make plans based on scenarios
• Stop marketing communication • Cut cost
• Continue M&A


Conclusions 23

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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