The Art and Science of Forecasting

by James Ray

ICIS Consulting

To view the recording of this webinar please visit

httpbcovemepefqf60t

1

ICIS Editorial is independent and has no involvement with ICIS Consulting Forecasts

wwwiciscom

ICIS Market intelligence for

the energy chemical

and fertilizer

industries

wwwiciscom

Prices News Analysis Consulting

Prices

News Analysis

Chemicals

Energy

Fertilizers

wwwiciscom

Support for Immediate through Long-term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long- Term

Long-Term Data-Driven Strategic Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

Prices News Analysis Consulting

Prices

News Analysis

Chemicals

Energy

Fertilizers

wwwiciscom

Support for Immediate through Long-term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long- Term

Long-Term Data-Driven Strategic Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

Support for Immediate through Long-term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long- Term

Long-Term Data-Driven Strategic Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

The Art and Science of Forecasting

by James Ray

ICIS Consulting

ICIS Editorial is independent and has no involvement with ICIS Consulting Forecasts

6

wwwiciscom

With varying levels of accuracy (amp consequences)

There are many different forecasts

7

wwwiciscom

bull Recent volatile markets have rendered

many previous methods ineffective for

most companies

bull As a result more complex forecasting is

necessary for budgeting and meeting

investor expectations

8

wwwiciscom

When forecasting we need to ask

1 What decision(s) do we want to make from this

andhellip

2 What is the potential impact of the deci$ion to the

bottom line

The answer to these should determine how much time

money and resources we spend on our forecast

9

wwwiciscom

Market Forecasting - Methods

1 Delphi method ndash ask a number of knowledgeable sources to get one

consolidate opinion (similar to ICIS Consulting Sentiment Index)

Strength ndash balanced professional opinion of multiple sources and

not dependent upon one person or one company

2 Economic correlations ndash use statistical correlations with econometric

data and establish regression equations with prediction confidence

intervals

Strengths ndash can be accurate and have a predicable margin of error

or range especially if good leading indicators are available

3 Market segment build-up ndash sum individual segment forecasts

Strengths ndash considers lots of factors that influence pricing

4 Other methods ndash use a combination of methods or various methods

like changes in inventory Heuristic techniques extrapolation or a

SWAG

10

wwwiciscom

Forecast Questions

1 What does the trade flow and value stream look like

2 What are the fundamental drivers behind it

3 What are the leading indicators in front of it Repercussions from

missing a forecast can be mitigated if you see it coming soon enough

4 What are the parallel events or alternate value streams that can

influence it

5 Which of these factors typically exert the most influence

6 Which factors can come into play as markets change and what are

their triggers

7 What are the time lags between their cause and effect relationship on

what we are forecasting

8 Is there seasonality or repeating patterns (usually the answer is yes)

9 Do the market characteristics or personalities change over time

10 Does the forecast model need to be self-correcting amp adapt to market

changes

11 What are the risks to the forecast - the price of oil foreign exchange

rates new technology or changes in consumer spending

11

wwwiciscom

What does the Value Chain look like

copy ICIS Consulting May 2013

Box Color Key Primary Product(s) Alternate Value Stream

12

wwwiciscom

NA rarr NEA uarr

ME rarr NEA uarr

ME rarr EU uarr

ME rarr SEA uarr

ME rarr Afr uarr ME rarr LatAm

uarr

SEA rarr NEA uarr

NEA rarr SEA uarr

EU rarr CIS darr

NA rarr LatAm uarr

SOURCE ICIS Consulting July 2013

Top 10 Global PE Trade Flows

13

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

With varying levels of accuracy (amp consequences)

There are many different forecasts

7

wwwiciscom

bull Recent volatile markets have rendered

many previous methods ineffective for

most companies

bull As a result more complex forecasting is

necessary for budgeting and meeting

investor expectations

8

wwwiciscom

When forecasting we need to ask

1 What decision(s) do we want to make from this

andhellip

2 What is the potential impact of the deci$ion to the

bottom line

The answer to these should determine how much time

money and resources we spend on our forecast

9

wwwiciscom

Market Forecasting - Methods

1 Delphi method ndash ask a number of knowledgeable sources to get one

consolidate opinion (similar to ICIS Consulting Sentiment Index)

Strength ndash balanced professional opinion of multiple sources and

not dependent upon one person or one company

2 Economic correlations ndash use statistical correlations with econometric

data and establish regression equations with prediction confidence

intervals

Strengths ndash can be accurate and have a predicable margin of error

or range especially if good leading indicators are available

3 Market segment build-up ndash sum individual segment forecasts

Strengths ndash considers lots of factors that influence pricing

4 Other methods ndash use a combination of methods or various methods

like changes in inventory Heuristic techniques extrapolation or a

SWAG

10

wwwiciscom

Forecast Questions

1 What does the trade flow and value stream look like

2 What are the fundamental drivers behind it

3 What are the leading indicators in front of it Repercussions from

missing a forecast can be mitigated if you see it coming soon enough

4 What are the parallel events or alternate value streams that can

influence it

5 Which of these factors typically exert the most influence

6 Which factors can come into play as markets change and what are

their triggers

7 What are the time lags between their cause and effect relationship on

what we are forecasting

8 Is there seasonality or repeating patterns (usually the answer is yes)

9 Do the market characteristics or personalities change over time

10 Does the forecast model need to be self-correcting amp adapt to market

changes

11 What are the risks to the forecast - the price of oil foreign exchange

rates new technology or changes in consumer spending

11

wwwiciscom

What does the Value Chain look like

copy ICIS Consulting May 2013

Box Color Key Primary Product(s) Alternate Value Stream

12

wwwiciscom

NA rarr NEA uarr

ME rarr NEA uarr

ME rarr EU uarr

ME rarr SEA uarr

ME rarr Afr uarr ME rarr LatAm

uarr

SEA rarr NEA uarr

NEA rarr SEA uarr

EU rarr CIS darr

NA rarr LatAm uarr

SOURCE ICIS Consulting July 2013

Top 10 Global PE Trade Flows

13

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

bull Recent volatile markets have rendered

many previous methods ineffective for

most companies

bull As a result more complex forecasting is

necessary for budgeting and meeting

investor expectations

8

wwwiciscom

When forecasting we need to ask

1 What decision(s) do we want to make from this

andhellip

2 What is the potential impact of the deci$ion to the

bottom line

The answer to these should determine how much time

money and resources we spend on our forecast

9

wwwiciscom

Market Forecasting - Methods

1 Delphi method ndash ask a number of knowledgeable sources to get one

consolidate opinion (similar to ICIS Consulting Sentiment Index)

Strength ndash balanced professional opinion of multiple sources and

not dependent upon one person or one company

2 Economic correlations ndash use statistical correlations with econometric

data and establish regression equations with prediction confidence

intervals

Strengths ndash can be accurate and have a predicable margin of error

or range especially if good leading indicators are available

3 Market segment build-up ndash sum individual segment forecasts

Strengths ndash considers lots of factors that influence pricing

4 Other methods ndash use a combination of methods or various methods

like changes in inventory Heuristic techniques extrapolation or a

SWAG

10

wwwiciscom

Forecast Questions

1 What does the trade flow and value stream look like

2 What are the fundamental drivers behind it

3 What are the leading indicators in front of it Repercussions from

missing a forecast can be mitigated if you see it coming soon enough

4 What are the parallel events or alternate value streams that can

influence it

5 Which of these factors typically exert the most influence

6 Which factors can come into play as markets change and what are

their triggers

7 What are the time lags between their cause and effect relationship on

what we are forecasting

8 Is there seasonality or repeating patterns (usually the answer is yes)

9 Do the market characteristics or personalities change over time

10 Does the forecast model need to be self-correcting amp adapt to market

changes

11 What are the risks to the forecast - the price of oil foreign exchange

rates new technology or changes in consumer spending

11

wwwiciscom

What does the Value Chain look like

copy ICIS Consulting May 2013

Box Color Key Primary Product(s) Alternate Value Stream

12

wwwiciscom

NA rarr NEA uarr

ME rarr NEA uarr

ME rarr EU uarr

ME rarr SEA uarr

ME rarr Afr uarr ME rarr LatAm

uarr

SEA rarr NEA uarr

NEA rarr SEA uarr

EU rarr CIS darr

NA rarr LatAm uarr

SOURCE ICIS Consulting July 2013

Top 10 Global PE Trade Flows

13

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

When forecasting we need to ask

1 What decision(s) do we want to make from this

andhellip

2 What is the potential impact of the deci$ion to the

bottom line

The answer to these should determine how much time

money and resources we spend on our forecast

9

wwwiciscom

Market Forecasting - Methods

1 Delphi method ndash ask a number of knowledgeable sources to get one

consolidate opinion (similar to ICIS Consulting Sentiment Index)

Strength ndash balanced professional opinion of multiple sources and

not dependent upon one person or one company

2 Economic correlations ndash use statistical correlations with econometric

data and establish regression equations with prediction confidence

intervals

Strengths ndash can be accurate and have a predicable margin of error

or range especially if good leading indicators are available

3 Market segment build-up ndash sum individual segment forecasts

Strengths ndash considers lots of factors that influence pricing

4 Other methods ndash use a combination of methods or various methods

like changes in inventory Heuristic techniques extrapolation or a

SWAG

10

wwwiciscom

Forecast Questions

1 What does the trade flow and value stream look like

2 What are the fundamental drivers behind it

3 What are the leading indicators in front of it Repercussions from

missing a forecast can be mitigated if you see it coming soon enough

4 What are the parallel events or alternate value streams that can

influence it

5 Which of these factors typically exert the most influence

6 Which factors can come into play as markets change and what are

their triggers

7 What are the time lags between their cause and effect relationship on

what we are forecasting

8 Is there seasonality or repeating patterns (usually the answer is yes)

9 Do the market characteristics or personalities change over time

10 Does the forecast model need to be self-correcting amp adapt to market

changes

11 What are the risks to the forecast - the price of oil foreign exchange

rates new technology or changes in consumer spending

11

wwwiciscom

What does the Value Chain look like

copy ICIS Consulting May 2013

Box Color Key Primary Product(s) Alternate Value Stream

12

wwwiciscom

NA rarr NEA uarr

ME rarr NEA uarr

ME rarr EU uarr

ME rarr SEA uarr

ME rarr Afr uarr ME rarr LatAm

uarr

SEA rarr NEA uarr

NEA rarr SEA uarr

EU rarr CIS darr

NA rarr LatAm uarr

SOURCE ICIS Consulting July 2013

Top 10 Global PE Trade Flows

13

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Market Forecasting - Methods

1 Delphi method ndash ask a number of knowledgeable sources to get one

consolidate opinion (similar to ICIS Consulting Sentiment Index)

Strength ndash balanced professional opinion of multiple sources and

not dependent upon one person or one company

2 Economic correlations ndash use statistical correlations with econometric

data and establish regression equations with prediction confidence

intervals

Strengths ndash can be accurate and have a predicable margin of error

or range especially if good leading indicators are available

3 Market segment build-up ndash sum individual segment forecasts

Strengths ndash considers lots of factors that influence pricing

4 Other methods ndash use a combination of methods or various methods

like changes in inventory Heuristic techniques extrapolation or a

SWAG

10

wwwiciscom

Forecast Questions

1 What does the trade flow and value stream look like

2 What are the fundamental drivers behind it

3 What are the leading indicators in front of it Repercussions from

missing a forecast can be mitigated if you see it coming soon enough

4 What are the parallel events or alternate value streams that can

influence it

5 Which of these factors typically exert the most influence

6 Which factors can come into play as markets change and what are

their triggers

7 What are the time lags between their cause and effect relationship on

what we are forecasting

8 Is there seasonality or repeating patterns (usually the answer is yes)

9 Do the market characteristics or personalities change over time

10 Does the forecast model need to be self-correcting amp adapt to market

changes

11 What are the risks to the forecast - the price of oil foreign exchange

rates new technology or changes in consumer spending

11

wwwiciscom

What does the Value Chain look like

copy ICIS Consulting May 2013

Box Color Key Primary Product(s) Alternate Value Stream

12

wwwiciscom

NA rarr NEA uarr

ME rarr NEA uarr

ME rarr EU uarr

ME rarr SEA uarr

ME rarr Afr uarr ME rarr LatAm

uarr

SEA rarr NEA uarr

NEA rarr SEA uarr

EU rarr CIS darr

NA rarr LatAm uarr

SOURCE ICIS Consulting July 2013

Top 10 Global PE Trade Flows

13

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Forecast Questions

1 What does the trade flow and value stream look like

2 What are the fundamental drivers behind it

3 What are the leading indicators in front of it Repercussions from

missing a forecast can be mitigated if you see it coming soon enough

4 What are the parallel events or alternate value streams that can

influence it

5 Which of these factors typically exert the most influence

6 Which factors can come into play as markets change and what are

their triggers

7 What are the time lags between their cause and effect relationship on

what we are forecasting

8 Is there seasonality or repeating patterns (usually the answer is yes)

9 Do the market characteristics or personalities change over time

10 Does the forecast model need to be self-correcting amp adapt to market

changes

11 What are the risks to the forecast - the price of oil foreign exchange

rates new technology or changes in consumer spending

11

wwwiciscom

What does the Value Chain look like

copy ICIS Consulting May 2013

Box Color Key Primary Product(s) Alternate Value Stream

12

wwwiciscom

NA rarr NEA uarr

ME rarr NEA uarr

ME rarr EU uarr

ME rarr SEA uarr

ME rarr Afr uarr ME rarr LatAm

uarr

SEA rarr NEA uarr

NEA rarr SEA uarr

EU rarr CIS darr

NA rarr LatAm uarr

SOURCE ICIS Consulting July 2013

Top 10 Global PE Trade Flows

13

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

What does the Value Chain look like

copy ICIS Consulting May 2013

Box Color Key Primary Product(s) Alternate Value Stream

12

wwwiciscom

NA rarr NEA uarr

ME rarr NEA uarr

ME rarr EU uarr

ME rarr SEA uarr

ME rarr Afr uarr ME rarr LatAm

uarr

SEA rarr NEA uarr

NEA rarr SEA uarr

EU rarr CIS darr

NA rarr LatAm uarr

SOURCE ICIS Consulting July 2013

Top 10 Global PE Trade Flows

13

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

NA rarr NEA uarr

ME rarr NEA uarr

ME rarr EU uarr

ME rarr SEA uarr

ME rarr Afr uarr ME rarr LatAm

uarr

SEA rarr NEA uarr

NEA rarr SEA uarr

EU rarr CIS darr

NA rarr LatAm uarr

SOURCE ICIS Consulting July 2013

Top 10 Global PE Trade Flows

13

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

What are the fundamental drivers

Housing Permits leads to Housing Starts which leads to PVC Pipe being purchased for construction

copy ICIS Consulting May 2013 14

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect

Understanding market time lags (Leading indicators)

These two lines do NOT fit well

15

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Understanding market time lags (Leading indicators)

Cause amp Effect lines fit well when shifted 2 time periods

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Market Cause amp Effect Time Lag

Cause

Effect Offset 2P

Watch the dotted line

16

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

After

Understanding market time lags (Leading Indicators)

Real World Example When one product goes up 10 cents in China It goes up 6 cents in the US 6 weeks later and 4 cents in EU 2 weeks later

-

10

20

30

40

50

60

70

80

P 1 P 2 P 3 P 4 P 5 P 6 P 7 P 8 P 9 P10 P11 P12

Before

Cause

Effect

Source ICIS Consulting copy 2013

When forecasting we need to ask

17

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

Q amp A Pause

18

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Forecast Considerations

1 What will be the Forecast basis GDP of X and Oil Price of Y (If oil goes to Z thenhellip)

2 Cost Factors Labor Utilities Overhead etc

3 Raw Materials - usually petroleum driven

4 Currency Strength ndash Foreign exchange rate

5 GDP Expectations for long term forecasts

6 Margins ndash Usually supply amp demand driven

7 Market Ceilings Floors Shift Triggers to alternatives

8 Population and economic growth regionally

9 Freight separation from global markets Logistics handicap

10 Seasonality amp long term cycles ndash Historically determined

11 Statistical Cause amp Effect Relationships

12 Model maintenance amp evolution

13 Forecasts models MUST be maintained either manually or automatically as market

conditions relationships and technology change

14 Risks ndash High Low expectations New developments ndash feedstock evolution like Shale Oil

When you know the risks you can plan for them

19

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Selecting the best data

bull Examples of leading indicators for some products in the US might be 1) consumer earnings increase then

2) consumer spending increases then

3) demand of consumer products increases

While many variables donrsquot have forward projections they can be leading indicators This is also

what is known as a cause amp effect relationship with a market time lag

bull Text books suggest a 95 correlation and a P value of less than 05 is required Free

Markets rarely demonstrate these degrees of predictability Only tightly regulated

markets or those that use formula pricing are likely to have text book correlations

bull Markets with few producers may have higher correlations due to less competition and

the ability to maintain fixed margins over raw materials

bull I would however caution you that theoretical statistics from a college class and real

world practical application are not always the same and can often result in misleading

statistics

20

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Cause amp Effect

bull It has been demonstrated by a University in California (CIU) that with

the increased sales of Popsicles (ice cold treats) that there is also an

increase in Shark Attacks

Some speculated that the tasty red sugar content in our system made

us more appealing to the sharks

bull It turned out that this was not a ldquocause and effectrdquo relationship but

rather an effect and effect relationship

bull As the summer temperature increased (cause) popsicles sales

increased (effect)

bull At the same time more people went to the beach (effect) increasing

the possibility of shark attacks

bull In addition as the water warms up the metabolism of the cold

blooded sharks also increased (another effect) making them feed

more

Moral of the story Understand your Cause and Effect relationships

21

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Look for cycles or seasonal factors

Products like this are more predictable because of seasonal demand

22

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Seasonality is important for short term accuracy but can be noise in long term forecasts

Seasonality can be a big factor

70

75

80

85

90

95

100

105

110

$040

$045

$050

$055

$060

$065

$070

$075

Jan Feb Mar Apr May June July Aug Sept Oct Nov Dec

$l

b

Sample Seasonal Price Average (10 Years)

Avg $lb

of Avg

23

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

bull Many times simple cost or prediction models use a Linear Equation like the slope-intercept equation y = mx +b

bull fixed cost (b) is constant within a given range (budget)

bull variable cost rate (m) is the slope or rate of change for the variable cost

bull Independent variable (x) is the independent factor often feedstock or raw material cost

bull Forecasting each component builds up to a total forecast

bull For example suppose a manufacturer has an $875 setup cost per order as his only fixed cost (b)

bull Then suppose the variable cost to convert a pound of raw materials into finished goods is 50 of the raw material cost

bull His cost equation y = mx +b becomes Cost = 15X + $875 where X is the raw material cost

Simple CostPrediction Models

24

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Excel can do simple regression

While this looks like a good fit and averages within 1 it has a 39 swing in error ranging from 22 low to 17 high

25

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

Q amp A Pause

26

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

PRICE COST MARGIN = +

Prices Consist of Cost and Margin

Market Segmentation forecasts each component separately often times at many levels

A function of feedstocks variable and fixed costs

A function of Competition ie supply amp demand

27

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Costs Correlates to Crude but Margin Does Not

Margins generally correlate more to supply amp demand

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Cost vs Crude Oil

(Historical US$Ton)

0

200

400

600

800

1000

1200

1400

1600

0 10 20 30 40 50 60 70 80

US

Do

llars

t

on

Crude Oil US$ per Bbl

Ethylene Variable Margins vs Crude Oil

(Historical US$Ton)

28

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Supply amp Demand = Key to Margin Portion of Market Price

NORTH AMERICA Q1 Q2 Q3 Q4 TOT YR

Capacity (pro-rata) 2176 2176 2200 2200 8753

Virgin Production 1804 1913 1867 1851 7434

Recycle 61 61 61 61 244

Total Production 1864 1974 1928 1912 7678

Av Plant utilization rate () 829 879 848 841 877

Regional Imports 48 51 49 49 197

Regional Exports 134 128 121 129 512

App Consumption 1778 1897 1856 1832 7363

Real Consumption 1798 1895 1834 1813 7340

2012

Source ICIS Supply amp Demand Database copy 2013

29

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Conceptual Derivative Market Structure

Cumulative Supply

Cas

h C

ost

$t

Advantaged feedstock

players

Low cost importers and integrated players run hard and are firmly in business Stand-alone and higher cost plants are exposed to all market shocks and are marginalised

lsquolongerrsquo Market

Integrated players

Regional Capacity

Total Capacity

Stand Alone

players

Laggard driven pricing

Best returns

Regional Production

lsquotighterrsquo Market

Marginal returns

Adequate returns

Source ICIS Consulting copy 2013

30

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Sample Forecast With High and Low Risk Lines and Key Feedstock Price History

Knowing the hilo range allows managers to anticipate and plan for potential risk

31

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom wwwiciscom

httpwwwiciscomcontactrequest-free-the-art-and-science-of-forecasting

httpwwwiciscomcontactrequest-free-icis-consulting-surviving-the-storm-market-outlook

More Information on Forecasting

32

wwwiciscom

Thank you

For further information please contact James Ray at

jamesrayiciscom

33

wwwiciscom

Case 1 Independent variable predicts well until market changes

occur

Market Change

Predictions not reliable

Predictions are reliable

30

35

40

45

50

55

60

65

70

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Prediction B

34

wwwiciscom

Case 2 Independent variable predicts poorly until market

changes occur

Predictions not reliable

Market Change

Predictions are reliable

30

35

40

45

50

55

60

65

70

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Prediction C

35

wwwiciscom

Case 3 Adaptive Weighted Prediction is more reliable as markets

change

Market Change

Predictions are reliable

20

30

40

50

60

70

80

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Weighted Prediction

36

wwwiciscom

ICIS Mission Statement

ldquoOur aim is to give companies a

competitive advantage We deliver

trusted pricing data high-value

news analysis and independent

consulting enabling our customers

to make better-informed trading

and planning decisionsrdquo

37

wwwiciscom

Global Presence Local Insight

Over 9200 price assessments in 1200 reports covering 180 commodities

More than 30 years of industry insight and data

Over 100000 industry customers

Customers include virtually every major chemical company

Weekly contact with thousands of market participants

17000+ annual news stories

700+ global employees

New York

Washington Houston

Bogota

London

Dusseldorf

Milan Tokyo

Mumbai

Beijing

Guangzhou

Singapore

Perth

Melbourne

Brisbane

Shanghai

38

wwwiciscom

Support for Immediate Through Long-Term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long Term

Long-Term Data-Driven Strategic

Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

39

wwwiciscom

Critical information as itrsquos needed

Pricing intelligence

bull Chemical price reports

bull Energy market reports

bull Fertilizer price reports

bull Chemical price alerts

bull Price history

Transactional Short-term planning Long-term planning

News

bull Global real-time news

service

Pricing intelligence

bull Outlook chemical reports

bull Outlook fertilizer reports

bull Chemical forward curves

bull Price history

Analysis amp forecasting

bull Annual studies

bull Price forecast reports

bull Margin reports

bull ICIS Chemical Business

magazine

bull Training amp conferences

Analysis amp forecasting

bull Supply amp demand

database

bull Annual studies

bull Consulting

40

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

Case 1 Independent variable predicts well until market changes

occur

Market Change

Predictions not reliable

Predictions are reliable

30

35

40

45

50

55

60

65

70

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Prediction B

34

wwwiciscom

Case 2 Independent variable predicts poorly until market

changes occur

Predictions not reliable

Market Change

Predictions are reliable

30

35

40

45

50

55

60

65

70

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Prediction C

35

wwwiciscom

Case 3 Adaptive Weighted Prediction is more reliable as markets

change

Market Change

Predictions are reliable

20

30

40

50

60

70

80

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Weighted Prediction

36

wwwiciscom

ICIS Mission Statement

ldquoOur aim is to give companies a

competitive advantage We deliver

trusted pricing data high-value

news analysis and independent

consulting enabling our customers

to make better-informed trading

and planning decisionsrdquo

37

wwwiciscom

Global Presence Local Insight

Over 9200 price assessments in 1200 reports covering 180 commodities

More than 30 years of industry insight and data

Over 100000 industry customers

Customers include virtually every major chemical company

Weekly contact with thousands of market participants

17000+ annual news stories

700+ global employees

New York

Washington Houston

Bogota

London

Dusseldorf

Milan Tokyo

Mumbai

Beijing

Guangzhou

Singapore

Perth

Melbourne

Brisbane

Shanghai

38

wwwiciscom

Support for Immediate Through Long-Term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long Term

Long-Term Data-Driven Strategic

Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

39

wwwiciscom

Critical information as itrsquos needed

Pricing intelligence

bull Chemical price reports

bull Energy market reports

bull Fertilizer price reports

bull Chemical price alerts

bull Price history

Transactional Short-term planning Long-term planning

News

bull Global real-time news

service

Pricing intelligence

bull Outlook chemical reports

bull Outlook fertilizer reports

bull Chemical forward curves

bull Price history

Analysis amp forecasting

bull Annual studies

bull Price forecast reports

bull Margin reports

bull ICIS Chemical Business

magazine

bull Training amp conferences

Analysis amp forecasting

bull Supply amp demand

database

bull Annual studies

bull Consulting

40

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

Case 2 Independent variable predicts poorly until market

changes occur

Predictions not reliable

Market Change

Predictions are reliable

30

35

40

45

50

55

60

65

70

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Prediction C

35

wwwiciscom

Case 3 Adaptive Weighted Prediction is more reliable as markets

change

Market Change

Predictions are reliable

20

30

40

50

60

70

80

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Weighted Prediction

36

wwwiciscom

ICIS Mission Statement

ldquoOur aim is to give companies a

competitive advantage We deliver

trusted pricing data high-value

news analysis and independent

consulting enabling our customers

to make better-informed trading

and planning decisionsrdquo

37

wwwiciscom

Global Presence Local Insight

Over 9200 price assessments in 1200 reports covering 180 commodities

More than 30 years of industry insight and data

Over 100000 industry customers

Customers include virtually every major chemical company

Weekly contact with thousands of market participants

17000+ annual news stories

700+ global employees

New York

Washington Houston

Bogota

London

Dusseldorf

Milan Tokyo

Mumbai

Beijing

Guangzhou

Singapore

Perth

Melbourne

Brisbane

Shanghai

38

wwwiciscom

Support for Immediate Through Long-Term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long Term

Long-Term Data-Driven Strategic

Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

39

wwwiciscom

Critical information as itrsquos needed

Pricing intelligence

bull Chemical price reports

bull Energy market reports

bull Fertilizer price reports

bull Chemical price alerts

bull Price history

Transactional Short-term planning Long-term planning

News

bull Global real-time news

service

Pricing intelligence

bull Outlook chemical reports

bull Outlook fertilizer reports

bull Chemical forward curves

bull Price history

Analysis amp forecasting

bull Annual studies

bull Price forecast reports

bull Margin reports

bull ICIS Chemical Business

magazine

bull Training amp conferences

Analysis amp forecasting

bull Supply amp demand

database

bull Annual studies

bull Consulting

40

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

Case 3 Adaptive Weighted Prediction is more reliable as markets

change

Market Change

Predictions are reliable

20

30

40

50

60

70

80

Jan2003

Feb2003

Mar2003

Apr2003

May2003

Jun2003

Jul2003

Aug2003

Sep2003

Oct2003

Nov2003

Dec2003

Jan2004

Feb2004

Mar2004

Apr2004

May2004

Jun2004

Jul2004

Aug2004

Sep2004

Oct2004

Nov2004

Dec2004

Adaptive Prediction Model

Actual Market Value Weighted Prediction

36

wwwiciscom

ICIS Mission Statement

ldquoOur aim is to give companies a

competitive advantage We deliver

trusted pricing data high-value

news analysis and independent

consulting enabling our customers

to make better-informed trading

and planning decisionsrdquo

37

wwwiciscom

Global Presence Local Insight

Over 9200 price assessments in 1200 reports covering 180 commodities

More than 30 years of industry insight and data

Over 100000 industry customers

Customers include virtually every major chemical company

Weekly contact with thousands of market participants

17000+ annual news stories

700+ global employees

New York

Washington Houston

Bogota

London

Dusseldorf

Milan Tokyo

Mumbai

Beijing

Guangzhou

Singapore

Perth

Melbourne

Brisbane

Shanghai

38

wwwiciscom

Support for Immediate Through Long-Term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long Term

Long-Term Data-Driven Strategic

Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

39

wwwiciscom

Critical information as itrsquos needed

Pricing intelligence

bull Chemical price reports

bull Energy market reports

bull Fertilizer price reports

bull Chemical price alerts

bull Price history

Transactional Short-term planning Long-term planning

News

bull Global real-time news

service

Pricing intelligence

bull Outlook chemical reports

bull Outlook fertilizer reports

bull Chemical forward curves

bull Price history

Analysis amp forecasting

bull Annual studies

bull Price forecast reports

bull Margin reports

bull ICIS Chemical Business

magazine

bull Training amp conferences

Analysis amp forecasting

bull Supply amp demand

database

bull Annual studies

bull Consulting

40

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

ICIS Mission Statement

ldquoOur aim is to give companies a

competitive advantage We deliver

trusted pricing data high-value

news analysis and independent

consulting enabling our customers

to make better-informed trading

and planning decisionsrdquo

37

wwwiciscom

Global Presence Local Insight

Over 9200 price assessments in 1200 reports covering 180 commodities

More than 30 years of industry insight and data

Over 100000 industry customers

Customers include virtually every major chemical company

Weekly contact with thousands of market participants

17000+ annual news stories

700+ global employees

New York

Washington Houston

Bogota

London

Dusseldorf

Milan Tokyo

Mumbai

Beijing

Guangzhou

Singapore

Perth

Melbourne

Brisbane

Shanghai

38

wwwiciscom

Support for Immediate Through Long-Term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long Term

Long-Term Data-Driven Strategic

Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

39

wwwiciscom

Critical information as itrsquos needed

Pricing intelligence

bull Chemical price reports

bull Energy market reports

bull Fertilizer price reports

bull Chemical price alerts

bull Price history

Transactional Short-term planning Long-term planning

News

bull Global real-time news

service

Pricing intelligence

bull Outlook chemical reports

bull Outlook fertilizer reports

bull Chemical forward curves

bull Price history

Analysis amp forecasting

bull Annual studies

bull Price forecast reports

bull Margin reports

bull ICIS Chemical Business

magazine

bull Training amp conferences

Analysis amp forecasting

bull Supply amp demand

database

bull Annual studies

bull Consulting

40

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

Global Presence Local Insight

Over 9200 price assessments in 1200 reports covering 180 commodities

More than 30 years of industry insight and data

Over 100000 industry customers

Customers include virtually every major chemical company

Weekly contact with thousands of market participants

17000+ annual news stories

700+ global employees

New York

Washington Houston

Bogota

London

Dusseldorf

Milan Tokyo

Mumbai

Beijing

Guangzhou

Singapore

Perth

Melbourne

Brisbane

Shanghai

38

wwwiciscom

Support for Immediate Through Long-Term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long Term

Long-Term Data-Driven Strategic

Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

39

wwwiciscom

Critical information as itrsquos needed

Pricing intelligence

bull Chemical price reports

bull Energy market reports

bull Fertilizer price reports

bull Chemical price alerts

bull Price history

Transactional Short-term planning Long-term planning

News

bull Global real-time news

service

Pricing intelligence

bull Outlook chemical reports

bull Outlook fertilizer reports

bull Chemical forward curves

bull Price history

Analysis amp forecasting

bull Annual studies

bull Price forecast reports

bull Margin reports

bull ICIS Chemical Business

magazine

bull Training amp conferences

Analysis amp forecasting

bull Supply amp demand

database

bull Annual studies

bull Consulting

40

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

Support for Immediate Through Long-Term Decisions

Price Assessments

Market News

Data Analysis and Short-Term Supply amp

Demand

Near-Term

Long Term

Long-Term Data-Driven Strategic

Planning

Price reports

Margin reports

Price alerts

Price indices

Industry-focused global consultancy

24 hour real-time news Training amp conferences

Plant capacities outputs amp shutdowns

Market insight amp analysis

Historical supply amp demand database

Forecast supply amp demand database

Available on Dashboard

39

wwwiciscom

Critical information as itrsquos needed

Pricing intelligence

bull Chemical price reports

bull Energy market reports

bull Fertilizer price reports

bull Chemical price alerts

bull Price history

Transactional Short-term planning Long-term planning

News

bull Global real-time news

service

Pricing intelligence

bull Outlook chemical reports

bull Outlook fertilizer reports

bull Chemical forward curves

bull Price history

Analysis amp forecasting

bull Annual studies

bull Price forecast reports

bull Margin reports

bull ICIS Chemical Business

magazine

bull Training amp conferences

Analysis amp forecasting

bull Supply amp demand

database

bull Annual studies

bull Consulting

40

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

Critical information as itrsquos needed

Pricing intelligence

bull Chemical price reports

bull Energy market reports

bull Fertilizer price reports

bull Chemical price alerts

bull Price history

Transactional Short-term planning Long-term planning

News

bull Global real-time news

service

Pricing intelligence

bull Outlook chemical reports

bull Outlook fertilizer reports

bull Chemical forward curves

bull Price history

Analysis amp forecasting

bull Annual studies

bull Price forecast reports

bull Margin reports

bull ICIS Chemical Business

magazine

bull Training amp conferences

Analysis amp forecasting

bull Supply amp demand

database

bull Annual studies

bull Consulting

40

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt

wwwiciscom

ICIS price forecast reports Supply demand and price trends at a glance

ICIS provides a clear view of prices margins capacity supply and demand for the next 12 months ndash all in a single report Packed with useful information this concise report includes everything you need to quickly understand exactly where the market is heading and maximise opportunities straight away

How the price forecast reports can help you

Negotiating contracts - Use the forecasts to help settle prices for the short- to medium-term with increased confidence Buying and selling- The concise market information enables you to quickly review your market positioning and decide the best time to trade Budgeting and planning- Whether you are calculating how much you will be spending in the short- to medium-term or making production or commercial decisions the report can help you assess the latest price supply demand and changes in capacity

Enquire about the price forecast reports gtgt