### 24 - PROJECT PROFITABILITY

```PROJECT PROFITABILITY
PROFITABILITY BASED ON
RETURN ON INVESTMENT
•
•
•
•
•
•
RETURN ON INVESTMENT IS DEFINED: ROI
= PROFIT/INVESTMENT
VARIATIONS IN BASES
ROIBT - RETURN ON INVESTMENT BEFORE
TAXES
ROIAT - RETURN ON INVESTMENT AFTER
TAXES
ROEBT - RETURN ON EQUITY BEFORE
TAXES
ROEAT - RETURN ON EQUITY AFTER TAXES
ROI/ROE ANALYSES ARE BASED
ON A PICTURE IN TIME
•
•
•
DEPRECIATION IS AVERAGED
CONSTANT \$ CALCULATIONS ARE APPLIED
SOME VARIATIONS HAVE BEEN SUGGESTED
TO ALLOW FOR TIME VALUES
N
ROI 
1  NETJ
N
j 1
F
(8  1b)
COMPARISON OF ROI AND DCRR
• PAPER PLANT EXAMPLE FROM
daran%20Book/Chap5.pdf
• 750,000 TPY CAPACITY
• CAPITAL INVESTMENT
– INITIAL = \$250 MILLION
– \$50 MILLION AT 5 YEARS FOR EXPANSION
EXAMPLE
• DEBT = \$100 MILLION @ 5.25% FOR 10 YEARS
W/ANNUAL EQUAL PAYMENTS
• PRODUCTION RATES
– FIRST YEAR 650,000 TONS
– SECOND YEAR 700,000 TONS
– THIRD YEAR 750,000 TONS
• ON STREAM TIME
– 90% FIRST 3 YEARS
– 95% AFTER 3 YEARS
EXAMPLE
• SALES PRICE = \$400/TON
• COM = 55% OF REVENUES VARIABLE + \$50
MILLION FIXED COSTS
• WORKING CAPITAL = 15% REVENUES
• DDB DEPRECIATION SCHEDULE AND PLANT
SOLD @ END OF 10YEARS
EXAMPLE – DEBT PAYMENTS
• AMORTIZATION SCHEDULE
• ANNUAL PAYMENT = \$13,108,000
EXAMPLE – INCOME FLOW
EXAMPLE – CASH FLOWS
• CASH FLOWS TO EQUITY ARE BASED ON
PAYMENTS TO NOTS
EXAMPLE - ROE
• ROE CALCULATED FOR EACH YEAR
EXAMPLE – NPV CASH FLOW
EXAMPLE – IRR
• DCRR = 18.06%
VARIABLES THAT AFFECT
RETURN
•
•
PRODUCTION RATES - HIGHER RATES
TYPICALLY REDUCED UNIT COM
SUPPLY - DEMAND - PRICE
SUPPLY
SUPPLY-DEMAND RELATIONSHIP
•
•
•
PRICE TYPICALLY INCREASES WITH
DECREASED SUPPLY
PRICE TYPICALLY DECREASES WITH
INCREASED SUPPLY
THIS AFFECT SHOULD BE CONSIDERED FOR
PRICE SENSITIVITY ANALYSES
SUPPLY-DEMAND -PRICE CURVE
• ANALYZE OPTIMUM SIZE FOR DESIGN, USES
INCREMENTAL ROI
Maximum Profit
Maximum
ROIBT
Maximum
Investment for
ROIBT base
Minimum
Investment for
ROIBT base
DCF
PAYOUT TIME
•
•
TIME REQUIRED IN CONSTANT \$ TO PAY BACK
ORIGINAL INVESTMENT
NORMALLY BASED ON DFC AND DOES NOT
INCLUDE LAND OR WORKING CAPITAL
LAND
WORKING CAPITAL
\$
PAYOUT TIME
LAND
CASH FLOW
TIME (YEARS)
FIXED
CAPITAL
WORKING CAPITAL
SABIC Training
Day 2 Price Forecasting
From T. Pavone
11th September 2012
London
Agenda – IHS Chemical Price Forecast Training
1. Introduction price definitions & forecasting techniques
11:00
9:30 –
 Short-term
 Medium-term
 Long-term
2. Production cost analysis
 Underlying energy and feedstock values
 Feedstock, variable, fixed costs, and co-product credits
 Alternative values
 Production cost models
 Cost curves
Coffee Break
11:20
3. Inherent margin analysis
11:00-
11:20 – 12:10
 Supply/demand balances
 Impact of operating rates
 Market momentum & psychology
 Return on investment
Lunch
12:10 – 13:30
Agenda – IHS Chemical Price Forecast Training
4. Price forecasting techniques
13:30 –
14:00
 Cost plus margin
 Diagnostic checks
 Regional relationships and arbitrage
 Price netbacks
5. Case Study 1-PBR Pricing
14:45
14:00 –
Coffee
15:00
14:45 –
6. Case Study 2- oxo-alcohols Pricing
15:00 – 15:45
7. Case Study 2- ethoxolates Pricing
15:45 – 16:30
Wrap-up
16:30 – 17:00
Current market chatter / status / emotions
impacts the perception of the forecast, but
the longer the forecast is into the future, the
less current status will realistically impact the
forecast
Short-term price forecasts (1 week to a few
months) are indeed impacted by market
psychology, momentum, manipulation, fear,
greed, etc.. But market fundamentals always
correct the prices eventually (Oil, Stock,
Housing, Technology bubbles).
Different interpretations and biases of “real data”
results in different forecasts
Historical data is real, and good to understand
past trends
Forecasts are an extrapolation of historical data
and historical relationships based on expectations
of the future and how those relationship may
change or stay the same in the future
Transition points in the forecast are easier to
determine than absolute levels
Transition points are often more important to get
right than absolute levels for “timing the market” for
investment decisions, inventory positions, sales
plans, etc.
Forecasts
)
•
•
•
•
A forecast is always wrong,
otherwise it would be history
(and the forecaster would be
very rich
Forecasts always come with
qualifications:
If Economy does this, then…
If Oil does that, then..
Country Leader: “I wish I knew a one-handed forecaster” His economic advisors would typically
give him economic advice stating, "On the one hand….And on the other hand...“
GIGO = Garbage In means Garbage Out – You Assumptions are very important!
Training Class Progress Check…
Introduction to short / medium / long term forecasting
Production Cost Analysis
Margin Analysis
Price Forecasting Techniques
Practical examples
Appendix – How to Access IHS Chemical’s Data to help
forecasting techniques
IHS Chemical Price Forecast Methodology
Price = Cost + Margin
•
IHS Chemical’s price forecast methodology considers numerous factors when projecting cost &
margins:
–
–
–
–
–
–
–
•
•
•
energy costs
economic growth
production costs
alternate values as a proxy for cost
competitive pressures
availability of supply/capacity
Underlying energy costs will drive the production costs of chemicals. Economic growth, or lack
thereof, will drive demand, which heavily influences the overall supply/demand balance.
To develop a general forecast, IHS Chemical considers all of these factors with a starting point
of a “cost plus margin equals price” approach.
IHS Chemical’s price forecast methodology provides a cycle forecast for one complete future
cycle, generally 5-7 years, and then reverts to a trend forecast for the long term. This is readily
accepted by financial community.
Short-Term / Medium-Term / Long-Term
•
1 Day
1 Week
•
•
•
Influenced by current market situation and
market momentum
Absolute values more likely accurate
Production cost often sets “soft” minimum
Consumer profitability sets “soft” maximum
• Influenced by supply outages, demand surges,
1 Month
1 Year
5 Years
30+ Years
and market over/under build cycle impacting
margins and pricing power
• Production cost sets “hard” minimum
• Historical profitability sets “soft” maximum
• Trend-line values tend to provide adequate returns
to justify new investments in growing business
• Relative values to underlying hydrocarbons more
likely accurate
• Technology, regulatory, demand, incremental
producer shifts can greatly impact prices
Short Term Forecast Methodology
Current through 3-6 Months
Price forecasts are based on individual experienced IHS Chemical consultants examining
inventories, trade, market momentum, contracts; in all, many different market indicators in
addition to the product cost structure.
3-6 Months Through 24 Months
Consultants are considering all the above and how they impact margins. Models build price
forecasts via a “cost plus margin” methodology as a “starting point”; then adjustments are made
on cost and margins for key considerations below
Key Considerations
Inventories
Operating schedules
Quarterly/Monthly supply/demand
Seasonality
Feedstock availability & price movements
Momentum
Mid-Term Price Forecast
Year 2 Through 1 Full Margin Cycle (5-7 years)
Consultants utilize historical understanding of margin cycles and supply/demand balances
coupled with analysis of any paradigm breaking market occurrences such as migration of
significant capacity to low cost regions of the world or breakthrough low cost production
technology
This should be strongly supported by annual supply/demand balance – new projects timing and
demand patterns are relatively well-known for the next 5-7 years.
Key Considerations
–
–
–
–
–
–
Cycle position in capital build cycle
Announced capacity changes
Some of the same factors as the short-term
Macro economic impact (jobs, recession, etc)
Relationships that hold? e.g.: propylene and ethylene ratio
Pricing sustainability with respect to whole value chain
Long Term Price Forecast
Trend Forecast (5-7 years to 30+ years)
– Cycles are no longer forecasted (although can be estimated if needed based on historical patterns)
Long term prices estimated to provide adequate return on capital invested for construction of new or
maintenance of existing marginal production
– Marginal production is a function of which incremental technology, location, and supply type will
clear demand growth in the future. This should be supported by long term supply/demand balance
Key Considerations
–
–
–
–
–
–
What is the price setting increment? Location? Technology? Size?
What are the investment return hurdles required?
What will be future trade flow patterns to justify regional differentials?
What effect do low cost regions have on future production?
What is derivative outlook? eg. PX is driven by fast growing polyester, but CHX is driven by slow
growing nylon.
– What are the chain margins for “integrated players”? Which part of the chain captures the margin?
– Are there regulatory considerations (MTBE phase out, low BZ in gasoline, etc.).
– What are the relationship to competing products; i.e.: Inter-polymer competition
Training Class Progress Check…
Introduction to short/medium/long term price definitions
Production Cost Analysis
Margin Analysis
Price Forecasting Techniques
Practical examples
Appendix – How to Access IHS Chemical’s Data
Production Cost Analysis
•
•
•
•
•
Commodity chemical prices are strongly
influenced by production costs
Production costs set a floor for prices
Movement in production costs are often a
basis for negotiation of prices
Production costs are often used in transfer
formulas internally and externally
Production cost is key element in “Cost +
Margin = Price” Methodology
Production Cost Analysis
Price = Production Cash Cost + Margin
What does a Production Cash Cost model comprise of?
Production Cash Cost = Net feedstock cost + Variable Cost + Fixed cost
Where:
Net feedstock (\$/Ton pdt) = Raw materials Cost (\$/Ton RM) – Co-credit Credit (\$/Ton pdt)
Raw materials Cost (\$/Ton pdt) = RM Price (\$/Ton RM)*(Ton RM/Ton pdt)
Co-credit Credit (\$/Ton pdt) = Co-credit Price (\$/Ton Co-credit)*(Ton Co-credit/Ton pdt)
Variable Cost (\$/Ton pdt) = All utilities (fuel, electricity, steam, boiler feed & cooling water, nitrogen,
etc) consumption cost + Consumables in the form of catalysts and non-feedstock chemicals are also
considered variable cost components, as would bagging costs for polymers
Where:
Power/Fuel/Steam/boiler feed water/cooling water are usually related to underlying energy pricing
Fixed Cost (\$/Ton pdt) = Labor cost + maintenance + insurance & taxes + overhead
Where:
Maintenance/insurance & taxes/overheads are usually estimated as a % of total capital
Production Cost Analysis
An example – To calculate SE Asia Ethylene C2 Cash cost via naphtha cracking
Product
Yield (/Ton C2)
Price Per Unit
Given:
Naphtha
-3.47 Ton/Ton
525 \$/Ton
Ethylene
Propylene
Crude C4s
Pygas
Hydrogen
Fuel
Pyrolysis Fuel Oil
Utility consumption
Power
Fuel
Cooling water
Catalyst & Chemicals
1.00
0.58
0.38
0.80
0.05
25.54
0.17
Ton/Ton
Ton/Ton
Ton/Ton
Ton/Ton
Ton/Ton
MMBtu/Ton
MMBtu/Ton
/Ton C2
213 KWh
26 MMBtu
206 Ton
7.41 \$
832
804
563
514
1144
8.52
360
\$/Ton
\$/Ton
\$/Ton
\$/Ton
\$/Ton
\$/MMBtu
\$/Ton
Price Per Unit
0.104 \$/Kwh
8.5 \$/MMBtu
0.028 \$/Ton
7.4 \$/Ton
Feedstock Cost (\$/Ton C2)
= Ton Nap/Ton C2 * Nap price (\$/Ton Nap)
= 3.47 * 525
= 1820
Co-product credit (\$/Ton C2)
= Ton C3/Ton C2 * C3 price (\$/Ton C3) + Ton CC4s/Ton C2 * CC4s price (\$/Ton CC4)
+ Ton pygas/Ton C2 * Pygas Price (\$/Ton pygas) + Ton H2/Ton C2 * H2 price (\$/Ton H2)
+ Ton fuel/Ton C2 * Fuel price (\$/Ton fuel) + Ton FO/Ton C2 * FO price (\$/Ton FO)
= (0.58 * 804) + (0.38 * 563) + (0.80 * 514) + (0.05 * 1144) + (25.54 * 8.52) + (0.17 * 360)
= 1426
Net feedstock Cost (\$/Ton C2)
= Feedstock Cost (\$/Ton C2) - Co-product credit (\$/Ton C2)
= 1820 - 1426
= 394
Variable Cost (\$/Ton C2)
= Power (Kwh/Ton C2) * Power price (\$/Kwh) + Fuel (MMBtu/Ton) * Fuel price (\$/MMBtu)
+ CW (Ton/Ton C2) * CW Price (\$/Ton CW) + Catalyst & Chem (\$/Ton C2)
= (212.5 * 0.10) + (25.5 * 8.52) + (206 * 0.02798) + 7.41
= 252
Production Cost Analysis
An example – To calculate SEA C2 Cash cost via naphtha cracking (cont’d)
Given:
Capacity (kta)
Oper. rate (%)
TFI (MM USD)
Labor (\$/Ton C2)
Fixed Cost component
Maintenance
Insurance and Local Taxes
Fixed Cost (\$/Ton C2)
430
92%
660
5.7
% of TFI
2.3%
1.0%
0.9%
= Maintenance (\$/Ton C2) + Insurance & local taxes (\$/Ton C2) + Plant ovhd (\$/Ton C2)
= {(2.3% + 1.0% + 0.9%) * 660}/(430 * 92%)*1000 + 5.7
= 74
Therefore, Prod CC (\$/Ton C2) = Net feedstock Cost (\$/Ton C2) + V.C. (\$/Ton C2) + F.C. (\$/Ton C2)
= 394 + 252 + 74
= 720
This summarizes the calculation of ethylene cash cost for one point in time in a time series – thank goodness for
Production Cost Analysis
Economic Snapshot (Single Period Model)
Southeast Asia Ethylene Economics
Full Range Naphtha
2009
Capacity (kTons / Year)
Operating Rate
430
92%
Factor
Ethylene (Spot)
1.000
Total Fixed Investment (MM US\$)
Working Capital (MM US\$)
per Ton
Product Quantity Units
Ton
395.6 k Tons
Freight
MM\$ / Yr
per Product Basis
\$ / Ton
831.7 \$ / Ton
329.0
832
60.0 \$ / Ton
23.7
60
305.3
772
8.7
85.9
2.3
22
217
6
Price Per Unit
Revenue
Power
Fuel
Cooling Water
Variable Cost
212.50
25.50
206.00
KWH
mill. Btu
Ton
84.1 GWH
10.1 trill. Btu
81.5 k Tons
103.7 \$ / MWH
8.52 \$ / mill. Btu
28.0 \$/ k Tons
Catalyst and Chemicals
Variable Operating Costs
Naphtha
Feedstock
Co-credits
Fixed Cost
3.466
Ton
1371.0 k Tons
660
93
525.2 \$ / Ton
2.9
7
99.8
252
720.0
1820
720.0
1820
185
85
163
22
86
24
467
214
412
54
218
60
Co-Product Credits
564.2
1426
Incremental Costs
255.6
646
2
15
7
6
6
38
17
14
29.3
74
285.0
720
Raw Material Costs
Crude C4s
Pygas
Hydrogen
Fuel
Pyrolysis Fuel Oil
Labor
Maintenance
Insurance and Local Taxes
Fixed Costs
Production Cash Costs
0.581
0.381
0.803
0.048
25.543
0.168
Ton
Ton
Ton
Ton
mill. Btu
Ton
230.0
150.6
317.5
18.8
10.1
66.3
k Tons
k Tons
k Tons
k Tons
trill. Btu
k Tons
2.3% of TFI
1.0% of TFI
0.9% of TFI
803.8
562.7
513.7
1144.4
8.52
360.0
\$
\$
\$
\$
\$
\$
/ Ton
/ Ton
/ Ton
/ Ton
/ mill. Btu
/ Ton
Production Cost Analysis
Economic Snapshot (Changing Geographic Location)
Northeast Asia Ethylene Economics
Full Range Naphtha
2009
Largest
capacity in that
region with
known regional
oper. rate
Estimated
regional
utility pricing
Capacity (kTons / Year)
Operating Rate
500
95%
Factor
Ethylene (Spot)
1.000
Total Fixed Investment (MM US\$)
Working Capital (MM US\$)
per Ton
Product Quantity Units
Ton
475.0 k Tons
Freight
Price Per Unit
823.9 \$ / Ton
60.0 \$ / Ton
Revenue
Power
Fuel
Cooling Water
212.50
25.50
206.00
KWH
mill. Btu
Ton
100.9 GWH
12.1 trill. Btu
97.9 k Tons
95.4 \$ / MWH
8.77 \$ / mill. Btu
26.6 \$/ k Tons
Catalyst and Chemicals
Variable Operating Costs
Naphtha
3.466
Ton
1646.1 k Tons
530.6 \$ / Ton
MM\$ / Yr
per Product Basis
\$ / Ton
391.4
824
28.5
60
362.9
764
9.6
106.3
2.6
20
224
5
3.5
7
122.0
257
873.4
1839
873.4
1839
235
102
202
27
106
30
494
214
424
56
224
62
Co-Product Credits
700.7
1475
Incremental Costs
294.7
620
4
19
8
7
8
39
18
15
38.0
80
332.7
700
Raw Material Costs
Estimated
regional
product pricing
832
113
Crude C4s
Pygas
Hydrogen
Fuel
Pyrolysis Fuel Oil
Labor
Maintenance
Insurance and Local Taxes
Fixed Costs
Production Cash Costs
0.581
0.381
0.803
0.048
25.543
0.168
Ton
Ton
Ton
Ton
mill. Btu
Ton
276.2
180.8
381.2
22.6
12.1
79.7
k Tons
k Tons
k Tons
k Tons
trill. Btu
k Tons
2.3% of TFI
1.0% of TFI
0.9% of TFI
850.3
562.7
528.7
1178.9
8.77
370.8
\$
\$
\$
\$
\$
\$
/ Ton
/ Ton
/ Ton
/ Ton
/ mill. Btu
/ Ton
Accounted for by
‘location indices’.
IHS Chemical will
include this index
when an asset is
cost modeled in a
Locale outside of
the one for which
the Yield data was
collected. The
index is expressed
as the % of USGC
cost basis.
Production Cost Analysis
(Estimating Capital Investment Costs)
Estimating for regional TFI:
(Region) TFI = USGC TFI * (region’s capacity/known capacity)^scale-up factor *
construction index w.r.t known year * location index
where TFI = OSBL + ISBL = The total fixed investment of a given plant includes the actual production unit
located on-plot (ISBL), outside battery limits (OSBL) equipment required to support the production unit.
Sometimes off-site plot expenditures are included in the definition of TFI, but for IHS Chemical’s forecasting
purposes, they are not.
TFI is used in IHS Chemical cost models
to USGC
estimate
forecast long term margin requirements
Known
TFIfixed costs
1000 and
MM to
USD
for investment return.
Known capacity
500 kta
Known year
2008
For example, gi
Estimating for e.g. Northeast Asia’s location index = 0.99; capacity = 650 kta; scale-up factor* = 0.65;
construction index to be inflated at 2% per year, therefore
NE Asia TFI in 2010 (MM USD) = \$1,000 * (650/1000) ^0.65 *1.04 * 0.99
= \$1,221
* scale-up factor: This value is widely used to take the known TFI value from a Yield set and to adjust it to the size we are actually
modeling in each location. It is this scaleup factor that allows us to use today's worldscale plant knowledge to estimate the
replacement cost of a different plant size in some other part of the world as capital does not move proportionately with capacity
increments.
Production Cost Analysis
(Utility & Variable Cost Calculations)
Electricity
Fuel
Cooling Water
Estimating for regional Utilities:
Steam
Catalyst &
Chems
IHS Chemical developed correlations between utility pricing and fuel value based on rigorous analysis and spot
checks with industry data sources.
est. electricity (\$/Kwh) = {V.C. + F.C. + %ROI*Capital (MM USD)/Kwh of elec. produced} of cogen unit.
est. cooling water (\$/Ton) = 0.03*Nelson Chem. oper index+0.63*est. electricity*(0.26466)
est. boiler feed water (\$/Ton)= cooling water (\$/Ton)+0.0074
est. MP steam (\$/Ton) = FV/0.87 + 0.75
est. HP steam (\$/Ton) = MP steam (\$/Ton)*1.1
est. LP steam (\$/Ton) = MP steam (\$/Ton)*0.90
where
FV = known regional/country fuel value, \$/MMBtu
Production Cost Analysis
Getting regional capacity & the respective operating rate
Example of Capacity output from IHS Chemical’s
CAPS database:
World
ETHYLENE
Average Annual Capacities (-000- Metric Tons)
COMPANY
LOCATION
PROCESS
2004
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
REMARKS
NORTHEAST ASIA
JAPAN
Idemitsu Kosan
Chiba
Tokuyama
(5)
(5)
Idemitsu PC
Chiba
Tokuyama
(5)
(5)
370
620
Keiyo Ethylene
Maruzen
Mitsub. Chemical
Mitsui Chemicals
Chiba
Chiba
Kashima
Kashima
Mizushima
Chiba
(5)
(5)
(4)
(4)
(4)
(5)
768
525
450
375
450
612
768
525
450
375
450
612
768
525
450
375
450
612
768
525
450
375
450
612
768
525
450
375
450
612
768
525
450
375
450
612
768
525
450
375
450
612
768
525
450
375
450
612
768
525
450
375
450
612
768
525
450
375
450
612
Nippon PC
Iwakuni, Yamaguchi
Kawasaki
(7)
(5)
(96)
433
(96)
433
(96)
433
(96)
433
(96)
147
(96)
----
(96)
----
(96)
----
(96)
----
(96)
----
Nippon Petrol.
Kawasaki
(5)
----
----
----
----
293
440
440
440
440
440
Osaka PC
Sanyo PC
Showa Denko
Sumitomo Chem.
TonenGeneral
TOSOH
Sakai
Mizushima
Oita
Chiba
Kawasaki
Yokkaichi
(4)
(5)
(5)
(5)
(5)
(5)
Subtotal - (4) EPB/Naphtha
Subtotal - (5) Naphtha
Subtotal - (7) Naphtha/Gas Oil/Residues
TOTAL - Japan
-------
415
686
-------
415
686
-------
415
686
-------
415
686
-------
415
686
-------
415
686
-------
415
686
-------
415
686
-------
415
686
-------
452
470
640
415
515
527
460
470
640
415
515
527
460
470
691
415
515
527
460
470
691
415
540
527
460
470
691
415
540
527
460
470
691
415
540
527
460
470
691
415
540
527
460
470
691
415
540
527
460
470
691
415
540
527
460
470
691
415
540
527
1,727
5,895
---7,622
1,735
6,006
---7,741
1,735
6,057
---7,792
1,735
6,082
---7,817
1,735
6,089
---7,824
1,735
6,089
---7,824
1,735
6,089
---7,824
1,735
6,089
---7,824
1,735
6,089
---7,824
1,735
6,089
---7,824
415
686 Kellogg MS, Exp. Oct.
02
------- Kellogg MS, Exp. Oct.
02
768 75 Maruzen/25 Sum.
525
450 From Mitsub. PC
375 From Mitsub. PC
450
612 From Ukishima, PC
No. 2
(96) From Mitsui PC
---- Merge with Nippon
Petrol. in Apr 2008
440 From Nippon PC wef
Apr 2008
460
470
691 No.2
415
540 From Tonen
527
1,735
6,089
---7,824
Production Cost Analysis
Getting regional capacity & the respective operating rate
Example of Supply/Demand output from IHS Chemical’s World Analysis database:
Japan
Ethylene
(-000- Metric Tons)
Balance is as of 2/18/2010
2004
2005
Actual
2006
2007
2008
2009
2010
Forecast
2011
2012
2013
2014
2004-09
%AAGR
2009-14
%AAGR
Capacity
Nameplate Capacity
Hypo (Rationalized) Capacity
Total Capacity
Oper. Rate
7622
0
7622
99.3
7741
0
7741
98.4
7792
0
7792
96.5
7817
0
7817
99
7824
0
7824
88
7824
0
7824
88
7824
0
7824
81.8
7824
-500
7324
86.3
7824
-1000
6824
92.5
7824
-1000
6824
91.8
7824
-1000
6824
90
0.5
0
0.5
-2.7
0
66
140
7364
0
0
7570
76
7646
0
109
231
7278
0
0
7618
93
7711
0
249
527
6745
0
0
7522
104
7626
0
257
543
6940
0
0
7739
55
7794
0
81
482
6319
0
0
6882
142
7024
0
103
513
6272
0
0
6889
41
6930
0
380
433
5560
27
0
6399
89
6488
0
427
487
5379
28
0
6321
106
6427
0
464
516
5302
28
0
6310
100
6410
0
477
529
5232
28
0
6266
100
6366
0
490
540
5081
28
0
6139
100
6239
0
9.2
29.7
-3.2
0
0
-1.9
-11.6
-1.9
0
36.5
1
-4.1
0
0
-2.3
19.5
-2.1
81
978
1311
689
1191
1214
815
239
692
7209
320
7529
78
990
1406
732
1152
1249
825
196
724
7351
288
7638
87
962
1413
685
1077
1240
819
194
752
7229
299
7528
84
1024
1396
701
1152
1061
795
210
763
7186
283
7469
72
852
1277
590
1068
1052
727
188
663
6490
202
6692
70
833
1383
642
1000
934
640
171
664
6338
561
6899
67
755
1255
557
909
1130
566
169
730
6138
350
6488
81
808
1194
523
928
1154
594
171
696
6149
278
6427
79
803
1187
499
944
1201
550
174
699
6135
275
6410
80
841
1191
501
967
1205
503
176
702
6166
200
6366
82
853
1183
499
987
1152
500
177
705
6139
100
6239
-2.8
-3.2
1.1
-1.4
-3.4
-5.1
-4.7
-6.5
-0.8
-2.5
11.9
-1.7
3.2
0.5
-3.1
-4.9
-0.3
4.3
-4.8
0.7
1.2
-0.6
-29.2
-2
Supply
Ethane
Propane
Butane
Naphtha
Gas Oil
Others
Production
Imports
Total Supply
Demand
Alpha Olefins
Ethylbenzene
EDC
Ethylene Oxide
HDPE
LDPE
LLDPE
Vinyl Acetate
Others
Domestic Demand
Exports
Total Demand
Oper. rate
Production Cost Analysis
Getting regional product pricing for Co-product credits
Example of Prices & Economics output from IHS Chemical’s Price Database:
Regional Cost Comparisons
(Economic Snapshot Results for Each Region)
Weighted Average Ethylene Production Cash Costs
1000
900
800
700
600
500
400
300
200
100
0
-100
Dollars per Metric Ton
World
North
America
South
America
Europe
Northeast
Asia
CIS & Baltic
States
Net Feedstock Costs
Variable Costs
Southeast
Asia
Middle East/
Africa
Fixed Costs
~
Cost Curve Analyses
Cost Curves –Economic Snapshots for every plant in the world
Production Costs, Dollars Per Metric Ton
750
Demand
Tight Market
650
550
Market Price
450
Weak Market
350
Plant K
150
Plant I
Plant J
Plant F
Plant G
Plant H
250
50
0
20
40
60
80
Cumulative Capacity (Million Tons)
100
120
Production Cost Analysis
Cost Curves – Why do we use them?
• Competitive Cost Analysis to investigate major cost drivers
• Feedstock
• Technology
• Scale
• IHS Chemical Cost Curve Models help to identify the following:
• marginal cost producer
• cost of the last metric ton – this cost ultimately sets the market price
• potential export opportunities and import threats
• the impact of new competitor technologies
• new capacity coming into the existing market
• The resulting cost curve for a particular industry can help you analyze a number of factors
regarding:
• market structure – strong vs. weak competitors
• price scenarios – floor price, marginal investment
• help to predict rationalization in a market
Production Cost Analysis
Cost Curves – How does IHS Chemical build them?
• IHS Chemical Cost Curve Models are built up in a logical consistent manner utilizing non-
confidential market intelligence.
• How do we build IHS Chemical Cost Curve Models?
(1)
(2)
(3)
(4)
(5)
IHS Chemical’s capacity database
IHS Chemical’s supply demand analysis
IHS Chemical’s prices and economics databases
IHS Chemical experienced consultants’ individual knowledge of process units
Information disclosed on a non-confidential basis or in public domain
• Confidential information obtained by IHS Chemical from the industry is NOT used.
–
–
NOT an industry benchmarking study.
Companies have NOT been interviewed to establish their actual costs
Production Cost Analysis
LLD - GLOBAL CASH COSTS BY SITE
2010
Integrated, Total Cash Cost Basis (Ethylene: Light Olefins Production Basis)
(Dollars per Ton)
1,500
1,300
1,100
900
700
500
300
100
0
5000
10000
15000
20000
25000
CUMULATIVE CAPACITY (Thousand Tons)
Total Cash Cost
Producer D, Site 4
Producer H, Site 8
Producer A, Site 1
Producer E, Site 5
Producer I, Site 9
Producer B, Site 2
Producer F, Site 6
Producer C,Site 3
Producer G, Site 7
30000
Production Cost Analysis
Ethylene Industry Production Cash Cost
Dollars per Metric Ton
1,400
Global Demand
1,200
1,000
EUR
SEA
NEA
600
MDE
200
EUR
SAM
CIS
400
NEA
NAM
SAM
800
NAM
SEA
CIS
MDE
2009
2014
0
0
10
30
20
40
50
60
70
80
90 100 110 120 130 140 150 160
~
Cumulative Production Capacity, Million Metric Tons
Ethylene Industry Production Cash Cost
Ethylene Industry Production Cash Cost
Dollars per Metric Ton
1,400
an increasingly large share of market.
1,200
Dollars per Metric Ton
1,400
Global Demand
1,200
1,000
1,000
800
800
600
600
400
400
200
200
2004
2009
12%
22%
Global
Demand
28%
2004
2009
2014
2014
0
0
0
20
40
60
80
100
120
Cumulative Production Capacity, Million Metric Tons
140
160
~
0
10
20
30
40
50
60
70
80
90 100 110 120 130 140 150 160
Cumulative Production Capacity, Million Metric Tons
~
Production Cost Analysis
Example – Delivered Cost to Market Analysis
LLD DELIVERED COSTS TO South China
2010
•
Integrated, Total Cash Cost Basis (Ethylene: Light Olefins Production Basis)
Dollars per Ton
1,400
1,200
1,000
800
600
0
Feedstock Cost
Packaging & Logistics
Other Freight
Variable Cost
Duty
Fixed Cost
Other Costs
Producer F
Producer E
Producer D
Producer C
Producer B
200
Producer A
400
Production Costs For By-Products And
Gasoline Blending Components
•
By-product streams and refined product
blending components usually do not have
easily assignable feedstocks & variable &
fixed costs
–
–
–
–
–
–
–
–
–
–
–
–
Pygas
Crude C4’s
Propylene
Hydrogen
Fuel oil
Methane
Benzene
Toluene
Mixed Xylenes
MTBE
Ethanol
ETBE
Production Costs For By-Products And
Gasoline Blending Components
•
How can production costs be estimated?
1. Use On-purpose technologies with easily assignable
feedstocks/variable costs
• PDH (propane dehydrogenation)for
propylene, HDA (hydrodealkylation) for
benzene, etc
• But, these might not be representative of the
most of the market
2. Approximate commercial mechanisms/formulas in use in the
market
• Crude C4’s or Benzene at ratio to naphtha
3. Use “alternative value” as an approximation of cost
(opportunity cost)
• Alternative in fuels markets:
– Burned as fuel (hydrogen, methane, ethane, etc.)
– Blended into motor gasoline (MTBE, toluene, etc.)
•
These values can be used as a “proxy” for production
1. On-Purpose Technologies
Where by-product can
produced on-purpose…
•
•
be
Similar to previous analysis of
feedstock costs – byproduct
credit + variable costs + fixed
costs
Key Questions:
–
–
Does on-purpose production
represent enough of the market
to impact average industry
costs structure?
Does on-purpose production
represent incremental
production capacity that will
market prices?
2. Commercial Mechanisms
= Nap X Ratio
Where by- product pricing is
•
•
•
•
There are some petrochemical
prices
that
have
commercial
mechanisms
that
move
with
naphtha/crude.
For example, the industry standard
price formula for crude C4 is usually
a percentage of naphtha. We use
MOPJ as the reference naphtha
basis.
Similarly to Crude C4s, Raffinate-1
prices in Northeast Asia is typically
based on a relationship to naphtha.
A ratio to naphtha is forecasted to
get the resultant product price, since
the is the commercially accepted
practice of establishing the product
value
Ratio to Naphtha
3. Alternative Blending Values
Where by-product is blended to a heating, transportation, or cooking fuel
o-XYLENE
Butane
Hydrogen
m-XYLENE
PAN/DOP/Plasticizers
PIA
XYLENES
p-XYLENE
TOLUENE
BTX
Crude
Oil
Refinery
Naphtha
Gas Oil
Ethane
Propane
Butane
Gas
HEATING
Separation
Unit
FUEL
OCTANE BLENDING
COMPONENTS
BTX
Reformate
Reformer
Steam
Cracker
PTA/DMT
PET
BENZENE
Extraction
Raffinate
Pygas
Propylene/PP
Ethylene
Natural
Gas
ETHYLBENZENE/
STYRENE
POLYETHYLENES
ETHYLENE DICHLORIDE/
PVC
ETO/MEG
Methanol
ETHANOL
EP RUBBER
Combustion Fuel Blending Values
Where a by-product is burned as a heating
or cooking fuel
•
•
•
Typically the product is simply valued at is
equivalent heating value using the heat of
combustion as a conversion factor
The local heating value is a function of the
typical energy source for heating, cooking, or
electricity generation ie, natural gas, fuel oil, etc.
Sometimes there are specifications and other
limitations on fuel gas blending: olefin content,
sulfur content, etc..
Combustion Fuel Blending Values
Fuel Value of Ethane
In Saudi Arabia
Assumptions:
Local Fuel Price = \$0.75 per million BTU
Heat Value of Ethane = 20,427 BTU / Pound
Calculation:
(\$0.75) x (20,427) / (1,000,000) x (2204) =
\$33/ton
Motor Gasoline Blending Values
Where a by-product is blended to
motor gasoline in a refinery
•
•
•
•
•
Typically, aromatic products (BTX) can be
blended into motor gasoline, but also MTBE,
ethanol, propylene/butylene alkylate, and other
products
A component’s Blend Value is the estimate of
the value a gasoline blender gets for adding
that component to gasoline
Octane & vapor pressure are key
specifications, but many other factors can
impact blending: sulfur, olefins, benzene,
oxygenates, etc.
Simplified blending correlations used for price
forecasting
Detailed non-linear models used in refinery
blending optimization
Gasoline Blending Fundamentals
Gasoline
Pool
Octane
Volume
Component
Gasoline
Component
Value
Price of
Regular
Gasoline
Market
Value of
“plain”
gasoline
Component Octane
Credit
for Octane
value of
component
Butane
Octane
Enhancement of
Butane
Volume
Enhancement of
Butane
Cost of
Butane
more butane into gasoline
limited by vapor pressure
specifications)
Component Blend Value
Regular
Gasoline
Price
Component
Octane Credit
Gasoline (87 Octane)
Octane
Value
Component
Octane
Number
Octane
Value
Regular
Gasoline
Octane
Number
Butane
Credit
Butane
Octane
Number
Regular
Gasoline
Octane
Number
Premium Gasoline Price - Regular Gasoline Price
Premium Octane Numbers - Regular Octane Number
Butane
Volume
Enhancement
Butane
Credit
Butane
Regular
Credit
Gasoline
Butane Cost
Butane
Credit
Price
Butane
Price
RVP Component - RVP Mogas
RVP Mogas - RVP Butane
Component Gasoline Blending Values
Product
Octane Value
Vapor Pressure
1/Density (gal/t)
Benzene
94
3.2
299.3
Toluene
103.5
1.36
303.7
Mixed Xylenes
103.5
0.3
304.3
Heavy aromatics
(C9+)
103.5
0.1
304.2
Butane
91.9
58.5
452.5
UNR gasoline
87
9.5
357.0
UNP gasoline
92
9.5
352.8
Sample Blend Value Calculation
Given:
UNR gasoline (87 octane) = US\$718/Ton
UNP gasoline (92 octane) = US\$745/Ton
Butane = US\$706/Ton
Then to calculate the blend value for Benzene –
BZE octane credit = octane value * {(BZE octane no. – UNR octane no.) + butane credit * (Butane octane no. – UNR octane no.)}
Octane value = (UNP gasoline price – UNR gasoline price) / (UNP gasoline octane no. – UNR gasoline octane no.)
= (745/3.53 – 718/3.57) / (92 – 87)
= 2.7
Butane credit = (RVP BZE – RVP mogas) / (RVP Mogas – RVP butane)
= (3.2 – 9.5) / (9.5 - 58.5)
= 0.129
BZE octane credit = 2.7 * {(94 – 87) + 0.129 * (91.9 – 87)}
= 20.6
Butane Vol. enhancement = butane credit * UNR gasoline price
= 0.129 * 718/3.57
= 25.9
Butane Cost = butane credit * butane price
= 0.129 * 706/4.53
= 20.1
Therefore,
BZE blend value = UNR gasoline price + BZE octane credit + butane Vol. enhancement – butane cost
= (718/3.57 + 20.6 + 25.9 – 20.1)*2.99
= 680 (US\$/Ton)
Tying Production Cost Models Into A
Time-Series Econometric Model
Production Cost Models to Support Price Forecasting
•
•
•
•
Production costs are generated from detailed
econometric models of chemical manufacturing
facilities throughout the world.
In our models, cash costs do not include
payments, taxes or a return on investment.
Only variable (raw material, utilities, and byproduct credits) and direct fixed costs are
included the cash cost
The econometric models represent a generic
unit of the technology dominant in the region.
Investment capital (Total Fixed Investment) is
based on a grassroots investment and is based
on U.S. dollars.
Economic production cost snapshots are
replicated for each period of time in
econometric model.
economodel example
Training Class Progress Check…
Introduction to short/medium/long term price definitions
Production Cost Analysis
Margin Analysis
Price Forecasting Techniques
Practical examples
Appendix – How to Access IHS Chemical’s Data
Margin Analysis
Price = Production Cash Cost + Margin
What impacts cash margins?
1)
Supply/demand fundamentals & effect of operating rates
2)
Return on Investment
3)
Other Factors
- Market Psychology
- Operating events
- Inventory levels
- Market Momentum
- Threat of Substitutes
- Etc…
Margin Analysis
What impacts margins?
1
Market momentum & psychology
In some cases, particularly short-term markets, major events or market speculation may
have a huge impact on petrochemical prices.








Energy price volatility
Demand surges / China imports
Inventory Levels
Operating Problems
Distressed cargos
Weather issues & Natural Disasters
New capacity start-ups
Other
Margin Analysis
What impacts margins?
1
Market momentum & psychology
Example
Asia Benzene and Naphtha Delta
“At the time of this data publication,
positive economic data was released
and the crude oil price rallied sharply,
while the European benzene price
skyrocketed due to significant benzene
plant trouble. The Asian benzene price
thus followed suit, although with a
smaller increase.”
Dollars per Metric Ton
1400
Forecast
1200
1000
800
600
400
200
0
-200
Jan-07
Jul-07
Jan-08
Jul-08
Delta
Jan-09
Jul-09
Benzene Spot
Jan-10
Jul-10
Naphtha
Jan-11
Jul-11
~
Margin Analysis
What impacts margins?
2
•
IHS Chemical utilizes Supply and Demand Models for analysis on a wide variety of
petrochemicals. In order to prepare historical and forecast demand for the basic
petrochemicals, we first prepare demand and production forecasts for all of the
derivatives.
–
•
Supply/demand fundamentals & effect of operating rates
For example, by first completing a comprehensive worldwide balance for ABS resins, country by
country, we can determine the amount of ABS resins that will be manufactured in each country and,
therefore, the amount of Butadiene that will be required for production of ABS resins.
With the model constructed, the key to the longer term forecast is how to establish demand
growth. IHS Chemical has developed a demand model driven by expected GDP growth, and
for each country and product a GDP elasticity forecast has been developed. The forecast of
elasticity is based on several factors:




Past Relationships
Per Capita Consumption
Prices
Margin Analysis
What impacts margins?
2
•
•
•
Supply/demand fundamentals & effect of operating rates
Having developed a demand forecast, IHS Chemical will then estimate how the demand will
be met. The balances for the “derivatives” are completed so that there is no trade imbalance
nor inventory swing in the forecast years. The production numbers generated for each
country are fed back into the “intermediate” or “petrochemical” balance in order to derive
demand for these products. Production and trade estimated for these products using the
guidelines indicated above.
Supply/demand example
This hierarchy of product balances ensures consistency throughout the IHS Chemical
database and creates a system that is flexible enough to reflect changing economic
assumptions.
Forecast of margins and profitability is also dependent on operating rates. High operating
rates lead to good margins and low operating rates lead to poor margins. Historic trends are
used to derive these forecasts.
Supply/Demand Balance Methodology
Margin Analysis
What impacts margins?
2
Supply/demand fundamentals & effect of operating rates
Operating Rates Correlate with Margins
Margin Analysis
What impacts margins?
Supply/demand fundamentals & effect of operating rates
2
An example
Southeast Asia Ethylene Economics
Dollars Per Ton
Oper. rate, %
1,600
120
Forecast
1,400
100
1,200
80
1,000
800
60
600
40
400
20
200
0
0
03
04
05
06
07
08
09
10
11
12
13
14
15
16
Cash Margin
Cash Cost
Ethylene, Spot CFR SE Asia
Oper. rate
17
18
19
20
The addition of a world scale steam cracker
(or loss of demand due to global economic
slowdown) can dramatically impact regional
operating rates due to the relatively small
size of the region.
Margin Analysis
What impacts margins
3
Return on Investment
For long-term price forecasting, an estimated percentage return on investment is then
forecast to determine the margin for a particular product for the marginal production
technology (the market setting technology).
This % return is a reflection of the product’s market outlook. This margin is then added
to the total production cost to get the price.
By evaluating what assets have returned over time we are able to determine what a
“reasonable” return is for a given plant and its risks.
Theoretically, the capital invested in the marginal production technology for a
product in a growing market must receive an adequate return on investment to
allow new supply to enter into the market to meet demand growth.
Margin Analysis
What impacts margins?
Return on Investment
Example
3
2005
2006
2007
2008
2009
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2021
2022
2023
2024
2025
Raw m aterials
Co-credit
US\$/Metric Ton
US\$/Metric Ton
1,201
1,457
1,632
2,142
1,325
1,781
1,779
1799
1871
1932
1999
2027
2088
2162
2243
2331
2425
2524
2625
2728
2831
-608
-728
-815
-1,027
-692
-867
-875
-918
-955
-986
-1021
-1036
-1069
-1107
-1150
-1196
-1246
-1297
-1351
-1405
-1459
Net feedstock
Variable
Fixed
US\$/Metric Ton US\$/Metric Ton US\$/Metric Ton
592
728
816
1,114
633
914
904
882
916
945
977
990
1020
1055
1093
1135
1180
1226
1275
1323
1372
95
110
126
166
125
156
157
160
163
164
166
169
174
180
187
195
203
212
221
229
238
70
70
77
75
79
80
81
83
84
85
85
86
87
88
89
90
91
92
93
94
95
Production Cash Cost
Total Cash Cost
Includes GSA
US\$/Metric Ton
US\$/Metric Ton
757
909
1,020
1,356
837
1,149
1,142
1125
1162
1194
1229
1245
1281
1323
1370
1420
1474
1530
1588
1647
1705
765
916
1,026
1,362
843
1,155
1,148
1131
1168
1200
1235
1251
1287
1329
1376
1426
1480
1536
1594
1652
1711
CMAI's forecast %IRR
BDE Margin Calc
US\$/Metric Ton
39%
40%
2%
60%
13%
47%
40%
34%
30%
25%
20%
17%
14%
14%
14%
14%
14%
14%
14%
14%
14%
448
458
41
734
179
604
533
465
419
356
291
253
214
217
219
222
224
227
230
234
237
BDE Price
Northeast Asia
Spot
US\$/Metric Ton
FOB S. Korea
1,213
1,374
1,068
2,096
1,022
1,760
1,681
1595
1587
1556
1526
1504
1501
1546
1595
1648
1704
1763
1824
1886
1948
Forecast % Return on Investment
Margin Analysis
What impacts margins?
Return on Investment
Example
3
Olefins/Polyolefins Operating Rate Vs Complex %ROI
Oper. rate, %
ROI, %
20
120
Forecast
100
15
80
10
60
5
40
0
20
-5
0
2000 2002 2004 2006 2008 2010 2012 2014 2016 2018 2020 2022 2024 2026 2028 2030
PE Complex % ROI
ETH SEA oper rate
HDP SEA oper rate
Introduction to short/medium/long term price definitions
Production Cost Analysis
Margin Analysis
Price Forecasting Techniques
Practical examples
Appendix – How to Access IHS Chemical’s Data
Price Forecasting Techniques
How to forecast?
2) Diagnostic checks
3) Price netbacks
Price Forecasting Techniques
How to forecast?
Cost + Margin
Example
1
Raw Materials
Co-Credits
Net Feedstock
Variable
Fixed
Prod Cash Cost
Btax Margin
Freight
US\$/Metric Ton US\$/Metric Ton US\$/Metric Ton US\$/Metric Ton US\$/Metric Ton US\$/Metric Ton US\$/Metric Ton US\$/Metric Ton
2010
2011
2012
2013
2014
2015
2016
2017
2018
2019
2020
2413
2394
2503
2602
2688
2784
2825
2913
3019
3136
3263
-1880
-1847
-1876
-1973
-2056
-2149
-2179
-2240
-2298
-2363
-2459
533
547
627
629
632
635
646
673
721
773
804
319
321
326
330
333
336
342
353
367
383
399
Cost
79
79
81
80
81
80
79
78
79
80
86
931
947
1034
1038
1046
1052
1066
1105
1168
1236
1290
+
92
14
26
111
176
244
269
263
206
148
150
Margin
(forecast)
60
60
32
33
34
35
35
36
37
38
38
Ethylene Price
Southeast Asia
Spot
US\$/Metric Ton
CFR SE Asia
1083
1021
1091
1182
1257
1330
1371
1404
1411
1422
1478
= Price
Price Forecasting Techniques
How to forecast?
2
Diagnostic Checks
• Although the “cost plus margin” approach is a
defendable starting point for price forecasting,
other factors/methods should be looked at to
fully scrutinize the forecast. “Diagnostic
Checks” are any other factors to consider to
check to make sure the forecast is defendable:
 Ratios or differentials to feedstock
(naphtha, crude oil, etc.)
 Production economics from alternative and
on-purpose technologies
 Arbitrage between regional pricing
 Integrated / non-integrated economics
Price Forecasting Techniques
How to forecast?
2
Diagnostic Checks – Example regional arbitrage
• There are plenty of examples of prices that are not derived from the cost of
making the product locally. These are considered to be “price taking” locales,
not “price setters”. This is often the case in areas where imports can quickly
flow in and satisfy demand if the price is attractive enough. This indicates that
the price elsewhere plus some kind of freight cost is setting or limiting the price
in this locale.
• The incremental production route with its associated costs serves as the
ultimate price setting mechanism. Identifying the price setting region is
important because it will determine how other regional prices are derived. For
e.g. For Benzene, U.S. will continue to be a large net importer and the high cost
production are HDAs. Global prices are based on North America HDA
production cash costs plus a delta based on freight differentials to other
markets.
Price Forecasting Techniques
How to forecast?
2
Diagnostic Checks – Example regional arbitrage
Example 2 (abstract taken from ‘Aromatics Asian Market Report’ – Feb’s issue on
Benzene International arbitrage.)
“Even
though China has very limited export volume, Asia has surplus benzene to export to
other regions. Benzene production has been ample from naphtha crackers and PX-related
aromatics plants with high operating rates. However, benzene production from gasoline
has been sluggish seasonally. Asia has no serious aromatics plant trouble and it is
currently the off-season for scheduled turnarounds. On the other hand, SM has started fullscale turnaround from January, especially in Japan. Total January- and February-loading
benzene to the US was approximately 100,000 tons. Market players are now trying hard to
load benzene in March to the US. A Japanese player fixed 5,000 tons of benzene to the
US, shipping directly from Chita. However, the US market is in very steep backwardation.
The arbitrage opportunity for Asian players to ship benzene to the US is at risk.”
Price Forecasting Techniques
How to forecast?
2
Diagnostic Checks – Example regional arbitrage
Example 1
Ethylene
International Spot Arbitrage Opportunities
Estimated Freight Rates
March 2010
(Dollars Per Metric Ton)
N.W. Europe
1,000 - 1,140 CIF
105 - 115
U.S Gulf
Mediterranean
1,500 - 1,545 delivered
370 - 380
210 - 220
N.E. Asia
45 - 55
1,130 - 1,200 FOB (n)
Middle East Gulf. 1,170 - 1,240 CFR (n)
290 - 300
180 - 190
S.E. Asia
80 - 100
1,120 - 1,150 FOB (n)
1,120 - 1,200 CFR (n)
40 - 60
Inter-region trade flows / arbitrage movements
Inter-region arbitrage movement limited / closed
Inter-region, potential arbitrage opening
Nomenclature
n - nominal as no / little movement
e - estimated
~
Price Forecasting Techniques
How to forecast?
3
Price Netbacks
• In-region net realized selling prices less the applicable, seller incurred expenses for
local delivery, storage and terminalling, ocean freights, duties, handling, and
miscellaneous other expenses.
Netback Price = Seller Price – ocean freight – duty – handling – local delivery –
storage & terminalling – other miscellaneous cost
• Price netbacks are used to calculate the margins for a certain product where:
Margin = Netback Price – Production Cash Cost
• The Cash cost margin excludes other major cost and accounting items that all
businesses must make a provision for (e.g. corporate charges, depreciation,
working capital, shareholder return etc).
Price Forecasting Techniques
How to forecast?
4
Price Netbacks
Example – Implication of netbacks on margins; abstract taken from ‘Aromatics Asian
Market Report’ – Feb’s issue
“Styrene netbacks have been unexpectedly good for the industry, particularly for the
integrated producers. SKE’s defiant defence of the price in the face of benzene
weakness also boosted non-integrated economics to the point that they are marginally
positive for the first time since last August.”
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