Methodology

MARKETS

Capacity Availability and Forecasting

methodology

The Petrochemical Simulator includes a full capacity database for every petrochemical producing plant in all the global regions to generate availability of petrochemical supply.  The capacity listings include the production process, the consumption factors of all raw materials, the yield of the main product and any co-products, the current capacity to produce and changes to the capacity due to expansions etc.

The capacity to produce petrochemicals from existing and planned projects is continuously researched and crosschecked with industry participants.  Announced new production plants and projects in the planning phase provide a guide on the likely capacity available in each region for the next five to eight years.  Thereafter, new capacity is forecast based on likely investment strategies under the macro-economic scenario being considered.

 

End-Use Consumption Forecasting

Consumption growth of commodity polymers and other "end-use" intermediates may be related to economic activity in the consuming region.  Consumption of end-use materials in the major economies is researched to determine the link between sectors of the economy and consumption.  Demand for a particular polymer or intermediate can be linked to the sum of the demand into each of the end use sectors.  Growth in each end use sector is made up of four additive elements:

  • demand due to growth of the end use sector
  • demand due to penetration into the sector for new applications
  • reduction in demand due to recycling, downgauging of polymers and substitution by other materials/polymers
  • demand due to cyclical downstream inventory changes.

For less developed economies, where data on individual sectors of the economy is less readily available and where the "services" sector of the economy is a much lower proportion, GDP is a fair substitute for petrochemical demand drivers.  In these regions the end-use growth is driven by the four elements but applied to a single economic driver.

 

Production and Trade Forecasting

Having forecast the regional consumption for each end-use the simulation model generates global production and trade of the end-use products and the monomers and feedstocks used to produce the end-use materials.  The consumption of monomers and intermediates is related directly to the regional production of the downstream derivatives.

The simulation model incorporates a detailed logistics and trade model to allow integrated forecasts of global trade balances.  The trade balances use demand forecasting, capacity availability and trade drivers to forecast global supply, demand and trade.

 

ECONOMICS  - Profitability and Price

The Petrochemical Simulator relates market demand drivers to petrochemical consumption.  From a database of petrochemical processes and plant capacity the regional consumption is then compared to the ability to produce.  Global trade algorithms, driven by a comprehensive logistics model, add to the simulation of trade and build to a full supply, demand and trade model of the industry. Basic commodity theory dictates that market tightness, measured by average operating rates, is the primary driver of profitability with inter-regional competition and inter-material competition adding to the complexity of price and cost drivers.

 

Price Influences

The primary drivers of price for most products are a combination of the cost of raw materials and the supply/demand balance of the market.  These two combine to drive cost plus margin to derive price.

The variable cost of production is determined from raw material costs and the cost of utilities less credits for co-products.  To this are added the fixed costs associated with running the plant consisting of operating labour, maintenance, general plant and works overheads and tax and insurance to give the cash cost of production.  Cash costs for the forecast period are projected based upon raw material costs (usually influenced by crude oil) and the other associated costs of production, making assumptions about the reduction of costs over time due to experience curve effects.  The margin is determined from the return on investment (ROI) forecast which, in turn, is derived from an analysis of the historical relationship of margin with average industry operating rate.  The combination, cost plus margin, making allowance where appropriate for freight and packaging, produces the price.

 

Secondary influences on the price forecasts include:

  • Forecast prices in other regions (USGC and South-East Asia)
  • Relationship to related products (e.g. inter-polymer relationships)
  • Profitability of upstream and downstream processes.

Analysis of the range of costs in a market uses the Leader/Laggard methodology to model the higher and lower cost producers and to forecast the limits on upper and lower margins.