Thursday, August 22, 2013

Chemistry Industry Financial Ratios from US Census Data

In an early blog (Chemical Industry Data Found at the US Census Bureau Website; click here), I identified some of the data that the US Census Bureau collects and makes available on its website about the chemical industry.  In this blog, I provide results of my analysis of some of the chemical industry financial data provide by the US Census Bureau.

From the financial data provided by the US Census Bureau (click here for the data), I determined an approximate gross profit margin percentage (GPM%) and a percentage of revenues that chemical companies, surveyed by the Census Bureau, have spent on capital projects and on payroll for 2010 and 2011.

For all chemical companies surveyed, the GPM% is 49% for 2010 and 48% for 2011.  This assumes that the data presented by the Census Bureau under the total value of shipments column represents revenues and under the total cost of materials column, the cost of the shipments.  All chemical companies’ capital expenditures were approximately 3% of revenues in both 2010 and 2011.  Payroll expenses were approximately 7% of revenues in 2010 and a slightly less 6% in 2011.

In addition to providing financial data on all chemical companies surveyed, the Census Bureau also categorizes the chemical companies into seven sub-categories and provides data for each sub-category.  These sub-categories are: basic; plastics; agricultural; pharmaceuticals; paints; soaps; and others.  A more detailed description of what the sub-categories cover can be found at the Census Bureau webpage I provide a link to above.  I also computed GPM%, capital expense as a percentage of revenues, and payroll expense as a percentage of revenues for these seven sub-categories.    I can provide these numbers to you if you email me.

The US Census Data used to compute these financial ratios (GPM%; capital expenditure as a percentage of revenues; and payroll as a percentage of revenues) are probably the best data available.  Being able to benchmark your company’s performance against the ratio results from the Census Bureau data should be useful.

Wednesday, August 21, 2013

Estimating Gross Profit Margin Percentages for Raw Material to Chemical Product Conversions

In my last blog, I wrote that the butadiene product price was on average 2.6 times the raw material oil price.  In other words, the sales price (revenue price) of butadiene, on average, was 2.6 times the cost of goods (cost of raw material - oil price), from which the butadiene was obtained. This 2.6 average is based on price data for oil and for butadiene from 1988 to 2012,

Using the same data, a gross profit margin percentage (GPM%) for the butadiene sales can be found since sales prices (of butadiene) and cost of good (oil) prices are known.  The average GPM% for the oil to butadiene process is 57% based on the 1988 to 2012 data.   Note this only includes the oil raw material as a cost of good sold.  It does not include other costs of goods, such as labor.  Including these other costs would reduce the 57% GPM% for the business of converting oil to butadiene.

The same GPM% computations should be possible for other raw material to chemical product conversions, if annual raw material and product price data is known.  To test this, I found Brazilian price data of sugarcane and ethanol and US corn and ethanol price data.  Then from this data I determine GPM%s for the conversion of raw material to chemical product..

Brazilian sugarcane and ethanol price data was found (e.g. click here and here) searching the Internet.  For anhydrous ethanol, I found the average GPM% to be 41% and for hydrous ethanol 33% (date range from 2008 to 2012).   In order to do these computations, an amount of ethanol from sugarcane needs to be assumed (85 liters from one ton of sugarcane for anhydrous alcohol and 89 liters for one ton for hydrous ethanol, as reported in Brazilian literature).  Again, as in the case of oil to butadiene, these GPM%s are only for the raw material to product conversion; they do not include other costs of goods.

For US corn conversion to ethanol (e.g. click here and here for price data for corn and ethanol), the average GPM% for the years 1996 to 2012 is 37%.  This includes an assumption of obtaining 2.8 gallons of ethanol from one bushel of corn, which seems to be the present accepted amount.

My experience is that knowing the actual GPM% for an industrial sector (e.g. butadiene and ethanol producers) can be a very useful benchmark metric for single producers to use in evaluating their performance.   I would be happy to research and analyze other raw material to chemical product price data and try to compute gross profit margin percentages from that data.  Please email me if you are interested.

Thursday, August 15, 2013

Butadiene Production and Price Trends

The first two graphs below show approximate annual butadiene global production amounts and approximate average price amounts for butadiene from 1988 to 2013.   This data was obtained from various websites found by exhaustively searching the Internet.  The data sources are believed to be reasonably reliable.  (One purpose of this blog is to indicate that such data is openly available on the internet.)

Another purpose of the blog is to analyze butadiene prices.

A regression analysis (using Excel) was done to determine the relationship between changes in butadiene production and butadiene price.  An R square value of 71% was found, indicating a reasonably good connection between changes in production with changes in price of butadiene.

A regression analysis was done to determine the fit of the changes in average oil (Brent) prices with changes in butadiene prices.  The R square value is 74%, indicating a reasonably good connection.  (Changes of Brent prices over time are shown in the third graph below.)

A regression analysis was done on the changes in butadiene production, year to year, from 1988 to 2012, with the changes in oil production over the same period.  The R square value was a convincing 98%, indicating a strong connection between the amounts of butadiene produced to the amount of oil produced.   So, it is not surprising that butadiene prices directly and strongly relate to oil prices.   (Changes in oil production over time are shown in the fourth graph below.)

From 1988 to 2012, the butadiene price was on average 2.6 times the oil price.  The standard deviation (using Excel) for this average 2.6 is 1.0.  Therefore, with a good probability, the butadiene price can be estimated to be between 1.6 and 3.6 times the expected oil price, assuming the above is correct.      

Another interesting idea about the 2.6 number is that it might represent a “premium” – an additional cost for the processing of butadiene from oil.  Such a number might be used as a benchmark, to achieve or surpass.

The close correlation between changes in butadiene prices with oil prices suggests to me that the raw material cost (e.g. cost of oil) is an important variable cost.   This would seem to offer a real opportunity for producers of butadiene using cheaper raw material costs, e.g. microbial fermentation of sugars.

With cheaper raw materials (and cheaper butadiene prices), more value should be created for both butadiene producers and users.  Also, with such a raw material as sugar, much less price variance in the raw material would be expected (compared to oil) leading to greater stability in planning and production, another value-creating result.