Chemical and Material Shortage Alerts – October 2013

The purpose of this blog is to identify chemical and material shortages reported on the Internet.  The sources of the information reported here are primarily news releases issued on the Internet.  The issue period of the news releases is from the middle of September 2013 to the middle of October 2013.

Section I below lists those chemicals and materials that were on the September 2013 Chemical and Material Shortage Alerts list and continue to have news releases indicating they are in short supply. Click here to read the September 2013 Chemical and Material Shortage Alerts list.

Section II lists the new chemicals and materials (not on the September list).  Also provided is some explanation for the shortage and when appropriate geographical information.  The blog attempts to list only actual shortages situations – shortages are being experienced currently as of the news release.   Chemicals and materials identified in news releases as only being in danger of being in short supply status are not listed.

Section I.   Chemicals and materials that continue from September to be reported as in short supply are: coal; copper scrap; helium; iron ore; hydrochloric acid; palladium; propylene; tin; and urea.  See the September list (click here) for explanations for the shortages and for geographical information.

Section II.   Shortages Reported in October Not Found on the  Previous Month’s List

Aluminum Scrap.  Scrap aluminum metal shortages are being experienced in China and in Europe.  One reason for the shortage is the increased demand for aluminum in China, with the supply of scrap aluminum not keeping up with the demand.

Jade.  A jade shortage is being experienced in Asia.  Prices have been increasing sharply.  The reason is that that jade is an important status symbol in many Asian cultures and the increased prosperity of many Asians is increasing the demand for jade.

Leather.   Leather has been in short supply in at least three countries: China; Pakistan; and Uruguay.  An explanation for shortages in Chain is the increased environmental regulations at tanneries, decreasing the leather output.   In Pakistan, an explanation is the decreased slaughter of sacrificial animals for religious observance purposes.   A reason in Uruguay is a much lower slaughter of cattle.

Sand.   Due to government-imposed restrictions on sand excavation, inadequate sand supply is available in parts of India to meet the construction industry’s demand.

Styrene.   Styrene production in China is not keeping up with its demand for use in polystyrene production.  One reason is that several styrene-producing facilities in many parts of the world have been down at the same time due to maintenance and technical problems.  This has resulted in a tight styrene supply world-wide.

Reasons for Section II shortages can be broadly categorized as: 

1.  Mining not keeping up with demand: jade;

2.  Production not keeping up with demand: leather; styrene;

3.  Government regulations: leather; sand;

4.  Sources no longer available: aluminum scrap.

Chemical Plant Concentrations by States in the United States

The United States Department of Labor’s Bureau of Labor Statistics (BLS) provides employment data by states for specific occupations.  One occupation for which employment data is provided is chemical plant and system operators.  Using this data as a surrogate for the number of chemical plants by states (the correlation between chemical plant operators and chemical plants should be high), I generated the two graphs below.

The first graph shows the BLS chemical plant operator employment data by state.  Red represents the state (Texas) with the highest number of chemical plant operators (6,820 operators; as of 2012; based on BLS surveys).  The graph shows in the lower left corner what the states’ colors indicate; from light green to dark green to blue to red signifying increasing numbers of chemical plant operators (and presumably numbers of chemical plants).   Based on the data used for the graph, Texas has the higher number, followed by Louisiana, South Carolina, and Ohio.  BLS reports no data for the states in white.

The second graph shows a somewhat different picture.   On the graph, states are colored by the number of chemical plant operators per state’s square mileage.   Note that whereas New Jersey and Massachusetts do not show high numbers of operators on the first graph, the second graph shows a high concentration of chemical plant operators (and presumably chemical plants) per square mile in these states.  Absolute numbers (number of employed plant operators) versus concentration numbers (density; rate) for other states are also different, e.g. Texas and California.  Data used to generate the second graph show that New Jersey has the highest density of operators (and presumably plants) followed by Louisiana, South Carolina, and Ohio.

Having relative comparisons of chemical plant operator concentrations (reflecting chemical plant density) seems to me to be a useful metric, perhaps more useful than the absolute numbers of operators and plants.   Such a metric could be of value to chemical companies seeking a site for a chemical plant.  Selecting a state with a high concentration (rather than only a high absolute number) might be advantageous.  For example, finding chemical operators to employ might be easier in high concentration states and high concentrations indicate clusters and clusters are known to lead to competitive advantages. 

The chemical plant operator employment data can be generated at a Bureau of Labor Statistics (BLS) site (click here).  Details on the BLS occupational employment statistics program can be found by clicking here.  Square mileage per state data is available at this site (click here).

Chemical and Material Shortage Alerts – September 2013

The purpose of this blog is to identify recent chemical and material shortages reported on the internet.

Coal.  Shortages of coal exist in India and Pakistan.  India is significantly increasing the amount of coal imported.  Primary suppliers of coal to India are Indonesia, Australia, and South Africa.  Although India has a fairly large amount of coal in the ground, quantities mined are not keeping up with demand.

Copper Scrap.   Scrap copper metal shortages are being experienced in China and in Europe.  This is leading to a decline of refined copper production.  One reason for the shortages is less construction is being demolished globally, and therefore less copper scrap.  Also, China is more restrictive on what copper scrap can be imported, due to increased environmental standards.

Helium.  Helium global shortages continue to be reported on the internet.  The primary problem seems to be that the US Government has been selling helium reserves at below market prices, so companies are reluctant to maintain and expand helium production.  Most helium production and inventory is currently in the United States.   The US Congress has recently directed a change in the US Government’s selling helium at below market prices, but this will take some time, e.g. a year or more, to correct the supply-demand in-balance.  

Iron Ore.   India’s steel mills continue to not have enough iron ore supplies available to meet their needs.  The supply problem apparently is caused, at least partially, by Indian-government bans, for environmental reasons, on iron ore mining in some regions.

Hydrochloric Acid.  Pakistan has been experiencing a shortage of hydrochloric acid, affecting certain manufacturing sectors.  The cause seems to be a ban on hydrochloric acid production imposed by the Pakistani government.

Natural Rubber. A global shortage of natural rubber supplies globally is increasing the use of synthetic rubber.  The shortage of natural rubber is primarily due to harvesters in Southeast Asia not being able to keep up with the demand.

Palladium.  Due primarily to increases in automobile production, especially in emerging markets, global demand for palladium is exceeding the supply in 2013.

Propylene.  Due to a higher reliance on natural gas as a petrochemical feedstock in the United States, due to the shale gas bonanza, less propylene is being produced from petroleum.  This is creating a propylene supply deficiency.

Tetracycline.  Tetracycline continues to be in short supply in the US market (since 2011).  A problem seems to be shortages in active ingredients that are used in producing tetracycline, a shortage due to economic reasons.

Tin.   The global supply of tin should continue to be tight due to Indonesian government restraints that applied to tin exports.  Indonesian production capacity of tin accounts for about 40% of world production capacity.   Demand has exceeded supply for several years causing tin prices to triple since 2005.  The primary use of tin is in soldering needed in electronic devices.

Urea.   India has a shortage of indigenously-produced urea.  One reason is that the price of imported urea is much less than the cost for Indian manufacturers to produce urea.  Pakistan also must import urea to meet the country’s needs.

This blog provides the locations and other limited information on 11 chemicals and materials recently reported on the internet as being in short supply.

Reasons for the shortages can be broadly categorized as: 

1.  Mining not keeping up with demand: coal, palladium;

2.  Production not keeping up with demand: natural rubber, propylene, urea;

3.  Government regulations: helium, hydrochloric acid, iron ore, tin;

4.  Sources no longer available: copper scrap, tetracycline.

Ideas Matter – Gross Profits and Research & Development Expenses Correlate in the Chemistry Industry

2010 and 2011 revenues, gross profits, and research & development (R&D) expenses were found for 25 chemical companies from data on submitted 10Ks to the US Securities & Exchange Commission.   These data was obtained because of an interest in examining the concept that a company's R&D expenses can represent how well a company’s “good” ideas (with R&D expenses reflecting such ideas) correlate with how well a company generates value.  The assumption is made that if there is such a correlation, than the R&D expenses as a percentage of revenues would show a correlation with gross profit margin percentages (GPM%) for a series of chemical companies.

The following two graphs show the GPM% for 25 chemical companies plotted against the corresponding R&D expenses as a percentage of revenues for the 25 companies.   These two graphs show what appears to me to be a good visual correlation between the two sets of data.  That is, as R&D expenses represent a higher percentage of the revenues for a company, the GPM% goes up.  Regression analysis (using Excel) was done to determine R-Square values for the two sets of data.  For the 2010 data, R-Square results were 63% and for the 2011 data, 65%.  Both R-Square values generally are considered to show good correlations between sets of data.

A basic finance principle is that it is primarily “ideas” (good projects) that matter in creating a company's financial value.  If the assumption is that a company that puts more emphasis on R&D could be expected to generate more good ideas, then one might expect a correlation between such companies’ emphasis and the value it creates.  Another assumption is that GPM% relate to values creation, with higher GPM% creating more value.

The chemical industry is a good sector to use R&D expenses as a surrogate for idea generations since,  antidotally, R&D has long been recognized as critical for chemical companies in creating new products (in having good ideas).   Two to five companies were selected from the US Census Bureau’s seven sub-categories (basic; plastics; agriculture; pharmaceuticals; paints; soaps; and other).  (The companies identify their business sub-categories as in the 10K filings.) 

The 2010 average GPM% and the percentage of R&D expenses of revenues for the 25 chemical companies are 41.7% and 5.5%, respectively.   In 2011, the respective averages are 42.6% and 5.2% for the 25 companies

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.

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.

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.