Consider new approach to estimate styrene industry metrics Key benchmarks define operations performance for global industry

R. B. Jones and J. s. Jeng, HSB Solomon Associates LLC, Dallas, Texas

The styrene industry is facing major restructuring due to current market oversupplies and future capacity additions under construction. These challenges are

not caused by the normal business cycle, overbuilding or the economy. Instead, they are a result of a chronic oversupply primarily related to the market losses in polystyrene (PS)—styrene’s largest derivative.

PS accounted for nearly 60% of the total styrene demand in the 1980s and more than 50% in the 1990s. In 2006, PS market demand for styrene fell to 43% of total monomer consumption. By 2011, forecasts estimate that PS demand will fall below 40%.1

This demand decline is driving the need to transform the styrene monomer (SM) industry. Such strategic restructur-ing will require data on the current and forecast economic value of SM production and on cost-efficiency options to reduce expenses and increase operational efficiencies wher-ever possible.

Performance metrics. Using information from a global styrene benchmarking study, a new approach to estimate styrene performance metrics has been developed. Benchmark-ing results can provide performance insights for individual participants as well as the industry as a whole. The key is the study’s industry participation is a sufficient representa-tive sample of the worldwide SM industry. Accordingly, the gathered information could be used to develop industry performance estimates. Using 2005 data, selected key per-formance metrics were developed for the global SM industry. The objective of this project is to provide the best informa-tion possible to industry corporate leaders to assist them in the planning and strategy analysis associated with the major restructuring challenges that lie ahead.

This statistical approach to estimate industry-level key performance indicators for the ethylbenzene-styrene (EBSM) processing industry is based on validated data from the global styrene benchmarking or comparative performance analysis (CPA) study. Statistical bootstrap techniques are applied to the EBSM performance data to compute industry-level esti-mates for capacity utilization, net energy consumption, return on investment (ROI) and maintenance cost indicators.2,3

Using these techniques, a Monte Carlo random selection method with replacement is applied to select processing plant data from the database to form an industry sample. These values are stored as one Monte Carlo trial. The computer simulation model performs several thousand re-sampling tri-als, and uses the re-sampling data to compute mean, variance, quartile, decile or other statistical estimates with associated confidence intervals.

The confidence intervals are computed directly from the data and do not rely on any assumptions about the statistics’ underlying distributions. This attribute is especially useful for evaluating quartile or other percentile statistics, since these performance boundaries are normally not reported with confidence intervals. In this paper, statistical bootstrap tech-niques are applied to develop EBSM industry performance value distributions for each of the operational attributes mentioned previously.

Historical origin of statistical bootstrapping. The origin of this statistical method comes from the conver-gence of computing technology, theoretical statistics and the practical need to infer information from sampling surveys. In the 1940s and 1950s, US statisticians—especially in the government—were combining stochastic (random) and sys-tematic sampling methods for finite populations to provide more insights from the sampling surveys. The idea of apply-ing random sampling from finite populations was not unique to the US. More than 80 years ago, statisticians in India were applying random sampling to estimate crop yields, and the origin of statistical bootstrapping appears to date back several decades further.4

The Monte Carlo method started systematic development around 1944, coupled with the development of specialized computers to model the transport of neutrons through shield-ing and fissile material. The technique used the computer as a record-keeping device to store the outcome of a large number of neutron trajectories where each path was determined by random selections from given probability distributions.

Until the late 1970s, computers required large capital and human investments that limited their availability to govern-ments, universities, large business and specialized industries. However, as computing technology and user-oriented soft-ware became more prevalent, computing access became avail-able to a growing segment of users in science, engineering, business and the general population worldwide.

Footnote:HSB Solomon Associates LLC recently conducted its third worldwide ethylbenzene–styrene monomer (EBSM) plant performance analysis using data for operating year 2005. Study participation represented 50% of the world’s styrene-producing capacity. All major styrene-producing regions were represented, including the Middle East/Africa, Asia-Pacific, Europe, North America and South America.

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Statistical bootstrapping got its name from the concept of “pulling oneself up by the bootstraps.” The analogy is that, although the collected sample may be limited, the data are representative enough to be applied to infer population-level information, thereby providing the necessary statistics for improvement. The method, designed uniquely for computer applications, has three basic steps:

1. Randomly select data values from a data set, allowing data values to be selected more than once, i.e., selection with replacement.

2. Compute the desired statistics from this computer-generated selection and store the current results for future reference.

3. Repeat Steps 1 and 2 for several iterations and then com-pute the final statistical calculations from the stored data.

Bootstrapping eBsM performance benchmarking. In performance benchmarking, the study data represents a sample from the total population of EBSM plants. The study results provide “exact” statistical indicators of performance for within study (sample) comparisons. This information is valuable in several ways, provid-ing insights on comparative performance, performance gaps and general guidelines for discovering areas where improvements can be made. However, there are ques-tions, such as:

How do a processing plant’s key performance metrics compare to the entire industry and not just to the plants

that participated in the study?

This question is a fundamental issue in benchmarking studies, since 100% industry participation is rarely achieved. It becomes especially relevant considering the EBSM indus-try’s global nature. To address this question, we first examined how representative the operational characteristics in the study data are, relative to active plants in the global marketplace.

Participant input data in the CPA database represents approximately 45% of the total number of EBSM plants and 50% of the total world EBSM production capacity [excluding production plants less than 200,000 metric tpy and propylene oxide/styrene monomer (POSM) plants)].5 These facts, cou-pled with the significant ranges of key operational character-istics shown in Table 1, indicate that the study participant data represents global EBSM industry operations adequately to apply the bootstrapping method to infer industry-level performance indicators.

The bootstrap method applies the inherent variability in the database (study) as a model of the variability of the population (industry). By random selection with replace-ment, the variability of the population is simulated by the variability in the statistics computed at each trial or boot-strap sample. This process eliminates the need to assume the form of underlying distributions of key statistical indicators. For example, the bootstrap approach does not require that quartile statistics, population distributions or any other statistic follow a normal distribution. This is a considerable advantage of bootstrap sampling, in that confidence intervals can be developed for any statistic directly from the sampled data without prior knowledge (or assumptions) of the theo-retical nature of the statistic.

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Capacity utilization distribution for eBsM plants.Fig. 1

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net-energy consumed distribution for plants operating various licensed sM technologies

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net-energy consumed distribution for plants operating the same licensed sM technology.

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return on investment distribution as percentage of total applied capital.

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We developed bootstrap estimates of the industry-wide distributions for these performance indicators:

• Capacity utilization as percent of capacity• Energy consumption, Btu/lb of SM• ROI as percent of replacement value (RV)• Maintenance cost indicator (MCI).A bootstrap sample size of 21 was applied from the study

database. In all cases, 20,000 iterations or re-samplings were used to compute the detailed percentile results and to con-struct the performance distributions.

Benchmark metrics. The capacity utilization (Fig. 1) is defined as the actual annual SM production divided by the annualized maximum 30-day capacity. To improve data consistency, planned turnaround provisions are excluded. The most probable capacity utilization rate is approximately 91%. The tails of the distribution suggest that more plants are operating in the 80 % –90% utilization range than in the 93% –95% range. Compared to historical performance, the current capacity utilization is relatively low, even for the sty-rene industry. In an up-cycle, we would expect this utilization to be closer to the mid-90% range.

Net-energy consumed values, shown in Fig. 2, are expressed in terms of the Btu/lb of SM produced. Net-energy consumed is defined as purchased energy plus consumed plant-produced energy minus exported energy. It is expressed on a net or lower heating value basis. The distribution’s bi-modal nature is probably due to the net energy consumption differences in the various SM manufacturing technologies represented in the database.

When the model was run with plants from a single SM technology group, the distribution curve (Fig. 3) did not exhibit the bi-modal nature as shown in Fig. 2. When study-ing the single-technology curve, more plants are operating in the 5,000– 6,000 Btu/lb range than in the 9,000 – 10,000 Btu/lb range. Although energy consumption varies with actual production rate, we do not expect an individual plant’s energy performance to change significantly unless there is a technology step-change or there have been targeted energy efficiency improvements. Current higher energy pricing may foster more energy improvements in the future.

ROI, shown in Fig. 4, is defined as net cash margin expressed as a percentage of total applied capital. Net cash margin is computed as gross margin minus cash operating expenses. Total applied capital is the 2005 calculated replace-ment value plus working capital required for inventories.

The distribution curve shows that the most likely ROI is approximately 4.8%. The distribution also depicts the true extent of the industry’s margin under-performance with 50% of plants operating at an ROI of approximately 5% or less. At these extremely low return rates, EBSM plants are barely breakeven on a full-cost basis (costs beyond cash cost). We would expect few or no grassroots styrene plants to be built until supply and demand balance reaches a point that will support higher margins. However, we are aware of planned new capacities in addition to the industry’s existing supply overhang. These new investors/owners are apparently either not concerned about tipping the supply and demand balance or they are anticipating rationing of less-efficient facilities.

Maintenance is the largest category of plant non-vol-ume-related cash expense and is second only to energy in determining cash expense performance. MCI is a calculated performance metric based on annualized turnaround cost plus routine maintenance cost expressed as a percent of plant replacement cost.

As shown in Fig. 5, the most likely MCI is approxi-mately 1.5%. About 50% of plants appear to be operat-ing at a MCI of 1.6% or more. The distribution curve shows that it is more likely that EBSM plants’ MCI are in the 1% –2% range than in the 2% –3% range. The distribution’s flatness also suggests that the industry’s performance on maintenance cost has a significant degree of variability. This variability and range of maintenance cost performance is quite typical across all of Solomon’s benchmarking studies, demonstrating clearly that very few plants can control maintenance costs consistently from year to year.

outlook of industry study. Statistical bootstrap meth-ods are widely accepted, robust tools for inferring industry-level information from a collected, validated sample (study) of performance data. Key success factors for the applicability of bootstrap methods for CPA studies are:

• Sample (study) size is reasonable relative to the industry population

• Study participation is representative of industry performance• For both of these factors, the current study database is

sufficiently representative to enable the useful application of bootstrap methods to infer industry-level performance.

The graphical results supply a semi-quantitative view of the overall performance variation that is being achieved across the industry. The results in this analysis reflect actual performance and market conditions of 2005. Therefore, the distributions are not forecasts of future industry per-formance but a quantified estimate of industry-level past performance. This information provides a basis for develop-ing forecasts, planning strategic restructuring, and analyzing expense efficiency.

Table 1. Range of key plant characteristics

eBsM range†

Average annual SM capacity, million lb 2,500–400

Capacity utilization, % of rated capacity 99–70

USGC base replacement cost, $ million 650–150† Range I Average of highest two plants – Average of lowest two plants

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0.40 0.90 1.40 1.90 2.40 2.90Maintenance cost indicator

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Maintenance cost indicator distribution for eBsM plantsFig. 5

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Additional analyses based on the bootstrap-derived indus-try model could include specific statistics, e.g., higher and lower percentiles, coefficients of variations (standard devia-tion/mean), and more advanced correlation investigations. However, given the current state of this industry, estimating industry-wide performance is more insightful and potentially strategically applicable than discrete percentile or quartile values. Additional metric distributions could be generated for other performance attributes and sub-populations, such as region and other peer group classifications. HP

LITERATURE CITED1 Sinclair, V., “The Styrene Market—life after polystyrene,” 2007 World

Petrochemical Conference, Houston, Texas, March 21–22, 2007.2 Efron, B., “Computers and the theory of statistics: Thinking the unthinkable,”

SIAM Rev., 21, pp. 460–480, 1979.3 Efron, B., “Bootstrap methods: Another look at the jackknife,” Ann. Statist.

No. 7, pp. 1–26, 1979.4 Hall, P., “A Short Prehistory of the Bootstrap,” Statistical Science, 2003, Vol. 18,

No. 2, pp. 158–159, Institute of Mathematical Statistics, 2003.5 ICIS Plants and Projects, Reed Business Information Limited. Registered

Office: Quadrant House, The Quadrant, Sutton, Surrey.

Richard Jones is responsible for the development and applica-tion of numerical and statistical methods to measure and analyze operational performance from Solomon’s and customers’ data-bases. He has more than 28 years of experience in corporate and industrial risk management, insurance and risk-based methods and

is a frequent speaker on the role of risk management as a tool to improve reliability. He has more than 50 publications and meeting contributions on various topics relat-ing to reliability and risk management. Dr. Jones has authored two books on risk management entitled, Risk-Based Management: A Reliability-Centered Approach and 20 Percent Chance of Rain: Your Personal Guide to Risk. He can be reached by e-mail at: Rick.Jones@solomononline.com.

Jeanne Jeng is the EBSM study manager and a senior petro-chemicals consultant at HSB Solomon Associates LLC, an interna-tional management consulting company headquartered in Dallas, Texas, providing performance benchmarking and performance improvement consulting services. She holds a BS degree in chemical

engineering from the Georgia Institute of Technology. Ms. Jeng has more than 27 years of petrochemicals manufacturing with consulting experience and is a registered engineer. Her areas of specialization include feedstock optimization, economic analy-sis, operations strategies, and development of computer models supporting various production planning and scheduling functions. Ms. Jeng can be reached by e-mail at: Jeanne.Jeng@solomononline.com.