benchmark transnet limited's petroleum pipelines
TRANSCRIPT
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Benchmarking Transnet Limited’s
Petroleum Pipelines
December 2011
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Table of Contents
Page
CHAPTER 1 ................................................................................................................ 1
1.1 Introduction ........................................................................................................ 1
1.2 Background ....................................................................................................... 2
1.3 Theoretical overview.......................................................................................... 3
1.3.1 Literature Review .................................................................................... 3
1.3.2 What is Benchmarking? .......................................................................... 3
1.3.3 Applications of Benchmarking for Regulatory Purposes ......................... 4
1.3.4 Performance Indicators and Benchmark Measures ................................ 5
1.3.5 Statistical Benchmarking Methods .......................................................... 6
1.3.6 Examples of Benchmarking Studies ....................................................... 7
CHAPTER 2: Benchmarking the Efficiency of Transnet’s Operations........................ 9
2.1 Ratio Analysis .................................................................................................. 14
2.2 Data Envelopment Analysis .............................................................................. 25
Conclusion and Recommendations ........................................................................... 31
Bibliography .............................................................................................................. 32
Annexure A ............................................................................................................... 33
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CHAPTER 1
1.1 Introduction
The National Energy Regulator of South Africa (NERSA or ‘the Energy Regulator’)
indicated in its Reasons for Decisions (RfD) on Transnet’s 2011/12 petroleum
pipeline tariff that it would investigate the possibility of meaningful benchmarking of
Transnet’s petroleum pipelines.
To fulfil this undertaking by the Energy Regulator, the concept of benchmarking
Transnet’s petroleum pipelines has been explored and as a result this report has
been produced to solicit public comment and guidance on this benchmarking
initiative. This report contains a theoretical overview of benchmarking including
reviews of international literature on benchmarking, precedents for the use of
benchmarking in regulation, and an overview of the three most-used benchmarking
methodologies in the energy sector.
To build organisational understanding of benchmarking and its role in the energysector, a two-day training course conducted by NERA on Benchmarking was held in
Pretoria on 13 and 14 October 2011. Delegates included staff from the three
divisions within NERSA, as well as other stakeholders affected by or interested in the
business of regulation.
A first attempt at benchmarking the efficiency of Transnet’s petroleum pipelines
operations has been conducted by comparing Transnet with a group of proxycompanies. NERSA has conducted a ratio analysis of performance measures based
on an international review of related literature and consultations with leading
consulting firm in energy regulation, NERA Economic Consulting.
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1.2 Background
Since the 1990s, many regulators of infrastructure industries around the world have
adopted incentive-based models of regulating natural monopoly activities, with the
aim of promoting improvements in efficiency in the absence of market mechanisms1.
A central issue to be confronted when promoting efficiency is how the efficiency
requirements are to be set. One approach is through the benchmarking of utilities
based on their relative efficiency.
Benchmarking identifies the efficiency levels of the population of firms in the sector
and measures the relative performance of the target firm against these based on
various metrics. Countries such as the Netherlands, the United Kingdom, Norway,
Canada and Japan have adopted benchmarking as part of their regulatory
processes.
Regulators can use cross-country benchmarking in order to evaluate the
performance of utilities within the larger context of international practice. International
comparisons enable regulators to measure the efficiency of utilities in comparison
with international best practice2.
A review of literature on the use of benchmarking in economic regulation, an
overview of the methods of benchmarking and examples of benchmarking are
discussed in the theoretical overview section of this report. The second chapter of
this report contains an overview of NERSA’s approach to benchmarking, as well as
the results of NERSA’s first attempt at benchmarking.
1Benchmarking and incentive regulation of quality of service: an application to the UK electricity
distribution networks, Energy Policy 33 (2005)2 International benchmarking and regulation: an application to European electricity distribution utilities,Energy Policy 31 (2003)
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1.3 Theoretical overview
1.3.1 Literature Review
This report focuses on utility benchmarking studies in the regulatory arena. There is
a vast amount of literature on regulatory benchmarking, including published
academic literature and work done by research firms that are specialists in the field.
This report relies heavily on publications by the Pacific Economic Group (PEG)3,
Frontier Economics4 and First Quartile Consulting and LLC (1QC) Elenchus
Research Associates (ERA), Inc5. These are leading consulting firms in utility
regulation and have done some benchmarking of energy utilities for Regulatory
bodies in North America and Europe.
1.3.2 What is Benchmarking?
Broadly, benchmarking can be defined as a comparison of some measure of actual
performance against a reference or benchmark performance6.
Frontier Economics defines a benchmark as a standard by which something may be
measured or judged and benchmarking as the process through which a benchmark
is identified7.
The Pacific Economic Group (PEG) describes benchmarking as a scientific approach
to performance measurement that makes extensive use of data on utility operations.
Indicators that reflect important dimensions of company performance are chosen.
Company values are then compared to benchmarks that reflect the performance of
other utilities8.
3 Pacific Economics Group (PEG), LLC report – Benchmarking The Cost of Ontario Power Distributorsfor further discussion, 20 March 2008 http://www.pacificeconomicsgroup.com4
The Future Role of Benchmarking in Regulatory Reviews May 2010 5 CAMPUT Benchmarking for Regulatory Purposes Prepared by: First Quartile Consulting, LLCElenchus Research Associates, Inc April 20106
Jamas, T, Pollitt, M, 2001. Benchmarking and regulation: International electricity experience. Utilities
Policy 9, 107 –1307 The Future Role of Benchmarking in Regulatory Reviews May 20108
(www.peg.com; accessed on 01 Aug 2011)
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PEG further describes benchmarking as a term that has been used more generally
to indicate something that embodies a performance standard and can be used as a
point of comparison in performance appraisals. PEG states that benchmarks are
often developed using data on the operations of agents that are involved in the
activity under study and statistical methods are useful in both the calculation of
benchmarks and the comparison process9.
Benchmarking treats firms as production entities which transform inputs into outputs.
The variables used may be physical or monetary units; monetary values of input
costs are preferable in a regulatory context.
1.3.3 Applications of Benchmarking for Regulatory Purposes
First Quartile Consulting, LLC (1QC) and ERA, Inc. (ERA) conducted a study for
CAMPUT (Canada’s Energy and Utility Regulators) in June 2009, which looked at
different options available for using benchmarking as a regulatory tool for Canadian
utilities10. Their report argues that from a regulator’s perspective, benchmarking can
be used for the following reasons:
a) Reducing Information Risk – to mitigate the risk associated with the imperfect
and incomplete information that regulators must rely on in making regulatory
decisions; benchmarks can provide an independent check on the reasonableness
of the information available to regulators.
b) Monitoring – to determine the utility’s accountability (delivering performance to
customers), individual efficiency assessment (delivering value to ratepayers), and
utility industry’s efficiency assessment (performing within the range of acceptable
values for the industry). Benchmarking in this context is for data collection and to
help establish a range of acceptable values or identify areas that require
additional review.
9(www.peg.com; accessed on 01 Aug 2011)
10
CAMPUT Benchmarking for Regulatory Purposes Prepared by: First Quartile Consulting, LLCElenchus Research Associates, Inc April 2010
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c) Audit – to support the financial and operational review of utility performance
including a systematic review and verification of results. In this case,
benchmarking provides standard definitions of performance and expected results.
d) Compliance – to ensure that a utility is compliant with regulatory requirements.
This may involve assessing whether the utility meets the requirements of
accepted practices, legislation, rules and regulations in the form of specific
standards or the terms of a contract. Benchmarking can be used to identify the
validity of an approach and best practices from other companies in the industry.
e) Rate-making – to assess the validity of the information presented and used to
set rates; address concerns about information risks and ensure that utilities are
performing as efficiently and effectively as possible. Benchmarking provides valid
comparison points across similarly performing utilities.
1.3.4 Performance Indicators and Benchmark Measures
The CAMPUT benchmarking report published by First Quartile Consulting and ERA
further outlines performance metrics useful for benchmarking utilities’ performance
by regulators. The metrics are designed to provide an overview of the information
required by regulators to understand the performance of the utility and in their view
should ideally cover the following areas:
Costs: to ensure that there is a prudent use of resources to ensure reasonable
rates, but also that enough is invested in the organisation to ensure continued
delivery of quality service.
Asset management: to address the balance between investing in new
infrastructure and establishing a robust maintenance programme to avoid
interruptions in service or unexpected repair costs.
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Customer care: allows regulators to understand how a utility is delivering on the
promise it is making to customers and meeting the objective of utility
accountability.
Operations: focus on the efficient delivery of the product and timely installation
of new connections; it refers to the reliability of the system and the way it is
managed.
1.3.5 Statistical Benchmarking Methods
The three most commonly used statistical benchmarking methodologies are:
econometric modelling, indexing and data envelopment analysis (DEA). 11
Indexing (Unit Cost & Productivity Indexes) – involves the comparison of a
company’s unit cost or productivity to historical values of such key performance
indicators for a peer group.
The challenge in using a unit cost approach is deciding which measure of output
should be used. Accuracy also hinges on the degree to which the cost pressures
faced by the peer group resemble those faced by the subject utility.
Econometrics – (Cost & Quality Models) – Econometric cost models explain
the relationship between utilities' costs and model parameters which are
estimated using the historical cost drivers of a sample of utilities. When feasible,
Econometric Cost Models have advantages over unit cost and productivity
metrics in performance measurement in that econometric models can be used to
predict the change in a company's cost given expected changes in local business
conditions (for example, input price inflation and customer growth).
11 See Pacific Economics Group (PEG), LLC report – Benchmarking the Cost of Ontario Power Distributors for further discussion, 20 March 2008 http://www.pacificeconomicsgroup.com
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Data Envelopment Analysis (DEA) – uses linear programming techniques to
‘envelop’ data on sample firms that relate outputs to inputs. It is therefore
essentially a technique for identifying what is known in economics as isocost and
isoquant curves. Efficiency is measured as the distance from the best attainable
curve.
DEA is a frontier-oriented method of benchmarking. It measures the performance
of firms against an efficient ‘frontier ’ or best practice. From a regulatory
perspective, frontier methods can be used to identify performance gaps,
particularly in the initial years of regulatory reform.
1.3.6 Examples of Benchmarking Studies
Utilities and regulators (in Europe) have been using benchmarks in support of rates
and regulatory proceedings. Sometimes these utilities choose to do so, and
sometimes the regulators require it. The reasons and the approaches vary by
jurisdiction and by utility. Below are some benchmarking studies that have been
undertaken in different countries. Most benchmarking studies in the energy sector
are on the electricity and gas industries. Benchmarking studies in the petroleum
pipelines sector are not common.
1. The use of large-scale benchmark studies in rate proceedings
British Columbia Hydro (BC Hydro) has been using a large-scale benchmarking
approach in rate proceedings for the past ten years. It chooses specific type of
information to demonstrate specific points about its capital investment levels, as
well as to highlight its operating and maintenance approach. BC Hydro considers
the benchmarks as a support tool, rather than a primary tool on which it bases its
decisions.12
12See BC Hydro 2011 Revenue Requirements Exhibit B-1, the report has a comprehensive list of
benchmarking studies conducted by BC Hydro during F2009 and F2010http://www.bcuc.com/Documents/Proceedings/2010/Doc_24719_B-1_BCHydro-F11RR-
Application.pdf
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2. Benchmarking the Cost of Ontario Power Distributors13
The Ontario Energy Board (OEB) consulted Pacific Economic Group (PEG) to
help it develop an operational benchmarking method for rate making. The study
by PEG considers the impact of service quality and capital use on operation,
maintenance, and administration (OM&A) expenses and explores the potential for
benchmarking capital costs. While the econometric method of benchmarking is
adopted in the regulation of the OM&A expenses of Ontario’s numerous power
distributors, the study explains in detail other most commonly used benchmarking
methods.
3. The Future Role of Benchmarking in Regulatory Reviews - prepared by Frontier
Economics for the Office of the Gas and Electricity Markets (OFGEM), May 2010
14
OFGEM, which regulates the electricity and gas markets in Great Britain,
requested Frontier Economics to conduct a study and write a report (the Frontier
Report) on the future role of benchmarking in regulatory reviews for electricity
distribution and transmission, as well as gas distribution and transmission.
OFGEM considered adopting a high-level DEA benchmark of the recent historic
costs of transmission operators amongst a small number of European peers.
Given the limitations on data availability, Frontier Economics recognised that this
approach was unlikely to provide definitive results.
13Ontario Energy Board (OEB) report by: Pacific Economics Group (PEG) 20 March 2008
14
For more detailed theory see report prepared by Frontier Economics for OFGEM: The Future Roleof Benchmarking in Regulatory Reviews May 2010. The report addresses areas like the context for benchmarking, criteria and approaches followed by companies to benchmarking.
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CHAPTER 2: Benchmarking the Efficiency of Transnet’s Operations
Due to the complexity of tariff-on-tariff comparison as a result of the various factors
that contribute to tariff administration, NERSA has considered the possibility of
benchmarking the efficiency of costs in Transnet pipelines operations against that of
international pipeline companies.
The steps to a benchmarking process are as follows15:
a) Select a benchmark (for example, distribution cost per customer).
b) Compare the chosen metric for the utility (Transnet ) to the average for the proxy
group.
c) Consider whether the cost pressures faced by the peer group resemble those
faced by the subject utility (Transnet ).
d) Numerators should reflect the fixed and variable costs of running a pipeline:
- the fixed cost component would be ‘net plant’; and
- the variable cost component would be the various categories of operating
expenses.
e) Denominators for consideration would be:
- volume of throughput;
- kilometres of pipeline; and
- volume per kilometre.
Using a consistent denominator helps in normalising the data.
To benchmark the efficiency of costs requires an understanding of the cost drivers in
the industry. This is crucial to ensure that appropriate benchmarking metrics are
selected.
Some of the cost drivers for a pipeline company are:
Capacity
Distance (km)
15Graham Shutterworth, Wayne P. Olson. Introduction to Benchmarking
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Terrain/Elevation
Flow rate (m3/h)
Number of entry and exit (off take) points
Pipeline diameter
The above are reflected in the value of assets, as well as the costs and revenues of
the companies. Differences in costs can be attributed to the differences in
economies of scale between the companies, as well as the geographic impact, which
can result in significant differences in the cost of the assets over similar distances.
Three ratios that were suggested by NERA for use in the benchmarking analysis are:
a)
This is a measure of the capital intensity of the company. Throughput is,
however, a function of economic growth. In the instance where international
comparisons are made, adjustments for the differences in the countries’
economic factors such as labour productivity, cost of labour, investments and
other cyclical components that impact on economic growth, would be
incorporated into the analysis.
b)
A key consideration in the analysis of costs is the reasonableness of costs, i.e.the costs ‘prudently’ incurred? To make the results more comparable, an
assessment of what percentage of allowable revenue (AR) is operating
expenditure (OPEX) is required.
c)
This is also a measure of efficiency of operations.
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Further assessment on whether Transnet’s financial statements and performance
reflect a sound business is conducted using its efficiency and productivity ratios.
Financial ratios worth considering include:
d) Profitability Ratios
- Return on Asset
- Return on Equity
These show the efficiency with which assets and equity are used to generate Net
Income.
e) Liquidity Ratios
- Current Ratio
- Quick Ratio
These show the company’s short-term solvency and financial flexibility.
f) Debt Utilisation Ratios
- Debt: Equity Ratio
This indicates what proportion of equity and debt the company is using to
finance its assets. Companies in capital intensive industries usually have high
debt: equity ratios, which reflect the financing of growth with debt.
- Asset: Equity Ratio
An increasing asset: equity ratio indicates that assets are increasing faster
than equity. The increase therefore reflects an expansion of assets to
generate earnings.
g) Asset Utilisation Ratios
- Asset Turnover
Asset turnover measures a company’s efficiency at using its assets in
generating revenue; the higher the number the better.
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NERSA has decided to conduct a ratio analysis of Transnet’s performance relative to
relevant international benchmarks as a first step in the benchmarking exercise.
These ratios are also used in a preliminary DEA model for benchmarking.
NERSA has decided to use the data of the companies used to determine a proxy
beta for calculating the value of the market risk premium (MRP) in the formula for
determining Transnet’s cost of equity (Ke). The data was obtained from the United
States of America’s Federal Energy Regulatory Commission (FERC).
The proxy companies considered by NERSA in the determination of the industry
beta for 2010/2011 tariffs were:
1 EQT Corporation (US)
2 Enbridge Inc. (CN)
3 El Paso Corporation (US)
4 Magellan Midstream Partners, LP (US)
5 Plains All American Pipeline, LP (US)
6 New Jersey Resources Corporation (US)
7 Piedmont Natural Gas Company, Inc. (US)
8 AGL Resources, Inc. (US)
9 Cabot Oil and Gas Corporation (US)
10 Crosstex Energy Inc. (US)
11 Devon Energy Corporation (US)
12 Encana Corporation (US)
13 Enterprise Products Partners, LP (US)
14 EOG Resource, Inc. (US)
15 Laclede Group, Inc. (US)
16 National Fuel Gas Company (US)
17 Nicor Inc. (US)
18 Provident Energy Ltd. (CN)
19 South Jersey Industries, Inc. (US)
20 Southern Union Company (US)
21 TEPPCO Partners, LP (US)22 UGI Corporation (US)
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23 WGL Holdings, Inc. (US)
For the purpose of this report, the list was limited to four companies based on the
availability of data from the FERC databases.
The proxy companies against which Transnet is benchmarked in this report are:
(i) Sunoco, Inc
Sunoco, Inc., through its 34% ownership interest in Sunoco Logistics, has
approximately 7,900 miles of crude oil and refined products owned and operated
pipelines; and approximately 40 product terminals.
(ii) Enbridge Inc.
Enbridge Inc. provides energy transportation, distribution and related services in
North America and internationally. The company operates a crude oil and liquids
pipeline system, and is involved in international energy projects, natural gas
transmission and midstream business. The company also distributes natural gas and
electricity, and provides retail energy products.
(iii) Kinder Morgan
Through its Products Pipelines business unit, Kinder Morgan transports over two
million barrels per day of gasoline, jet fuel, diesel, natural gas liquids and other fuels
through more than 8,000 miles of pipelines. The company also has approximately 50
liquids terminals in this business segment that store fuels, and offers blending
services for ethanol and other products.
(iv) Magellan Midstream Partners, LP
Magellan Midstream Partners, LP is primarily involved in the storage, transportation,and distribution of refined petroleum products and ammonia. The company assets
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include a pipeline system serving the mid-continent region of the United States (US),
petroleum products marine terminal facilities, petroleum products terminals, and an
ammonia pipeline system.
The following assumptions have been made in terms of the conversion from United
States metrics to South African metrics:
Miles to Km 1.609344
Exchange Rate (R/US$) 7.04
(this is the 2007 average rate, used as a base to convert
all the periods. A constant rand-dollar rate was utilised to
eliminate the fluctuation of currencies and to simplify the
analysis)
Litres per Barrel 158.99
All monetary values used are real values with US$ values converted to Rand values,
as Transnet operations are Rand based.
2.1 Ratio Analysis
The ratio analysis compares Transnet and the proxy companies’ ratios over the
years 2008, 2009 and 2010.
A primary challenge faced in developing ratios is data availability. While NERSA has
access to Transnet’s information, obtaining the required comparable data for the
proxy companies has proved to be a challenge. This has resulted in the analysis
being limited to the following ratios:
1. Asset Turnover =
where RAB refers to the Regulatory Asset Base
2.
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3.
4.
5.
6.
a) Asset Turnover
Asset turnover measures a firm's efficiency at using its assets in generating sales or
revenue - the higher the number the more efficient is the firm. The ratio measures
the revenue that is generated for every Rand of asset owned by the company. For
most companies, their investment in fixed assets represents the single largest
component of their total assets. The same applies for the capital intensive. For a
capital intensive company, capital productivity should result in low tariffs.
Figure 1: Transnet’s Asset Turnover
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
NOTE: The Transnet Ann ual Financial Report for 2008 presents Transnet 's perform ance
for th e year ended 31 March 2008. The same app lies for the years 2009 and 2010.
0.00
0.05
0.10
0.150.20
0.25
0.30
0.35
0.40
2008 2009 2010
Transnet: Asset Turnover
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In 2008, Transnet returned R0.38 in revenue on each Rand of asset owned. By
2010, this had dropped to R0.12. These asset turnover ratios are low, which is
understandable as Transnet’s asset base has been increasing due to the
capitalisation of the NMPP.
Figure 2: Asset Turnover - Transnet and Proxy Companies
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
http://www.ferc.gov/docs-filing/forms/form-6/data/asp
Asset turnover for all four companies has dropped since 2008, with the exception of
Enbridge, whose asset turnover increased in the period 2009 to 2010. Transnet,
however, has a lower asset turnover in comparison to the proxy companies.
0.00
0.10
0.20
0.30
0.40
0.50
0.60
0.70
0.80
2008 2009 2010
Enbridge Inc.
Kinder Morgan
Magellan Midstream
Partners, LP
Sunoco
Transnet Pipelines
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b) Throughput/RAB
Figure 3: Transnet’s Throughput/RAB
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
Transnet’s capital intensity increased by 62% in the period from 2008 to 2010. These
results are consistent with the results in 2.1.1 above.
Figure 4: Throughput/RAB – Transnet and Proxy Companies
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
http://www.ferc.gov/docs-filing/forms/form-6/data/asp
0.00
1.00
2.00
3.00
4.00
5.00
6.00
2008 2009 2010
m 3 / R
Throughput/RAB
0
5
1015
20
25
30
35
40
45
2008 2009 2010
m 3 / R
Throughput/RAB
Enbridge Inc.
Kinder Morgan
Magellan Midstream
Partners, LP
Sunoco
Transnet Pipelines
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The trend is quite different from what is seen in Figure 2 above. Magellan’s capital
intensity is relatively constant throughout the three years. While Enbridge’s asset
turnover dropped in the period 2009 to 2010, a similar trend is not evident in this
case. An investigation into the underlying reasons will be conducted in the follow-up
discussion paper.
c) Operating Expenditure/RAB
Figure 5: Transnet’s OPEX/RAB
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
Both OPEX and RAB increased in the period 2008 to 2010. The downward sloping
curve is therefore a result of an increase in RAB that is growing faster than the
increase in OPEX.
0
0.01
0.02
0.03
0.04
0.05
0.06
0.07
0.08
0.09
2008 2009 2010
Opex/RAB
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Figure 6: OPEX/RAB – Transnet and Proxy companies
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
http://www.ferc.gov/docs-filing/forms/form-6/data/asp
All companies have a downward sloping curve except for Enbridge, with Transnet
having the lowest ratio.
d) Net Plant/Kilometres of Pipeline
Figure 7: Net Plant/Kilometres of Pipeline – Transnet
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
0
0.1
0.2
0.3
0.4
0.5
0.6
2008 2009 2010
OPEX/RAB
Enbridge Inc.
Kinder Morgan
Magellan Midstream
Partners, LP
Sunoco
Transnet Pipelines
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
4500000
2008 2009 2010
R / k m
Transnet: Net Plant/Kilometres of Pipeline
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This ratio reflects an increase in Transnet’s plant over the three years under review.
This increase is due to the massive infrastructure expansion programme Transnet
has embarked on.
Figure 8: Net Plant/Kilometre of Pipeline – Transnet and Proxy Companies
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
http://www.ferc.gov/docs-filing/forms/form-6/data/asp
Transnet and Enbridge have a higher plant per kilometre than the other three
companies. There was a drop in Sunoco’s Net Plant in 2009, followed by an increase
in 2010.
Table 1: Net Plant and Kilometres of Pipeline values
Kilometres of Pipeline Net Plant
2008 2009 2010 2008 2009 2010
Mean 5 402 5 829 5 598 6 080 451 078 8 511 757 822 11 488 043 581
Min 681 681 681 57 500 092 57 280 602 74 717 497
Max 13 593 13 593 13 670 14 696 558 352 22 291 529 919 30 552 051 587
Enbridge Inc 6 233 6 233 7 139 14 696 558 352 22 291 529 919 30 552 051 587
Kinder Morgan 681 681 681 57 500 092 57 280 602 74 717 497
Magellan 13 593 13 593 13 670 8 776 552 982 10 444 954 109 13 125 466 381
Sunoco Inc 4 080 6 217 4 080 3 397 543 962 3 785 954 478 4 086 682 440
Transnet 2 423 2 423 2 423 3 474 100 000 5 979 070 000 9 601 300 000
0
500000
1000000
1500000
2000000
2500000
3000000
3500000
4000000
4500000
2008 2009 2010
R / k m
Net Plant/Kilometres of Pipeline
Enbridge Inc.
Kinder Morgan
Magellan Midstream
Partners, LP
Sunoco
Transnet Pipelines
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From Table 1 we see that in 2010, Enbridge’s regulatory asset base (Net Plant) is
three times the average and is the largest in comparison to the other companies,
with a relatively long average pipeline length. Transnet has a large asset base but
the length of its pipelines is less than half the average. While the asset base has
been increasing over the three years, the length of its pipelines has not changed.
The length of Sunoco’s pipelines increased in 2009, but dropped to the 2008 value in
2010. This is a possible data capturing error and will be investigated in the follow-up
report.
e) Operating Expenditure/Volume per kilometre
Figure 9: Operating Expenditure/Volume per kilometre – Transnet
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
The increase reflected over the three years is due to an increase in operating
expenses, which is likely to be due to the increasing costs of maintenance on the
Durban to Johannesburg Pipeline (DJP). NERSA needs to further investigate what
the desirable value for this ratio is.
0
20
40
60
80
2008 2009 2010
R / m 3 / k m
Transnet: Operating Expenditure/Volume per kilometre
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Figure 10: Operating Expenses/Volume per kilometre – Transnet and Proxy Companies
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
http://www.ferc.gov/docs-filing/forms/form-6/data/asp
Transnet’s performance is in line with two other companies. Magellan’s performance
over the three years and Enbridge’s performance in 2010 are outliers in this analysis.
Table 2: Operating Expenditure and Volume per Kilometre values
Operating Expenditure (R) Volume per kilometre (m /km)
2008 2009 2010 2008 2009 2010
Average 981 066 896 1 087 654 438 1 858 814 157 9 504 312 8 364 436 9 517 690
Min 28 653 487 19 047 985 18 087 401 3 484 747 3 074 595 2 474 968
Max 1 923 355 088 2 185 507 688 6 388 956 935 17 765 056 15 894 402 20 020 498
Enbridge 1 800 445 687 2 185 507 688 6 388 956 935 15 689 942 15 894 402 13 872 321
Kinder
Morgan 28 653 487 19 047 985 18 087 401 3 610 293 3 074 595 3 894 620
Magellan 1 923 355 088 1 816 681 480 1 435 565 828 3 484 747 3 509 015 2 474 968
Sunoco 874 853 311 1 029 546 708 1 002 754 962 17 765 056 12 257 951 20 020 498
Transnet 278 026 906 387 488 329 448 705 657 6 971 523 7 086 215 7 326 042
Magellan started with a high operating expenditure in 2008 compared to the other
companies, but has since decreased its operating expenditure steadily over the three
years. Magellan’s volume per kilometre is on average three times smaller than the
average, and has decreased over the period 2009 to 2010. This results in an inflated
0
100
200
300
400
500
600
700
800
900
2008 2009 2010
R / m 3 / k m
Operating Expenditure/Volume per kilometre
Enbridge Inc.
Kinder Morgan
Magellan Midstream
Partners, LP
Sunoco
Transnet Pipelines
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value for this ratio. Enbridge’s operating expenditure tripled from 2009 to 2010, while
its volume per kilometre decreased during the same period. An investigation into the
underlying factors will be considered in a follow-up report.
f) Operating Expenditure/Volume
Figure 11: Operating Expenditure/Volume - Transnet
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010NERSA Reasons for Decision, Transnet Pipelines Tariff Application
The ideal trajectory for this graph in terms of efficiency is a downward slope. The
results suggest that Transnet could be becoming less efficient.
0
0.005
0.01
0.015
0.02
0.025
0.03
2008 2009 2010
R / m 3
Transnet: Operating Expenditure/Volume
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Figure 12: Operating Expenditure/Volume – Transnet and Proxy Companies
Sources: Transnet Annual Financial Report for 2008, 2009 and 2010
NERSA Reasons for Decision, Transnet Pipelines Tariff Application
http://www.ferc.gov/docs-filing/forms/form-6/data/asp
Transnet’s performance, however, remains in line with three of the proxy companies.
Magellan’s performance is consistent with the results from the above ratios, which
shows that the company’s operating expenditure is high in comparison to volumes.
The ratio analysis shows that while there are some possible inefficiencies in
Transnet operations, its performance is comparable with that of the proxy
companies.
0
0.2
0.4
0.6
0.8
1
1.2
2008 2009 2010
R / m 3
Operating Expenditure/Volume
Enbridge Inc.
Kinder Morgan
Magellan Midstream
Partners, LP
Sunoco
Transnet Pipelines
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2.2 Data Envelopment Analysis
Data Envelopment Analysis (DEA) is a non-parametric method and uses piecewise
linear programming to calculate (rather than estimate) the efficient or best-practice
frontier of a sample. The decision-making units (DMUs) or firms that make up the
frontier envelop the less efficient firms. The efficiency of the firms is calculated in
terms of scores on a scale of 0 –1, with the frontier firms receiving a score of 1.
DEA models can be input- or output-oriented, and can be specified as constant
returns to scale (CRS) or variable returns to scale (VRS). The CRS hypothesis
suggests that companies are flexible to adjust their sizes to the optimal firm size. In
contrast, the VRS approach is less restrictive since it compares the efficiency of
companies only within similar sample sizes. This approach is adopted if the
companies are not free to choose or adapt their size. The comparison between the
two approaches also provides some information about the underlying technology: if
the results of the CRS and the VRS approaches are similar, then the returns to scale
do not play an important role in the process.
Output-oriented models maximise output for a given quantity of input factors.
Conversely, input-oriented models minimise input factors required for a given level of
output. Given that most distribution utilities have an obligation to meet demand, they
can only become more efficient by providing a predefined output level with fewer
inputs.
Over the past 20 years, DEA has attracted much attention from among the wide
spectrum of energy and environmental modelling techniques. DEA has been
accepted as a major frontier technique for benchmarking energy sectors in many
countries, particularly in the electricity industry.
An international survey on regulatory benchmarking for distribution companies found
that Chile, Columbia and Brazil were all employing benchmarking, with DEA analysis
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being the most popular approach16. Similarly, the Finnish Energy Market uses a DEA
model for distribution company efficiency benchmarking. In Norway there are a large
number of utilities (approximately 180), and the regulator uses the DEA technique
with multiple inputs and outputs and directly converts the benchmarking scores into
price caps. A growing number of studies demonstrate the application of DEA in the
benchmarking of electricity distribution, gas distribution and water utilities.
An important step in DEA is the choice of appropriate input and output variables. The
variables should, to the extent possible, reflect the main aspects of resource-use in
the activity concerned. DEA can also control the effect of environmental variables
that are beyond the control of the management of firms but affect their performance.
Also, the basic DEA model illustrated above does not impose weights on model input
and output variables, but it can be extended to incorporate value judgements in the
form of relative weight restrictions imposed on model inputs or outputs.
NERSA’s preferred model is input-oriented and assumes constant returns to scale
(CRS) so that the measured relative efficiency of firms is not affected by their size
(costs vary with for example the units of energy delivered). The model uses a single
cost input reflecting the OPEX of the distribution business of the utilities. As reported
in the paper on benchmarking in electricity regulation by Jamasb and Pollitt17, the
most widely used output variables for modelling of electricity distribution utilities are:
i. units of electricity delivered;
ii. number of customers; and
iii. length of network.
The comparable variables in the pipelines industry are:
i. volume/throughput
ii. number of customers
iii. kilometres of pipeline
16Performance Benchmarks for Electricity Distribution Companies in South Asia, USAID SARI/Energy
Program, November 200417 Jamasb, T. and Pollitt, M. (2001), Benchmarking and regulation: International electricityexperience, Utilities Policy, Vol. 9/3, pp. 107-130.
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In this benchmarking study, Transnet’s petroleum pipelines performance is
compared to that of the four proxy companies listed at the beginning of this chapter.
In order to smooth the variables, a three-year average of each of the ratios is used in
the model.
Table 3: Summary Statistics for the dataset
OPEX VolumeKilometres of
Pipeline
Target Company:
Transnet Pipelines 371 406 964 17 270 966 667 2 423
Proxy Companies:
Sunoco 969 051 660 76 786 546 107 4 792
Enbridge Inc. 3 458 303 437 98 652 434 961 6 535
Kinder Morgan 21 929 625 2 400 675 476 681
Magellan 1 725 200 799 1 899 887 715 13 618
Sample:
Mean 1 309 178 497 39 402 102 185 5 610
Minimum 21 929 625 1 899 887 715 681
Maximum 3 458 303 437 98 652 434 961 13 618
The linear equations to be solved in the calculation of the efficiency scores are
provided in Annexure A.
Table 4: Model Specifications
Variable Model 1 Model 2 Model 3
OPEX I I I
Volume O O
Kilometres of Pipeline O O
Key: O – Outpu t Variable
I – Input Variable
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The three DEA models were run on MaxDEA software developed by Cheng Gang
and Qian Zhenhua (2011)18.
Model Results
Efficiency scores for the five companies considered are reported in Table 7.
Note that the efficiency of the companies is calculated in terms of scores on a scale
of 0 –1, with the frontier firms receiving a score of 1.
Table 5: Efficiency scores using both constant returns to scale and variable returns to scale
specifications
Enbridge Inc.
Kinder
Morgan
Magellan
Midstream
Partners, LP Sunoco
Transnet
Pipelines
Model 1 CRS 0.260581 1 0.254287 0.723829 0.42478
Model 2 CRS 0.260581 1 0.01006 0.723829 0.42478
Model 3 CRS 0.060873 1 0.254287 0.159302 0.210158
CRS Average 0.194012 1 0.172878 0.535653 0.353239
All three models have Kinder Morgan as the benchmark company (efficiency
score=1). In none of the models is Transnet the least efficient company. With the
exception of Kinder Morgan, the efficiency scores for all companies are very low
under Model 3, an indication of a possible inappropriateness of the model
specification.
On closer inspection, we notice that Kinder Morgan is a significantly smaller
company in comparison to the other four companies, even in terms of the asset base
as seen Table 6 below.
18http://MaxDEA.cn
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Table 6: Summary Statistics
OPEX RAB Volume Kilometres of Pipeline
Target Company:
Transnet Pipelines 371 406 964 6 351 490 000 17 270 966 667 2 423
Proxy Companies:
Sunoco 969 051 660 3 756 726 960 76 786 546 107 4 792
Enbridge Inc. 3 458 303 437 22 513 379 953 98 652 434 961 6 535
Kinder Morgan 21 929 625 63 166 064 2 400 675 476 681
Magellan 1 725 200 799 10 782 324 491 1 899 887 715 13 618
Sample:
Mean 1 309 178 497 8 693 417 493 39 402 102 185 5 610
Minimum 21 929 625 63 166 064 1 899 887 715 681
Maximum 3 458 303 437 22 513 379 953 98 652 434 961 13 618
We therefore ran a fourth model excluding Kinder Morgan with OPEX as the input
and volume as the output. The results are as follows:
DMU Score
Enbridge 0.360003
Magellan 0.013898
Sunoco 1
Transnet 0.586852
Under the model Sunoco is the benchmark company with Transnet’s score above
50%.
Figure 12 illustrates the main features of our model. The figure shows the five
companies used in our analysis with OPEX and RAB as the inputs and volume for
the output. The vertical and horizontal axes represent the OPEX and RAB per unit of
output respectively.
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Figure 11: Data Envelopment Analysis – All Five Companies
A - Transnet
B - SunocoC - Enbridge
D - Kinder Morgan
E - Magellan
This graph, however, shows Magellan to be the outlying company. Drawing the
graph without Magellan we see the following:
Figure 12: Data Envelopment Analysis – Excluding Magellan
Once again we see clearly that Kinder Morgan uses less RAB and OPEX per unit of
output. Transnet requires more RAB per unit of output than all the companies.
While the results of this analysis are not definitive, they reinforce the results of the
ratio analysis: the efficiency with which Transnet is using its assets must be
investigated.
AB CD
E
-2
0
2
4
6
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
R A B /
V o l u m e
OPEX/Volume
A
B
C
D0
0.1
0.2
0.3
0.4
0 0.005 0.01 0.015 0.02 0.025 0.03 0.035 0.04
R A B / V o l u m e
OPEX/Volume
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Conclusion and Recommendations
Results from the ratio analysis in Chapter 2 suggest that Transnet is within the range
of the proxy companies in terms of efficiency. However, while its performance is in
line with the proxy companies, the upward-sloping operating expenditure curves
(suggesting that Transnet is becoming less efficient), as well as the downward
sloping cash flow curve (another sign of inefficiency) are an indication of the need for
further investigation into Transnet’s performance. The next step in the benchmarking
exercise could include an investigation into the underlying factors driving the
trajectory of the various ratio curves, the desirable slope for the curves in terms of
efficiency, as well as further analysis to establish desirable benchmark values for
each of the ratios.
NERSA decided to investigate the possibility of conducting meaningful
benchmarking. It is possible that this preliminary analysis could be made more
meaningful if it were to be customised to suit local conditions, but this too faces data
and other challenges. This will be further considered following input from
stakeholders.
The use of benchmarking as an instrument for the setting of South African petroleum
pipeline tariffs is not recommended as benchmarking does not easily provide
definitive results. Rather, the results of a benchmarking exercise may be a useful
step in a debate about the cost efficiency of a utility. Benchmarking, therefore, may
be more useful in pointing to the questions that could be posed in relation to the cost
performance of a target utility.
NERSA’s efforts thus far point to the limited usefulness of benchmarking for South
African petroleum pipelines given the limited data availability. However, this
benchmarking exercise has suggested that benchmarking may be a more useful tool
in efforts to improve the efficiency of petroleum storage and loading licensees where
more local data should be available. This will be considered in future.
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Bibliography
1. Pacific Economics Group, LLC (20 March 2008): Benchmarking the Costs of Ontario
Power Distributors
2. Frontier Economics (May 2010): The Future Role of Benchmarking in Regulatory
Reviews
3. First Quartile Consulting, LLC Elenchus Research Associates, Inc (April 2010):
CAMPUT Benchmarking for Regulatory Purposes
4. Cheng Gang (June 2011): MaxDEA manual version 5.2
5. Amit Kabnurkar (14 March 2001): Mathematical Modeling for Data Envelopment
Analysis with Fuzzy Restrictions on Weights
6. BC Hydro 2011 Revenue Requirements Exhibit B-1.
http://www.bcuc.com/Documents/Proceedings/2010/Doc_24719_B-1_BCHydro-
F11RR-Application.pdf
7. Transnet Annual Financial Report 2008
8. Transnet Annual Financial Report 2009
9. Transnet Annual Financial Report 2010
10. NERSA Reasons for Decision, Transnet Pipeline Tariff Applications.
www.nersa.org.za/PetroleumPipelines/Tariffs/Pipelines/TariffDecisions/Current
11. Form 6/6-Q - Annual/Quarterly Report of Oil Pipeline Companies.
http://www.ferc.gov/docs-filing/forms/form-6/data/asp
12.Jelena Zorić, Nevenka Hrovatin, Gian Carlo Scarsi (April 2009): Gas Distribution
Benchmarking of Utilities from Slovenia, the Netherlands and the UK - an Application
of Data Envelopment Analysis
13. Tooraj Jamasb, Michael Pollitt, (2003): International benchmarking and regulation -
an application to European electricity distribution utilities. Energy Policy 31 (2003)
1609 –1622
14. USAID SARI/Energy Program (November 2004): Performance Benchmarks
for Electricity Distribution Companies in South Asia. www.sari-energy.org
15. Working Paper CMI EP 19/DAE 0312, January 2003, Dept. of Applied Economics,
University of Cambridge
16. Jamasb, T and Pollitt, M. (2001), Benchmarking and regulation: International
electricity experience, Utilities Policy, Vol. 9/3, pp. 107-130.
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Annexure A
Assume there is information on K inputs and M outputs for each of N firms. For the i -
th firm, these are represented by the column vectors xi and yi, respectively. The K×N
input matrix X and M×N output matrix Y represent the data for all N firms. The linear
programme of input-oriented CRS envelopment model is formulated as follows:
min θ, λ θ
st -yi + Yλ ≥ 0
θxi – Xλ ≥ 0 (1)
λ ≥ 0,
where θ is a scalar and λ is a N x1 vector of constants. The value of θ obtained
will represent the technical efficiency score of the i-th firm. The linear programming
problem must be solved N times, once for each firm.
Essentially, the problem takes the i -th firm and then seeks to radially contract the
input vector xi as much as possible, while still remaining within the feasible input set.
The inner-boundary of this set is a piece-wise linear isoquant, determined by the
observed data points. Since θ is a feasible solution to (1), the optimal value θ ≤ 1. If
θ = 1, the current input levels can no more be proportionally reduced, indicating that
a firm is on the frontier. Otherwise, if θ < 1, then the firm is dominated by the frontier.
In the VRS DEA model, a convexity constraint is added to (1):
(2)
This additional constraint ensures that the firm is compared with other firms of a
similar size. When not all the firms are operating at the optimal scale, then technical
efficiency as calculated by the constant returns to scale model (TE CRS) will include
‘pure’ technical efficiency (TE VRS) as well as scale efficiency (SE ):
TE CRS = TE VRS x SE (3)
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By conducting both CRS and VRS DEA, one can obtain a scale efficiency measure
for each firm.19
19 G Di t ib ti B h ki f Utiliti f Sl i th N th l d d th UK