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Benchmarking European rail freight transport companies.

Abstract

This article focuses on answering the following research question: How efficient are European rail transport companies compared with each other? When the focus in benchmarking is placed on "employee productivity performance," Railcargo Austria and VR Cargo perform relatively well when compared with their competitors. RENFE and SNCF Fret are the worst performers. If the focus is on "sales productivity performance," the best performers are Green Cargo, SBB, and VR Cargo. The worst performers are Trenitalia, CP, and SNCF Fret. The best in "railcar productivity performance" are Green Cargo, VR Cargo, and Railcargo Austria. The worst performers are SNCF Fret, Trenitalia, and RENFE. Analysis of effects, using a mixed model approach, showed that there are large differences among the companies. The overall trends were mostly not significant, but the distributions of the signs of the trends (+ or -) are quite plausible and significant. Two main conclusions arise: First, the inputs (employees, locomotives, and railcars) have been reduced over the years. Second, the partial productivity performances (tons/ railcar, tonkms/railcar, sales/railcar, employees/railcar, sales/ton, sales/employee, sales/tonkm, and tonkms/employee) have improved over the years. Overall, less freight is transported more efficiently.

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In Europe, the importance of the road transport sector is measured in terms of the volume of transported goods. It represents about 75 percent of the total ton kilometers (tonkms) transported in the EU. Whereas road transport in the period 1985-1995 has grown by 163 percent (with respect to tonkms), rail transport has increased by only 20 percent (EC 2002). Although the total rail transport is growing, its market share is in decline. "Closed" national systems and decreasing networks have caused customers to abandon rail transport in the course of the past three decades in favor of road. Now, however, the European Union is trying to persuade companies to come back to rail to transport their goods. By liberalizing rail freight services and opening them to competition, the European Union wants to cut greenhouse gas emissions and revitalize the rail freight market. And indeed, albeit slowly, freight transport by rail is changing.

Changes have taken place in the type of freight that is transported by rail (e.g., high-value and high-quality goods have been added to the product portfolio) and in the transport units (more standardized units, such as containers). However, even more changes are needed, and in particular the productivity of rail freight transport companies might be improved as a first step to lure customers back to rail. More efficiency creates stronger companies that focus on their valuable customers and that make profits. That might create space for investments in new market segments and/or customers, in order to start growing again.

The challenge in this article is to examine the extent to which the efficiency of rail freight transport can be improved. The research question posed in this article is, How efficient are European rail transport companies compared with each other? To answer this question only publicly available data have been used. First, the article will describe the rail freight sector on a European level (English, Welsh & Scottish Railway [EWS] in the United Kingdom is not included in the analysis because it does not publish an annual report); second, a mixed model approach to analyze trends and benchmarking theory will be described; and third, the efficiency of rail freight transport companies in Europe will be compared.

RAIL FREIGHT TRANSPORT IN EUROPE

There are a number of reasons cited for the diminishing attractiveness of rail. Historically, most rail freight companies in Europe were nationally held. This has resulted in a lack of international integration and has reduced the railway companies' chances of offering fast, reliable, and efficient international services. In the United States, where most rail networks are private and transcontinental transportation is open to competition, railway companies maintain a 40 percent share of the overall freight market (European Commission 2001). Currently, only 9 percent of all the freight transported in Europe is moved by rail, down from 21 percent in 1970. The problems are numerous: incompatible forms of traction electrification, differing track gauges, and lengthy border checks, to name but a few. Nijkamp (1995) observed twelve years ago that a drastic reorientation of the management of railways was required. However, in a recent report, the European Commission found that the average speed of international freight services has fallen to eighteen kilometers per hour: "slower than an icebreaker opening up a shipping route through the Baltic Sea" (European Commission 2001). Figure 1 shows the main developments in rail freight transport in the EU and reveals that in terms of tonkms, almost all EU countries have shown an increase from 1995 to 2000.

[FIGURE 1 OMITTED]

In recent years, important changes in European rail transport have occurred: e.g., containerization and shuttle trains (Bouwknegt et al. 2004; Logitech 2003). However, in spite of these advantages, it still proves difficult to improve the competitive position of rail freight transport. This is supported by several research studies. Ferreira (1997) proved that rail freight market share increases are closely related to the level of service offered, particularly with respect to transit times and reliability of arrivals. Keaton (1991) has shown that significant reductions in transit times require large increases in both the number of connections and the operating costs. In addition to this, FitzRoy and Smith (1995) demonstrated that high train frequencies have contributed to a strong rail performance for freight in Austria and Switzerland. For the development of new rail freight markets, Barthel and Woxenius (2004) identified the factors that severely hamper the attraction of small flows over short distances. These are market and financial uncertainties; insufficient network connectivity; limited track time for extra services vis-a-vis existing passenger and freight movements; and policies that favor the existing technology paradigm. Furthermore, cost diseconomies-an increase in output comes with a relatively large increase in total costs--limit the potential for short-haul rail freight transport. All of these studies reveal that it is quite difficult to improve the competitive position of rail freight transport and underscore the urgent need for seeking ways to improve the efficiency and effectiveness of current rail operations.

Markets can be divided according to product (service) or according to geography (de Vries et al. 2001). The rail freight product market can be divided according to the way the freight is transported (based on EC 2002; IBM 2002; Community of the European Railways 2003; Lupo 2003: Wiegmans 2003):

a) Dry bulk: This sector consists of materials like gravel, sand, coal, detritus, wood, and agrarian products.

b) Liquid bulk (or tanker transport): This sector consists mainly of chemicals and fuels.

c) Intermodal transport unit: This sector is growing rapidly and consists of ISO containers, semi-trailers on railcars, the rolling road (transport of complete trucks and trailer by train), and swap bodies (the swap body is a container of lightweight construction and relatively low tare weight. Swap bodies are available in a variety of lengths and other design characteristics [Muller]).

d) Railcar loads: This sector transports mainly large parts and semi-manufactured articles (e.g., steel articles, cars, agriculture machines).

Alternatively, the freight can be divided into type of products to be transported (see Figure 2).

[FIGURE 2 OMITTED]

Another way to analyze the rail freight market is by looking towards transport services. In general, three types of services can be distinguished (de Wit and van Gent 2001):

a) Shuttle trains transport mainly maritime containers. The transport takes place at regular time intervals between the same origin-destination combinations.

b) Mixed trains transport mainly continental containers, trailers (on train), and packaged freight (fresh foods and other agrarian products, bulk materials, and cars). Usually these types of trains carry relatively smaller shipments that together form a train.

c) Unit (charter or block) trains transport chemicals, oil, ore and coal, other dry bulk, and heavy loads. This type of train is operated exclusively for movement of a single client's cargo. Such trains usually have a minimum length of twenty railcars (for cost considerations). Charter trains have the advantage of offering faster origin-to-destination transit times because no shunting and/or transshipment are needed (Konings and Kreutzberger 2001).

It is also possible to define rail freight markets according to geography. This is possible for countries by looking at imports, exports, and national freight by rail. Some rail freight transport companies make a distinction between national and international transport (e.g., OBB). Table 1 shows the main competitors in the European rail freight market.

In general, the competitive position of rail freight transport is determined by its strengths, weaknesses, opportunities, and threats (see Table 2). The strengths of rail transport refer to those characteristics that form the basis of its competitive position. Weaknesses refer to characteristics that do not enhance (maybe even worsen) its competitive position. Opportunities exist when external factors influence the sector and create chances for new sources of profitable traffic. Threats exist when external developments might result in a decrease of the market share of rail transport.

Spychalski and Swan (2004) found that deregulation in the U.S. resulted in steeply declined freight rates and large improvements in productivity. This primarily resulted from elimination of money-losing services, use of more efficient equipment, more flexible work rules, and large reductions of employees. The same developments might be expected to take place in Europe. Cantos et al. (1999) showed that the greater the degree of autonomy and financial independence, the higher the levels of efficiency and technical change.

RAIL FREIGHT TRANSPORT EFFICIENCY ANALYSIS

When looking at efficiency, several questions arise: Is a private car energy efficient (i.e., which car travels the most kms on one liter of fuel)? Is a company producing efficiently? Is a company financially efficient? Efficiency can be defined as the quality of being able to do a task successfully and without wasting time or energy (Sinclair 1992). Kim and Marlow (2001) present a similar definition: "Efficiency refers to how well the resources expended are used." According to Anthony et al. (1992), efficiency is doing things right. This definition appears to be more general and is therefore less suitable for this research. A characteristic of efficiency is that there often exists a distinction between input, process, output, and outcome. Input consists of resources, such as money, power, or workers, that are given to something such as a machine or a project to make it work (Sinclair 1992). Efficiency can be measured by comparing the amount or value of goods (output) with the time and money spent on producing them and the number of workers who produce them (input). Process refers to resources consumed in the process (relative to minimum possible levels) (Sheffield Hallam University 2003). It can also be defined as a series of actions that are carried out in order to achieve a particular result (Sinclair 1992). Output is the ability of a process to deliver products or services according to specifications (Sheffield Hallam University 2003). It may also be defined as the amount of something that they make or produce (Sinclair 1992). According to Tortosa-Austina (2002), in a context of major changes, primarily due to deregulation, the estimation of efficiency depends heavily on the output specification. This might also be the case in the rail freight market. Outcome is the ability of outputs to satisfy the needs of customers (Sheffield Hallam University 2003). However, because customer needs are less well known, outcome is difficult to trace for this research.

In this article, therefore, the focus is on inputs and on the benchmarking of partial productivity measures. According to Ockwell (2001), efficiency is either a minimizer or a maximizer concept. Minimizing would then be applied to inputs, whereas maximizing could be applied to outputs. Sena (2006) concludes that a restriction in the availability of financial resources can affect efficiency positively. In addition to this, Cantos and Maudos (2001) proved that rail freight companies that are more efficient in costs behave inefficiently with regard to revenue.

In the short run, firm profitability depends on the firm's ability to efficiently combine inputs to produce an output target. Or, in other words, it depends on input price, technology (including management, X-inefficiency, etc.), and output price (McCarthy 2001). The firm's production technology (used in its processes) is characterized by a production function (and its dual, the cost function), which gives the maximum possible output (minimum possible cost), given the inputs (output target). When a firm is not able to produce the maximum possible output (or at the minimum possible cost), the firm is inefficient. Too many inputs are used, and/or the input combination is not necessarily optimal. There may be various reasons for this. First, the employees may not have an incentive to behave optimally (X-inefficiency); the shareholders assume (or believe) that profits are maximized, while this is not the case. Second, the firm maximizes profits (or minimizes cost, which is not necessarily the same), but for one reason or another (e.g., regulation, lack of information, or "unexpected events" such as weather), the optimal output cannot be reached. In Europe, for a long time, companies may have focused on maximizing output. Liberalization forces the companies to change their behavior to maximizing profits or minimizing costs.

Efficiency analysis usually is performed with the final goal being efficiency improvement. Wilson (1997) found for the U.S. railroad industry that--due to deregulation--cost savings were impressive and productivity gains were large. The measurement of efficiency has received considerable attention in recent decades. Important methodological contributions are (1) Fare et al. (1994), who showed how Data Envelopment Analysis (DEA)-like linear programs could be applied to construct nonparametric production frontiers; (2) Charnes et al. (1981), who developed DEA, a performance measurement technique that can be used to evaluate the relative efficiency of companies; (3) Nishumizu and Page (1982), who applied linear programming methods to panel data to construct parametric production frontiers; and (4) Solow (1957), who measured Total Factor Productivity (TFP) using the residuals from a Cobb-Douglas production function. Wang and Liao (2006) showed that TFP growth for the Taiwan Railway is mainly driven by technological progress and scale economies. DEA and Stochastic Frontier analysis are two much-used methods to measure efficiency. The advantages of the Stochastic Frontier analysis over DEA are that it accounts for noise and it can be used to conventionally test hypotheses. But the disadvantages are the need to specify a distributional form for the inefficiency term; the difficulty in accommodating multiple outputs; and the need to specify a functional form for the production function. Because of the limited number of observations in the data set, the models mentioned above could not be used. Instead, we made an informal comparison on a number of relevant variables and we used a mixed modeling approach to study trends in the variables in question.

Mixed modeling, also known as multilevel modeling, has been developed as a reaction to the focus on a basic unit for analysis. Especially in education, there has been an intensive discussion about the "unit of analysis" problem (see, e.g., Burnstein et al. 1980). In education, the debate is about whether to focus on the teacher or the student as the unit of analysis in order to identify the effectiveness of teaching. Before mixed modeling was introduced, hierarchical structures in data were difficult to solve because powerful tools were unavailable. Therefore, the hierarchical structures were simply ignored (Goldstein 2003). Mixed modeling solves the problem. Mixed modeling has several advantages (Goldstein 2003): First, it enables statistically efficient estimates of regression coefficients to be obtained. Second, by using the clustering information, it provides correct standard errors, confidence intervals, and significance tests, and these generally will be more conservative than the traditional ones that are obtained by simply ignoring the hierarchical structures of the data. Third, by allowing the use of covariates measured at any of the levels of a hierarchy, it is possible to explore the extent to which differences in average performance results between rail transport companies are accounted for by factors such as organizational practice or possibly other characteristics of the companies. Finally, there may be interest in the relative ranking of individual rail freight transport companies, using performance after adjusting for start positions.

As well as efficiency analysis, benchmarking also provides insight into relative productivity performance. Benchmarking is the process of determining who is the very best, who sets the standard, and what that standard is. To be beneficial to management, the benchmark concepts must be translated into meaningful indicators (Martland 1992). A benchmark is something whose quality, quantity, or capability is known and which can therefore be used as a standard with which other things can be compared (Sinclair 1992). Benchmarking is, therefore, the process of making comparisons with other companies and then learning the lessons that arise. Essential elements of benchmarking are that it is continuous, systematic, implementable, and best practice (Sheffield Hallam University 2003). There are seven main approaches to benchmarking (Sheffield Hallam University 2003):

a) strategic benchmarking

b) performance or competitive benchmarking

c) process benchmarking

d) functional and generic benchmarking

e) external benchmarking

f) internal good practice benchmarking

g) international benchmarking

Lawrence et al. (1997) have benchmarked several infrastructure sectors in Australia. Their main focus was on price, service, labor, and capital. When performing (relative) efficiency analysis it is important to choose a relevant benchmark and then find the most similar company in terms of efficiency (Gonzalez and Alvarez 2001). In the efficiency analysis in this article, several benchmarks of partial productivity measures are used in order to present a full overview of different viewpoints on the efficiency of rail freight transport companies. Benchmarking of rail freight competitors results in a relative ranking. Who is the best in rail freight sales? Who has the best-cost performance?

BENCHMARKING AND MIXED MODELING

The efficiency is analyzed in two ways: (1) mixed modeling to test for trends; and (2) relative economic efficiency analysis (benchmarking). The initial database consists of the ten main European rail freight transport companies (excluding EWS). The mixed modeling has been carried out using publicly available data from the largest rail freight transport companies to study the trends in each variable. These trends are important for the benchmark since they indicate how stable the results of the benchmark are. Moreover, these trend analyses can reveal past developments that might hint at future changes. For each analysis, a mixed model was used to determine the relation between time and each variable present in the benchmark. Three mixed models are suitable to perform this analysis:

1. Both the intercept (the value for the year = 0 (= 2000)) and the regression weight (the connection with the year) depend on the company. This implies that the companies follow individual trends. The analysis of the overall regression weight of time is then the analysis of the average regression weight.

2. Only the intercept depends on the company, but the effect of time (the trend) is the same for all companies.

3. Neither of the above (1 and 2) depends on the company.

The appropriate model is determined on the basis of Akaike's Information Criterion (AIC). Table 3 shows the dependent variable, the model used, and the p-value for the regression weight and the sign.

It should first be noted that there are large differences between the companies regarding the trends for most of the dependent variables, since model 1 is in most cases the appropriate model. Second, the results of the analysis of the overall regression weights are not significant, which means that these weights cannot be interpreted. The signs (+ or -), however, are quite plausible. Two main conclusions that arise from the table are that the inputs (employees, locomotives, and railcars) of the main rail freight carriers in Europe have been reduced over the years and that the productivity performance of the rail freight transport companies (tons/railcar, tonkms/railcar, sales/railcar, employees/railcar, sales/ton, sales/employee, sales/tonkm, and tonkms/employee) has improved over the years. Overall, it can be concluded that less freight is transported more efficiently.

Benchmarking rail operations may focus on train size, equipment utilization, and labor productivity (Ockwell 2001). In the database, the main determining elements for the maximum capacity are the network length; the number of employees; the number of locomotives; and the number of railcars.

The first focus in benchmarking is on employees' productivity performance (see Table 4). Up till now, most companies have had large numbers of employees, but almost all are reducing them. The available data show the total number of employees. So far, no distinction between drivers, transshipment personnel, and management is made in the data. It would, however, be interesting to connect employee groups to type of service delivered. However, because of the need for confidentiality it is difficult to obtain these data from the companies concerned. Benchmarking tons/employee might suggest that the company that performs the best has the most tons/employee. In this case that would be Railcargo Austria (RCA) (26,565). The worst performer in terms of tons/ employee is RENFE (598). The numbers show that the differences between the companies are quite large. But a good performance in tons does not always mean a good performance in overall sales and profits. Instead, higher tonkms/employee (in millions of tonkms) seems better. The best and worst performers are again, respectively, RCA (5.45 mln) and RENFE (0.23 mln). However, it is also important to have insight into average distances. For example, large volumes multiplied with short distances might give comparable results (in terms of tonkms) to small volumes multiplied with long distances. In general, rail freight transport concentrates on large volumes on long distances. But, in terms of financial results, small volumes on long distances could offer opportunities due to less transshipment, longer shipment length, and longer driving time, although smaller volumes appear to be transported by single-mode road transport. For sales/employee it would be good to maximize the outcome. Again, RCA is the best performer and RENFE performs the worst. RCA realizes sales of more than 250,000 [euro]/employee, which is quite good compared with its competitors. RENFE, on the other hand, achieves sales of less than 10,000 [euro]/employee. For RENFE, this offers considerable opportunities for the improvement of its sales productivity performance.

For employees/locomotive, at first glance it might seem that it would be required to minimize this to the maximum extent possible. Again, RCA is the best performer (2.15), but now SNCF Fret has the worst performance (22.51). But maximizing sales may require other types of services (more employee-intensive), and therefore more insight is needed into the connection between type of services offered and the number of employees/locomotive. However, these types of data are not available in annual reports. It seems that minimizing the number of employees/railcar is the optimal strategy for this indicator. Again, RCA performs the best (0.19), RENFE has the worst performance (2.61). On the other hand, a strategy aiming for maximum sales may require different types of services (more employee-intensive). Therefore, a reduced performance in employees per railcar (more employees per railcar) may be wise in order to, for example, offer more customer service.

The second focus in benchmarking is on the sales productivity performance of the companies. So far, most companies have steady or slightly decreasing total sales. The available data show the total sales of the companies. The rail freight operators do sometimes provide a distinction between different types of services (commodities) offered by the companies, but accompanying sales numbers are lacking. The benchmarking would gain in detail if this commodity mix structure could be included. For example, bulk transport will show another sales structure when compared to containers. Furthermore, short- or long-term buyer-seller relationships and small or large clients will further influence the sales structure. Benchmarking the sales/employee would suggest that the company that performs the best has the most sales/ employee. In this case that would be RCA (252,518 [euro]). The worst performer in terms of sales/employee is RENFE (6,651 [euro]).

Sales/employee is not only important in terms of sales volume, but also in terms of the quality of sales. Sales/ton provide some insight into the quality of sales. For sales/ton it would be wise to maximize the outcome. Green Cargo is the best performer (16 [euro]) and B-Cargo (6 [euro]) performs the worst. Overall, the table shows that companies that are efficient in "employee terms" (e.g., RCA) are less efficient in "sales terms." This confirms the findings of Cantos and Maudos (2001), who proved that rail freight companies that are more efficient in costs (e.g., employee productivity performance) behave less efficiently with regard to sales. Nevertheless, the overall performance of RCA is impressive.

In terms of sales/tonkm, the best and worst performers are, respectively, SBB Cargo (0.09 [euro]/tonkm) and RENFE (0.03 [euro]/tonkm). However, it is also important to have insight into tons and the corresponding transport distances to assess these numbers. For sales per locomotive, it would be necessary to maximize this. Green Cargo is the best performer (1,617,743 [euro]). B-Cargo has the worst performance (364,333 [euro]). However, maximizing sales/ locomotive will be characterized by other types of services (more locomotive-extensive) and therefore more insight is needed into the connection between types of services offered and the number of locomotives used. Unfortunately, all rail freight operators report their locomotives as either passenger or freight. Therefore, in the benchmarking analysis it is not possible to take into account differences in size and power of locomotives or to distinguish between owned or leased locomotives. In terms of sales/railcar Green Cargo performs the best (69,936 [euro]), and Trenitalia has the worst productivity performance (14,551 [euro]). Overall, companies that perform well in "sales terms" are Green Cargo and SBB. The worst performers are SNCF Fret, CP, Trenitalia, and RENFE.

The third focus in benchmarking is on the railcars. So far, most companies have large pools of railcars, but almost all are reducing them. The publicly available data show the total number of railcars. So far, not all rail freight operators distinguish between different types of railcars (e.g., covered hoppers, intermodal, tank wagons) and also not between owned and leased wagons. However, it would be interesting to connect type of railcars to traffic mix (coal, containers, intermodal, etc.), type of service (shuttle, mixed or unit trains), and different productivity performances (e.g., employees, sales, etc.). In terms of tons/railcar, Green Cargo transports most (5,118) and SNCF Fret transports least (1,144). The desired service portfolio by the rail freight operator might require more railcars and this would result in a relatively less intensive use of railcars. Most tonkms/railcar are transported by Green Cargo (1,505,882) and the least by SNCF Fret (446,952). The two best performers in terms of railcars are Green Cargo and RCA.

CONCLUSIONS

The conclusions in this study must be treated with caution for several reasons. First, the number of companies that have been analyzed is limited. This is because only a limited number of large rail freight transport companies in Europe publish annual reports available to the public. A comparison with American rail freight transport companies is less relevant because these companies are very efficient when compared with their European counterparts. Second, not all publicly available data from all companies are fully reliable (as a result of changes inside the companies during recent years). However, the data do give a good indication of the efficiency performance of the largest European rail freight transport companies. The data suggest that, for most companies, a (large) efficiency improvement is possible. Third, differences between the main rail freight operators (e.g., capital cost, different types of service, investment levels, network characteristics) may explain part of the efficiency differences between rail freight companies. For example, Railion, SNCF Fret, and RENFE have different networks (longer distances) than B-Cargo or CP (short distances), and thus differences in efficiency might occur due to these differences.

In the article, the focus has been on answering the following research question: How efficient are European rail transport companies compared with each other? The analysis has been performed by means of mixed modeling and benchmarking. The overall results of the mixed modeling analysis are not significant, which means that the values cannot be interpreted. The signs (+ or -), however, are quite plausible. Two main conclusions that arise from the tables are that the inputs (employees, locomotives, and railcars) of the main rail freight carriers in Europe have been reduced over the years. Second, the productivity performance of the rail freight transport companies (tons/railcar, tonkms/railcar, sales/railcar, employees/railcar, sales/ton, sales/employee, sales/tonkm, and tonkm/employee) has improved over the years. Overall, therefore, it can be concluded that less freight is transported more efficiently.

The main relative benchmarking results are shown in Table 5. When the focus is placed on "employee performance," RCA and VR Cargo perform relatively well when compared with their competitors. RENFE and SNCF Fret are the worst performers. When compared to each other, it can be observed that the differences are considerable. The difference between the best and worst performer in tons/employee is a factor 44, in tonkms/employee it is a factor 23, in sales/employee it is a factor 38, in employees/locomotive it is a factor 10, and in employees/railcar it is a factor 13. In "employee terms," the differences between the European rail freight operators are quite large.

If the focus is on sales, it can be observed that the best performers in sales are Green Cargo, SBB, and VR Cargo. The worst performers are Trenitalia, CP, and SNCF Fret. When compared to each other, the differences can be considerable. The difference between the best and worst performer in sales/ton is a factor 2.6, in sales/tonkm it is a factor 3, in sales/locomotive it is a factor 4, in sales/railcar it is a factor 4.8. In "sales terms," the differences between the European rail freight operators are considerable but not as large as in "employee terms." This might be due to the fact that a large part of their business takes place in an international competitive environment.

The best performers with their railcars are Green Cargo, VR Cargo, and RCA. The worst performers are SNCF Fret, Trenitalia, and RENFE. When compared to each other, it can be observed that the differences are considerable. The difference between the best and worst performer in tons/railcar is a factor 4.5, and in tonkms/railcar it is a factor 3.4. In "railcar terms," the differences between the European rail freight operators are comparable to that of sales.

The overall most efficient rail freight company is RCA. The company performs well in "employee" and "railcar terms." Its productivity performance in "sales terms" is acceptable. The overall worst performers are SNCF Fret and RENFE. Several indications for paths for the industry to follow can be given. First, it seems that a strategy to minimize inputs (employees, locomotives, and railcars) is a good strategy to realize efficiency. For several companies this suggests to (further) reduce the number of employees because in this area the differences between them are the largest. Second, a strategy that puts the focus on sales performance also seems a valid strategy. This means that the rail freight company should concentrate on its best customers. For many companies, 80 percent of profits originate from 20 percent of the customers. The other 80 percent of the customers should be evaluated and eliminated if needed. Thirdly, most companies (Railion, SNCF Fret, CP, Trenitalia, B-Cargo. and RENFE) appear to have no strategy at all. They have no excellent performance in either employees and railcars (inputs) or sales (output). This suggests that they need to develop a clearly focused strategy if they want to become more efficient.

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Mr. Wiegmans is research associate, Department of Transport and Infrastructure, OTB Research Institute for Housing, Urban and Mobility Studies, Delft University of Technology, Delft, The Netherlands; e-mail b.wiegmans@tudelft.nl. Mr. Donders is research associate, Department of Innovation Management, Utrecht University, Copernicus Institute, Utrecht, The Netherlands; e-mail r.donders@geo.uu.nl.
Table 1. The Main Rail Freight Transport Companies in Europe

 Railion SNCF Fret RCA

Tons (mln, 2003) 268 120 87
--% change (year to year) 0% -6% 0%
Tons (mln, 2002) 267 128 87
--% change (year to year) -4% 2% 1%
Tons (mln, 2001) 277 126 86
--% change (year to year) -3% -11% 1%
Tons (rain, 2000) 287 142 85
Tonkm (bln, 2003) 73.95 46.83 17.84
--% change (year to year) 2% -6% 1%
Tonkm (bln, 2002) 72.42 50.03 17.63
--% change (year to year) -3% -1% 2%
Tonkm (bln, 2001) 74.45 50.4 17.35
--% change (year to year) -3% -9% 1%
Tonkm (bln, 2000) 76.82 55.35 17.11
Sales (mln, 2003) 3288 1806 827
--% change (year to year) 1% 1% 0%
Sales (mln, 2002) 3259 1781 824
--% change (year to year) -5% -1% 0%
Sales (mln, 2001) 3421 1798 828
--% change (year to year) -3% -7% 4%
Sales (mln, 2000) 3509 1939 796
Employees (2003) 23733 41470 3275
--% change (year to year) -9% 1% -3%
Employees (2002) 26155 41168 3376
--% change (year to year) -10% 10% -5%
Employees (2001) 29101 37527 3554
--% change (year to year) -19% 0% -3%
Employees (2000) 35996 37398 3658

 TRENITALIA B-Cargo SBB-Cargo

Tons (mln, 2003) 83 57 55
--% change (year to year) 0% 0% 0%
Tons (mln, 2002) 83 57 55
--% change (year to year) -5% 0% -7%
Tons (mln, 2001) 87 57 59
--% change (year to year) -1% -8% -3%
Tons (rain, 2000) 88 62 61
Tonkm (bln, 2003) 22.6 7.29 9.9
--% change (year to year) -2% 0% 2%
Tonkm (bln, 2002) 23 7.3 9.73
--% change (year to year) -6% 3% -8%
Tonkm (bln, 2001) 24.4 7.08 10.53
--% change (year to year) -3% -8% -2%
Tonkm (bln, 2000) 25.03 7.67 10.79
Sales (mln, 2003) 713 335 844
--% change (year to year) 0% 1% -4%
Sales (mln, 2002) 712 333 882
--% change (year to year) -4% 3% 3%
Sales (mln, 2001) 740 323 860
--% change (year to year) -1% 8% 24%
Sales (mln, 2000) 750 300 694
Employees (2003) 9706 3000 (4) 4851
--% change (year to year) -1% 0% -5%
Employees (2002) 9811 3000 (4) 5107
--% change (year to year) -4% 0% 0%
Employees (2001) 10200 3000 (4) 5091
--% change (year to year) -4% 0% 16%
Employees (2000) 10608 3000 (4) 4370

 Green Cargo VR Cargo

Tons (mln, 2003) 43.5 44
--% change (year to year) 4% 5%
Tons (mln, 2002) 41.7 42
--% change (year to year) 0% 0%
Tons (mln, 2001) 41.7 42
--% change (year to year) 3% 2%
Tons (rain, 2000) 40.5 41
Tonkm (bln, 2003) 12.8 10.05
--% change (year to year) 2% 4%
Tonkm (bln, 2002) 12.51 9.66
--% change (year to year) 4% -2%
Tonkm (bln, 2001) 12.06 9.86
--% change (year to year) -25% -2%
Tonkm (bln, 2000) 16.07 10.11
Sales (mln, 2003) 679 352
--% change (year to year) 1% 7%
Sales (mln, 2002) 674 330
--% change (year to year) -1% -1%
Sales (mln, 2001) 682 335
--% change (year to year) -14% 2%
Sales (mln, 2000) 790 330
Employees (2003) 3512 2324
--% change (year to year) -3% -5%
Employees (2002) 3614 2446
--% change (year to year) -7% -3%
Employees (2001) 3902 2522
--% change (year to year) -12% -3%
Employees (2000) 4434 2600

 RENFE CP

Tons (mln, 2003) n.a. 9.3
--% change (year to year) n.a. 7%
Tons (mln, 2002) 18.8 8.7
--% change (year to year) 4% n.a.
Tons (mln, 2001) 18.1 n.a.
--% change (year to year) -1% n.a.
Tons (rain, 2000) 18.2 n.a.
Tonkm (bln, 2003) n.a. 2.09
--% change (year to year) n.a. -6%
Tonkm (bln, 2002) 7.369 2.22
--% change (year to year) 0% n.a.
Tonkm (bln, 2001) 7.397 n.a.
--% change (year to year) 2% n.a.
Tonkm (bln, 2000) 7.260 n.a.
Sales (mln, 2003) n.a. 71.2
--% change (year to year) n.a. -3%
Sales (mln, 2002) 209 73.4
--% change (year to year) 1% n.a.
Sales (mln, 2001) 207 n.a.
--% change (year to year) 1% n.a.
Sales (mln, 2000) 204 n.a.
Employees (2003) n.a. 1024
--% change (year to year) n.a. -3%
Employees (2002) 31422 1053
--% change (year to year) -4% n.a.
Employees (2001) 32584 n.a.
--% change (year to year) -3% n.a.
Employees (2000) 33747 n.a.

* email from Bcargo. Sources: DB Cargo AG, 2003; DB Cargo AG,
2002; Deutsche Balm, 2001; Railion Deutschland AG, 2004; SNCF
Fret 2001-2004; OBB, 2001-2004; SBB CFF FFS, 2001-2004; SJ
Group, 2001; Green Cargo, 2002-2004; VR Group, 2001-2004;
Nationale Maatschappij der Belgische Spoorwegen, 2001-2004;
RENFE, 2003; CP, 2004

Table 2. Strengths, Weaknesses, Opportunities, and Threat
(SWOT) Analysis of Rail Freight Transport in Europe

Strengths Weaknesses

high level of safety lack of flexibility

less harmful to the lack of accessibility (low network and
environment than terminal density compared with road)
road transport

fuel efficiency not reliable due to execution of rail freight
 transport

not expensive if lack of reliable and consistent market
distances are information
relatively long
and door-to-door
solutions are
possible

ability to carry less suitable for smaller shipments and; or
large volumes small load units

 bad image

 relatively slow transport compared with road

 insufficient cooperation between national
 railway companies

 incompatible technologies

 incompatible rules

 considerable variations in performance between
 operators (price, speed, reliability)

 track access charges vary strongly among EU
 Member States

 access to local facilities (terminals, fuelling
 stations) sometimes problematic

 differences in rail infrastructure
 characteristics (power systems, signaling
 systems, maximum train length, axle gauge

 and train weight, differences in track gauge)

 rail infrastructure capacity shortages (limited
 number of train paths, difficulties for finding
 alternative paths in case of delay)

 variations between national railway regulations
 on operations and safety (braking sheets,
 working conditions for locomotive drivers)

Strengths Opportunities Threats

high level of safety increasing road road transport gets
 congestion more sustainable

less harmful to the increasing cost improvements in
environment than of road transport barge transport
road transport

fuel efficiency Longer EU-hauls industry changes
 (coal) to barges

not expensive if cost reductions transfer of freight
distances are flows to other
relatively long origins and; or
and door-to-door destinations
solutions are
possible

ability to carry quality improvements
large volumes of rail transport

 travel time
 reductions

 high-speed freight
 transport

 double-stacked
 containers

Sources: Based on Wiegmans et al., 1999a-b; EC, 2001; Wiegmans
et al., 2001; EC, 2002; IBM, 2002; Scherp, 2002; Community of
the European Railways, 2003; Hylen, 2003, Lupo, 2003; Mynard,
2003; Wiegmans, 2003; Bouwknegt et al., 2004; VR Group, 2004;
www.admin.ch.

Table 3. Results of the Mixed Modeling Analysis

 p-value Sign regression
Dependent variable Model Time weight

Tons (mln) 1 0.152 -
Tonkms (mln) 1 0.068 -
Sales (mln) 1 0.550 -
Employees 1 0.372 -
Locomotives 1 0.082 -
Railcars 3 0.117 -
Tons/railcar 1 0.124 +
Tonkms/railcar 2 0.183 +
Sales/railcar 2 0.001 +
Employees/railcar 1 0.199 +
Sales/ton 1 0.804 +
Sales/employee 1 0.023 +
Sales/tonkm 1 0.186 +
Tonkms/employee 1 0.439 +

Sources: DB Cargo AG, 2003; DB Cargo AG, 2002; Deutsche Bahn, 2001;
Railion Deutschland AG, 2004; SNCF Fret 2001-2004; OBB, 2001-2004;
SBB CFF FFS, 2001-2004; SJ Group, 2001; Green Cargo, 2002-2004; VR
Group, 2001-2004; Nationale Maatschappij der Belgische Spoorwegen,
2001-2004; RENFE, 2003; CP, 2004

Table 4. Overview of the Benchmarking Results (absolute numbers)

 Railion SNCF Fret CP

Tons/employee 11,292 2,894 9,082
Tonkms in mln/employee 3.12 1.13 2.04
Sales in Euro/employee 138,541 43,549 69,531
Employees/locomotive 9.30 22.51 8.00
Employees/railcar 0.22 0.37 0.29
Sales in Euro/employee 138,541 43,549 69,531
Sales/ton 12 15 8
Sales/tonkm 0.04 0.04 0.03
Sales/locomotive 1,288,906 980,456 556,250
Sales/railcar 30,116 15,911 20,337
Tons/railcar 2,455 1,144 2,656
Tonkms/railcar 677,328 446,952 597,258
Sales/railcar 30,116 15,911 20,337
Employees/railcar 0.22 0.37 0.29

 RCA Trenitalia BCargo

Tons/employee 26,565 8,500 19,000
Tonkms in mln/employee 5.45 2.33 2.43
Sales in Euro/employee 252,518 73,457 111,667
Employees/locomotive 2.15 5.44 3.28
Employees/railcar 0.19 0.20 0.20
Sales in Euro/employee 252,518 73,457 111,667
Sales/ton 10 9 6
Sales/tonkm 0.05 0.03 0.05
Sales/locomotive 543,364 399,664 364,333
Sales/railcar 48,621 14,551 22,443
Tons/railcar 5,115 1,684 3,819
Tonkms/railcar 1,048,621 461,224 488,578
Sales/railcar 48,621 14,551 22,443
Employees/railcar 0.19 0.20 0.20

 SBB GreenCargo

Tons/employee 11,297 12,386
Tonkms in mln/employee 2.04 3.64
Sales in Euro/employee 174,056 193,466
Employees/locomotive 6.25 8.36
Employees/railcar 0.25 0.41
Sales in Euro/employee 174,056 193,466
Sales/ton 15 16
Sales/tonkm 0.09 0.05
Sales/locomotive 1,088,077 1,617,743
Sales/railcar 43,726 69,936
Tons/railcar 2,838 5,118
Tonkms/railcar 512,688 1,505,882
Sales/railcar 43,726 69,936
Employees/railcar 0.25 0.41

 VR Cargo RENFE

Tons/employee 18,718 598
Tonkms in mln/employee 4.32 0.23
Sales in Euro/employee 151,461 6,651
Employees/locomotive 5.42 n.a.
Employees/railcar 0.21 2.61
Sales in Euro/employee 151,461 6,651
Sales/ton 8 11
Sales/tonkm 0.04 0.03
Sales/locomotive 820.513 n.a.
Sales/railcar 31,084 17,347
Tons/railcar 3,841 1,560
Tonkms/railcar 887,231 611,637
Sales/railcar 31,084 17,347
Employees/railcar 0.21 2.61

Note: The benchmark has been made mainly for the year 2003. For
the years 2000-2002 the number of missing data is considerable.

Sources: DB Cargo AG, 2003; DB Cargo AG. 2002: Deutsche Bahn.
2001; Railion Deutschland AG, 2004: SNCF Fret 2001-2004: GBB,
2001-2004; SBB CFF FFS, 2001-2004: SJ Group. 2001; Green Cargo,
2002-2004: VR Group, 2001-2004; Nationale Maatschappij der
Belgische Spoorwegen, 2001-2004; RENFE, 2003; CP, 2004

Table 5. Overview of the Benchmarking Results (ranking)

 Railion SNCF Fret CP RCA

Tons/employee 6 9 7 1
Tonkms/employee 4 9 7 1
Sales/employee 5 9 8 1
Employees/locomotive 8 9 6 1
Employees/railcar 5 8 7 1
Sales/employee 5 9 8 1
Sales/ton 4 3 9 6
Sales/tonkm 5 6 8 3
Sales/locomotive 2 4 6 7
Sales/railcar 5 9 7 2
Tons/railcar 7 10 6 2
Tonkms/railcar 4 10 6 2
Sales/railcar 5 9 7 2
Employees/railcar 5 8 7 1
Total 70 112 99 31

 Trenitalia BCargo SBB

Tons/employee 8 3 5
Tonkms/employee 6 5 7
Sales/employee 7 6 3
Employees/locomotive 3 2 5
Employees/railcar 2 2 6
Sales/employee 7 6 3
Sales/ton 7 10 2
Sales/tonkm 9 4 1
Sales/locomotive 8 9 3
Sales/railcar 10 6 3
Tons/railcar 8 4 5
Tonkms/railcar 9 8 7
Sales/railcar 10 6 3
Employees/railcar 2 2 6
Total 96 73 59

 GreenCargo VR Cargo RENFF

Tons/employee 4 2 10
Tonkms/employee 3 2 10
Sales/employee 2 4 10
Employees/locomotive 7 4 n.a.
Employees/railcar 9 4 10
Sales/employee 2 4 10
Sales/ton 1 8 5
Sales/tonkm 2 7 10
Sales/locomotive 1 5 n.a.
Sales/railcar 1 4 8
Tons/railcar 1 3 9
Tonkms/railcar 1 3 5
Sales/railcar 1 4 8
Employees/railcar 9 4 10
Total 44 58 105

Note: The benchmark has been made for the year 2003. For the
years 2000-2002 the number of missing data is considerable.

Sources: DB Cargo AG, 2003; DB Cargo AG, 2002; Deutsche Balm,
2001; Railion Deutschland AG, 2004; SNCF Fret 2001-2004; OBB,
2001-2004; SBB CFF FFS, 2001-2004; SJ Group, 2001; Green Cargo,
2002-2004; VR Group, 2001-2004; Nationale Maatschappij der
Belgische Spoorwegen, 2001-2004; RENFE, 2003; CP, 2004
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Author:Wiegmans, Bart W.; Donders, A. Rogier T.
Publication:Transportation Journal
Date:Mar 22, 2007
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