Simulating contestability in freight transportation – The

Canadian grain handling and transportation system

Russell Lawrence

James Nolan*

Richard Schoney

*Corresponding author

Dept. of Agricultural and Resource Economics

University of Saskatchewan

Saskatoon, SK Canada S7N 5A8

Abstract: Modern supply chains are characterized by multiple interactions among individuals

and evolving temporal patterns of interaction. The grain handling and transportation system

(GHTS) in Canada is a good example of a large modern supply chain. In this paper, we develop

an agent-based simulation model of this regional supply chain and use the simulation output to

assess the viability of an open rail access policy designed to mitigate railway market power in

the system. We find that if open rail access in the Canadian GHTS were to be implemented, at

best profitable entry opportunities would be very limited.

Date of this version: May, 2016

Keywords: agent based simulation, rail, contestability, grain transportation, Canada.

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1. Introduction

Modern supply chains are characterized by numerous interactions among individuals as well

as evolving temporal patterns of interaction. The grain handling and transportation system

(GHTS) of Western Canada is a good example of a large scale, modern supply chain. While

almost all grain grown in Western Canada is ultimately exported, grain entering the Canadian

GHTS comes from numerous individual farms. During the crop year, Prairie farms harvest

grain and transport their product to grain elevators by truck. Grain is blended at the elevator to

specification, and is then put into railcars for transport to port elevation facilities. Once at port,

grain is finally loaded onto cargo ships for delivery to overseas customers.

Mostly due to economies of scale, rail has been the dominant mode for transporting grain across

the region. This dominance is so complete that in spite of almost complete deregulation of the

entire freight transportation sector in Canada through the 1970’s, there remains residual

regulatory oversight over grain movement with the potential for exertion of market power by

rail in this supply chain. While the form of this regulation has changed over time, currently, a

policy of revenue limits on grain movement is still used to control the behavior of the two

major Class 1 rail carriers serving the Canadian grain handling system.

With continuing modernization of the supply chain, there remains considerable controversy

over whether and how much the current revenue based formula for regulating grain

transportation benefits either grain shippers or the railways. Neither grain shippers nor railways

are entirely satisfied with the current regulatory framework, while both sides seem to want a

competitive supply chain that requires minimal regulatory oversight. Future policy intervention

in this sector will need to better accommodate the kinds of spatial and temporal shocks that are

endemic to the vast Canadian grain handling supply chain.

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In the rail sector, one approach to create more competition that has been attempted in other

jurisdictions is a policy referred to as ‘open’ or ‘competitive’ access (Carlson & Nolan, 2005).

Similar to policies in other network industries, open rail access accommodates priced entry by

competing railways on existing rail infrastructure. Rail entrants are permitted to lease rail right-

of-way and also solicit traffic over existing track. Due to complications related to issues of

track ownership and infrastructure management, to date the evidence on societal benefits

stemming from implementation of open rail access in other countries has been mixed

(Mitzutani, 2013). In Canada and the U.S., open access in freight rail is further complicated

because the freight rail network is privately owned (Bonsor, 1995). Implementation of such a

policy would entail considerable operational and financial risks to both carriers and shippers.

Even today, open access regimes in rail are still essentially public policy “experiments”,

implemented with little prior practical understanding of the likely economic and financial

consequences to the rail industry. In this paper, we build upon the expanding literature in

computational economics and develop an agent-based simulation model of a complex regional

supply chain served by a concentrated rail network. We then use the simulation output to assess

the viability of open access to mitigate rail market power in this supply chain. From a public

policy perspective, this type of analysis should allow a rail regulator to effectively pre-test both

the feasibility and sustainability of rail access policies prior to implementation.

The simulation data allows us to evaluate the feasibility of implementing an open or

competitive access policy on wheat/grain movement over a large regional freight rail network.

On one hand, while the type of access policy considered here is an effort to address on-going

concerns of agricultural shippers about market power in grain transportation, the regulator must

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also ensure that both entrant and incumbent railway remain financially viable. In this regard,

we will define “entry opportunity” in the system as that moment when wheat is loaded at an

elevator into hopper rail cars ready to be transported to final destination, but for some reason

the incumbent railway is not able to move the cars in a timely manner. Any delay or opportunity

cost arising from immobile cars in the rail network represents not only a lost marketing

opportunity for the shipper, but also creates a window of opportunity for a rail competitor to

access the loaded cars and subsequently transport them to final destination. It is this opportunity

which constitutes open access in our simulated grain handling and transportation system.

2. The Canadian grain handling and transportation system

The current Canadian grain handling and transportation system has evolved from a web of

mainlines and branch lines serving numerous small wooden grain elevators, into a modern

supply chain composed largely of a few dozen large concrete elevators served by the two

Canadian Class 1 railways. In any given year, about 75-80% of all Western Canadian grain is

sent for export through one of the ports of Vancouver, Thunder Bay, Churchill, or Prince

Rupert.

The current system is also dominated by large volume trains and increasingly efficient elevator

throughput. The economics of grain elevation and commodity movement means that although

newer large concrete elevators have higher fixed costs, they also have much lower marginal

costs than the bygone wooden elevators (White et al., 2015). Both grain companies and

railways have strong incentives to move large quantities of grain from elevators to port position

in a timely manner. With large economies of scale in grain movement, the two Canadian Class

1 railways are effectively natural monopolies in their respective markets (see Figure 1) and this

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market power has led to a history of regulatory policies in order to mitigate this behavior (Nolan

and Carlson, 2005).

Even so, with respect to system logistics things are very much the same as they have been since

the system was built decades ago. Grain companies order and receive rail cars from the

railways. The information used to determine these orders includes prior grain deliveries, world

prices, and ability of the railway to transport demanded hopper cars. Once at port, grain cars

must be emptied either directly into ships, into terminal grain elevators for future loading, or

moved to adjacent rail yards (if space is available) to be loaded when a suitable ship arrives.

After unloading, grain cars are returned to the Prairies for re-allocation and re-loading

(Canadian Transportation Agency, 2008). While there is variation year to year, as an example

in the 2008-2009 crop year it took an average of 50.1 days for Canadian grain to move from an

inland elevator to port position. This can be broken down into an average of 27.7 days in store

on the Prairies, 5.7 days of loaded transit time, 16.7 days at a terminal elevator at port, and then

4.6 days of vessel time in port (Quorum Corporation, 2010).

Contestability in freight transportation

In order to develop policies to improve competition among firms possessing market power, we

briefly review the concept of contestable markets (Baumol et al., 1982). A market is defined as

contestable when a single (or few) firms in a market face explicit or implicit pressure on prices

from other firms that could potentially operate in that market. Contestability also implies the

existence of conditions such that firms operating outside the market can readily enter (or exit)

that market if they perceive an opportunity to earn economic profit. A market is therefore

contestable if there are any similar firms capable of entering, producing output and then exiting

that particular market (Carlton, 1994).

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For a market to be perfectly contestable, the following conditions are necessary (Baumol et al.,

1982): 1) entry must be costless and without limit, so that any entrant should be able to entirely

displace an incumbent firm if this situation obtains the lowest industry cost; 2) entry needs to

be absolute, meaning that an entrant should be able to enter without it being possible for the

incumbent to reduce price in response to entry; and 3) the firm with the lowest costs should

always displace a higher cost firm. The purest form of a contestable market is called “ultra-free

entry” or “hit-and-run entry”, meaning that the purest form of a contestable market is often

called “ultra-free entry” or “hit-and-run entry”.

Contestability theory has already been utilized within the U.S. rail sector to help formulate

regulatory policy. The U.S. Surface Transportation Board uses a pricing test founded in

contestability theory (the “stand alone cost” or SAC test) to compute upper limits on rates in

rate disputes (Carlson & Nolan, 2005). However, continued shipper dissatisfaction with the

SAC test and the allowable rate levels it has permitted has led to criticism about the use of

contestability theory as applied to the rail sector (Tye, 1990). These critiques focus on the fact

that the conditions necessary for a perfectly contestable market are often very difficult to meet

in a vertically integrated rail industry. Contestability theory applied to rail policy should instead

help lead to situations where viable potential entrants become credible competitors, thereby

imposing some (possibly incomplete) price discipline on the incumbent railway(s).

In this analysis, we simulate a situation where a concentrated freight rail sector is instead

mandated to operate using “competitive” or “open” access rail policies (BTRE, 2003). Such

access policies set prices for entrant access on the incumbent rail bed, with the goal of fostering

greater competition in the rail sector according to the spirit of contestability theory.

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One key issue to resolve is how to share and pay for the use of rail infrastructure. Prior

experience with other access regimes has shown that designing a fair and sustainable pricing

regime for the use of track infrastructure by competing railways is not a simple task (BTRE,

2003). The fixed cost of the track infrastructure is common for all users of the track, but

apportioning these costs across users is complicated at best because differences in track

condition and trains create difficulties in determining those costs associated with a particular

train using particular sections of track.

3. Simulating the Canadian grain handling and transportation system

In contrast to equilibrium models that rely on assumptions about aggregate behavior, agent

based economic simulation models (ABM) use individually programmed computer agents that

act according to their own individual objectives, while every agent also interacts with others in

a changing operational environment. Agent based modeling provides something akin to a

controlled laboratory setting to test certain dynamic characteristics of a system, as well as a

method for empirical examination of counterfactual scenarios of interest (Gintis, 2005).

While the scope of our study is novel, the agent based simulation approach is founded upon

prior research in both the social sciences and the economics of competition. For example,

simulated competition has been used to examine the coordination effects of mergers (Davis &

Garces, 2010), while intra and intermodal transport competition has been simulated using a

game theoretic approach (Ivaldi & Vibes, 2008). In supply chain analysis, agent based

simulation has been used to develop better intermodal container transport handling schedules

by exploring the inland exchanges between modes (Gambardella, Rizzoli, & Funk, 2002). And

novel work by Preston et al. (1999) developed a related (but not agent based) numerical

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simulation model that evaluated the potential for on track rail competition in the UK. This work

is closely related to our research in it attempt to assess the potential for rail competition under

a priced track access regime (Preston, Whelan, & Wardman, 1999).

The software used to perform the simulations is known as NetLogo© (Wilensky, 1999).

Among a growing number of available agent-based software packages, NetLogo© is relatively

easy to implement and has been used by a number of researchers to simulate various social and

natural phenomena. With the incorporation of a geographic information system (GIS)

extension capability, real physical landscapes and associated characteristics (like transportation

networks) can be more accurately modeled and represented in Netlogo models.

The scope and complexity of this particular research led to the development of a multi-tiered

simulation environment built upon both agent-based software and a GIS application. The

simulation uses both Netlogo and GIS data to accurately represent key locations as well as the

spatial interactions between participants in the Canadian grain handling system. We calibrated

the simulation to specific real world data in order to validate the simulation, then ran the model

over multiple replicates to generate output. It is these results which we report here, along with

a discussion of the implications of our findings for concentrated supply chains and grain

transportation.

The region and the agents in the simulation

To keep the analysis tractable yet relevant, the scope of the simulation is limited to the major

grain/wheat producing province of Saskatchewan (see Figure 1). Note that wheat output is not

homogeneous across the province, so that each of the sub-regions (called census agricultural

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regions or CARs) shown on the provincial map differ by soil type and productivity. A detailed

map of this information was incorporated into the simulation.

The agents in the simulation consist of farms, grain elevators and the railways. In the model,

farms deliver wheat to elevators, then elevators assemble delivered product into shipments that

are to be moved by rail to final port destination (see Figure 1). Due to their large numbers

relative to the other participants, farms are assumed to be distributed randomly across each

agricultural sub-region, and remain there for the duration of each simulation run. In addition,

each farm is given an initial store of wheat to move into the system.

For simplicity, the number of trains moving on rail lines in the simulation were randomized.

Testing revealed there were few gains from using proxy schedules to emulate railway behavior

within the supply chain. Actual grain elevators in the region were located with the aid of a GIS

shape file that also contained the locations of rail lines. Elevators are assumed to start the

simulation with no inventory, but the elevator agents constantly try to obtain wheat from the

network of farms across the region.

Furthermore, we assume in the simulation that a monoculture of wheat is grown across the

region (Lawrence, 2011) and is directly seeded on all arable acres of each farm every year.

While this was done for tractability and does approximate historical reality, in fact the

simulation does not account for other significant regional crops (like canola) or summer fallow,

nor is there a specific accounting for adverse weather events.1 While we feel that these

assumptions do not fundamentally affect the qualitative outcomes of the model, future work

1 The wheat production data we use effectively contains built-in weather adjustments by time of year and

season.

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will incorporate other commodities into the analysis since other crops have assumed growing

importance in regional crop rotations over time.

Figure 1.

Rail network and census agricultural region (crop districts) boundaries (Saskatchewan).

Simulation initialization

The initialization process used actual production, in tonnes, for all wheat produced in

Saskatchewan from 1977 to 2006, by CAR (see Lawrence, 2011). Regional level production

was assumed to be divided equally among the farms in each farming district, meaning

simulated farms within the sub-regions are necessarily assumed to be the same size (Statistics

Canada, Agricultural Division, 2006). Based on this information, each farmer agent makes its

own production and delivery decisions, with each agent accessing relevant information for the

appropriate year of the simulation.

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Train lengths used in the simulation are set according to the capacities of three most common

lengths of grain/wheat trains serving the region. These comprise a 100 car unit train, a 50 car

train, and a smaller 25 car train. Using hopper car capacities, these trains have capacities

(respectively) of 10,000 tonnes, 5,000 tonnes, and 2,500 tonnes. In addition, we assume world

wheat prices are known to the agents. In the simulation, the offer made to farms by elevators

to obtain wheat is a cash price.

In turn, actual production levels were used to determine the percentage of each type of wheat

produced in each year. This computed percentage was converted to a weighted average in order

to determine an average price for wheat to be offered to farms in the simulation. Finally, this

latter price was adjusted according to a Farm Product Price Index for Saskatchewan grains to

ensure the simulated wheat prices were indexed relative to each other (Lawrence, 2011).

The total number of farms initialized within the simulation was determined using the 2006

Census of Agriculture. To give a sense of the scale of the model, the number of farms located

on the simulated landscape was just over 25,000. This closely matches farm numbers listed in

publicly available data on grain farming (Statistics Canada, 2006).

In order to maximize gross margins, farms are assumed to plant wheat with all acreage seeded

every year.2 Since almost all of the production costs are sunk by the time wheat is harvested,

we further assume the farm agents do not take cost of production into account when pricing

their wheat.3 In summary, every simulated farm within each separate sub-region is identical in

2 This assumption simplifies the model, but in fact for many years, wheat has been by far the most common crop

grown in the province. Very recently, other similar crops (like canola) have gained importance on Saskatchewan

farms, but this assumption is valid across the data and time covered in this analysis. 3 This assumption stems from the experience of field experts (including one of the authors).

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size and harvests the same amount of wheat as every other farm in the same sub-region. Since

actual agricultural yields vary across the province, the model generates yield variability through

the randomized location of farm agents in each replicate of the simulation.

The supply chain for wheat

Trucking rates for moving wheat from farm to elevator are a function of distance (Weyburn

Inland Terminal, 2011). This required using a grid distance calculation to determine applicable

road distance from farms to elevators. Given the structure of the regional road network

modeled, a grid calculation more accurately simulates actual truck routings.

Over the latter time periods covered by the simulation, there were approximately 185 grain

elevator locations (Canadian Grain Commission, 2009) across the province. These were

operated by 37 companies, while 22 of these companies possessed primary handling facilities.

Next, we excluded grain processing facilities because they do not collect grain for the purpose

of export, so this left grain delivery facilities in the simulation with a capacity of 2.91 million

tonnes, or about 90% of total elevator capacity in Saskatchewan (Canadian Grain Commission,

2009). A complete listing of the elevators used in the simulation, listed by company as well as

total capacity, can be found in Lawrence (2011).

For tractability and to best capture reality within the simulation, we distinguished between three

primary sizes of grain elevator. Based on industry information, elevator capacities were

configured as follows – those less than 7,000 tonnes (small), those between 7,000 and 25,000

tonnes (medium), and those greater than 25,000 tonnes (large). Drawing upon real elevator

capacities, these discrete size categories were then imposed on the (over 150) primary elevator

locations located within the province.

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Using average capacities and the actual number of handling facilities falling within each

category, we then generated a “synthetic” population of grain elevators matching actual

capacity distribution for a representative crop year (2006; see Table 1). The categories

generated 51 small elevators, 57 medium elevators, and 49 large elevators, each of which had

average simulated capacities of 4,076 tonnes, 13,829 tonnes, and 38,961 tonnes respectively.

Simulated total grain elevator capacity was 2,913,000 tonnes, only fractionally higher than

actual capacity at that time. Details on other particular aspects of the grain elevator calibration

exercise, including handling fees, storage and elevator tariff rates are contained in Lawrence

(2011). Topographically, it is worth noting that each elevator in the simulated landscape was

accessible from just a single Class 1 railway.

Table 1.

Simulated elevator capacities in Saskatchewan, by Grain Company

Finally, rail rates for wheat from Saskatchewan to the port of Vancouver applicable to the

simulated period were obtained from the Saskatchewan Ministry of Agriculture (Saskatchewan

Ministry of Agriculture, 2009), while data from the Government of Alberta’s Agriculture and

Rural Development website (Mah, 2010) were also used to match each Class 1 railway with

appropriate rate data for various elevator locations across the region.

Served by CN Served by CP Served by CN Served by CP

Cargill 11 2 8.9% 1.8% 312,000 10.7%

Parrish & Heimbecker 7 3 4.7% 4.0% 255,000 8.8%

Richardson Pioneer 19 15 7.4% 9.6% 496,000 17.0%

Viterra 27 25 20.7% 17.2% 1,103,000 37.9%

Remainder 13 35 8.0% 17.7% 747,000 25.6%

Total 77 80 49.7% 50.3% 2,913,000 100.0%

Simulated Elevator Capacities in Saskatchewan by Company

Company# of Locations % of Capacity Sum of Primary

Capacity (tonnes)%

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System and delivery efficiency – delays and penalties

The simulation tracks delivery delays and associated costs within the grain supply chain in

order to assess the viability of another rail operator transporting any delayed grain shipments.

While demurrage costs can be significant in the industry, in fact any costs incurred through

wheat not moving in a timely manner can be greater than any demurrage costs at destination.

For example, if the market price for wheat varies dramatically, there is the possibility of

significant foregone profits if grain does not move reliably to export position. The full cost to

the shipper in this case is not only the pre-contracted demurrage fee, but also the foregone

opportunity of not being able to sell wheat at a desired price. For this reason, our simulated

total delayed wheat volume is a essentially a proxy for shipper costs due to transportation

delays.

In the simulation, we define a delivery penalty “event” as a situation in which wheat does not

move in a timely or reliable fashion from grain elevator (origin) to port (destination). Like

many industries where time is money, grain shipment delays generate additional costs for

shippers. In this sense, our delivery penalty is similar to demurrage or delay costs, but in the

simulation the penalty is calculated over commodity volume instead of value. In effect, we are

assuming the delay penalty is equivalent to the capacity of a train serving a particular size of

elevator in the regional grain handling system.

Grain elevators are collection points. In the simulation, farm agents can deliver into the elevator

system all the wheat they choose to deliver. In turn, wheat gathered and stored in an elevator

must be moved out as soon as possible in order to minimize storage and holding costs. In the

simulation, elevators do not want to hold wheat for too long before shipping it out by rail

because delays also generate losses for the elevator. While these decisions are important, in

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fact, the extant literature provides surprisingly little guidance regarding formal modeling of

elevator behavior in this respect. Therefore we made some assumptions about what constituted

“too much” wheat carried over in an elevator for a defined period of time in the simulation.

Delivery penalties are computed for grain elevators using a heuristic that is a function of the

capacity of an individual elevator.4 If at the end of a simulated time period the actual wheat

level in a large or medium elevator is greater than 75% of its total capacity, then a delivery

penalty is imposed for carrying over too much stock. But for small elevators, the measured

wheat level in the elevator must fall at 85% or greater of total capacity to incur a penalty. Note

that the different penalty percentages reflect the different relative train capacities that can serve

the elevator sizes used in the simulation.

For delay to be “flagged” in the simulation, the serving railway must not have delivered a train

to that elevator in the simulated time period (month). If a particular elevator satisfies the

conditions necessary in its size class to create a delivery penalty event, the simulation records

the amount of tonnage that did not move, the location of the elevator, the elevator company,

and the month this happened.

Simulation heuristics were validated by checking and comparing carryout stocks of wheat.5

Table 2 summarizes the data used for initialization of the simulation environment. Farms are

not evenly distributed across the province, and farms are endowed with a starting wheat

inventory so that deliveries can start entering the supply chain prior to the first harvest.

4 This heuristic was chosen based on industry knowledge, and to approximate basic inventory optimization

solutions. 5 Carryout stock is the amount of wheat left over once all demand is satisfied.

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Table 2.

Simulation initialization summary

Additional information about the simulation

This section lists several other important elements and assumptions that were important to the

development of the simulation framework. Aside from time scale, each of these points better

describes governing behavior of the agents as programmed in the simulation. The interested

reader is referred to Lawrence (2011) for more exact details about the simulation

environment beyond those provided here, including a flowchart of the model sequence and

the Netlogo code used to conduct the analysis.

Elevator

Size

Number

of

Elevators

Total

Capacity

(Tonnes)

Percent

Capacity at

Initialization

Train

Capacity

(Tonnes)

Small 49 4,000 0% 2,500

Medium 57 14,000 0% 5,000

Large 51 39,000 0% 10,000

1A 1017 3BS 717 7A 1403

1B 770 4A 313 7B 1212

2A 906 4B 696 8A 1377

2B 1587 5A 1978 8B 1685

3AN 644 5B 2077 9A 1766

3AS 1208 6A 2102 9B 958

3BN 1422 6B 1584

Initialzation Information Summary

Number of Farmers by CAR

Farmer Starting Inventory N ~ (300, 50)

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1) Time scale - The simulation is replicated for 360 individual months, with 12 months

equating to one calendar year and each replicate proceeding for 30 years. This is repeated for

a total of 1,024 iterations, yielding 368,640 months of data.

2) Railway behavior – The railways charge rates based on historical data (see Appendix for a

listing). Due to the large scale of the rail network and considering on-going complaints in the

sector about rail service (for example, see Transport Canada, 2011; Annand & Nolan, 2003)

we assume that from the perspective of shippers, rail car allocation to each elevator is

effectively a random process. In the simulation, trains arrive randomly at every elevator, with

arrivals are drawn from a uniform distribution.

The parameters of the (uniform) train arrival distribution were set in the following manner.

The average number of trains delivered in a year to grain elevators was determined by

calculating average total wheat production from 1977 to 2006 (about 13.5 million tonnes;

Statistics Canada, 2007), while the yearly number rail cars available for each elevator was

computed such that this average wheat production could be transported from all elevators

within a single crop year. Using the amount of each category (large, medium, and small) of

elevators in the simulation, we then assigned grain car spots of 100, 50, and 25 cars to each

respective size of grain elevator. From this, we found that on average the large elevator

locations receive 21 trains per year, medium sized elevators get an average of 12 trains per

year, and small elevators receive six trains in an average year. Table 3 summarizes this basic

grain system information.

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Table 3.

Number of elevators and average total simulated tonnes of wheat transported per year

There is an additional model calibration done within train allocation that allows elevators to

reject one train spot under certain conditions, including a situation when a train arrives at the

elevator but too little wheat is in the elevator to move it cost effectively.6 Furthermore,

measured elevator capacity changes slightly every month as a consequence of the number of

trains actually allocated to the elevator. This is due to the fact that the more trains actually

received by an elevator, the greater the real capacity of the elevator since the train also acts as

additional storage for the elevator within the base time period of one month.

3) Farmer behavior - Farmer agent decisions to deliver wheat to an elevator are fixed for all

months of the year, and are the same for each year of the simulation. Like reality, we assume

farms do not make any September deliveries, unless they are queried by an elevator short of

grain. In every period, each farmer chooses a percentage of their total wheat inventory that they

will deliver to an elevator. In turn, these amounts are based roughly upon historical data and

are tied to cycles of the wheat crop year. This information is listed in Table 4. Ultimately, based

6 This was done to better approximate real world delivery conditions. If a shipper has a low volume of inventory,

they will not call for rail cars. If the shipper does make a call for cars, the number of cars they actually receive is

effectively random since they may or may not get all of the rail cars they requested in the time specified.

Small 49 25 6 735,000

Medium 57 50 12 3,420,000

Large 51 100 21 10,710,000

Total 14,865,000

(Statistics Canada, 2007)

Number of Elevators and Average Total Simulated Tonnes of Wheat Transported per Year

# of Elevators # of Rail Cars per TrainAverage # of Trains per

Year

Average Total Tonnes

Transported per YearElevator Size

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on these parameters and other minor heuristics, farms in the simulation move approximately

98% of simulated wheat production to the region’s elevators in a given simulated year.

Table 4.

Attempted delivery volumes, by percentage of inventory per month

In order to keep the model temporally efficient, farm grain deliveries are done in the following

manner. Each time period, ten thousand randomly chosen farms try to deliver wheat to their

closest (in this case, assessed as the “crow flies”) large capacity elevator, regardless of offered

prices. Next, twenty five hundred randomly chosen farms attempt to deliver to their closest

medium sized elevator. Finally, two thousand randomly selected farms do the same and try to

deliver wheat to the closest small elevator. The remainder of farms who were not selected to

deliver to a set location make their delivery choices to the elevator (regardless of size) with the

largest differential between offered price and transportation cost (using computed trucking

distance) from their farm to that elevator (Hoover & Giarratani, 1985). For simplicity, we also

MonthDelivery

Volume

January 40%

February 40%

March 40%

April 50%

May 50%

June 80%

July 90%

August 100%

September 0%

October 10%

November 20%

December 30%

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assume that individual farmer deliveries cannot be divided, so that farms deliver all or none of

their desired delivery volume.

4) Elevator behavior - Once all farms have attempted deliveries, that month their wheat is

moved to the appropriate elevators and levels deducted from individual farm storage

inventories. Once all deliveries are made, the elevator compares current inventory against the

amount the serving railway is scheduled to load in that particular month. If the elevator has

sufficient inventory to fill what the railway can load that month, then the elevator allocates that

amount for rail movement. If the elevator does not have enough to cover what the railway can

load, the elevator is allowed to call farms again for more wheat (this is similar to heuristics

used by Bunn and Olivera (2001) to model the electricity industry).

Once all farms called to help fill elevator capacity shortfalls have delivered their wheat, those

elevators that were short add up their inventories again. If the elevator’s inventory is large

enough to cover what the railway will take away, then it allocates the amount the railway will

move and deducts this from inventory. But if an elevator is still short of wheat to transport, it

delivers whatever amount sits in inventory to the railway and reports how many tonnes it was

short in meeting the specified delivery amount.

Railways take delivery of wheat from the elevators and subsequently transport it to port

destination, the final action in the simulated time interval. Deliveries at port are assumed to be

filled and shipping tonnage is allocated in similar proportions to elevator capacity. One of the

major grain companies is allocated a fixed amount (almost 40 percent) of a given ship capacity,

while the other major companies receive declining capacities. Finally, the remaining smaller

grain companies and elevators are assumed to fill the remaining 25 percent of the ship. Total

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port delivery is summed according to company and compared to the percentage allocated to

each grain company. Finally, the number of ships needed to move wheat is determined after

the total port deliveries are tallied.

4. Model Validation and Results

We conducted a very basic validation exercise to determine if our simulation results were

broadly consistent with reality. The volume of wheat stock in the province of Saskatchewan

was chosen as our validation metric. Figure 2 shows real and simulated provincial wheat stock

data from 1981 up to the final year of the simulated model run (2006).

While the simulation consistently overestimates carryout stocks of wheat, we note that carryout

stocks generated with the simulation track actual carryout stocks quite closely. In fact, the

simulation consistently overestimates carryout stocks because in the simulation, while trains

arrive at elevators randomly, they are ultimately configured to transport long run average

provincial wheat production. The simulated railways can adjust for years where production is

lower than the long run average by reducing the number of cars delivered. However, due to

efforts to avoid congestion in the simulated rail system, the railways were given limited ability

to increase car deliveries in those years with greater than average production. See Figure 3. A

year with higher wheat production means that carryout stocks remain high until a point where

wheat production drops towards the long run average.

As an ambitious simulation framework, our efforts to build it led to many simplifications, some

of which may not mirror system behavior for the full duration of the simulation. But given the

limitations as discussed, we conclude that the ability of the simulation to closely track, if not

exactly emulate, real wheat carryout stock data is indicative that the model is a reasonable

21

representation of the regional grain handling and transportation system. While certain

assumptions and heuristics have affected the long-term accuracy of the simulation, the model

generates realistic output for many other measures of system performance.

Figure 2.

Wheat Stocks in Saskatchewan (simulated vs. actual), 1981 to 2006

(Statistics Canada, 2006)

22

Figure 3.

Simulated rail deliveries to port, compared to wheat production.

Delivery penalty events

The amount of data generated by the simulation means that we had to develop parsimonious

yet effective representations of system performance. We opted to track and compute the

approximate chance of delivery penalty events happening within the entire simulated grain

handling system, both through space and time. This particular representation is actually a

measure of system ‘failure’ (from a logistics perspective) but offers a concise way to track and

visualize overall system (in)efficiency.

Over the entire simulation timeline (30 years by 1,024 iterations, or 368,640 months of data)

the model generated over 57 million elevator delivery events, with almost 12 million delivery

penalty events in the system.7 In turn, each elevator yielded about 96 delivery penalty events

7 The data generated for Year 1 in the simulation was somewhat lower than other years because elevators were

assumed to start the simulation with zero inventories.

23

per run (i.e. a simulated year). The total wheat volume associated with delivery penalties over

all runs averaged just over eighty-five thousand tonnes per month, which translates into

approximately seventeen 50 car unit trains for transport. Subject to the various assumptions

and heuristics used in the model, the overall odds of a delivery penalty event occurring at any

given time and location were approximately one in five.8

Grain transportation delays in the simulation could also be broken down according to the train

size required to move the commodity. The likelihood of a 2,500 tonne penalty event occurring

at any given time was 19.3%, while that for a 5,000 tonne penalty event occurring was 1.25%.

Finally, the likelihood of a 10,000 tonne penalty event in the simulation was about 0.01%. So

as in reality, simulated grain handling system delivery penalty events occured most often at

small elevator locations and only rarely at medium and large elevator locations.

Examples of delivery penalty events

Staying mindful of our efforts to assess the viability of contestability policies in grain

transportation, we next describe some specific delivery penalty tonnage events in more detail.

These events help us to determine the feasibility of a rail entrant seeking to move delayed grain

in the supply chain.

Examining the broader simulation output, the amount of wheat that did not move in a timely

manner appears to be significant. But given the sheer expanse of this supply chain, the location

of any delayed wheat is crucial in assessing the viability of rail entry. To this end, GIS mapping

tools were used to illustrate the likelihood of a delay penalty event occurring at any particular

8 To the knowledge of the authors, this kind of data has never been tracked in Canada. However, anecdotal

evidence from local shippers indicates that this likelihood of delivery “failure” is close to that historically

experienced by grain shippers in the system.

24

elevator location in the simulation. This analysis generates a visual representation we refer to

as “probability maps”. These indicate where delivery delays occurred in a given time period,

along with the associated frequency of occurrence. To see more of these maps over several

years of the simulation, see Lawrence (2011). Figure 4 is just one of these maps, chosen to

represent a typical year of the simulation (2006).

Figure 4.

Map of regional delivery delay probability or likelihood, 2006

In the simulated 2006 crop year, average total wheat volume delayed throughout the system

was 981,121 tonnes. The lighter shaded areas represent locations with a lower likelihood of a

delivery penalty event occurring, while the darker shaded areas represent locations with a

25

greater likelihood. We can see that delivery events and penalties are somewhat localized and

often occurred at the edges of the modelled region.

As noted on the map, specific examples in 2006 include a high 65 percent chance of a delivery

penalty event at the elevator in Shellbrook (operated by Pioneer), whereas the elevator at

Tribune (also operated by Pioneer) had just a 52 percent chance of a delivery penalty event. In

fact, all the grain elevator companies were affected similarly in that each of them had varying

likelihoods of delay events for their elevator networks. For instance, the elevator at Wadena

(Viterra) had a 38 percent chance of a delivery penalty event, while the elevator at Maple Creek

(Viterra) had just a one in 100 chance of a delivery penalty event. Since it is well known that

many smaller elevators have limited connectivity to the trunk rail network that (over the time

of the simulation) continues to prioritize high volume unit trains (Nolan and Skotheim, 2008),

it is not surprising that for smaller outlying grain elevators, the simulation generated a higher

likelihood of a delay event.

5. Evaluating the possibility of rail entry into this grain transportation system

Carlson and Nolan (2005) evaluated the costs of third party access for a potential entrant into

the Canadian rail system who wanted (or was restricted) to move wheat. That research offers a

foundation for computing access prices for a potential entrant in the simulated network, where

an entrant in our context is defined as a railway that can readily identify delay events and then

exploit these through the transport of that delayed wheat.

Using the access pricing mechanism in the Carlson and Nolan paper (adjusted for inflation),

we generate a total charge for access of C$0.015883685 per tonne kilometre, broken down

between C$0.009089376 per tonne kilometre compensation for the use of track, and

26

$0.006794309 per tonne kilometre for track access (Lawrence, 2011). A single EMD SD40-2

diesel locomotive (a commonly used type) has an approximate market value of C$225,000.

Using available data, we assume these locomotives can be leased by the entrant for C$250 per

day (Simmons-Boardman Publishing Corporation, 2008). Furthermore, grain cars are assumed

to have a tare weight of 20 tonnes, while carrying 100 tonnes of wheat.

Locomotives employed by an entrant might not be used every day, but the entrant would need

to have access to them at all times due to the uncertain nature of when, and more importantly

where, a delay event might occur. To this end, we assume a crew of three is needed to operate

each unit train and that labour is compensated at C$65,000 per year. We also assume that 25

and 50 car trains require only two locomotives and one crew, while the large 100 car spots need

three locomotives and one crew.

In conducting this analysis, we also assume that a potential entrant has access to very good

information. This implies the following: 1) the entrant knows the likelihood of each elevator

possessing delayed rail car spots; 2) the entrant is informed immediately where and when those

events occur; and 3) the entrant can readily purchase (at a regulated rate) track capacity on the

host railway to move delayed grain cars in a timely manner. Using conservative business rules,

we also assume the entrant railway requires a 15 percent rate of return on their investment.9

We offer that this rate of return is reasonable since some of the risk associated with operating

as a competing railway purchasing access over a very large rail network is reduced because

potential entrants know where delivery penalty events are most likely to occur.

9 The rate of return was assumed to be 15% based on average venture capital rates of return, combined with the

potential cost savings that a rail entrant would have under perfect information.

27

Potential entrant feasibility: Entrant serves all delivery penalty events

Next, we generated a basic financial statement for the entrant using the assumptions in

association with the simulated system data on wheat deliveries and delays. This exercise is

done to evaluate under what conditions contestable entry in the grain transportation system

might be profitable. To start, an accounting breakeven threshold with a net income of zero was

used, along with assumptions of an (average) C$41.85 per tonne freight rate10 with an average

shipment distance from origin to destination of 1,857 kilometres.11 An economic breakeven

point was identified by adjusting the freight rate to a level that generated a 15 percent return

on investment.12 Table 5 lists relevant parameters used in the entry calculation.

In 2006, the simulation generated an average just over 1 million tonnes of wheat that was either

outright delayed or not moved in a timely manner. Given the structure of the elevator industry

in 2006, we assumed that approximately 950,000 tonnes (about 94 percent) of this total would

need to be moved in smaller 25 rail car spots, while the remaining tonnage (about 6 percent)

would be moved in larger 50 car spots (based on the simulated likelihood of a 100 car spot

delay as near zero). We also assumed that average railway car cycle in 2006-2007 for all car

spot sizes was 14.7 days (Quorum Corporation, 2007).

10 Average freight rate in the system (2006) 11 Average distance to Vancouver of all elevator locations used in the simulation 12 Return on Investment was calculated as: Net Income / Expenditures

28

Table 5.

Parameter settings, determination of potential entrant viability (Canadian dollars)

Average Distance by Rail to Vancouver (Km) 1,857

Inflation 2%

Operating Charge ($ / Tonne) 0.00909

Access Charge ($ / Tonne) 0.00679

Total Charge ($ / Tonne) 0.01588

Empty Railcar Weight (Tonnes) 20

Loaded Railcar Weight (Tonnes) 120

Locomotive Mass (Tonnes) 120

Lease Cost of EMD SD40-2 ($ / Day) 250

Cost of One Labourer ($ / Year) 65,000

Average Car Cyle Length 14.7

Number of Sets* 16

*The number of locomotive sets needed to move all of the delivery penalties.

Assumptions used for Determining Revenues and Expenses for Potential Rail Entrant

29

Table 6.

Income statement for the rail entrant (2006 data, Canadian dollars)

REVENUE

Freight Rate ($ / Tonne) 41.85

Tonnes 1,017,500

Total Revenue 42,582,375

EXPENSES

Fixed Cost

Locomotive Cost

Lease Cost / Day ($ / Day) 250

Number of Sets 16

Locomotives / Set 2

Days / Year 365

2,920,000

Labour

Wages / Month ($) 65,000

Members / Crew 3

Number of Sets 16

3,120,000

Variable Cost

Access

Locomotive Mass

Mass / Locomotive 120

Locomotives / Set 2

Number of Sets 16

Trips / Year 24

92,160

Railcar Mass (Tonnes) 1,221,000

Dist to Vancouver (km) 1,857

Tonne-km 2,438,538,120

38,732,971

Total Expenses 44,772,971

Net Income -2,190,596

Return on Investment -4.9%

Income Statement for Potential Railway Entrant

30

Table 6 shows that a single entrant trying to move delayed wheat throughout the full system

will incur significant costs not only in the form of track access fees, but also from leasing costs

of the locomotives necessary to move that wheat. As the simulation generated an average of

395 yearly delivery delay events, considering the turnaround time for trains and the size of

trains needed to move the delayed grain/wheat, we computed that a minimum of sixteen sets

of dual locomotives would be needed to transport all the wheat delayed in the system in the

given time period. We conclude that under these assumed parameters, a single rail entrant

cannot earn a positive rate of return trying to serve the entire system for every delayed wheat

shipment.

Examination of critical control variables in this analysis (Table 7) yields some interesting

information about potential rail competition in this market. Most importantly, we find that

under our assumed conditions, if an entrant could increase the freight rate charged to

approximately C$51 per tonne (about a 20% increase over the rates assumed in the simulation)

this would generate an economic break-even point. Alternatively, if an entrant could somehow

shorten its average length of grain haul to less than 1,500 kilometers (meaning that many of

the more distant small elevators in the system would not be served), the same economic break-

even outcome would be achieved with the lower freight rates from the simulation. While freight

rates are certainly adjustable, the likelihood of enough penalty events by happenstance

occurring close enough to destination to permit the latter situation to occur on behalf of an

entrant is small. Finally, we also note that if a rail entrant into the entire system could instead

decrease average car cycle length to approximately 10 days, it would also achieve a zero net

income result, all else equal.

31

Table 7.

Critical control variables

Considering this analysis, we find that by charging either greater freight rates (i.e. greater than

the incumbent railway in this case) or by chance only needing to access wheat delay events that

happen to be located relatively close to final destination, a potential rail entrant into this grain

transportation system might break even. Given the layout of the grain handling system, the

latter situation is not likely to occur within any given time period. In a similar fashion, it would

be very difficult for an entrant to decrease average car cycle length independently of other

factors in the network.

Alternate potential entry conditions: Focus on large elevator delay events

Now we consider if an entrant railway could operate under a true “hit-and-run” approach at the

largest delay events. Using the same assumptions as above, all else equal we find that an entrant

would be slightly better off conducting hit and run entry on larger delay events than serving all

wheat delay events. Over the duration of the entire 30 year simulation period, we generated

approximately eight delay events per crop year where the volume of wheat delayed was very

large, at 10,000 tonnes or greater. Specifically, we know that such events occurred at just 37

elevator locations in the region. The highest likelihood of such an event occurring was at the

major grain elevator hub of Moose Jaw, SK (located in the Western part of the province), which

generated a large volume delay event in the simulation approximately once every two years.

Base Case

Accounting Economic

Freight Rate ($/T) $41.85 $44.00 $50.60

Distance to Vancouver (km) 1,857 1,752 1,486

Car Cycle Length 14.7 ~ 10 NA

Break Even

32

Table 8.

Income statement for potential railway entrant transporting 100 rail car spots, 2006

REVENUE

Freight Rate ($ / Tonne) 41.85

Tonnes 80,000

Total Revenue 3,348,000

EXPENSES

Fixed Cost

Locomotive Cost

Lease Cost / Day ($ / Day) 250

Number of Sets 1

Locomotives / Set 3

Days / Year 365

273,750

Labour

Wages / Month ($) 65,000

Members / Crew 3

Number of Sets 1

195,000

Variable Cost

Access

Locomotive Mass

Mass / Locomotive 120

Locomotives / Set 3

Number of Sets 1

Trips / Year 8

2,880

Railcar Mass (Tonnes) 96,000

Dist to Vancouver (km) 1,857

Tonne-km 183,620,160

2,916,565

Total Expenses 3,385,315

Net Income -37,315

Return on Investment -1.1%

Income Statement for Potential Railway Entrant

(Only transporting 100 rail car spots)

33

As was found in the case of a full system entrant, distance to final port destination is a crucial

control variable for determining the success of a potential entrant under hit and run entry even

for larger penalty events. In effect, those elevators located closest to port were the most likely

locations for profitable hit and run entry.

Table 8 is the income statement for a hit and run entrant in this supply chain, staying mindful

of our model assumptions. Similar to the case of system wide entry, we see that if a rail entrant

instead employed a hit and run approach on the larger delay events, it could still not earn a

positive return by transporting only the infrequent large grain car shipments. But a potential

entrant could be profitable in the latter case if it could increase the (average) freight rate charged

on shipments to about C$49.00 per tonne (approximately 18% greater than those rates assumed

in the simulation).

In summary, using both spatial and temporal freight data generated by the simulation and a

single representative year (2006) along with our base set of assumptions, we found that the

overall return on investment for a single potential entrant attempting to transport all generated

delivery penalty events in the region would be approximately -4.9% (loss). In fact, wheat that

does not move in a timely manner is widely dispersed across the region, both through time and

space. Alternatively, hit and run entry in this market on limited large volume events yields a

ROI of approximately minus 1.1%. In either case, we find that an entrant into the system would

necessarily need to raise freight rates (compared to the actual rates used in the simulation) by

about 20% to become profitable.

An entrant trying to locate and then serve all delayed wheat shipments would require a

considerable amount of timely information. Without considerable oversight and co-ordination

34

by shippers, it is difficult to envision how an entrant could both process and act on this diffuse

(both in time and space) information in a timely and effective fashion. Considering the

difficulties this situation could present, we did discover that a more limited hit and run entrant

serving only the largest penalty events from those points closest to port destination could be

profitable. The latter is a less complicated and more profitable set of entry conditions than

having an entrant attempting to serve all wheat penalty events in a given time period across the

entire region.

In spite of continued complaints about freight rates from the agricultural sector in the region,

we find that in most cases freight rates for grain as used in the simulation approximate levels

consistent with competition. Further, our analysis shows that for much of the region and its

elevator network, the freight rates charged do not permit easy nor profitable entry by another

contesting carrier. If policies permitting open rail access in the Canadian grain handling and

transportation system were to be implemented, at best profitable and contestable rail entry

opportunities would be very limited.

6. Conclusion

The primary purpose of this research was to assess the viability of (contestable) rail entry into

the vast grain handling and transportation system in Canada. This was achieved by building a

detailed agent based simulation of the grain handling system serving the arable portion of the

Canadian province of Saskatchewan. For tractability and realism, standard varieties of wheat

were assumed to be the only commodity moving through the supply chain. Then using the

simulation output, we identified (over both time and space) instances where delivery of wheat

from grain elevators in the region gets delayed due to lack of timely railway availability.

35

The simulation generated an annual average of approximately one million tonnes of wheat (out

of a typical volume of 13 million tonnes moved annually in the region) delayed in delivery by

a lack of timely railway availability. The wheat volume delayed averaged approximately

85,000 tonnes per month, translating to about 30 rail shipments across the province.

Delayed wheat movement is not randomly distributed across the elevator locations in the

region, but instead possesses particular characteristics. For example, the model generated a

high (94 percent) chance that delayed shipment events occur at small elevators, noting that

these elevators are frequently located at the edge of the province. In addition, small elevators

are also often located a great distance from the main Class 1 rail lines, and in areas with lower

grain production per farm. On the other hand, the simulation generated a 6% chance that any

given delay event will require a medium sized 50 car spot, while the chance of a significant

transportation delay at the largest elevators spotting 100 or more cars (i.e. a typical unit train

size today) is negligible. Overall, the capacity of both medium and large elevators combined

with the available rail car spots dramatically decreased the likelihood of wheat being delayed

in transportation at these elevators.

Tracking where and when delivery delay events occurred in the simulation, we then conducted

an economic break-even analysis to determine if a hypothetical entrant railway could be

profitable by serving delayed wheat shipments in the system. We found that even under ideal

conditions, an entrant moving delayed wheat and serving the entire system would be

unprofitable under extant rail rates. Referring to simulation data from 2006, the freight rates

charged by a hypothetical entrant into the system would need to increase by about 20 percent

in order to make entry profitable. Alternatively, an entrant transporting delayed grain from

36

those large grain elevators closest to the port destination would be profitable under the extant

freight rate structure.

Our findings also help clarify long-standing but heretofore unanswerable questions about the

future of policies governing the Canadian grain handling and transportation system. In effect,

we find that the system works well for larger and well located elevators in the region. But not

surprisingly, for an entrant railway to profitably serve the more distant and smaller elevators

would require the entrant to charge higher relative freight rates, a situation that will not alleviate

the concerns of the agricultural community about grain rates. And from a broader supply chain

perspective, our findings also seem to indicate that additional consolidation (both in volume

and location) of the grain elevator system in the region is likely to occur as the system moves

towards increased efficiency and throughput.

37

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40

APPENDIX

Rail rates

The following is a list of the rail freight rates used in the simulation. Actual historical freight

rates for all delivery points could not be found, so efforts were made to build realistic rates and

compare them to data that could be found. In fact, our rate data tracks what we know about

actual rates very well, including tracking a major increase from 1994 to 1995 reflecting the

removal of direct grain transportation subsidies from the system.

Some recent freight rate data was obtained from the Parrish & Heimbecker grain company in

Saskatoon (the largest city in the region), moving grain on CN lines. Calculated applicable CP

rates used in the simulation are based on a simple regression on freight rates, with a differential

applied against the known CN data. In addition, freight rate data from Saskatoon were used as

a benchmark and then other rates were adjusted based on the distance by rail from the delivery

point to Vancouver. Finally, Alberta Agriculture Ministry data was used to help determine the

average differential between CN and CP freight rates across other locations in the province.

41

Table A1.

Base freight rates used in the simulation in $ per tonne, 1977 to 2006

Base Freight

Rate

CN Rail Rate,

in $/t/km

CP Rail Rate,

in $/t/km

Year $ / tonne

1977 4.85 0.0032 0.0034

1978 4.85 0.0032 0.0034

1979 4.85 0.0032 0.0034

1980 4.85 0.0032 0.0034

1981 4.85 0.0032 0.0034

1982 4.85 0.0032 0.0034

1983 5.33 0.0035 0.0037

1984 7.57 0.0050 0.0052

1985 5.90 0.0039 0.0041

1986 5.87 0.0039 0.0041

1987 6.23 0.0041 0.0043

1988 7.15 0.0047 0.0049

1989 8.86 0.0058 0.0060

1990 10.03 0.0066 0.0068

1991 10.37 0.0068 0.0070

1992 11.23 0.0074 0.0076

1993 12.86 0.0085 0.0087

1994 13.37 0.0088 0.0090

1995 33.01 0.0194 0.0196

1996 35.37 0.0208 0.0210

1997 36.08 0.0212 0.0214

1998 35.67 0.0210 0.0212

1999 35.74 0.0210 0.0212

2000 34.31 0.0202 0.0204

2001 35.68 0.0210 0.0212

2002 37.11 0.0218 0.0220

2003 37.85 0.0223 0.0225

2004 35.65 0.0210 0.0212

2005 38.52 0.0227 0.0221

2006 41.85 0.0246 0.0241

$ / tonne / km