Simulating contestability in freight transportation The Canadian grain handling and transportation system

Transcription

1 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.

2 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. 1

3 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 rightof-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 2

4 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 3

5 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 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). 4

6 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. 5

7 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 6

8 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 7

9 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. 8

10 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. 9

11 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). 10

12 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. 11

13 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 Simulated Elevator Capacities in Saskatchewan by Company Company # of Locations % of Capacity Sum of Primary Served by CN Served by CP Served by CN Served by CP Capacity (tonnes) % Cargill % 1.8% 312, % Parrish & Heimbecker % 4.0% 255, % Richardson Pioneer % 9.6% 496, % Viterra % 17.2% 1,103, % Remainder % 17.7% 747, % Total % 50.3% 2,913, % 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. 12

14 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 13

15 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. 14

16 Elevator Size Table 2. Simulation initialization summary Initialzation Information Summary 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 Farmer Starting Inventory N ~ (300, 50) Number of Farmers by CAR 1A BS 717 7A B 770 4A 313 7B A 906 4B 696 8A B A B AN 644 5B A AS A B 958 3BN B 1584 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. 15

17 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. 16

18 Table 3. Number of elevators and average total simulated tonnes of wheat transported per year Elevator Size Number of Elevators and Average Total Simulated Tonnes of Wheat Transported per Year # of Elevators # of Rail Cars per Train Average # of Trains per Year Average Total Tonnes Transported per Year Small ,000 Medium ,420,000 Large ,710,000 Total 14,865,000 (Statistics Canada, 2007) 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. 17

19 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 Month Delivery 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% 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 18

20 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 19

21 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 20

22 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) 21

23 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. 22

24 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. 23

25 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 24

26 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$ per tonne kilometre, broken down between C$ per tonne kilometre compensation for the use of track, and 25

27 $ 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. 26

28 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 rate 10 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 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 27

29 Table 5. Parameter settings, determination of potential entrant viability (Canadian dollars) Assumptions used for Determining Revenues and Expenses for Potential Rail Entrant Average Distance by Rail to Vancouver (Km) 1,857 Inflation 2% Operating Charge ($ / Tonne) Access Charge ($ / Tonne) Total Charge ($ / Tonne) 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. 28

30 Table 6. Income statement for the rail entrant (2006 data, Canadian dollars) Income Statement for Potential Railway Entrant REVENUE Freight Rate ($ / Tonne) 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% 29

31 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 breakeven 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. 30

32 Table 7. Critical control variables Base Case Break Even 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 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. 31