one-third of the management team for MBC Farms, and is in charge of the Farms cropping enterprise.

Transcription

1 MBC Farms Horse Hay Enterprise On a cold December morning, Craig steps out of Mike s house, thoughtful of the future. Craig is one-third of the management team for MBC Farms, and is in charge of the Farms cropping enterprise. Craig s sister, Betty, manages the dairy operation, while their Uncle Mike acts as the chief executive officer of all operations. At the meeting that morning, Craig reviewed crop records with Betty and Mike while discussing their marketing strategy for the remainder of the crop year. During the meeting, Craig suggested that MBC Farms investigate expansion into the high quality horse hay business. Craig was looking for value-added opportunities because MBC Farms chose to contract the majority of the dairy operation s hay and silage needs after a recent expansion. This out-sourcing strategy had given Craig a little extra time and land to pursue other options. As he left the meeting that morning, Craig promised to research hay market opportunities and financial risks so that he could present findings to Betty and Mike at the next meeting. Craig is certain that the horse hay enterprise will make money after all, most horse owners are very selective when purchasing hay, and Craig produces some of the highest quality hay in the area. Craig has spoken to several hay buyers and horse owners and senses strong demand for the hay. But some questions are left unanswered: How much is horse hay worth? How much hay can be supplied? Can he count on steady profits or will revenues vary too much? Will the enterprise be profitable at all? Market Planning Before tackling the problem of whether or not the horse hay enterprise will be profitable, Craig thinks about the marketing mix product, place, price and promotion. That s not to say he is neglecting the production side of the hay business, but: I ve got a pretty good handle on the costs of producing hay. Marketing is my major concern. In the past, any hay that was not used in the dairy was sold through my old friend in Louisville, Freddie. Freddie is a hay broker with good contacts at stables and always gives slightly more than the market price. This simple way of marketing was fine for the small amounts of hay that 1

2 we sold in the past. But if MBC Farms is going to go after the horse hay market, a lot more hay will be sold and we ll need to think more concretely about how it is priced. Once Craig outlines the basics of a marketing plan, he can effectively evaluate the profit potential of the hay enterprise and the risk that the enterprise might bring to MBC Farms. A Differentiation Strategy Craig believes earning a premium for his hay is possible, provided he can differentiate himself from the competition. To find out a little bit more about the horse hay business and devise a differentiation strategy, Craig spoke with some local horse owners. Based on his conversation with the horse owners, he decided to create a value bundle, a bundle of both the hay and related hay services, that will better meet the needs of his target market. The value bundle that Craig defined includes: high quality hay free of mold, noxious weeds, blister beetles and dust; year round just-in-time (JIT) inventory delivery by storing on-farm and trucking with a local trucking agency; a liberal return policy that uses high quality hay to replace poorer hay even if it has been improperly stored. Craig is confident that supplying high quality hay and these services is a good differentiation strategy. He now needs to consider two other marketing mix components: how much to charge for the hay, and how to develop and maintain a hay market channel. Pricing decisions for the hay market are quite different from the pricing that MBC Farms currently uses. A market advisor assists in pricing commodity grains, and the specialty corn is sold under a contract written by Frito Lay. In both of these cases, Craig essentially takes what the market is willing to give. Pricing horse hay is unique Craig will dictate the price as well as the other terms and conditions of the sale. Craig considers several pricing rules, and decides to adopt a value-based pricing rule. The value based pricing rule uses a reference price, and then Craig subjectively adds premiums to the price 2

3 reflecting the value of the services he provides. Using last year s average hay price as a reference point, he establishes the value pricing rule as: $80 per ton.last Year s Price (Reference Price) $15 per ton..high Quality Estimated Value $15 per ton Returns accepted without freight charges, $15 per ton..jit delivery $125 per ton..total Value-added Price The $125 per ton represents Craig s target price, and it may be adjusted depending on the market price and the comments he gets from customers. Craig has established the product and its price. A question remains: How can he coordinate the deliveries and moldy hay returns? Craig needs to create a hay market channel and has several options from which to choose. One market channel is the status quo; he can call Freddie and let his friend sell the hay for him. A second alternative is to contact some of the feed stores and farm suppliers in the area surrounding Indianapolis, Louisville and Lexington, all communities with weekend horse owners that are short on hay and hay storage. Finally, Craig could market direct to the customer via newspaper advertisements and an e-market on the Internet. (Craig s daughters swear that it is easy to develop an online ordering, procurement and management system for the new horse hay enterprise). Craig decides to focus on hay production for now and leave the market channeling to someone with contacts and a good reputation. On the next trip to Lexington, Craig will have coffee with Freddie, and pitch the high quality hay idea to him. Craig hopes to emerge from the conversation as Freddie s exclusive hay supplier for next year. A Few Weeks Later Craig and Freddie enjoyed a cup of coffee and catching up on old times. When Craig pitched the idea of high quality hay, Freddie was guardedly optimistic about the prospects and felt Craig s valuebased pricing system was in the ballpark for what customers would pay. Freddie s clientele are stable 3

4 owners who rely on high quality hay for the horses boarded at their facilities. These clients manage more than 650 horses in the Louisville and Lexington area, and need hay 9 months out of the year. The two agreed to meet again in a few weeks to hammer out details once Craig sought approval from Betty and Mike. As Craig drove home on I-65, he was excited about the opportunity, but a little troubled about the profit potential and risks of the new enterprise. Freddie insisted that they sign a contract at the next meeting, and that Craig deliver either 500 or 1,000 tons of hay in equal quantities over a nine-month period (September 1 June 1). Craig knows of a local trucking firm that will ship hay for $1.45 per loaded mile, and Freddie s place is about 220 miles from the farm. The trucking firm uses 53 foot trailers to haul hay, and usually carries 14 tons per trip. If Craig supplies 1,000 tons, then he will need to ship a semi load of hay twice per week. Craig usually hires the local high school s football players to load the trucks the night before shipment, and it takes about two and-a-half hours to load the semi. If Craig s hay is of poor quality, he will have a high return rate from his customers meaning he ll have to purchase hay to fulfill the contract. When Craig s hay is bad, everyone s is bad, so prices are likely to be high. Some years though, Craig s hay crop is quite good and in those years the hay price is terrible. Wouldn t it be great to forward contract all of that high quality hay to horse owners at high prices rather than face the low prices the market would surely give him? But, if Craig hinges his product on high quality, how will his business suffer when quality is low? Decision Analysis Under Uncertainty A lot of uncertainty surrounds the profitability of Craig s horse hay enterprise, and he ought to have some solid estimates of net revenues before he faces Mike and Betty at their meeting next week. How much hay should he contract? Should he even sign a contract? What if it rains while hay is on the ground ruining its quality? What if quality is really good? Can he guarantee to Betty and Mike that the enterprise will make money? 4

5 Beginning the Decision Process: A Contributing Factor Diagram Craig stepped into his office, sat down at his desk, and pulled out a blank sheet of paper from the middle drawer. Before turning on his computer to build a budget, he first wanted to think through the economic variables that would influence MBC Farm s decision to join the horse hay business. Craig (as well as Betty and Mike) measure the success of this hay enterprise by the net revenues that it generates, so draws a rhombus around the words net revenues in the center of the sheet (Figure 1). Craig then diagrams other factors that will influence net revenues including production costs, the hay price, and quality. To show the interaction between all of these factors, Craig connects them with arrows. The resulting picture is the Contributing Factor Diagram shown in Figure 1. The contract amount (0, 500, or 1000 tons) is the only variable that Craig really has total control over; uncertain quality and prices are factors that influence net revenues and are beyond Craig s complete control. Craig s Assumptions The Contributing Factor Diagram summarizes the important variables and interactions that will impact Craig s decision. To reduce the complexity of the problem, Craig focuses on three key variables in the diagram: the contract amount, the uncertain hay price, and the uncertain level of hay returns. Craig will be able to choose a contract amount, so it makes sense to calculate the net revenues for each contract level (0, 500 and 1000 tons). In order to calculate returns, he ll have to make assumptions on costs (Table 1), hay returns (Table 2) and horse hay prices (Table 3). Determining the hay return rate is a challenge, and Craig relies on his subjective assessment of historical hay quality to solve the problem. Noting how the hay quality has varied in each year of his records, Craig divides the potential return rates into 3 categories: high (20% of hay will be returned), medium (10% of hay returned), and low (5% of hay returned). Once the categories are developed, Craig assesses how often the high, medium and low return rates are likely to occur. He summarizes the information in Table 2. Using three categories of return rates reduces the complexity of the hay net revenue problem, and Craig uses the same strategy when approaching uncertain prices. Craig reviews his records and clusters 5

6 hay prices into 3 categories: high ($135/ton), medium ($100/ton), and low ($80/ton). He then notes how often a particular price category (i.e. $80/ton) occurs with a particular hay return rate category (Low Return Rate), and then records the information (Table 3). For the most part, the table confirms Craig s intuition, when hay quality is good (low return rate), every other producer s hay is good too, so hay prices are not high. Now that Craig has made assumptions, he can compute net revenues under different price and return scenarios. Craig s Decision Tree Craig uses decision trees when calculating net revenues because it allows him to list all possible net revenue outcomes while checking the logic of his assumptions. The contract amount is Craig s key decision variable, so he makes contract amounts the first branch on the left hand side of the decision tree in Figure 2. These three branches, marked Contract 0, Contract 500, or Contract 1000, are mutually exclusive and completely exhaustive of Craig s contracting opportunities. Craig begins with the 500 ton branch, and from this node creates additional branches that represent return rate and price scenarios from Table 3. In all, nine possible scenarios exist for each contract amount, and twenty-seven scenarios are available for the entire decision tree. As an example, the upper most branch of the Contract 500 section represents a low return rate, low price scenario (low, low). Craig also writes the frequencies from Table 3 on the appropriate branch, so the low,low branch has a frequency of 17% on it. The scenarios on these branches are exhaustive of all outcomes, which explains why their frequencies sum to 100% (17% + 6% + 11% % = 100%). By spending some time with his spreadsheet, Craig calculates net revenues for each of the 27 outcomes. As an example from the 500 ton branch, Craig calculates net revenues when the return rate and prices are low. Two sources of revenue exist: contract sales and hay sales on the open market. Craig assumes that total production of hay will be 1200 tons each season, and with 500 tons contracted, open market hay sales are 700 tons times the difference in the open market price ($80/ton) and production costs ($70/ton) or $7,000. 6

7 Contract net revenues have two components: total revenues from contract sales and the costs associated with poor quality hay returns. Contract sales are the contract amount multiplied by the combination of the contract price ($125/ton) minus production costs ($70 per ton) and trucking costs ($23/ton) or 500* [ ] = $16,000. The low hay return rate is 5%, or 25 tons, which Craig replace with purchases on the open market at the prevailing price ($80/ton) and ship to customers ($23/ton). Contract net revenues are contract sales ($16,000) minus the replacement costs for hay returns ($2,575) for a net of $13,425. Adding these contract net revenues ($13,425) to the open market sales ($7,000) yields net revenue to the horse hay operation of $20,425. Multiplying the net revenues by the frequency (17%) generates the expected payoff of this scenario, or $3,404. Craig s Payoff Matrices Craig initially believed the decision tree would be easy to analyze, but the twenty-seven outcomes found in Figure 2 are more difficult to compare than he thought. To make the job of weighing contract alternatives easier, Craig organizes a payoff matrix that list contract amounts in columns and the return rate/price outcomes in rows (Matrix 1 of Table 4). The net revenue values in the interior of the first payoff matrix come directly from the decision tree, so Craig simply writes them in. As an example, the net returns from a low hay return rate and low prices are $12,000 when 0 tons are contracted, $20,425 when 500 tons are contracted and $28,850 when 1000 tons are contracted. Using this payoff matrix, Craig can easily view the outcomes of the three contracting alternatives under differing return rate and price scenarios. If one contract always has a better (more profitable) outcome than another, he can eliminate that contract amount from consideration. The size of potential profit matters to Craig, but he s also interested in how probable the profit outcome is after all, he might prefer a smaller profit that happens frequently to a large profit that has a small chance of occurring. Matrix 1 is a listing of outcomes only; the frequency with which outcomes occur has been omitted. In order to include frequencies with the outcomes, Craig builds a second payoff matrix (Matrix 2) that lists the expected payoff in the interior of the table that are taken from the decision tree. The expected payoff is the outcome multiplied by its frequency; in essence, the expected value 7

8 weights the potential profit by how likely it is to occur. As an example, the expected payoff of a low return and a low price are $2,000; $3,404 and $4,808 for contracting 0, 500 and 1,000 tons respectively. The pessimist in Craig wants to choose a strategy that minimizes his regret should he choose the wrong contract amount, so he builds a regrets payoff matrix (matrix 3). The regrets matrix shows the maximum that Craig misses out on (regrets) if he makes the wrong contracting decision for any given return rate and price event. For example, the best decision to make for a low return and low price is to contract 1,000 tons and generate $28,850. Of course, Craig knows this is the best decision only in hindsight, he does not know his return rate and price until after the contracting decision made. Suppose that instead of contracting 1,000 tons in the low return and low price scenario, he contracts 0 tons. Then his net returns are $12,000 or $16,850 less than if he had made the best possible decision, contract 1,000 tons. Thus $16,850 represents the regret of choosing the wrong contract amount, and this is what he writes under the 0 contract alternative in the low price, low return scenario of matrix 3. Likewise, if Craig were to choose 500 tons rather than 1,000 tons in this scenario, his regret is equal to $8,425 or the difference between $20,425 and$28,850. Craig now has three payoff matrices, each of which provides information with which he can make a decision. The first matrix is strictly an outcomes matrix that shows the size of the payoff for each event under the contract amount. The second matrix shows expected profits, the elements in the matrix combine the size of the payoff with how likely it is for the event to occur. The final matrix looks back at a decision and illustrates missed opportunities its elements show the maximum returns Craig will miss out on if he chooses poorly. The three payoff matrices help organize the decision tree into an easy-to-read form, but Craig still has to choose among the three contract amounts. Which contract alternative should he recommend to Mike and Betty? 1 Discussion Questions 1. Craig s analysis considers a production risk (quality) and a market risk (price risk). What other risks does Craig s hay venture face? 8

9 2. Craig adopts a value-based pricing rule for his horse hay. Is this pricing rule consistent with the overall marketing strategy? How does Craig s place and promotion strategy support the overall product strategy? 3. Craig keeps good records with which to make assumptions for the analysis. In the absence of records, how might he acquire information about variables? 4. What decision rule is the best for Craig to use when choosing among risky alternatives? How might he use the payoff matrices to choose among alternatives? Why does Craig create three different payoff matrices rather than relying on one? 5. Suppose Craig is able to negotiate a higher contract price from Freddy. How might the increased price change his decision on the amount to contract? 6. Suppose Craig were to abandon the hay return policy. How should Craig change his analysis to reflect the change in strategy? How would the results change? 9

10 Demand Substitutes Hay Cubes w eather Hay Prices Quality (Hay Returns) Everyone Else's Production Net Revenues Total Production How Much to Contract Acres Planted Trucking Cost Production Costs Figure 1. Craig s Contributing Factor Diagram Table 1. Craig's Assumptions on Hay Production, Trucking Costs, and Contract Price Item Amount ($/ton) Contract Price $125 Production Costs $70 Trucking Costs $23 Table 2. Historical Return Rates- Central Indiana Hay Return Category Amount Returned How Often? Low 5% 3 in every 6 years Medium 10% 1 in every 6 years High 20% 2 in every 6 years Table 3. Craig's Joint Frequencies for Hay Returns and Hay Prices in Central Indiana Hay Return Category Low Price Years ($80) Medium Price Years ($100) High Price Years ($135) Low 17% 25% 8% Medium 6% 8% 3% High 11% 17% 6% 10

11 Contract 0 Hay Decision Contract 500 low, low low, medium low, high medium, low medium, medium medium, high high, low high, medium high, high 17% $3,404 $20,425 6% $992 $17,850 11% $1,411 $12,700 25% $8,481 $33,925 8% $2,571 $30,850 17% $4,117 $24,700 8% $4,869 $58,425 3% $1,538 $55,350 6% $2,733 $49,200 Contract 1000 Figure 2. Craig s Hay Contract Decision Tree (500 ton contract portion) 11

12 Table 4. Craig's Payoff Matrices and Regrets Matrix Tons Contracted Prices Return Rate Matrix 1: Potential Hay Net Revenue Payoffs Low Price Low $12,000 $20,425 $28,850 Medium $12,000 $17,850 $23,700 High $12,000 $12,700 $13,400 Medium Price Low $36,000 $33,925 $31,850 Medium $36,000 $30,850 $25,700 High $36,000 $24,700 $13,400 High Price Low $78,000 $58,425 $37,100 Medium $78,000 $55,350 $29,200 High $78,000 $49,200 $13,400 Tons Contracted Prices Return Rate Matrix 2: Hay Net Revenue Payoffs Weighted by Frequency Low Price Low 17% $2,000 $3,404 $4,808 Medium 6% $667 $992 $1,317 High 11% $1,333 $1,411 $1,489 Medium Price Low 25% $9,000 $8,481 $7,963 Medium 8% $3,000 $2,571 $2,142 High 17% $6,000 $4,117 $2,233 High Price Low 8% $6,500 $4,869 $3,092 Medium 3% $2,167 $1,538 $811 High 6% $4,333 $2,733 $744 Tons Contracted Prices Return Rate Matrix 3: Regrets Matrix for Hay Net Revenue Payoffs Low Price Low $(16,850) $(8,425) - Medium $(11,700) $(5,850) - High $(1,400) $(700) - Medium Price Low - $(2,075) $(4,150) Medium - $(5,150) $(10,300) High - $(11,300) $(22,600) High Price Low - $(19,575) $(40,900) Medium - $(22,650) $(48,800) High - $(28,800) $(64,600) 12