Dream Dinners: Evaluating a Franchise Opportunity
John Stocker, Alfred Lerner College of Business and Economics,
University of Delaware, 319 Purnell Hall, Newark, DE 19716, 302 419 7665, stocker@udel.edu
Dinara Maskulova, Alfred Lerner College of Business and Economics,
University of Delaware, dinara@udel.edu
Paul Roseman was deep in concentration reflecting hard on his recent business venture. The franchise opportunity was only an idea a year ago. The investment seemed reasonable, the time to profitability appeared short, and the required skill set matched up well his background and interests. Unfortunately, the new venture had not panned out as expected. The business was bleeding cash and a decision had to be made to continue or pull the plug. Regardless of the decision made, Roseman was determined to learn from this experience. Looking back, he wondered if there was anything else they could have done in assessing the venture.
Paul Roseman was hunched over the latest set of financials from his family’s venture into the world of franchising and the numbers did not look good. For 18 months “Dream Dinners” hovered on the brink of profitability, but had consistently fallen short. After an initial investment of $40,000, signing on to a $250,000 SBA loan, and pumping another $36,000 into the business, Roseman was wondering when they would turn the corner. He knew he needed to make a decision to cut his losses and fold up shop or continue trying to forge his way to profitability. Roseman wondered if there was a better way to assess the true risk of a new venture. At the time of the initial investment the numbers looked good and the assumptions seemed reasonable. At the end of the day, Roseman was determined that if the business did go south he wanted to learn enough from the experience to avoid a similar situation in the future.
Background
Dream Dinners is a retail service center hosting pre-arranged sessions for the preparation of self- prepared home meals. Its primary value proposition is to help families both save time and enjoy healthy, delicious dinners in their own home. At a Dream Dinners location a client can assemble six to 12 entrees or more for the month in about two hours and then take the assembled meals home to cook. Dream Dinners provides the meal plans, ingredients, and kitchen facilities for preparation.
Dream Dinners was co-founded by Stephanie Allen, a working mother searching for a way to provide higher quality meals for her family. In 1986 Allen began preparing and freezing entrees for later cooking and consumption. She found that freezing the raw materials resulted in better tasting meals when compared to reheating previously-cooked entrees. Over time she developed a collection of recipes.
In 2002, Allen hired Tina Kuna, a friend with management experience, to help with the growing demand. The pair hosted “meal assembly” sessions that generated enough interest to warrant opening a retail establishment. Allen and Kuna opened two more locations within six months and, by December of 2003 the partners opened 35 new stores after reviewing more than 6,800 applications. In January 2004 they moved to new offices in Snohomish, Washington. The new facilities included a recipe test kitchen, corporate training facility, and playground for employees’ children. In less than three years Dream Dinners made the leap from home kitchen to national prominence. To a potential franchisee the value proposition seemed appealing:
At Dream Dinners, busy parents who want their kids to eat right can crank out 12 meals, up to 72 servings, in less than two hours for just $250. The company insists (based on its own research) that preparing those same meals at home would require 18 to 20 hours of shopping and cooking and cost between $525 and $585.
The Opportunity
Margaret Kramer was searching for a business opportunity in 2007 when she came across Dream Dinners. She liked Dream Dinners commitment to providing high quality, nutritious meals at a reasonable price. By 2007 Dream Dinners was on track to launch almost 200 new retail establishments for the year. Based on the franchise information, Kramer believed the opportunity provided a good return on investment with limited financial resources. Dream Dinners estimated a required initial investment ranging from $245,000 to about $370,000 with the potential for profitability in just a few short months. The initial investment would cover the site preparation and modifications, equipment purchases, permits and licensing, and operating expenses for six months of operations.
Exhibit 1 shows the pro forma financials distributed by Dream Dinners to potential new franchisees. The financials indicate that with only 187 monthly customers spending an average of $178 per month, an individual store could generate approximately $75,000 a year in operating profit. After reflecting on the numbers Kramer believed achieving 350 customers a month was highly achievable.
The Market
Santa Fe, the Capital of New Mexico, has a population of approximately 150,000 and a median family income of approximately $53,000 per year. A tourist destination and home to St John’s University, Santa Fe is the oldest capital city in the United States. The city seemed well suited to the Dream Dinners concept. While this would be the first Dream Dinners in Santa Fe the “meal assembly” concept appeared to be gaining traction as two other firms have also made plans to establish retail outlets in the city.
The Initial Investment
Kramer pulled the trigger and invested $80,000 in personal savings and took out a $253,000 SBA-backed loan to establish the franchise. After the initial euphoria wore off Kramer quickly realized that she was overmatched. Handling the day to day responsibilities of keeping the retail operation running smoothly while seeking out new customers required more hours in the day than were available.
At about the same time that Kramer launched the new venture Paul and Carrie Roseman were also looking for a new business opportunity. Both had success in past business ventures. Paul currently ran a successful consulting firm while Carrie had extensive experience in catering and marketing. The plan was for Carrie to take the lead on the new venture and for Paul to help when needed.
Margaret and Carrie were social acquaintances that had known each other for several years. About four months after launching Dream Dinners Margaret ran into Carrie and updated her on Dream Dinners. Carrie was intrigued by the concept and after numerous discussions with Margaret began to feel that it would be a great opportunity. The Rosemans opted to buy into the franchise, purchasing 50 percent of the equity for $40,000 and co-signing on the SBA loan.
The Results
Exhibits 2 and 3 present the results of operations for the last 11 months operated under the partnership and the beginning and ending balance sheets. During this time Dream Dinners had lost over $50,000 with monthly results ranging from an operating profit of approximately $5,000 to an operating loss of close to $14,000. During the six months prior to Carrie’s involvement Dream Dinners had lost a total of $58,000. Margaret had not taken a salary during the last 17 months and Carrie had gone without a salary for 11 months). Both were working full time in the business, although Margaret was currently running out of money and was seriously considering taking a full time job. The combined salaries of Carrie’s and Margaret’s last full time jobs was in excess of $10,000 a month and both felt they could be employed at this level if necessary. The SBA loan carried an interest rate of 6.5% per year and a 20 year amortization. Fixed assets had an average life of 10 years and were depreciated using the straight-line method to a zero salvage value. Intangibles were amortized on a straight-line basis over eight years.
Questions
⦁ Compare to the pro forma projections in Exhibit I to the actual results presented in Exhibit II – how was Dream Dinners performing? What expenses did the pro forma statements conveniently leave out?
⦁ Given the recent performance calculate the breakeven point in sales. With the size of the market and level of competition is it reasonable to expect to reach this level of sales?
⦁ Based on Exhibit I prepare a discounted cash flow analysis (NPV). Assume the different scenarios in Exhibit I represents year one through four. Are there any other operating costs that should be considered? Assume a 35% tax rate, depreciation rates as presented in Exhibit II, a cost of equity capital of 20%, and an operating profit valuation multiple of 5. Apply the valuation multiple to the projected cash flow in year four add it to the projected cash flow for year three. Does the venture appear profitable?
⦁ Use a Monte Carlo simulation approach to gain a better appreciation for the risk of the venture. Assume that the average number of monthly customers will vary between 187 and 350 and COGS will vary between 45 and 60 percent of sales. Based on running 5,000 iterations what is the expected NPV and standard deviation? How do these results compare to the analysis conducted in question 1 above?
⦁ What are the lessons learned?
Appendix
Monte Carlo Simulation
Effectively evaluating any investment opportunity requires estimating potential cash flows, the risk associated with those cash flows, and evaluating the potential return relative to risk. Net Present Value (NPV) and the Internal Rate of Return (IRR) are two methods widely used by businesses throughout the world to evaluate investment opportunities. NPV requires an estimate of likely cash flows discounted back to the present at the appropriate cost of capital, while the IRR equates the initial investment to the present value of future cash flows. The decision rule calls for the acceptance of projects with a positive NPV or IRR greater than the cost of capital. Adjustments for risk typically take place through an adjustment of the cost of capital. For example, assume the following investment opportunity in which a $250,000 investment generates expected annual cash flows of $200,000 per year:
Exhibit 1
Assuming a 12 percent cost of capital the project generates an NPV of approximately $470,000. With no capital constraints and a positive NPV, we would proceed with implementing the project.
Given the uncertainty underlying the revenue and expense projections, the point estimates for NPV and IRR only tell part of the story; by themselves they tell us nothing about the distribution around the point estimate. Simulating the projected results by allowing key inputs to vary over a large number of iterations is one approach for generating a probability distribution of possible outcomes. Monte Carlo (MC) simulation is one of several techniques available to assess the risks surrounding the investment decision.
Simulation
Extending the above example by simulating the results utilizing a MC approach let’s assume that the expected number of units sold is 7,000 per year with a range from 4,000 to 8,500 and that the expected cost of a key input is $150 per unit with a range from $130 to $200. Using the random number generator in excel, it is possible to simulate product demand and variation in the cost of the key input. (Exhibit 2 illustrates this point).
Exhibit II
Demand = $D$10+RAND()*($E$10-$D$10). Where D10 is the minimum units sold and E10 the maximum units sold. RAND() generates a random number between 0 and 1. The cost of the input is calculated in a similar fashion.
Tying the demand functions to the NPV analysis and hitting the F9 key will “reset” the random number generator and calculate a new NPV estimate each time the F9 key is hit. Exhibit 3 illustrates one possible outcome.
Exhibit III
On a separate worksheet we can set up each row to generate an NPV estimate. Again, each time F9 is hit a new NPV value is generated.
Exhibit VII
Finally, we can calculate a number of descriptive statistics based on the distribution of NPVs. This example yields a range of possible NPV values from a negative $640,000 to $749,000 with a standard deviation of almost $200,000. Not only does the simulation provide a more conservative point estimate of the NPV and IRR, but it also provides the decision-maker with a much richer understanding of the characteristics of the possible outcomes. All of the measures calculated below are done with standard excel functions. The probability of the positive NPV is calculated as the number of positive NPVs divided by the number of iterations (5,000). While the initial analysis looked positive, a deeper analysis suggests that significant risk exists that the probability of losing money on the venture is significant—64 percent based on this simulation!
Exhibit IV
