CASE STUDY.

Lupine Enterprises is a small family-run niche manufacturer of specialty appliances used in commercial food preparation (mostly restaurants). The company is always on the lookout for new products to introduce. The couple who founded the company are inveterate tinkerers and the company holds several patents on products that they have invented. They recently built some prototypes of an appliance that speeds up the processes involved in preparing cheesecakes and other custard-like desserts, appetizers, and entre items. The Vice President for Marketing has shown the appliance to selected customers and has run several focus group sessions and reports that there is strong enough interest to warrant serious consideration of whether to manufacture and sell the appliance. As a business school student who is interning at Lupine enterprises, the owners feel this is a perfect project for you to work on to apply your knowledge to a real-world situation. They want you to gather data and make projections, and, using those, generate a spreadsheet model that they can use to decide whether to move ahead or scrap the project.

Project Layout Phase Questions:

1. Sketch out the process you plan to use to attack this project.2. What will be the decision variable(s) you need to calculate in order to assist Lupine’s owners in

their decision?3. What information will you need to acquire in order to complete the project? How do you

propose acquiring/deriving such information?

To complete this project, you decide that building a pro forma business model projection will be needed. You also decide to build the model based on a timeline that will consist of

a. Estimating the costs of designing the assembly line and supporting activities;b. Estimating the costs of building the assembly area and acquiring the equipment and capital

required to build the appliance;c. Estimating costs associated with producing and selling the appliance over the period of the

projection;d. Estimating the market sales conditions, including the sales price and quantity customers will

buy each year over the period of the projection.

As you are going through this list, you find that you also will have to determine the best period of time over which you should make your projections. You know that since the major design and investment capital cost outlays will come in the beginning of the project, you will have to project sufficiently into the future to allow for there to be a reasonable chance that the net revenues from sales can justify the initial design and capital investments. If you cut the projections off too early, your revenue projections will be less and you run the risk of incorrectly projecting that the project is not worth undertaking. On the other hand, the farther out you take your projections, the less confidence you can have in the validity of the numbers. So, one of your tasks will be to learn what is appropriate for time horizon.

You have an accounting friend in the company who suggests that you use a spreadsheet format that will allow you to calculate the net cash flows for the business. This gives more structure and precision to the

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data you must collect. The spreadsheet would have you calculate the following data for however long you think the projections should be made.

Year (Time from now)Year of ProductionDesign CostsCapital InvestmentPriceVolumeSales RevenueUnit Production CostOverheadCost of Goods SoldGross MarginOperating ExpenseNet Before TaxesDepreciationLoss Carried OverTaxable IncomeTaxes OwedNet After Taxes

Net Cash Flow

You think this structure will be helpful. You also decide that since you will have multiple years for which to gather information, you will aggregate your data into yearly amounts (rather than monthly or weekly).

Preliminary Modeling and Time Horizon Questions:

1. Do you think the structure suggested above will give you data necessary to determine the net cash flows over the projected period of time? What other factors should be considered? Will the data required for this model be acquirable? How?

2. Under what category would you put assembly-line labor costs? Under which category would you put marketing expenses? Are there any costs or expenses that you can anticipate that would not fit into this model? If there are, how would you propose dealing with them?

3. Why are you attempting to estimate the Net Cash Flows of the project? How does that differ from the Net After Taxes?

4. What are the pros and cons of aggregating data into yearly amounts rather than monthly or weekly?

5. What specific issues will you want to consider in deciding how far out you should make projections?

6. What will be the general rubric you will use in making your decision (i.e., to project for x, but not x+1 or beyond).

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With the basic format of the project decided, you need to “fill in the blanks.” To do so, you seek out people in the company who you deem to be most likely to provide the answers you need. You stick with your basic general breakdown of the types of data you need and start meeting with people. Here is what you find:

Stage 1: Design

Manufacturing this appliance will necessitate expansion of Lupine’s manufacturing facilities. Fortunately, the architectural and facility design costs can be limited by the fact that the manufacturing process somewhat mirrors a process you have fine-tuned for making another product. The industrial architecture and engineering firm you have used in the past indicates that they could modify your current processes and provide specs and blueprints for $120,000 if you can give them a year to fit the project in amongst their other work. However, if you want quicker delivery, they will charge you $200,000 for delivery in 4-6 months (since speedier delivery will require that they hire additional personnel to just work on your project).

Stage 2: Building

Despite not having a precise set of blueprints and design specs for the manufacturing process, the process is similar enough to another recently-completed capital project that your building contractor is reasonably confident that she can estimate construction and capital acquisition costs at $1,550,000 provided that materials, permitting, and other costs remain close to what current conditions indicate. Since it will take about a year to complete the project once the blueprints and specs are set, the $1,550,000 estimate includes adjustments for anticipated price changes due to inflation and the probability of increased regulatory costs. However, the contractor will not commit 100% to the price and reserves the right to be paid for legitimate cost overruns, but will agree to place a cap of 10% or $155,000 on cost overruns except under a few circumstances that are extremely unlikely.

Stage 3: Manufacturing

Production Costs

After consulting with the marketing folks and looking at past data for appliances in your industry, you feel that this product will have to pay for itself within 8 years from the time the product is first sold; projecting much past that has been shown to be very risky since products in this industry tend to become obsolete as new appliances are introduced and food preparation techniques evolve. While this does not mean that all manufacturing and sales will cease after 8 years, projecting past then is generally not done. If a project cannot pay for itself within that time frame, it is not viable. You note that since production will not start for 2 years (unless you shave a half year by paying the extra $80,000 to the industrial design firm), your total cost and revenue estimates for this project are for a span of 10 years from the present.

Once you start manufacturing, your research indicates that the unit production costs of this appliance will be $32 a unit. However, due to maintenance, inflation, etc., you estimate that to be safe, you should project the unit production costs to increase by an average of 2.5% each year. (You arrive at that number by looking at past data across the industry and in consulting with the company accountants.

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You found that you should plan on a minimum of a 2% increase with a most likely increase of 2.5% and a maximum expected increase of 4%.) Allocating overhead is difficult since this manufacturing line will be sharing some resources with existing manufacturing lines, but you decide that it will probably add $12,000 in overhead to the company’s expenses. To be on the safe side, you project that overhead will increase at the same rate each year as the unit production costs. You calculate General Operating Expenses by reviewing monthly data for the company over the past 4 years. The average percentage of gross margin for operating expenses was 21.29%. Since the standard deviation of your sample data was fairly small at 1.66%, you feel confident in using 21.29% as the factor to determine Operating Expense projections for the periods over which you are projecting.

Taxes

Since your cash flow projections require accounting for taxes, you seek out Lupine’s chief accountant to find out how to handle them. After reviewing your preliminary numbers, the accountant informs you that for tax purposes, you can fully depreciate the capital investment over a 7-year period starting in the year the capital expenditures are made (year 2). For simplicity, you choose to use straight-line depreciation. However, IRS rules allow you to only claim half of the allowable depreciation in the year you incur the expense, so you will have to tack on the other half at the end of the depreciation period. While the costs associated with drawing up the blueprints can be expensed for tax purposes, the capital and building costs cannot and are spread out over 8 years through depreciation.

The corporate tax rate on taxable income is a flat 21% regardless of level of income. Since taxable income can never be negative, you are allowed to “carry forward” losses in excess of what was used as an offset against net before-tax earnings in the current period. Such losses are carried forward to the next period to be used as an offset against net before-tax income in that period. This process is repeated until all losses are used to offset Net Before-tax Income. However, the new tax law requires that losses and depreciation in any year cannot offset more than 80% of the Net Income Before Taxes. This means that if Net Before Taxes is positive, the Taxable Income cannot be less than 20% of the Net Before Taxes.

Stage 4: Marketing and Sales

Lupine’s Vice President for Marketing has both decades of experience and considerable archives of data on the food preparation industry, the target consumer for the new product. After describing to him the product and its capabilities, he gets back to you with a strategy for pricing the appliance and provides some sales projections. He believes that in the introductory year, the price should be set at $79 (though he also indicates that he might change his mind and go up or down by an average of $1 when the time actually comes to put a firm price on the product.) In the second year of sales, he thinks it will be reasonable to increase the price to $89 (again plus or minus an average of $2 depending on how sales go the first year and the shape of general economic conditions and other intangibles that cannot be predicted—or possibly even known—until you are closer to the time for the decision.) For the third year of sales, he estimates a price of $99 for the product as demand picks up. But, again, he sees the possibility of varying that prediction by an average of $4. For subsequent years, he feels that the price will level off—meaning that each successive year’s expected price will be unchanged from the previous year. But, again, each year’s price may change by an average of $6. He explains that the reason the possible average price change increases is due to the fact that it is more difficult to predict market reactions the further out one is projecting.

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Though the company sets the price it wants for the appliance, the sales quantity totally depends on consumers’ decisions regarding whether to buy. Marketing can influence those decisions by making potential customers aware of the benefits the product offers (advertising in trade journals, participating in trade fairs and expositions, obtaining high-profile endorsements, etc.), but ultimately, it is up to the customer to buy. That said, the V.P. of marketing provides you with some projections based on his research and knowledge of the market. His initial guess is that you can project sales of 5000 units the first year at the $79 price. He estimates that the sales volume will have a standard deviation of plus or minus 10% (which could occur due to both unpredictable reasons or if the actual price is changed as explained earlier). He estimates that each successive year will see an average increase or decrease in sales from the previous year based on his estimate of the product’s life cycle in the market. And, as you predict further into the future, there is less certainty, so the standard deviation of the prediction increases. He sketches out his best guesses to you on a scrap of paper:

Production Year 2 3 4 5 6 7 8Projected Average Change in Sales Volume from Previous Year +1500 +1100 -500 -200 -200 -100 -100

Standard Deviation of Total Sales Projection 12.5% 15% 17.5% 20% 22.5% 25% 27.5%

Model Building and Resulting Questions and Issues:

1. Using the information you have gotten, build an Excel spreadsheet that shows a pro forma projection of the net (after tax) cash flows over the next 10 years for the project. Since you are not sure how to handle the variability that you have learned is inherent in the projections of specific data, you decide to simply use the best guesses and and/or average values of data you have been given.

2. What are the concerns you have about the results you have generated?3. Do you need additional information in order to allow you to make a decision regarding the

viability of undertaking this project? If so, what?

You have the cash flow projections complete, but need a decision variable. You have taken finance and know that you will need to calculate the Net Present Value of the projected cash flows to determine if it will be good idea to start the project. You also think that calculating the “Internal Rate of Return” associate with the net cash flows may be helpful.

NPV and IRR Questions:

1. What is the difference between what the NPV and the IRR calculations tell you? What are the similarities?

2. What is the “Decision Rule” associated with using the NPV calculation in this situation?3. Do you need more information to calculate the NPV of the projected net cash flows?4. What is the “Decision Rule” associated with using the IRR calculation in this situation?5. Do you need more information in order to use the IRR in making your decision?

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Ah, ha! You require one important piece of data in order to render your final calculations and present your findings: the appropriate interest rate needed to discount the net cash flows and determine the NPV. Though not needed to calculate the IRR, the same interest rate is needed to compare to the estimated IRR in determining the viability of undertaking the project.

Interest Rate Questions:

1. What considerations do you want to take into account in determining the interest rate you should use in making the NPV calculations or in using the IRR in your decision?

2. How will the riskiness of the project come into play in your decision?3. How will possible inflation affect your choice of interest rate?4. What other considerations do you need to use in deciding on the appropriate discount factor?

You meet with the owners and talk to them about “debt” versus “equity” financing, seeking out “angels” or “venture capital”, as well as “crowd funding” as a possibility. You also ask them if they have enough in liquid assets to finance the project themselves.

Financing Questions:

1. What does an inquiry into financing have to do with establishing an interest rate to use in your decision regarding the viability of the project?

2. In addition to the financing possibilities listed above, what might you add to the list if you were undertaking this project?

3. For each of the funding possibilities that are identified (including additional ones you have identified in your answer to the foregoing question), how will a usable “interest rate” be calculated?

4. Why will the interest rate decided on be called the “Cost of Capital”?

After discussing the options with the owners, they tell you that they have a long-established relationship with their bank and that they think a straightforward business loan will be the answer. They have sufficient equity in the business (though it is illiquid) that the bank will accept as collateral and give them a more favorable rate on their loan. Since that is their decision, you meet with the banker to learn what the possibilities are for a loan. You learn that Lupine’s credit worthiness is exemplary—especially with pledged collateral—and that the bank is willing to float a loan for the full amount on a “prime plus” basis for the interest rate. The loan will be a variable rate loan where the rate can be changed on an annual basis. The banker tells you that the current rate would be 7.89%. You ask the banker what the projected variation in the interest rate might be and are told that under similar economic conditions, it has been as low as 7.10% and as high as 8.80%. You decide to use the 7.89% as the relevant interest rate in your calculations.

You complete your spreadsheet but before you present it to the owners, you decide to show it to the company’s financial accountant to make sure you have properly modeled the task:

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The accountant tells you that your overall methodology is sound if the owners choose to undertake this project as a new entrepreneurial startup that is independent from the current Lupine Enterprises corporate structure. However, the spreadsheet is not appropriate if the owners wish to undertake this project as an expansion within the current structure of Lupine Enterprises. She explains that Lupine Enterprises’ current and projected financials have Corporate Net Before Taxes of sufficient size that there would be no corporate loss carry overs from this project if the project were to be undertaken as an expansion within Lupine Enterprises. She suggests that you have for presentation an additional spreadsheet showing the financial impacts (if any) in that scenario.

Expansion Structure Questions:

1. Explain how the accountant could tell upon quick inspection that your spreadsheet was for an entrepreneurial startup rather than an expansion within an existing business?

2. How will you make adjustments in the spreadsheet to incorporate the impact of this project on the whole of Lupine Enterprises if it is treated as an expansion project?

Rather than make an additional separate spreadsheet, you decide to make the changes that show the different scenarios within one spreadsheet and are ready to present to the owners:

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Analysis Questions:

1. Why are the NPV and IRR measures higher if the project is handled as an internal expansion when the sums of the Net Cash Flows in the two scenarios are the same (check it out!)?

2. Starting in 2018 the corporate income tax rate was set at a flat 21% of taxable income for all levels income. Prior to 2018, marginal tax rates generally increased so that higher levels of taxable income were taxes at higher rates. What effect would that have had on the relative NPV and IRR calculations for these two scenarios?

Decision Questions:

1. Would the owners prefer to undertake this project as a startup or an expansion?2. What information is on this spreadsheet that will help Lupine’s owners decide whether to

undertake the proposed project? How will that information be used in making the decision?3. You discovered in your talks with people who supplied the numbers you entered in the

spreadsheet that there was possible, if not likely, variation in the projected numbers. You have used average (mean) values and/or best guesses in the numbers entered in the spreadsheets without accounting for the variation. Would the numbers you entered change if the variability (standard deviations of the estimated values) were larger than stated above? What if they were lower?

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4. Would changing the standard deviations of the projected values change the riskiness of the project? Why?

5. What would the standard deviations of the estimates have to be for the estimates to be “certain”? How would that affect the riskiness of the project?

6. Since the information you have gotten makes this project risky, who will bear that risk?7. How would you propose either adjusting the numbers in the spreadsheet or changing how you

use those numbers to take risk into account in deciding whether to do take on this project?8. Since the NPV and IRR values could change depending on the values you placed into the

spreadsheet, do you think that every entered value will affect the calculated values of the NPV and IRR to the same degree? Why? If not, can you propose a way to determine which entries have the greatest effect on the NPV and IRR? Why would knowing that information be helpful when you advise the owners of your findings?

After confronting the foregoing questions, you are not sure the spreadsheet you have made gives Lupine’s owners sufficient information on which to make their decision. Sure, the NPV is positive and the IRR is greater than the Cost of Capital, but you are not sure how to handle the variation and risk. And you are not sure which of the variables you used in your spreadsheet are the most critical in determining the decision variable values of NPV and IRR.

You are aware of several theoretical ways to handle risk in decision making, but are unsure regarding how to translate the theory into practice for this application.

Risk Questions:

1. How would you suggest measuring risk in the context of this decision?2. What adjustments to the existing calculated numbers and/or additional numbers would you

want to calculate to use in deciding whether to undertake this project?3. Would Lupine’s owners’ attitude toward risk be critical in making the decision regarding

whether to go forward with this project? Why? If it is critical, how would you measure their degree of risk tolerance and incorporate that into the decision process?

4. If risk is a consideration, are there ways the owners can “spread” the risk, “hedge” against their risk or do anything else to lessen the impact of risk on their decision? (What do the words in quotes mean in the context of this situation?)

You hoped that completing the spreadsheet and calculating the NPV and IRR for the project would complete your task and you could return to school confident that you had successfully applied what you learned in classes to a real-world problem. Most importantly, you had provided Lupine’s owners with the best possible information they could have in order to make their decision. Now, you are full of doubt; there seem to be more questions popping up than you have answered. What to do?

You search the internet and stumble across a program called “Top Rank” that is an add-in to Excel. It purports to perform “sensitivity analysis” in spreadsheet models. You download the demonstration copy and open it up on your spreadsheet. It seems intuitively easy to use: it asks you to identify the outputs you are concerned about in your spreadsheet. You identify the cells that contain the NPV and IRR calculations. Then, you press the button to “Run What-If Analysis” and the program opens a new workbook and puts several diagrams into several tabs that are reproduced on the following pages:

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(Data for the Spider Graphs is not shown here; it is similar to that shown with the Tornado Graphs.)

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The sensitivity analysis graphs shown are for the Internal Expansion option for this project. The graphs for the Entrepreneurial option are substantially the same.

Sensitivity Analysis Questions:

1. What is the information provided in the Tornado Graphs?2. What is the information provided in the Spider Graphs?3. Are the graphs associated with the NPV giving similar information as those associated with the

IRR? Why or why not? 4. What data that you entered into your spreadsheet is most critical in determining the viability of

the proposed project?5. Why is the Capital Investment line in the Spider Graph upward sloping while the Unit Production

Cost line is downward sloping?6. What data that you entered into your spreadsheet would you want to go back to your sources

and ask them if there is a way to give more certainty to the numbers they provided?7. You note that prices are among the top 10 factors in affecting the bottom line (NPV or IRR), but

the associated sales volumes are not. You know that prices affect sales volumes (the “law of demand”) so that a change in price should have an effect on Sales Revenue not just from the change in the price, but also from the accompanying change in sales volume. Does this outcome suggest unidentified issue in your modeling of future expected prices and sales volume? (It does; what is it?)

8. How does TopRank perform the Sensitivity Analysis? In other words, what does TopRank do to your spreadsheet to generate the Sensitivity Analysis output?

9. How might Sensitivity Analysis of the sort performed by TopRank help Lupine’s owners in making their decision?

You solved the sensitivity analysis conundrum, but when you went back and talked to the people who provided the data, they did not think they could be any more certain of the information they provided than they were earlier. So, how to handle the risk factor still remains a problem for you. Without having a way to address risk, you are not sure how to advise Lupine’s owners. Back to square one.

Heading back to the Internet, you discover that the company that made the TopRank add in, Palisade Corporation, also makes an Excel add-in called “@Risk.” In doing further research, you learn that @Risk allows you to enter stochastic functions into Excel so you can account for the degree of risk associated with any particular cell value. This is done through a process called “Monte Carlo Simulation.” You also learn that there is a competing program called “Crystal Ball” that is produced by Oracle Corporation. Though they both conduct Monte Carlo Simulations within spreadsheets, you decide to see if you can use the @Risk program with your spreadsheet and download a trial version of @Risk. You discover that you need to brush up on your knowledge of stochastic processes, but feel fairly confident that you can remember enough of your statistics to get the basic concepts down correctly.

You go back and review your notes from when you were talking to people about data. In a nutshell, here is what you have.

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Design Costs: fixed at $120,000; firm (though you are prepared to run a scenario where the cost is $200,000 and move all other periods ahead by half a year.)

Building Capital Expenditures: at least $1,550,000, but possibly as much as 10% greater, or $1,705,000. The contractor emphasized that she always builds in buffers in her bids, so cost overruns are truly unanticipated and the probability of a particular magnitude of overrun diminishes rapidly the greater the overrun until there is virtually no chance of an overrun greater than 10%. After a little research, you decide that this outcome can be modeled using a Pareto distribution. You tinker with the inputs and choose =RiskPareto(55,1550000) as a good fit for your data. To make sure that the functions doesn’t generate outliers in excess of $1,705,000 you use a function in @Risk that piggy-backs on the RiskPareto function to truncate it and prevent those outliers from being computed.

Unit Production Costs: As before, these start at $32 in the first year of production, but will increase by between 2% and 4% each year with the most likely increase being 2.5%. You decide to model this with a triangular distribution by entering =RiskTraing(.02,.025,.04) into the cell that contains the number for “Increase in Unit Production Costs.”

Operating Expenses Factor: Recalling that you have 4 years of monthly data for this factor, you use the @Risk Distribution Fitting ability to determine the probability distribution that most closely fits the data. You do so and enter =RiskNormal(0.212886,0.016554) into the cell that contains the number for “Operating Expense Factor.” You are not surprised that the parameters of the RiskNormal distribution are the mean and standard deviation that you calculated from the sample data.

Price: going back over your notes, you decide to model the price with a normal distribution by setting the initial mean value in period 3 (the first of manufacturing and sales) at $79 with a standard deviation of $1, the second mean at $89 with a standard deviation of $2, and the third mean at $99 with a standard deviation of $4. Successive means were set equal to the previous period’s realized value with a standard deviation of $6.

Quantity: This, too, you decide to model using a normal distribution starting in period 3 with a mean value of 5000 and a standard deviation of 500, or 10% of the expected value. You recall the table that the VP of marketing sketched for you. It is reproduced here:

Production Year 2 3 4 5 6 7 8Projected Average Change in Sales Volume from Previous Year +1500 +1100 -500 -200 -200 -100 -100

Standard Deviation of Total Sales Projection 12.5% 15% 17.5% 20% 22.5% 25% 27.5%

Cost of Capital: Referencing your notes from the banker, you see that he expected this to be right around 7.89% within a range from a low of 7.1% to a high of 8.8%. You decide to model this using a PERT distribution by entering =RiskPert(.071,.0789,.088) into the cell that contains the number for “Cost of Capital.”

You change your spreadsheet to take these risk factors into account and get set to run the simulations. Your computer is fast and you do not think it will be taxed if it simulates this model 100,000 times. But, just before you push the “Start Simulation” button, you pause to make sure you have properly modeled

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this to account for uncertainty-caused variation in the numbers you are using to determine the feasibility of the project.

Stochastic Modeling Questions:

1. Review the model carefully. Do the changes of adding stochastic variation affect the underlying structure and assumptions used in forming the original static spreadsheets?

2. Are there any specific additional considerations you will have to incorporate into the model given any concerns expressed in your answer to question 1?

You remember that your principle of microeconomics teacher kept pounding into your head that there is an inverse relationship between the price and the quantity demanded of a good. Will that inverse relationship be taken into account in your present model where you have both price and quantity subject to change with each iteration of the simulation? The answer, sadly, is “no;” there is no connection between the price generating cell and the volume sold cell in each period; they are independently calculated and therefore do not conform to the laws of demand.

You open the @Risk ribbon in Excel and spot a button named “Define Correlations” and look over what it does and what information you need to make it work. So, you head back to the marketing department and seek out the VP for Marketing once again. This time you need to know the strength of the relationship between changes in price and changes in sales volume (quantity sold). In other words, how are these two correlated? The VP of Marketing tells you that past experience shows that when a product is first introduced, the inverse correlation is not as strong as it becomes after the product has been on the market for a while. His estimate is that the correlation for the first year of sales (period 3) will be -0.7, then get stronger to -0.75 in the second year of sales, and then move to -0.8 in the third year of sales. He believes it will remain at -0.8 in all successive years.

You return to your spreadsheet and enter the information to set up the required correlations and now are ready to run the simulation.

Download the spreadsheet shell, Case Study Template.xlsx (which is the static model shown on page 8), make the modifications to account for the risk (stochastic variability in the data projections), and run a simulation of 100,000 iterations.

@Risk Questions:

1. Do the results from this simulation change the way you will present your findings to the owners?2. What is the probability that this project will add to the profitability of Lupine Enterprises?3. What is the average (or expected) NPV from this project after accounting for risk? How does

this answer compare to the NPV calculated when you could not account for variability? Can you explain why they are different?

4. What is the probability that the project will not generate enough revenue to recover the design and investment costs even if the company did not have to borrow to pay for it and otherwise would have eked out a return on its idle cash reserves just enough to compensate for inflation?