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Benchmarking Menu Analysis Algorithms

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July 19, 2011
A Look At | Technology
Michael L. Kasavana, Ph.D., CHTP - kasavana@pilot.msu.edu

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© 2002 Hospitality Upgrade. No reproduction without written permission.

The increased popularity and availability of menu analysis software is becoming an important component of the foodservice technology toolbox. Unfortunately, one potential implementation problem deals with the fact that menu analysis programs often rely on different evaluative algorithms. In other words, analyzing a specific set of menu data with one analysis scheme may produce a different result than with a competitor’s software product. What are the popular menu analysis algorithms and which may be best? The prevailing models are explained and illustrated below.

Menu Analysis Models
Historically, the concept of menu analysis hinged primarily on the minimization of food cost. As operators became more sophisticated, reflecting the number of items sold along with food cost appeared more logical. More recently, practitioners moved toward contribution margin analysis and less toward food costing metrics. Three popular menu analysis models are minimum cost analysis, cost/margin analysis and menu engineering.

Minimum Cost Analysis. One of the earliest forms of foodservice evaluation is minimum analysis. This method requires that operators know the direct cost of plating each menu item. In a minimum cost analysis, the best items on the menu are judged to be those with the lowest food cost and the highest number sold (menu mix). This method appeals to operators with a focus on food cost control.

Cost/Margin Analysis. Similar to the minimum cost analysis model, in cost/margin analysis a restaurateur needs to know the food cost of each menu item, but in addition must also know the item’s contribution margin (selling price minus food cost). Under this analytical format, the best items on the menu will be those having the lowest food cost and the highest contribution margin. This method tends to be biased toward lower food cost items without regard to the number of items sold. This method is most effective when menu items tend to sell equally.

Menu Engineering. In a menu engineering analysis, the best menu items are those having the highest number sold and the highest menu item contribution margin. The strength of this analysis is that it reflects menu item popularity and profitability and establishes a benchmark against which future menus can be evaluated.

The Basics
Perhaps the oldest misconception in foodservice management is that food cost is directly related to profitability. In other words, the lower an operation’s food cost percentage (food cost divided by food revenues), the more profitable the restaurant is assumed to be. This simply is not always the case. Consider the traditional steak and chicken example. If Chicken Hawaiian has a food cost of $1.50 and a menu price of $4.50 it will have a 33 percent food cost. Similarly, if New York Strip Steak has a food cost of $3.50 and sells for $7, it will carry a 50 percent food cost. When asked which of these two items they would prefer to sell, some may be quick to identify Chicken Hawaiian since it will yield a lower food cost percentage.

The hidden factor in this analysis, however, is consideration of the difference between each menu item’s selling price and its food cost. This monetary difference is termed contribution margin. Chicken Hawaiian has a contribution margin of $3 ($4.50 minus $1.50); while New York Strip Steak has a contribution margin of $3.50 ($7 minus $3.50). This margin represents the number of dollars gained as gross profit. When a single portion of Chicken Hawaiian is sold, $3 in gross profit is earned. New York Strip Steak, on the other hand, produces $3.50 in gross profit with each sale. Since foodservice operators bank dollars and not percentages, sale of New York Strip Steak (higher contribution margin) should be considered more desirable as it is more profitable.

To compare menu analysis models, consider the four-item menu with corresponding food cost, selling price and menu mix data in Figure 1.

Menu Item: Selling Price
Shrimp Dinner $8.50
Veal Dinner 8.95
Lamb Dinner 8.95
Swordfish Dinner 10.95

Menu Item: Food Cost
Shrimp Dinner $3.17
Veal Dinner 3.01
Lamb Dinner 3.33
Swordfish Dinner 4.65
Menu Item: No. Sold
Shrimp Dinner 16
Veal Dinner 7
Lamb Dinner 12
Swordfish Dinner 15

Minimum Cost Analysis
The minimum cost analysis algorithm requires computation of two metrics: average number sold (total menu items sold divided by number of items on menu) and average menu food cost (menu food cost divided by number of items sold). These dimensions are used to construct a four-cell model in which the best menu items are classified as winners, the worst items as losers and all other items are deemed marginal.

In this example the average number of items sold (Avg NoSold) is found by dividing the 50 items sold by four (number of items on the menu). This produces an Avg NoSold of 12.5 (50/4). The average menu food cost (Avg Food Cost) is found by dividing the menu’s total food cost (each item’s cost times its number sold) of $181.50 by the number of items sold (50). This yields an Avg Food Cost of $3.63 ($181.50/50). Applying the minimum cost algorithm, Shrimp Dinner is classified as the best item on this menu.

Cost/Margin Analysis
A cost/margin analysis algorithm requires computation of two metrics: average menu contribution margin (total menu contribution margin divided by number of items sold) and average menu food cost (menu food cost divided by number of items sold). These dimensions are used to construct a four-cell model in which the best menu items are classified as prime, the worst items as problems, those low in both dimensions are sleepers, while those high in both dimensions are standard.

In this example the average menu contribution margin (Avg CM) is found by dividing the menu’s CM of $288.80 by the number of items sold (50). This produces an Avg CM of $5.78 ($288.80/50). The average menu food cost is found by dividing the menu’s total food cost of $181.50 by the number of items sold. This yields an Avg Food Cost of $3.63. Applying the cost/margin algorithm, Veal Dinner will be classified as the best item on this menu.

Menu Engineering
The menu engineering analysis algorithm requires computation of two metrics: average menu contribution margin (Avg CM) and a 70 percent menu mix index (70% MM). These dimensions are used to construct a four-cell model in which the best menu items are classified as stars, the worst items as dogs, those low in CM but high in MM are plowhorses, while those high in CM and low in MM are labeled puzzles.

The Avg CM is found by dividing the menu’s CM of $288.80 by 50 for an Avg CM of $5.78. The 70% MM average is found by multiplying 70 percent times 1/number of items on the menu. In this example, there are 4 menu items producing a factor of ¼. Multiplying this factor by .7, the 70% MM is found to be 17.5 percent. Applying the menu engineering algorithm, Swordfish Dinner is classified as the best item on this menu.

Summary
While most foodservice menu analysis software packages are primarily concerned with controlling food cost and/or monitoring menu sales data, a menu’s contribution margin should also be considered. While percentages serve as important managerial controls by providing feedback that cannot otherwise be obtained, contribution margin analysis orients management to profitability. Instead of asking what a satisfactory food cost percentage is, operators should be concerned with the restaurant receiving a reasonable contribution to profit from its menu mix. Menu engineering software goes beyond traditional evaluative approaches by assisting operators in obtaining a reasonable level of profitability through increased customer demand and/or increased average contribution margin per menu item and establishes a benchmark against which future menu changes can be evaluated.

Michael L. Kasavana, Ph.D., CHTP, is NAMA Professor in Hospitality Business, School of Hospitality Business, Michigan State University. He can be reached at kasavana@pilot.msu.edu.

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