Restaurant Menu Engineering: A Profit Framework

Menu engineering is a structured framework for classifying every item on a restaurant's menu by two variables: how often it sells and how much gross profit it generates. It was developed by Michael L. Kasavana and Donald I. Smith at the Michigan State University School of Hospitality Business around 1982, and it gives operators a decision method for pricing, promotion, and removal that is more precise than food cost percentage alone.

What Menu Engineering Actually Measures

Menu engineering works on two numbers for every item: contribution margin and menu mix percentage.

Contribution margin (CM) is the selling price minus the food cost of a dish. A $16 pasta with a $4.80 ingredient cost generates an $11.20 contribution margin. That number represents what each plate puts toward labor, overhead, and operating profit before any other cost enters the calculation.

Menu mix percentage (MM%) is the proportion of total covers a given dish accounts for. If a restaurant serves 800 guests in a week and 120 of them order that pasta, the MM% is 15%.

The thresholds for high versus low on each axis are derived from the menu itself, not from external benchmarks. The CM threshold is the average contribution margin across all items. For menu mix, the standard rule treats an item as high if its MM reaches at least 70 percent of its equal menu share: for a 20-item menu, equal share is 5%, so the threshold is 3.5%. Anchoring both thresholds to the menu's own performance data is what makes the matrix diagnostic rather than decorative.

The Kasavana-Smith Matrix

With both variables measured, every item lands in one of four quadrants.

Stars are high-CM, high-MM% items. These are the menu's best performers and deserve protection. Price changes on Stars should be gradual and tested. Menu placement and photography investment are well-justified here.

Plow Horses are high-MM%, low-CM items. Customers order them reliably, but they do not earn their keep on contribution per plate. The usual response is a modest price increase, a food cost reduction through portion calibration or ingredient substitution, or bundling with a higher-CM item. Removing Plow Horses typically depresses cover counts in ways that cost more than the thin margin does.

Puzzles are high-CM, low-MM% items: profitable per plate but underordered. These warrant repositioning on the physical menu (first or last position in a section, larger typography, a callout box), a name revision, or a measured price reduction to test whether demand responds.

Dogs are low-CM, low-MM% items. The default decision is removal. Occasionally a Dog earns a seasonal rotation spot or fills a dietary requirement for a significant customer segment, but it should not hold permanent menu real estate.

Building the Numbers

Running a menu engineering analysis requires three data points per item, drawn from the review period (most operators use 8 to 12 weeks for a stable read):

1. Total units sold
2. Selling price
3. Food cost per unit, including prep waste and yield loss

From these, calculate the contribution margin per item, total contribution for the period, and each item's share of total covers. Then compare each item against the CM and MM% thresholds to assign a quadrant.

The first full analysis on a complete menu typically takes two to three hours. Quarterly re-runs take 30 to 45 minutes when POS data is clean.

POS item taxonomy is the most common point of failure. Restaurants that route modifiers, combo variants, and split-check versions of the same dish through separate item codes produce fragmented MM% figures that distort the matrix. Cleaning up item coding is a prerequisite, not an afterthought.

One Restaurant's Quarterly Menu Audit

A 48-seat neighborhood bistro found its food cost climbing from 29% to 33% over a single quarter with no corresponding revenue increase. The ownership assumed the problem was a recently introduced protein-heavy entree. The matrix disagreed.

The restaurant's most-ordered appetizer, a house-made charcuterie board priced at $18, carried a food cost of $9.20 per order once prep time and spoilage were factored into the ingredient calculation. Its contribution margin was $8.80 against a menu-wide appetizer CM average of $11.80. A textbook Plow Horse: ordered at 19% of all covers, but pulling the average down on every ticket.

Two high-margin items that server staff rarely recommended turned out to be Puzzles: a seasonal grain salad ($16 price, $2.90 food cost, $13.10 CM) and a roasted half-chicken ($28 price, $7.60 food cost, $20.40 CM). Both appeared at the bottom of their menu sections, well below the visual fold of the menu card.

The operator made three changes: raised the charcuterie board to $21, moved the grain salad to the lead position in the appetizer section, and included the half-chicken in a prix-fixe pairing. Food cost returned to 30% over the following six weeks. The matrix did not identify a single villain. It organized the data so the pattern became visible.

How Often to Run the Analysis

Menu engineering works best as a quarterly practice and as a mandatory step before any seasonal menu change. Running it more frequently than quarterly produces noise unless a specific pricing or sourcing change warrants an interim review.

Two conditions should trigger an off-cycle analysis: food cost percentage rising more than two percentage points in a single quarter without a revenue increase, or a key ingredient's wholesale cost increasing sharply. The USDA Economic Research Service publishes a Food Price Outlook that forecasts food price changes up to 18 months ahead, including producer-level price movements at farm and wholesale stages, which helps an operator judge whether a cost spike is restaurant-specific or market-wide.

Restaurants that treat menu engineering as an annual exercise rather than a quarterly discipline tend to discover margin problems long after the window for a painless correction has closed. Running the analysis takes less than an hour when POS data is clean. The cost of skipping it compounds.

The Kasavana-Smith Matrix converts a price list into a performance portfolio. Every menu item carries a classification and a corresponding action, which moves pricing and placement decisions from instinct to measurement. Operators who run the analysis quarterly tend to catch food cost drift earlier and respond with more precision than those relying on monthly P&L reports alone.

MyDashBorg builds restaurant dashboards that surface item-level contribution margin and menu mix data from POS systems automatically. See the restaurant dashboard templates or review plan options at the pricing page to find the tier that fits your operation.

Frequently Asked Questions

What are the two numbers menu engineering is built on?

Contribution margin and menu mix percentage. Contribution margin is selling price minus food cost in dollars, which is what each plate puts toward labor, overhead, and profit. Menu mix percentage is the share of total covers a given dish accounts for. Every item gets plotted against both.

How are the high and low thresholds set?

Both come from the menu itself rather than an outside benchmark. The profitability threshold is the average contribution margin across all items. The popularity threshold is 70 percent of an item's equal menu share, so on a 20-item menu where equal share is 5 percent, the cutoff sits at 3.5 percent.

What should an operator do with a Plow Horse?

Plow Horses sell reliably but contribute little per plate, so the usual responses are a modest price increase, a food cost reduction through portion calibration or ingredient substitution, or bundling with a higher-margin item. Removing them outright tends to depress cover counts in ways that cost more than the thin margin does.

How often should the analysis be run?

Quarterly, plus before any seasonal menu change. Running it more often than that produces noise unless something specific has shifted. Two conditions justify an off-cycle run: food cost percentage rising more than two points in a quarter without a revenue increase, or a sharp move in a key ingredient's wholesale cost.

Why does POS item coding matter so much?

Because fragmented item codes distort the menu mix figures the matrix depends on. When modifiers, combo variants, and split-check versions of one dish run through separate codes, a single popular item can look like several unpopular ones. Cleaning up the taxonomy is a prerequisite to the first analysis, not a follow-up task.

Sources

• The Power of Menu Engineering, Part One, AHLEI · ahlei.servsafebrands.com
• The Power of Menu Engineering, Part Two, AHLEI · ahlei.servsafebrands.com
• Food Price Outlook, USDA Economic Research Service · www.ers.usda.gov