Tech Tuesday: The Grocery Concept AI Forgot—Understanding Fungibility

You walk into the grocery store looking for an avocado.

More accurately, you are looking for that avocado—the one that yields gently under your thumb and will be ready for tonight’s guacamole. You do not want the rock-hard one beside it. You certainly do not want the bruised one hiding underneath.

That small shopping decision exposes a major weakness in many grocery inventory and fulfillment systems. The system sees a product. The shopper sees an individual choice.

The technical concept behind that difference is fungibility.

Technical Deep Dive: What Does Fungible Mean?

An item is fungible when one unit can replace another without changing its practical value or usefulness.

Imagine someone borrows a $20 bill. When the person repays you, you do not expect the same physical bill. Any genuine $20 bill will settle the debt.

Money is highly fungible.

Now imagine loaning someone your grandfather’s pocket watch. You do not want a pocket watch returned. You want that pocket watch, with its scratches, history, and family meaning intact.

That object is non-fungible because the specific item matters.

A grocery store contains both kinds of products. It also contains a large middle category where interchangeability depends on expiration date, intended use, package condition, weight, quality, and the shopper’s own preferences.

A Simple Grocery Classification

HIGH FUNGIBILITY
An equivalent item is usually acceptable.

CONDITIONAL FUNGIBILITY
An equivalent item may be acceptable after checking preferences.

LOW FUNGIBILITY
Preserve the exact item selected by the shopper.

Food and Kitchen Analogy: A Cup of Flour or That Tomato?

Suppose a recipe calls for one cup of all-purpose flour. Any cup from the same bag will work. Grandma probably does not care which individual flour particles land in the mixing bowl.

Now suppose she points toward the windowsill and says, “Bring me that tomato.”

She may have chosen it because of its ripeness, aroma, color, size, or lack of bruising. Bringing her another tomato may satisfy the product category, but it replaces her judgment.

Many grocery products are highly interchangeable:

A sealed bag of flour from the same brand and size
A can of tomatoes with the same product code
A box of spaghetti
A package of frozen vegetables from the same production lot

Other purchases depend heavily on the characteristics of one individual item:

An avocado selected for tonight
A ribeye chosen for its marbling
A peach selected for aroma and ripeness
A bakery loaf chosen for its crust
A fish fillet selected for thickness and appearance

In those cases, the shopper has made a personal judgment that the fulfillment system should preserve.

The Computer Sees a SKU

Traditional inventory systems are designed to count standardized products. A basic record might look like this:

SKU: 4810
Product: Avocado
Inventory: 214
Price: $1.29 each

To the inventory system, the store has 214 units of the same product.

To the shopper, the store has 214 different avocados.

Some are ready today. Some will be ready this weekend. Some are bruised. Some are too soft. A few are exactly right.

The inventory system counts units. The shopper evaluates possibilities.

That distinction matters whenever a store employee, delivery service, or automated system chooses food on someone else’s behalf.

Technology Already Handles Exact Purchases

The idea of reserving one specific item is common in other industries.

When you buy a concert ticket, you may select Section 112, Row G, Seat 14. Another seat in the same arena is not necessarily equivalent.

When reserving a campsite, Site 27 may offer shade, privacy, a lake view, or enough level ground for your family’s tent. Site 31 may cost the same and still be a poor substitute.

Car buyers compare individual vehicles on the lot. Mileage, options, color, condition, and history distinguish one vehicle from another even when both share the same model name.

Home buyers do not ask for any three-bedroom house. They select a particular property.

Fresh food often works the same way. The product category matters, but sometimes the individual item matters more.

From Object Detection to Exact-Item Recognition

Computer vision is often discussed as though it were one capability. An exact-item grocery system would require several separate technical steps working together.

Camera Image
     |
     v
Object Detection
     |
     v
Instance Segmentation
     |
     v
Feature Extraction
     |
     v
Quality and Ripeness Analysis
     |
     v
Item Matching
     |
     v
Cart, Bin, or Purchase Record

Object Detection

Object detection identifies and locates objects in an image. A camera might report that it sees twelve avocados and draw a bounding box around each one.

The same system could run a second model that estimates visible condition. One avocado might be labeled ready to eat, another likely ready in three days, and another past its prime.

Those labels remain estimates. A camera cannot feel firmness the way a shopper can, but it can provide useful evidence.

Instance Segmentation

Instance segmentation goes beyond drawing rectangular boxes. It identifies the pixels belonging to each individual avocado.

This becomes important when produce overlaps, touches neighboring fruit, or sits partly hidden in a crowded bin. The model must distinguish one object from the others before it can attempt to preserve that object’s identity.

Feature Embeddings

A computer-vision model can convert visible characteristics into a sequence of numbers called a feature embedding.

The values may encode patterns involving shape, color, skin texture, stem position, surface markings, and other visible traits. The resulting vector acts like a compact mathematical description of the item.

Avocado A:
[0.18, 0.72, 0.44, 0.09, 0.61, ...]

Avocado B:
[0.21, 0.69, 0.47, 0.11, 0.58, ...]

Individual values have little meaning to a person reading them. Taken together, they allow the system to compare two images and estimate whether they show the same avocado or merely two similar avocados.

Re-Identification

Re-identification asks whether an object seen now is the same physical object observed earlier.

Recognizing an avocado is a classification problem. Recognizing the same avocado later is an identity problem.

The second problem is harder because lighting changes. The avocado rotates. Another piece of fruit may partly cover it. A produce bag may hide its skin texture. Many nearby avocados may look almost identical.

Researchers have demonstrated fruit instance segmentation and re-identification across separate observations. Much of that work remains focused on agriculture, controlled environments, and research systems rather than crowded supermarket displays.

The underlying problem is still the same: preserve the identity of one physical object after it moves.

A Photograph May Not Be Enough

Visual recognition will sometimes fail. Two avocados may look nearly identical. One may be turned over, hidden under another piece of fruit, or photographed under different lighting.

A practical grocery system could improve accuracy by combining several forms of evidence through sensor fusion.

Visual Similarity
        +
Measured Weight
        +
Shelf Location
        +
Selection Time
        +
Cart Association
        +
Employee Confirmation
        =
Item Identity Confidence

Sensor fusion allows the system to compare several clues rather than betting everything on a single photograph.

A camera supplies visual features. A nearby scale supplies weight. The shopper’s phone records the time of selection. Shelf-location data narrows the search. The smart cart or fulfillment bin supplies a destination. An employee can confirm the handoff when confidence remains uncertain.

Checkout-free stores and smart-cart systems already combine computer vision, sensors, RFID, and virtual purchase records to determine which products shoppers take or return.

The exact-avocado problem applies those ideas to a more difficult task: preserving the shopper’s selected instance of a loose fresh product.

The Grocery Store of Tomorrow

Imagine selecting an avocado for tonight’s dinner.

You inspect the color, feel its firmness, and decide that it is the one you want.

You scan it with your phone or a store-provided device. The system records:

Product category and price
Visual appearance and surface markings
Approximate size and measured weight
Time and location of selection
Your cart or purchase record

If you place the avocado in your cart and take it home, the cart already protects the selection.

If you choose pickup or delivery, an employee can move that exact avocado into a customer-specific staging bin. The system associates the item with your order and protects it from sorting errors, accidental substitutions, or reassignment.

The shopper makes the decision. Technology preserves it.

Turning Fungibility Into Data

For software to act on fungibility, the concept must become part of the product and customer data.

A product record could begin with a default classification:

Product: All-Purpose Flour
Default Fungibility: HIGH

Product: Whole Milk
Default Fungibility: CONDITIONAL

Product: Ribeye Steak
Default Fungibility: LOW

The default classification provides a starting point. The system can then consider package condition, shelf life, weight differences, natural product variation, and the shopper’s substitution preferences.

An Illustrative Fungibility Score

The following model is a simplified example created for this discussion. It is not an established grocery-industry standard.

Fungibility Score =

    0.30 x Package Standardization
  + 0.25 x Quality Uniformity
  + 0.20 x Shelf-Life Similarity
  + 0.15 x Weight Similarity
  + 0.10 x Shopper Substitution Tolerance

Each input receives a value between 0 and 1. A result close to 1 suggests that equivalent units are usually acceptable. A result close to 0 signals that the exact selected item should be protected.

Consider two simplified examples:

Sealed Flour:

Package Standardization       1.00
Quality Uniformity            0.95
Shelf-Life Similarity         0.95
Weight Similarity             1.00
Substitution Tolerance        0.90

Estimated Fungibility Score: HIGH


Selected Ribeye Steak:

Package Standardization       0.60
Quality Uniformity            0.25
Shelf-Life Similarity         0.70
Weight Similarity             0.45
Substitution Tolerance        0.10

Estimated Fungibility Score: LOW

Several ribeyes may share the same product code, but differences in marbling, thickness, weight, shape, and shopper preference make the individual package important.

The formula does not need to be perfect to improve the process. Its purpose is to force the system to ask the right operational question:

Is an equivalent unit truly equivalent to this shopper?

Decision Thresholds Control Substitutions

Once the system assigns a fungibility level or score, clear rules can determine what happens next.

IF fungibility_score >= 0.80
    allow an equivalent substitution
    within customer-defined limits

ELSE IF fungibility_score >= 0.40
    check expiration, condition, price,
    weight, and shopper preferences

ELSE
    preserve the exact selected item
    or request customer approval

The customer still defines the boundaries.

One shopper may allow any equivalent can of tomatoes from the same brand. Another may reject a different package size. Someone buying milk for tonight’s gravy may accept any sealed gallon. A shopper buying milk for the entire week may insist on the farthest expiration date available.

The product data describes the item. Customer preferences describe the decision.

What Technology Exists Today?

Many pieces of the proposed system already exist:

Camera-based fruit and vegetable recognition
Object detection and instance segmentation
Computer-assisted quality and defect inspection
Mobile scanning and payment
Electronic shelf labels
Digital inventory systems
Indoor positioning
Weight sensors and distributed scales
RFID for suitable packaged products
Smart carts and virtual shopping carts
Delivery and fulfillment optimization

Retail produce-identification systems have been demonstrated using cameras, convolutional neural networks, scales, and small computers. Checkout-free stores and smart carts already combine AI, computer vision, sensors, RFID, and digital purchase records.

The immediate opportunity is coordination. A grocery store does not need one magical new machine. It needs cameras, scales, inventory systems, mobile devices, carts, and employees to share information through a clear workflow.

Computer vision, weight sensors, mobile payment, digital inventory, and cart tracking can handle much of the work. No “blockchain for bananas” required.

At least... probably not.

What Still Belongs to the Near Future?

Some capabilities are available now. Others work best in controlled environments or remain active research problems.

Capability	Current Position
Identify an avocado as an avocado	Available now
Detect visible surface defects	Available in research, sorting, and controlled inspection systems
Estimate ripeness using images or specialized sensors	Available in research and selected commercial applications
Associate an item with a virtual shopping cart	Available now in supported retail environments
Re-identify one exact loose avocado after it moves	Research and pilot territory
Preserve an exact fresh item through staging and delivery	Operationally possible now with human-supported workflows
Automate exact-item identity through the full store	Plausible near-future capability requiring reliable sensor fusion

The easiest current solution may be procedural rather than futuristic.

The shopper scans the exact item. An employee moves it directly into a customer-specific purchased bin. The system only needs to maintain identity long enough to confirm the transfer.

That simple workflow reduces the technical difficulty while preserving the customer’s choice.

Fungibility Depends on the Shopper

Many products sit between fully interchangeable and completely unique.

Milk may appear interchangeable, but shoppers use it differently. One person wants the farthest expiration date because the gallon must last all week. Another needs milk for tonight’s recipe and considers any sealed gallon acceptable.

The product is the same. The intended use changes the substitution rule.

Eggs may be interchangeable until one carton contains a cracked shell. Ground beef may be interchangeable until packages differ in weight, color, packaging condition, or expiration date.

A useful grocery system therefore needs two kinds of information: the product’s normal degree of fungibility and the customer’s expectations for this purchase.

Product	Fungibility	System Response
Canned tomatoes	High	Equivalent package is usually acceptable
Flour	High	Match brand, type, and package size
Milk	Conditional	Check expiration and shopper preference
Eggs	Conditional	Inspect carton and shell condition
Avocado	Low	Preserve the exact shopper selection
Ribeye steak	Low	Protect the chosen cut and marbling
Fresh fish	Low	Deliver the exact fillet selected

Practical Food Connection: Better Substitutions

Fungibility gives grocery systems a practical set of substitution rules:

For highly fungible products, substitute an equivalent item automatically within customer-defined limits.
For conditional products, check expiration date, package condition, size, price, dietary requirements, and stated preferences.
For low-fungibility products, preserve the exact selection or request approval before making any change.

Those rules can improve online ordering, curbside pickup, home delivery, smart carts, employee fulfillment, and inventory management. They also tell employees where human judgment matters most.

Recognizing the Decision

Artificial intelligence is becoming very good at recognizing objects. One of its next challenges will be recognizing when a human has made a decision that should be preserved.

Sometimes the shopper wants any equivalent bag of flour. Sometimes the shopper wants that avocado, that steak, or that fish fillet.

The less fungible the product, the more carefully the system should protect the shopper’s exact choice.

Knowing the difference may become one of the smartest things an AI grocery system ever learns.

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Tech Tuesday: Fungible Food