AI Food Waste Reduction System Development

Development of an AI system for reducing food waste (Food Waste AI)

Food waste accounts for 30-40% of purchased products in restaurants and 15-20% in retail. The AI system reduces this through accurate demand forecasting, dynamic inventory management, intelligent pricing of expiring items, and optimized use of leftovers.

Sources of food waste

Primary causes:

• Overproduction: they made more than they sold
• Overordering: purchased more than necessary
• Expiry: failed to sell before expiration
• Spoilage: storage failure, cold chains

By chain links:

• Production and farms: overproduction
• Distribution: damage during transportation
• Retail: expired, substandard
• Catering: overproduction, plate waste
• Households: the largest share of all losses

AI systems for businesses (retail, catering) affect distribution and sales.

Demand Forecasting for Retail

The problem of perishable goods: Dairy, bread, vegetables/fruits – 1-7 days. A 5% forecast error = real money debited.

Special methods:

• Intermittent demand: for niche positions (organic, premium) with rare sales
• Shelf-life aware replenishment: ordering based on the remaining shelf life

def shelf_life_adjusted_order(forecast, current_inventory, expiry_dates, min_shelf_life_at_sale=2):
    """
    The remainder that will be sold before the expiration date minus 2 days
    = Sellable inventory
    Net need = forecast - sellable_inventory
    """
    sellable = sum(qty for qty, exp in zip(current_inventory, expiry_dates)
                   if (exp - today).days >= min_shelf_life_at_sale)
    return max(0, forecast - sellable)

Dynamic Markdown Pricing

Expiring items should be sold while they still have value. Dynamic markdown:

def calculate_markdown(current_price, days_remaining, daily_demand, units_remaining):
    """
    Optimal Discount: Maximize Revenue from Remaining Stock
    under restriction: sell everything before the deadline
    """
    # Probability of selling all units at current price
    prob_sell = survival_model.predict_proba(days_remaining, units_remaining, daily_demand)

    if prob_sell > 0.8:
        return 0 # no need to lower

    # Optimal discount: price × demand(price) = max revenue
    optimal_price = price_optimizer(daily_demand, price_elasticity, days_remaining, units_remaining)
    markdown_pct = (current_price - optimal_price) / current_price
    return markdown_pct

# Typical markdowns: D-3 before expiration: -15%, D-1: -30%, D0: -50%

For restaurants - Daily Specials: Leftover ingredients → AI generates high-margin "Dish of the Day" offers using ingredients with expiring dates. Recipe database + inventory → LLM generates dish descriptions.

Production Planning for Catering

Optimizer setup:

def calculate_mise_en_place(cover_forecast, menu, inventory, prep_times):
    """
    Cover forecast → expected food orders
    → ingredients to prepare
    → comparison with current inventory
    → what needs to be prepared, in what quantity
    """
    expected_dishes = cover_forecast @ menu.dish_probability_matrix
    expected_ingredients = expected_dishes @ menu.recipe_matrix

    # Buffer for walk-ins and variability
    prep_with_buffer = expected_ingredients * 1.15

    # Minus what is already ready
    net_prep = prep_with_buffer - current_prepped_inventory
    return net_prep

Batch cooking optimization: For large volumes, cook in batches. AI determines the optimal batch size: a balance between freshness (smaller = better taste) and labor efficiency (larger = fewer kitchen adjustments).

IoT waste monitoring

Smart bin system:

• Scales under garbage bins (Winnow, Orbisk): automatic weighing
• Chamber above the tank + CV: classification of what is emitted
• Real-time data → the chef sees waste by item

# Example of waste monitoring system output
daily_waste_report = {
    'total_kg': 12.3,
    'value_usd': 45.80,
    'top_wasted_items': [
        {'item': 'Salmon', 'qty_kg': 2.1, 'cause': 'overproduction'},
        {'item': 'Mixed salad', 'qty_kg': 1.8, 'cause': 'plate_waste'},
        {'item': 'Croissants', 'qty_kg': 1.4, 'cause': 'expired'}
    ]
}

Donations and B2B sales of leftovers

Automatic surplus management:

• 24 hours before expiration: publish on Too Good To Go / Eatwith (magic bag)
• In 12 hours: Offer to local charities via the Last Mile API
• 6 hours: transfer to the food bank (logistics via our own courier or platform)

Legally: GOST R 55787-2013 regulates the transfer of products to social protection organizations.

System metrics:

• Food waste reduction: 20-35% от baseline
• Food Cost %: decrease by 1-3 percentage points.
• Markdown recovery rate: revenue from discount sales / cost of potentially written-off inventory
• Waste per cover (restaurants): kg/guest

Deadlines: A basic system with demand forecasting and a Markdown engine will take 4-5 weeks. A full-fledged platform with IoT scales, a donation API, and a recipe optimizer will take 3-4 months.