Precise Planning for the Ready‑to‑Eat Food Market: How Machine Learning Reduces Waste and Improves Profitability
The ready‑to‑eat food market continues to show impressive growth, driven by changes in consumer preferences and lifestyle. The global ready‑to‑eat food market is estimated at $170–180 billion in 2026, reflecting year‑over‑year growth of around 7–9%. The segment continues to show strong consumer interest in convenient, ready‑to‑consume products and pushes manufacturers to adapt their assortments to evolving preferences.
However, the industry also faces challenges. According to statistics and surveys, the main constraints are not demand, but the ability of companies to produce the required volume while maintaining quality and efficiency. For example, the average EBITDA profitability in the category does not exceed 10%, and write‑offs due to short shelf life reach 9–11%.
Traditionally, demand forecasting for stores, logistics planning, and production scheduling are handled using simple algorithms in accounting systems or Excel spreadsheets. However, as assortment and sales volumes grow, such planning becomes labor‑intensive and inaccurate, leading to increased write‑offs and losses.
The task of optimizing the supply of ready‑to‑eat food, as well as raw materials for production, can be solved using machine learning (ML). Let’s look at a case to understand how this works in practice.
However, the industry also faces challenges. According to statistics and surveys, the main constraints are not demand, but the ability of companies to produce the required volume while maintaining quality and efficiency. For example, the average EBITDA profitability in the category does not exceed 10%, and write‑offs due to short shelf life reach 9–11%.
Traditionally, demand forecasting for stores, logistics planning, and production scheduling are handled using simple algorithms in accounting systems or Excel spreadsheets. However, as assortment and sales volumes grow, such planning becomes labor‑intensive and inaccurate, leading to increased write‑offs and losses.
The task of optimizing the supply of ready‑to‑eat food, as well as raw materials for production, can be solved using machine learning (ML). Let’s look at a case to understand how this works in practice.
Automating Raw Material Ordering for Ready‑to‑Eat Food Production
The assortment includes 80 ready‑to‑eat SKUs, and demand must be forecasted per store, per day, with a six‑week horizon. The number of stores is 1,260.
Step 1. Forecasting demand by SKU and store
Demand forecasting is performed using machine learning methods that take into account factors such as promotions, seasonality, trends, weekdays, holidays, and weather conditions. Manually accounting for all these factors is practically impossible, so using ML models at this stage is justified due to their flexibility and ability to identify complex dependencies.
Step 2. Translating demand for finished products into ingredient requirements
Each dish has a recipe that defines its ingredients and proportions. Once demand for the dish is forecasted based on sales, the required amount of each ingredient can be calculated.
Step 3. Mapping ingredients to purchase SKUs (raw materials and supplies)
An ingredient is an abstract concept in the accounting system that links the recipe to a specific SKU. For example, a dish may contain tomatoes. Depending on the season, these may be “Turkish” or “domestic” tomatoes. For the dish, the specific SKU does not matter, but for the business it does — and priorities may change over time. For such cases, a mapping directory is needed. It allows converting an ingredient into a specific purchase SKU.
Step 4. Calculating purchase requirements for each SKU
Recipes allow calculating the ingredient requirement per unit sold. The mapping directory allows converting ingredients into purchase SKUs. Thus, based on direct sales forecasts, the required amount of raw materials (linked to specific SKUs) for production can be obtained.
Step 5. Automatic generation of purchase orders
Forecasting and order generation are moved outside the accounting system and the transactional data storage layer — ideally into a cloud environment. This approach avoids overloading the system when running ML algorithms and significantly speeds up calculations.
Generated orders are transmitted via API to the accounting system, where centralized purchase orders for suppliers are created.
Forecasting ingredient consumption using machine learning reduces labor intensity by automating the planning process. As a result, labor costs were reduced by half. Forecast accuracy improved, which allowed reducing markdowns and write‑offs by 25%.
Step 1. Forecasting demand by SKU and store
Demand forecasting is performed using machine learning methods that take into account factors such as promotions, seasonality, trends, weekdays, holidays, and weather conditions. Manually accounting for all these factors is practically impossible, so using ML models at this stage is justified due to their flexibility and ability to identify complex dependencies.
Step 2. Translating demand for finished products into ingredient requirements
Each dish has a recipe that defines its ingredients and proportions. Once demand for the dish is forecasted based on sales, the required amount of each ingredient can be calculated.
Step 3. Mapping ingredients to purchase SKUs (raw materials and supplies)
An ingredient is an abstract concept in the accounting system that links the recipe to a specific SKU. For example, a dish may contain tomatoes. Depending on the season, these may be “Turkish” or “domestic” tomatoes. For the dish, the specific SKU does not matter, but for the business it does — and priorities may change over time. For such cases, a mapping directory is needed. It allows converting an ingredient into a specific purchase SKU.
Step 4. Calculating purchase requirements for each SKU
Recipes allow calculating the ingredient requirement per unit sold. The mapping directory allows converting ingredients into purchase SKUs. Thus, based on direct sales forecasts, the required amount of raw materials (linked to specific SKUs) for production can be obtained.
Step 5. Automatic generation of purchase orders
Forecasting and order generation are moved outside the accounting system and the transactional data storage layer — ideally into a cloud environment. This approach avoids overloading the system when running ML algorithms and significantly speeds up calculations.
Generated orders are transmitted via API to the accounting system, where centralized purchase orders for suppliers are created.
Forecasting ingredient consumption using machine learning reduces labor intensity by automating the planning process. As a result, labor costs were reduced by half. Forecast accuracy improved, which allowed reducing markdowns and write‑offs by 25%.
Conclusion
The ready‑to‑eat food market is growing, but companies face low profitability and high write‑offs due to short shelf life. Traditional planning methods become ineffective as assortment and business scale increase. Machine learning enables automated demand forecasting, conversion into ingredient requirements, and generation of raw material orders.
A key element is the use of cloud resources: they enable running complex ML algorithms without overloading accounting systems, and allow rapid scaling of computations as assortment, suppliers, or delivery points grow. This approach reduces write‑offs, optimizes logistics, and gives manufacturers a competitive advantage in the rapidly growing ready‑to‑eat food category.
A key element is the use of cloud resources: they enable running complex ML algorithms without overloading accounting systems, and allow rapid scaling of computations as assortment, suppliers, or delivery points grow. This approach reduces write‑offs, optimizes logistics, and gives manufacturers a competitive advantage in the rapidly growing ready‑to‑eat food category.