Driving Efficiency in the Ready-to-Eat Food Industry with AI and Machine Learning
The global ready-to-eat food market continues to show impressive growth, driven by changes in consumer preferences and lifestyles. It is projected to reach $278.60 billion by 2031, with a CAGR of 5.50% from 2024 to 2031. This trend reflects the ongoing interest in convenient and ready-to-eat products, prompting food manufacturers to adapt their product offerings to meet market needs.
Traditionally, ordering ingredients for ready-to-eat food production was handled using simple algorithms in accounting systems or Excel spreadsheets based on average product consumption. However, as the variety and volume of ready-to-eat food items grow, such planning becomes labor-intensive and inaccurate, affecting service levels and increasing waste and losses.
Optimizing the supply of raw materials for ready-to-eat food production can be addressed using automatic forecasting methods based on machine learning.
Traditionally, ordering ingredients for ready-to-eat food production was handled using simple algorithms in accounting systems or Excel spreadsheets based on average product consumption. However, as the variety and volume of ready-to-eat food items grow, such planning becomes labor-intensive and inaccurate, affecting service levels and increasing waste and losses.
Optimizing the supply of raw materials for ready-to-eat food production can be addressed using automatic forecasting methods based on machine learning.
Example: Automating Raw Material Ordering for Ready-to-Eat Food Production
The following example illustrates the process of automating raw material ordering for ready-to-eat food production. The total number of ready-to-eat food items is 80, and forecasting is required for each retail location daily, with a six-week horizon. The number of forecasting locations is 1260.
Step 1: Demand Forecasting by Item and Retail Location
Forecasting is carried out using machine learning methods, considering parameters such as order history, sales, promotions, seasonality, sales trends, days of the week, and holidays. Additionally, the machine learning model can forecast demand based on weather predictions, which would be very labor-intensive to do manually on a daily basis for each item.
Step 2: Translating Finished Product Demand Forecast to Ingredients
Each production item has a recipe or process sheet that includes the composition and quantity of ingredients. Once the demand forecast for the finished product is obtained, it can be translated into ingredient forecasts.
Step 3: Mapping Ingredients to Order Items (Raw Materials)
Each ingredient may correspond to different products for ordering. Reference guides help translate ingredients into specific items for ordering from suppliers.
Step 4: Calculating the Requirement for Each Order Item
Process sheets allow the calculation of the need for each ingredient, translating it into a specific order item based on the consumption indicated in the recipe, resulting in the forecasted raw material requirements for production.
Step 5: Automatic Purchase Order Generation from Suppliers
The calculated forecasted requirement values for production items are transmitted via API to the accounting system, where centralized purchase orders from suppliers are generated.
Cloud computing power is used to speed up and optimize calculations.
Machine learning-based forecasting of ingredient consumption reduces labor intensity by automating the planning process. As a result, labor costs were halved. Forecast accuracy improved, reducing markdowns and write-offs by 25%.
The implementation of advanced solutions not only reduces costs, increases service levels and efficiency, but also successfully responds to and adapts to changes in consumer demand.
Step 1: Demand Forecasting by Item and Retail Location
Forecasting is carried out using machine learning methods, considering parameters such as order history, sales, promotions, seasonality, sales trends, days of the week, and holidays. Additionally, the machine learning model can forecast demand based on weather predictions, which would be very labor-intensive to do manually on a daily basis for each item.
Step 2: Translating Finished Product Demand Forecast to Ingredients
Each production item has a recipe or process sheet that includes the composition and quantity of ingredients. Once the demand forecast for the finished product is obtained, it can be translated into ingredient forecasts.
Step 3: Mapping Ingredients to Order Items (Raw Materials)
Each ingredient may correspond to different products for ordering. Reference guides help translate ingredients into specific items for ordering from suppliers.
Step 4: Calculating the Requirement for Each Order Item
Process sheets allow the calculation of the need for each ingredient, translating it into a specific order item based on the consumption indicated in the recipe, resulting in the forecasted raw material requirements for production.
Step 5: Automatic Purchase Order Generation from Suppliers
The calculated forecasted requirement values for production items are transmitted via API to the accounting system, where centralized purchase orders from suppliers are generated.
Cloud computing power is used to speed up and optimize calculations.
Machine learning-based forecasting of ingredient consumption reduces labor intensity by automating the planning process. As a result, labor costs were halved. Forecast accuracy improved, reducing markdowns and write-offs by 25%.
The implementation of advanced solutions not only reduces costs, increases service levels and efficiency, but also successfully responds to and adapts to changes in consumer demand.