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Fill Weight Giveaway Reduction with AI

AI & ML Customer Case

Being efficient in production processes is key in the manufacturing industry, especially for food manufacturers. Waste during the production and filling processes is a common issue that can significantly impact profitability and production uptime. At Elisa Industriq, we utilised the power of artificial intelligence to create a solution that reduces fill weight giveaway enabling food manufacturers with a tool that saves significant costs on material waste.

Overfilling product pouches is a widespread problem in the food manufacturing industry. This not only leads to excessive material costs but also results in wastage and inefficiency. Traditional methods of managing fill weight often lack the precision needed to optimise production, leading to either overfilled bags, which give away free product, or underfilled bags, which must be scrapped. Both scenarios negatively impact the bottom line and production efficiency.

Algorithms for Filling Precision

Our AI-powered fill weight giveaway reduction solution integrates seamlessly with existing production systems. By running continuously in the background, it analyses the complex relationships between current giveaway rates and machine settings. The system then formulates precise recommendations for optimal machine settings to ensure accurate filling.

Operators can leverage this tool in two distinct modes: manual and automatic.

  • Manual Mode: Operators receive real-time recommendations on their control tablets. They can review and explicitly accept these recommendations, allowing for hands-on control and oversight.
  • Automatic Mode: The system autonomously applies the recommended settings in the background at regular intervals, ensuring continuous optimization without requiring manual intervention.

Implementing this fill weight giveaway reduction solution can lead to substantial cost savings and enhanced production efficiency. Key benefits include:

  • Cost Savings: By minimising overfilling, the solution reduces the amount of product wasted. Additionally, it decreases the number of underfilled bags that need to be scrapped, further reducing waste and associated costs.
  • Improved Efficiency: Operators are provided with precise, data-driven recommendations, reducing the guesswork and manual adjustments typically involved in the filling process. This leads to more consistent product quality and a smoother production workflow.
  • Sustainability: Reducing material waste aligns with sustainability goals, contributing to a more environmentally friendly production process.

Pet Food Manufacturer Use Case

At a pet food production of Mars, multiple filler machines operate daily. These filler machines distribute the ready food products into pouches. Each product has its own composition and pouch weight. If a pouch is underfilled, it is rejected and removed since it cannot be sold. Consequently, overfilling is often preferred to avoid underfilled pouches. However, overfilling results in a significant amount of product being given away for free or wasted. The optimal approach is to minimize this giveaway while ensuring pouches are filled to or above the target weight.

We have created an algorithm model by analysing the parameters of the filler machines for each product type. The algorithm proposes adjustments to the machine settings (automatic or manual) to optimise the filling process and minimise giveaway. The algorithm is trained on relative adjustments of the filling history, as each product shows different behaviour on different filling machines.

With the fill weight giveaway reduction algorithm, we provide food and beverage manufacturers with an advanced solution that can drive significant improvements by optimising the filling process, reducing waste, and saving costs.

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