Link company website: https://www.varonalavella.com/

On the 6th in the morning, we visited Varona la Vella oil mill. Below the complete audio of the visit and a brief about the presentation and the interview we had with the owner. 

Interview Audio

Potential Applications of Artificial Intelligence in Oil Production Management and Traceability

The interview focused on the proposals presentation and identifying current challenges in production management, traceability, and sales planning within an olive oil production environment. The discussion explored how this Artificial Intelligence (AI) could support the automation of administrative tasks, improve data management, and assist decision-making by integrating information from different stages of the production process.

Intelligent Data Management

One of the first topics was the large amount of information generated throughout the production process. Data currently comes from multiple sources, including machinery, production records, inventories, storage systems, and operational databases, making it difficult to maintain a unified and organised view.

Team suggested that an AI-based system could automatically collect, clean, classify, and organise this information, creating structured records for each production batch and reducing the risk of errors caused by manual data management.

Improving Product Traceability

After presentation, traceability was identified as one of the most important areas where AI could provide added value. 

The proposed solution consists of assigning a unique identifier to every production batch, linking it to photographs, production records, processing information, and other relevant data. AI would automatically connect all these elements, ensuring that each batch has a complete and easily accessible history.

Such a system would not only improve internal organisation but also facilitate the generation of detailed traceability information for customers.

Automation of Documentation

Another key objective discussed during the interview was reducing the time spent on administrative work.

Our team we proposed that AI should automatically request any missing information during the production process—such as photographs, temperature readings, or storage data—and integrate it with the existing records.

Based on this information, the system could automatically generate traceability reports, PDF documents, or QR codes containing all relevant production data, significantly reducing manual paperwork.

Sales Forecasting and Production Planning

We explored the use of AI for predictive analysis. By analysing historical sales, customer behaviour, production records, pricing information, and inventory levels, AI could estimate future demand and assist in planning production more efficiently.

Rather than replacing human decision-making, the system would provide reliable forecasts to support production scheduling and resource allocation.

Limitations of Current Software Solutions

Company manager explained that a specialised traceability software package had already been implemented. However, despite its capabilities, it requires a considerable amount of manual data entry and administrative work.

Large amounts of information still need to be imported, verified, and completed manually, making the software time-consuming to use. As a result, the interviewee often prefers using spreadsheets, which are perceived as more practical despite offering fewer automation features.

Requirements for an Ideal Digital Solution

Throughout the interview, a clear vision emerged regarding the characteristics of an effective AI solution.

The ideal system should operate directly on a tablet used during daily production activities. It should automatically collect information from scales, sensors, and other connected equipment while prompting the operator only for data that cannot be captured automatically.

The primary objective is not to introduce additional complexity but to eliminate repetitive administrative tasks and avoid transferring information to a computer after the production process has been completed.

AI-Based Image Analysis

The final part of the interview focused on the potential of AI image recognition technologies.

Several practical applications were discussed, including:

• identifying olive varieties from photographs;
• assessing fruit maturity;
• comparing current images with previous harvests;
• retrieving historical images associated with a specific batch;

• automatically generating descriptions and technical reports.

   

The interviewee considered AI's ability to interpret images and combine them with historical production data as one of the most promising opportunities for improving both production management and traceability.

The interview highlighted that the main need is not the development of another management software platform, but rather an intelligent assistant capable of supporting operators throughout the production process.

The most valuable AI functionalities identified include automated data collection, digital traceability, automatic document generation, integration of images with production records, predictive analytics for production planning, and the reduction of repetitive administrative work.

Overall, the interview demonstrates a strong interest in practical AI solutions that simplify daily operations while improving data quality, operational efficiency, and decision-making.