AS Kalve coffee is participating in the Recovery and Resilience Facility project "Latvian Food Competence Centre."

From October 1, 2025, AS Kalve coffee is participating in the Recovery and Resilience Facility (RRF) research project "Latvian Food Competence Centre" No. 2.2.1.3.i.0/1/24/A/CFLA/002: "Development of an Artificial Intelligence-based, unified digital solution for supply, production, and accounting to improve the work efficiency and automation of a food industry manufacturing company."

Project Objective: The objective of the project is to develop a unified digital solution integrating artificial intelligence to enhance the operational efficiency and automation of a manufacturing enterprise within the food industry. The developed solution is anticipated to focus primarily on optimizing core manufacturing processes, including raw material procurement, production (roasting), finished goods logistics, and inventory management. This will ensure higher forecasting accuracy (e.g., in demand planning), streamlined supply chain planning for raw materials, automated routine tasks, and the overarching capability to make data-driven decisions in real time.

Expected Outcome: The development of a solution prototype that, utilizing artificial intelligence components, will be capable of effectively planning and forecasting essential production processes, raw material requirements, and other critical operations within a food industry enterprise.

In the first phase of the project (October 1, 2025 – December 31, 2025), a study of the production process and data sources will be implemented.

Project Implementation Period: October 1, 2025 – December 31, 2026.

Total Project Costs: 336,005.00 EUR, including co-financing from the Recovery and Resilience Facility amounting to 247,125.52 EUR.

During the first quarter, research on data and data sources was conducted. This included fieldwork, process and data mapping, and the integration of available data within a laboratory environment. The following activities were performed, yielding these results:

1. Business Process Analysis: Identified key processes and mapped interactions across
   the production, warehouse, and logistics dimensions.
2. Warehouse Data Research: Analysed inventory turnover and warehouse data, including data structures and schematic representations. Focus was placed on historical data and data granularity. Warehouse data sources are suitable for further structuring and processing.
3. Production Resource and Process Data Analysis: Identified specific resources used in the coffee production process (human resources, equipment, raw materials, etc.). The production stages and steps for the coffee product (coffee cans) were mapped schematically, including a time dimension.
4. Recipe and Roasting Data Research: Examined coffee roasting recipes and validated the data structure for use in production planning and forecasting algorithms. Evaluated the potential for using recipe data to predict production time, resources, and costs.
5. Identified necessary supplemental data (seasonality, holidays, external demand factors, logistics delays) and assessed its availability for further use and integration in laboratory conditions.

Achieved results:

Acquired a precise technical dataset required for the development of automation and optimization algorithms, including:

1. A structured and validated technical dataset suitable for algorithm development. Existing and potential data sources and their applications have been explored.
2. Defined requirements for data processing, enrichment, and integration for future project activities.
3. Prepared information for algorithm architecture design and laboratory testing in the next research phase.

Reporting Period Progress (January 1, 2026 – March 31, 2026):

During the reporting period, Activity 2, "Design of the Technological Solution," was executed. Throughout this activity, the design of the technological solution, the modeling of the algorithm architecture schematic, and the exploration of the potential solution's system architecture were conducted in a laboratory environment, utilizing previously acquired and integrated technical data sets.

Achieved Results:

The technological schematic of the solution, the algorithmic execution architecture schematic, and the overall system architecture schematic have been thoroughly researched and defined. This includes:

1. The development of a data architecture and database (DB) schema concept that links orders, inventory, resources, recipes, and work tasks within a unified structure;

2. The formulation of a data collection and aggregation workflow (initially based on manual data import, with a planned transition toward automated data acquisition) to ensure a repeatable and consistent algorithm testing cycle.