Business Intelligence Optimization

Problem Description

Business Intelligence Optimization encompasses a broad category of problems aimed at enhancing how organizations store, analyze, and use data to guide decision-making. In this context, combinatorial optimization techniques play a crucial role in:

• Data storage planning: Structuring and indexing data for efficient retrieval.
• KPI and ROI monitoring: Identifying key performance indicators (KPIs) and maximizing return on investment (ROI) through operational adjustments.
• Expert system construction: Designing intelligent decision support systems using heuristic rules and historical data.
• Process mining and optimization: Extracting actionable insights from logs and workflow data.

These tasks are increasingly central to data-driven enterprises, especially in the era of Industry 4.0 and digital transformation.

Industrial Context

Modern businesses generate vast volumes of data from operations, logistics, customer interactions, and production systems. Turning this raw data into insight requires optimization at multiple levels—both in how data is managed and in how decisions are formulated and executed.

RedHEUR4.0 emphasizes the integration of business intelligence systems with optimization engines, especially through open-source ERP extensions. These tools help SMEs make data-informed decisions without relying on costly proprietary software.

Common Challenges

• Data preprocessing and storage optimization
• Automated performance analysis using KPIs
• Recommender systems and decision rule mining
• Balancing transparency, interpretability, and efficiency in expert systems

The solutions must deal with data volume, variability, and the need for fast, interpretable outputs.

Solution Approaches

Business intelligence problems benefit from:

• Heuristics and rule-based systems
• Metaheuristics combined with feature selection or clustering
• Hybrid models that blend statistical analysis, machine learning, and optimization
• Visualization and explainability to support managerial decision-making

RedHEUR4.0 fosters open, reusable models that can be embedded into customizable ERP or BI platforms for wide accessibility.

References

1. RedHEUR4.0 Proposal. (2022). Red Española de Optimización Heurística 4.0: Digitalización.
2. Ministerio de Asuntos Económicos y Transformación Digital (MINECO). (2020). Estrategia Nacional de Inteligencia Artificial. Link
3. EUCO (2021). Study on the impact of open source software and hardware on technological independence and innovation. Link

Acknowledgments

This work forms part of the RedHEUR4.0 initiative to modernize and digitalize Spanish industry through applied heuristic optimization and data-driven methodologies. Funded by the Spanish Ministry of Science and Innovation.