<Melón: concrete dosage optimization with machine learning
Machine learning and optimization platform to recommend concrete mix designs, reducing cement usage without compromising quality.
Client
Melón
Industry
Industry
Focus
Industrial optimization with AI
Result
Recommendations with quality safeguards
<Applied capabilities
Industrial machine learning
Data engineering
Mathematical optimization
Integration with internal APIs
On-premise operation
Monitoring and safeguards
<Executive summary
Melón needed to evolve the way it defined ready-mix concrete dosages in its plants.
Nnodes participated in the development of a machine learning and optimization platform that integrates production, raw material, operating condition and quality result data, trains predictive models by product family and generates dosage recommendations with safety controls.
The strategic impact lies in connecting advanced analytics with operational decisions: moving from static formulas to data-based recommendations, with traceability, monitoring and safeguards to protect product quality.
<Context
In ready-mix concrete production, cement dosage has a direct impact on costs, quality and carbon footprint.
Using more cement than necessary increases costs and emissions. Using less than required can affect concrete strength and create technical, commercial and legal risks.
<Challenge
The challenge was to build a platform capable of operating on real industrial data, with variability by product, plant, raw materials, weather and dispatch conditions.
- Replace static theoretical criteria with data-based models.
- Model compressive strength for different concrete families.
- Recommend lower cement dosage without compromising quality compliance.
- Operate in on-premise environments.
- Deliver recommendations consumable by dashboards and operational systems.
- Maintain safeguards for late, inconsistent or out-of-range data.
<The solution
Nnodes developed a modular data, machine learning and optimization platform based on production pipelines.
The solution combines data engineering, predictive modeling, optimization, integration with internal APIs and an interface for review and simulation.
Cement optimization
Recommendations aimed at reducing dosage when data indicates that strength and quality can be maintained.
Data-based decisions
Theoretical formulas are complemented with real production, material, weather and historical result data.
Quality safeguards
Confidence bands, safety factors, range filters and formula restoration protect the operation.
Operational integration
Recommendations are published to APIs, dashboards and internal datamarts to connect with daily operations.
<Technical depth, in business language
The platform was built as a production system of data and model pipelines, not as an isolated analysis.
Each execution takes operational data, transforms it, generates relevant variables, applies predictive models and then evaluates alternative dosage scenarios. The final recommendation goes through safety rules, operating limits and validations before reaching internal systems.
The model does not treat all products as if they were the same. The platform separates concrete families and allows specific settings to be maintained for each one, which is key in an industrial process where small differences in product, raw material or context can change the right decision.
<Why it matters
This case demonstrates how Artificial Intelligence can create value when applied to a concrete operational decision, with economic and environmental impact.
The key was not building a predictive model in the abstract. It was connecting data, models, optimization, internal systems and quality safeguards to support a daily production decision.
<Relevance for other clients
The Melón case is relevant for organizations with industrial processes, high input costs, multiple products or plants, and an interest in incorporating AI without compromising quality, traceability or operational continuity.
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