AI applied to sales, forecasting and inventory in technology distribution
The challenge
Integration of multiple sources
Connection of data from multiple ERPs, manufacturer portals, invoicing systems and stock management tools.
Predictive modelling
Building ML models for sales and demand forecasting with heterogeneous data and complex seasonality.
Intelligence coordination
Orchestration of AI agents for automatic analysis of trends, alerts and recommendations in real time.
APCOM, a Midis Group company, is a multi-country technology distributor with complex sales, stock and logistics operations across multiple channels.
Multi-market expansion with the need to unify commercial intelligence, improve forecasting and automate the detection of opportunities.
Designed architecture
Data architecture with ETL pipelines, a centralised data warehouse and an ML layer for predictive models and intelligent agents.
Unified data pipelines
ETL/ELT pipelines to consolidate transactional data from multiple sources into a centralised data warehouse.
Predictive demand models
ML models for sales forecasting with exogenous variables, seasonality and market trends.
Agentic architecture
AI agent system for automatic anomaly detection, alerts and the generation of recommendations.
Tech stack
Implementation model
Incremental rollout prioritising data pipelines, predictive models and commercial intelligence dashboards.
Measurable impact
Improved sales forecast accuracy with ML models
Shorter multi-source data integration times
Automatic detection of deviations and early alerts
Better stock levels and fewer stockouts
Greater visibility for sales and operations teams
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