Digital Twin Strategy

Data warehouse and AI forecasting

A technical roadmap to decouple operational intelligence from legacy ERP constraints and build a data foundation for AI and predictive analytics.

Year

2025

Client

Australian Fresh Produce Logistics Company

Scope

Enterprise data architecture

The client operated a complex, high-velocity supply chain environment spanning growers, transport providers, warehousing, and retail distribution. Operational data was embedded within a legacy ERP system, limiting cross-entity visibility and constraining advanced forecasting capabilities. The challenge was to build analytics capability and solve architectural dependency: intelligence was structurally tied to transactional software. The mandate was to design a digital twin framework capable of representing operational reality independently of the ERP, while preserving financial integrity and operational continuity.

We developed a canonical data model to abstract core business entities into a structured warehouse architecture. The strategy evaluated Azure, Snowflake, and Databricks environments against cost, governance, and scalability criteria, and defined a phased migration roadmap. The resulting blueprint enabled demand forecasting, scenario modelling, and AI-assisted planning without vendor lock-in. By separating operational truth from application logic, the organisation gained digital agency, cost transparency, and long-term architectural resilience.