Hitachi Technological Centre of Excellence
Unlocking Operational Intelligence: A Cloud-Native Analytics Platform for Autonomous Haulage
This case study details WorkingMouse’s collaboration with Hitachi Construction Machinery Australia—first to
modernise their Map Authoring Tool and later to co-develop a data analytics platform with Hitachi’s Technological Centre of Excellence
(TCoE). Together, these initiatives form a foundational digital suite powering Hitachi’s Autonomous Haulage System (AHS) and broader
mining optimisation strategy.
Phase One: Legacy Reimagined — Modernising the Map Authoring Tool
The initial engagement focused on replacing legacy C++ applications with a cloud-native interface for managing autonomous dump truck operation zones. WorkingMouse introduced a modular architecture underpinned by domain-driven design and user-centric workflows. Delivered in April 2022, the Proof of Concept validated the feasibility of the transformation and set the technical stage for long-term digital capability building.
Key methods included:
- Microservice decomposition and transition from monoliths
- Use of user journey and story mapping to expose operational pain points
- Incremental delivery through scoped MVP milestones
Phase Two: Engineering Insight — The TCoE Analytics Platform
With terabytes of telemetry data generated daily by autonomous haulage vehicles, Hitachi’s Centre of Technology Excellence sought a solution to make this data actionable for both internal stakeholders and their mining customers.
WorkingMouse responded by delivering a data ingestion and analytics platform designed for high-volume, high-fidelity operational intelligence. At the heart of the solution are model-driven pipelines, developed using WorkingMouse’s Codebots platform engineering suite.
Platform Architecture Highlights
- CETL Engine (Codebots Extract, Transform, Load): Enables structured ingestion of raw telemetry, transforming it into a Star Schema timeline database optimised for analytical workloads.
- Visual Query Builder (CQL in WYSIWYG): Users construct complex queries through a visual interface that generates CQL (Codebots Query Language)—a domain-specific language designed for readable, structured, and executable queries.
- Self-Service Reporting: Reports and dashboards can be created, exported (API, CSV, PDF, or email), and scheduled without developer intervention—driving autonomy across analytics workflows.
- API-First Data Integration: All ingestion occurs through secure, tenant-scoped RabbitMQ pipelines using API key authentication, maintaining isolation and scalability across mining clients.
Strategic Impact
Hitachi now leverages a model-driven analytics product that combines automation with human oversight—the principle of Jidoka, reinterpreted for modern data platforms. Users at every layer—from technical teams to operational managers—can explore, analyse, and optimise their autonomous systems using real-time insights.
WorkingMouse’s role extended beyond delivery to capability uplift: training internal developers, defining platform governance standards, and co-developing a custom HitachiBot under licence. The result is a sustainable platform that Hitachi can extend independently—backed by a solid architecture and a living model of engineering knowledge.
Closing Reflection
This engagement demonstrates the value of applying Model-Driven Engineering at scale—moving from hard-coded pipelines and static data tools toward adaptive platforms defined by live models and reusable transformation logic. By embedding software intelligence into infrastructure, Hitachi has not just digitised operations, but industrialised insight itself.
