Manufacturing systems, production tooling, automation and analytics inside a live automotive environment.
During a 13-month placement in Digital Innovation, I designed and delivered production-facing software used across the manufacturing environment. My work ranged from enterprise asset tracking and Power Platform solutions to Python analysis, operational automation and live reporting.
The placement involved developing and supporting software inside an automotive manufacturing environment where reliability, usability, traceability and operational impact mattered immediately.
Unlike university projects, solutions were deployed into real workflows and used by manufacturing teams and senior stakeholders. The role combined software engineering, stakeholder management, systems thinking, process understanding and rapid problem solving under enterprise constraints.
“Systems were expected to work reliably, integrate cleanly with existing processes, and provide measurable operational value.”
The largest project of the placement was the collaborative design and development of an Oracle APEX platform for production asset tracking.
The work covered the full delivery lifecycle: identifying operational problems, gathering requirements, designing database structures and workflows, developing frontend and backend functionality, deploying to live users, and producing documentation, training and handover material.
The platform tracks more than 2,100 manufacturing jigs and tools using QR identification and live condition reporting. A normalised Oracle data model sits behind the application, alongside a Zebra label-printing pipeline using REST services, ZPL encoding and automated queue handling.
It is used by 100+ concurrent users and feeds live Power BI reporting for shift-start and operational readiness decisions.
“The platform generated a calculated annual operational saving in the high tens of thousands of pounds.”
PYTHON · ANALYSISAnalysed 270,000+ historical production records spanning more than eight years. Internal restrictions meant the solution was written in base Python without pandas or other external data libraries.
A custom CSV parsing and dictionary-based workflow completed the full analysis in roughly seven seconds. The results supported engineering investigation and quality/safety review processes.
AUTO · PYTHONDeveloped an automation system for a repetitive manufacturing workflow that previously consumed roughly two hours per day.
The solution interacted with Windows UI controls, interpreted application state, routed through deterministic workflows and used Teams webhook alerting to surface failures. The result saved around a working day of manual effort each week.
POWER · PLATFORMDelivered multiple production-facing Power Platform solutions, including operational startup tooling, stock management and technical assessment applications.
Supporting dashboards and reports were built for manufacturing analytics, operational readiness and workforce/capability analysis.
The placement accelerated the transition from academic projects to systems that had to be reliable in production. The work required balancing software engineering, stakeholder communication, manufacturing constraints, operational reliability and rapid iteration at the same time.
The placement concluded with an Outstanding Plus (O+) performance grade, the highest classification available within the programme.
“Setting a standard worthy of imitation.”
The year also meant relocating more than 300 miles from home and adapting to full-time industry work for the first time. It materially changed both my technical confidence and my understanding of effective systems delivery.
This page is deliberately written as a public portfolio summary. Internal source code, datasets, exact manufacturing workflows, credentials and non-public operational details are not included.
The emphasis is on the engineering approach, scale, technologies and outcomes that can be discussed safely in a recruitment context.