I’m a Certified Lean Manufacturing Trainer with deep experience improving processes across manufacturing, logistics, R&D, distribution, and service operations.
My work blends Lean/TPS principles + data analytics to design smarter, faster, and more adaptive operational systems.
I use SQL, Python, and OR-Tools to uncover inefficiencies, optimise supply chains, and deliver measurable improvements in productivity, cost, throughput, and service performance across both manufacturing and service environments.
I specialise in turning operational challenges into data‑driven optimisation solutions, including:
- Routing optimisation & scheduling
- Demand forecasting & capacity planning
- Workforce modelling
- Inventory & flow optimisation
- Automation of repetitive operational tasks
My goal is simple: use data + Lean thinking to build operations that scale intelligently.
- Systems thinking & structured problem‑solving
- Root‑cause analysis and structured problem‑solving
- Designing efficient workflows and value streams
- Leading cross‑functional improvement initiatives
- Turning operational challenges into measurable, data‑driven solutions
- Standardised Work, JIT, Kanban
- SMED, 5S, Mistake Proofing
- Built‑in Quality, Andon
- Visual/Daily Management
- Facilitation of structured problem‑solving
- Operations research
- Plant layout & flow optimisation
- Motion/time study
- Ergonomics
- Queuing systems
- Capacity planning & scheduling
- Python (Pandas, NumPy, Matplotlib, NetworkX)
- SQL (analysis, reporting, optimisation)
- OR-Tools (routing, scheduling, optimisation)
- Data automation & workflow optimisation
- Machine learning fundamentals
Tech Stack: Python · PostgreSQL · Pandas · NetworkX · EUROCONTROL Cost Framework · BITRE/ACCC Public Data
Overview: An independent lean-thinking diagnostic of Australian domestic aviation disruptions (2023–2025). Mapped 21 cost elements across four waste categories (Waiting, Rework, Unconverted Capacity, Overprocessing) using public operational and financial data. Built a relational cost model and visualisation suite to quantify ~AUD $2.99B in annual carrier and passenger economic loss. Demonstrates rigorous data engineering, cost modelling, and lean mapping applicable to complex service networks, maintenance operations, and logistics optimisation.
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Tech Stack: Python · OSRM Routing API · NetworkX · Dijkstra’s Algorithm · Folium · Matplotlib
Overview: Independent logistics cost model for Liebherr’s mining equipment parts distribution and field service network. Used real-road routing, shortest-path optimisation, and industry benchmarks to model a $9.35M annual baseline. Identified strategic regional hub placements (Pilbara + Olympic Dam) to reduce SLA breach rates from 15% to 3%, projecting $5.66M in annual savings. Includes sensitivity analysis, lean waste classification, and executive-ready visualisations. Directly applicable to mining OEM service networks, warehousing, distribution, and supply chain optimisation.
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👉 Explore the full portfolio here:
https://github.com/erick-m-lean-analytics/Transport-Operations-Analysis
I enjoy bridging operational reality with analytical and technical solutions. Whether improving a production line or building an AI model, I focus on clarity, practicality, and measurable impact.