Senior Process Engineer
I combine process engineering, analytics, and Lean Six Sigma to turn complex, high-volume operations into measurable gains in throughput, cost, quality, and stability.
01 / Results visualized
Each comparison uses figures from my resume. Baselines are shown only to make the documented change clear; no additional performance data has been inferred.
Python automation surfaced excess capacity and supported a leaner operating configuration.
Load-balancing models improved throughput stability and service reliability.
Power BI and cause-and-effect analysis turned operating data into targeted improvement priorities.
02 / Live sorting systems
Animated engineering schematics showing how sensing, routing logic, and mechanical handling work together across two distinct material streams.
Live flow
Live flow
Reliable sortation is a system problem: stable induction, trustworthy measurement, balanced flow, correct routing, and a control plan that keeps them aligned.
03 / Selected projects
Four detailed case studies reconstructed from my professional experience. The focus stays on transferable methods and documented outcomes.
Automated system-performance analysis to identify underused capacity and support a reduction in MLOCR utilization from 10 units to 7.
Python / Pandas / Operations data
Test whether ten MLOCR units were necessary under real operating conditions.
Built a repeatable performance-analysis workflow in Python to compare asset demand, operating patterns, and service requirements.
Supported a reduction from 10 units to 7, lowering maintenance demand and operating cost.
Analyzed throughput and material-flow data to support equipment removal, outbound lane expansion, better space utilization, and capital deferral.
Power BI / Material flow / Layout
Create outbound capacity within an established facility footprint.
Mapped plant-floor flow and combined throughput, equipment, and space data in decision-ready Power BI views.
Enabled equipment removal, lane expansion, stronger space utilization, and deferred capital investment.
Built data models and standardized operating practices to stabilize throughput, improve conveyor efficiency, and strengthen dispatch-lane performance.
Python / Pandas / Scenario modelling
Reduce unstable PPH that complicated staffing, flow, and service planning.
Developed load-balancing and scenario models, then translated the findings into SOPs and operator training.
Reduced variability from 8% to 5% while improving throughput stability and service reliability.
Combined Power BI reporting with cause-and-effect analysis to expose constraints, focus improvement work, and sustain operational gains.
Power BI / Fishbone / Lean Six Sigma
Identify the operational causes behind long cycle times and inconsistent productivity.
Connected visual reporting with cause-and-effect analysis to focus teams on the most consequential constraints.
Delivered a 20% cycle-time reduction and a 15% productivity improvement.
04 / About
I am a Toronto-based Professional Engineer who enjoys making complicated systems easier to understand and better to operate.
My work sits at the intersection of mechanical engineering, operational excellence, and data science. That perspective helps me move between the plant floor and the analytical model without losing sight of the people who make the system work.
05 / Working method
My approach combines DMAIC structure with modern analytics. Each stage produces something practical for the next decision.
Frame the operating problem, customer need, scope, and decision criteria.
Charter / VOC / SIPOCValidate the data, establish the baseline, and make variation visible.
MSA / SQL / Power BISeparate symptoms from causes using models, statistics, and process knowledge.
Python / FMEA / RCATest scenarios, balance trade-offs, and translate insight into workable change.
Simulation / DOE / PilotsStandardize the new method and monitor the signals that protect the gain.
SPC / SOPs / Training06 / Expertise
Lean Six Sigma, DMAIC, FMEA, SPC, Cp/Cpk, root-cause analysis, and practical operating standards.
Decision-ready analysis and reporting with Python, SQL, Power BI, Excel, Tableau, R, and Minitab.
Data-led equipment, material-flow, and facility decisions supported by SolidWorks, CATIA, AutoCAD, and ANSYS.
07 / Experience
Canada Post / Toronto
Leading data-driven improvements across high-volume logistics operations, with a focus on flow, capacity, quality, reliability, and cost.
Education / In progress
Education
Credentials
08 / Contact
Have a process challenge, analytics opportunity, or engineering role in mind? Send me a private message and include a little context about what you are working on.