MHMuhammad Hassan
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Hi, I'm Muhammad Hassan

Senior Process Engineer · P.Eng · Lean Six Sigma Black Belt

Muhammad Hassan wearing an orange safety vest in an operations facility
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25%reduction in MLOCR utilization
20%cycle-time reduction
15%productivity improvement
8% to 5%PPH variability reduction

01 / Results visualized

Improvement you can see.

Each comparison uses figures from my resume. Baselines are shown only to make the documented change clear; no additional performance data has been inferred.

Before After
Asset utilization10 → 7 units

Python automation surfaced excess capacity and supported a leaner operating configuration.

PPH variability8% → 5%

Load-balancing models improved throughput stability and service reliability.

Normalized operating indexCycle time down / Productivity up

Power BI and cause-and-effect analysis turned operating data into targeted improvement priorities.

02 / Live sorting systems

Flow made visible.

Animated engineering schematics showing how sensing, routing logic, and mechanical handling work together across two distinct material streams.

System A

Letter-mail sorting

Live flow

01 Induct & singulate02 Read & classify03 Divert by destination
System B

Parcel sorting

Live flow

01 Measure & identify02 Assign destination03 Track & divert
Engineering principle

Reliable sortation is a system problem: stable induction, trustworthy measurement, balanced flow, correct routing, and a control plan that keeps them aligned.

Projects

Six resume-based examples showing the focus, technical contribution, and documented operational value of my work.

Python automation01

Equipment utilization optimization

Automated MLOCR system-performance analysis using Python to identify excess equipment capacity.

Python / Pandas / Operations data

Focus

Equipment utilization and operating cost.

Contribution

Automated system-performance analysis and compared actual operating requirements across the available MLOCR units.

Impact

Reduced utilization from 10 units to 7, lowering maintenance demand and operating costs.

Result25% reduction
Flow and capacity02

Facility flow optimization

Analyzed plant-floor throughput and material-flow data in Power BI to improve the use of equipment and space.

Power BI / Material flow / Layout

Focus

Outbound flow, facility capacity, and capital efficiency.

Contribution

Connected throughput, material-flow, equipment, and layout information in decision-ready Power BI analysis.

Impact

Enabled MLOCR removal, outbound lane expansion, improved space utilization, and capital-equipment deferral.

ResultCapacity unlocked
Predictive modelling03

Load-balancing and service reliability

Built Python load-balancing models and translated the findings into repeatable operating practices.

Python / Pandas / Scenario modelling

Focus

PPH stability, conveyor efficiency, and service reliability.

Contribution

Developed Pandas-based load-balancing models, SOPs, and operator training for conveyor and dispatch-lane flow.

Impact

Reduced PPH variability from 8% to 5% while improving throughput stability and reducing handling errors.

Result8% to 5% variability
Quality analytics04

Cycle-time and productivity improvement

Designed Power BI dashboards incorporating cause-and-effect analysis to focus improvement work on the right constraints.

Power BI / Fishbone / Lean Six Sigma

Focus

Cycle time, productivity, and process visibility.

Contribution

Combined operational dashboards with Fishbone analysis to make causes, priorities, and performance trends clear.

Impact

Delivered a 20% cycle-time reduction and a 15% productivity improvement.

Result20% faster / 15% more productive
Measurement systems05

Parcel data integrity

Evaluated parcel dimensioning and weight-capture systems to strengthen the quality of downstream decisions.

Gage R&R / MSA / Parcel systems

Focus

Measurement accuracy for sorting and load planning.

Contribution

Performed Measurement System Analysis and Gage R&R on parcel dimensioning and weight-capture systems.

Impact

Strengthened data integrity for downstream sorting and load-planning decisions.

ResultMore reliable data
SQL and data pipelines06

Parcel staging and decision support

Developed high-volume SQL and analytics solutions for parcel staging, transport utilization, and operational planning.

Teradata SQL / ETL / Power BI

Focus

Inbound and outbound parcel staging across multiple depots.

Contribution

Built a delivery-space calculator plus Teradata SQL and ETL pipelines for operational and strategic analysis.

Impact

Improved skid staging and transport utilization while reducing loading-damage risk.

ResultFlow and planning improved
Muhammad Hassan on the Brooklyn Bridge at sunset
Muhammad Hassan with the Toronto skyline
Muhammad Hassan near the Manhattan Bridge

04 / About

Curious by nature.
Rigorous by practice.

I am a Toronto-based Professional Engineer with a mechanical engineering foundation and a record of improving high-volume logistics operations.

My work combines plant-floor engineering, Lean Six Sigma, and practical data science. I use that combination to improve material flow, equipment utilization, process capability, parcel quality, and the reliability of operational decisions.

Currently expanding the toolkitMS Computer Science
Artificial IntelligenceGeorgia Tech / 2025 - 2027

05 / Working method

A disciplined path from signal to sustained result.

My approach combines DMAIC structure with modern analytics. Each stage produces something practical for the next decision.

01

Define

Frame the operating problem, customer need, scope, and decision criteria.

Charter / VOC / SIPOC
02

Measure

Validate the data, establish the baseline, and make variation visible.

MSA / SQL / Power BI
03

Analyze

Separate symptoms from causes using models, statistics, and process knowledge.

Python / FMEA / RCA
04

Improve

Test scenarios, balance trade-offs, and translate insight into workable change.

Simulation / DOE / Pilots
05

Control

Standardize the new method and monitor the signals that protect the gain.

SPC / SOPs / Training
DataDecisionChangeLearningRepeat

06 / Expertise

From the plant floor to the decision room.

01

Programming and data

Python with Pandas, NumPy, and Scikit-Learn; SQL with Teradata; R; and MATLAB.

02

Analytics and BI

Power BI, Excel, Tableau, and Minitab for operational analysis, dashboards, and decision support.

03

Process and quality

Lean Six Sigma Black Belt, DMAIC, FMEA, SPC, Cp/Cpk, root-cause analysis, and Gage R&R.

04

Engineering design

SolidWorks, CATIA, AutoCAD, and ANSYS supported by a mechanical engineering foundation.

05

Operational systems

High-volume logistics, material flow, conveyor systems, parcel staging, load planning, SOPs, and operator training.

Experience

Data-driven process engineering across high-volume logistics operations.

2019 - Present

Canada Post / Toronto

Senior Process Engineer

Delivering data-driven improvements across high-volume logistics operations, with responsibility spanning process efficiency, material flow, product quality, measurement systems, data pipelines, and cost control.

PythonPandasSQLTeradataPower BILean Six SigmaFMEASPCCp/CpkGage R&RMinitab

Education / In progress

MS in Computer Science - Artificial Intelligence

Georgia Institute of Technology, 2025 - 2027

Education

BASc in Mechanical Engineering

University of Windsor, 2015 - 2019

Credentials

Professional Engineer, P.Eng

Professional Engineers Ontario

Lean Six Sigma Black Belt

01

Process improvement and efficiency

  • Reduced MLOCR utilization by 25%, from 10 units to 7, through automated Python performance analysis.
  • Used Power BI throughput and material-flow analysis to support equipment removal, lane expansion, better space use, and capital deferral.
  • Reduced PPH variability from 8% to 5% with Python load-balancing models.
  • Created SOPs and trained operators to improve conveyor and dispatch-lane efficiency while reducing handling errors.
  • Led FMEA work to eliminate gravity-conveyor blockages and reduce parcel damage, jams, and rework.
02

Product quality and cost control

  • Improved process stability and Cp/Cpk using Python, R, and Minitab to reduce defects, rework, and operating cost.
  • Delivered a 20% cycle-time reduction and 15% productivity improvement through Power BI and Fishbone analysis.
  • Built a Teradata SQL delivery-space calculator to optimize parcel-skid staging and transport utilization.
  • Performed Gage R&R on parcel dimensioning and weight-capture systems to improve data integrity.
03

Technical expertise and data innovation

  • Built Python scenario models for lane configurations, staffing strategies, and service-level trade-offs under variable parcel volumes.
  • Designed SQL and Power BI analytics for inbound and outbound staging across multiple Canada Post depots.
  • Developed Teradata SQL and ETL pipelines for high-volume operational data and strategic decision-making.

Contact

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