Industrial Engineering
Graduate Seminar
NOTICE OF SEMINAR PRESENTATION
CANDIDATE: Shovo Sarker
DEGREE SOUGHT: MASc
DATE: 3/13/2026
TIME: 10:30am
PLACE: Room 2102 CEI
TITLE: IIoT Enabled Lean 4.0 Model for Process Difficulty and Skill Identification
Abstract
This study proposes a Lean 4.0 framework to overcome the limitations of static performance metrics in labour-intensive garment manufacturing. By integrating Industrial IoT (IIoT) with advanced data modelling, the research replaces subjective assessments with objective, dynamic standards. The methodology employs hyperbolic learning-curve modelling to track operator progression, and a multi-layered anomaly detection suite ensures data integrity. Central to the framework is a hybrid Multi-Criteria Decision Making (MCDM) model that calculates a Process Difficulty Index, validated through K-means clustering. Experimental results demonstrate that this difficulty-adjusted skill metric enables more equitable evaluations and facilitates a strategic "right worker to right workstation" allocation, ultimately driving operational transparency and productivity