Date of Award
2026
Document Type
Open Access Master's Thesis
Degree Name
Master of Science in Manufacturing Engineering
Administrative Home Department
Department of Manufacturing and Mechanical Engineering Technology
Advisor 1
Anis Fatima
Advisor 2
John Irwin
Committee Member 1
David Wanless
Committee Member 2
Scott Wagner
Abstract
In the contemporary industrial landscape, energy efficiency has evolved from a discretionary objective to a priority for both competitiveness and sustainability in manufacturing. Excessive energy consumption is increasingly recognized as an additional form of waste beyond the traditional lean taxonomy, known as the ninth waste. This thesis presents a unified Lean-Energy Industrial Management (LEIM) framework, structured using the Plan-Do-Check-Act (PDCA) methodology, that systematically integrates lean manufacturing principles with energy management strategies to advance sustainable manufacturing practices.
The proposed framework introduces a classification of energy consumption into primary and secondary categories and employs Specific Energy Consumption (SEC) as the core diagnostic metric. A key innovation of this work is the incorporation of dynamic adjustment factors—the congestion coefficient (βWIP) and the product variability coefficient (βMM)—which account for work-in-process congestion and mixed-model production effects that conventional static analyses overlook. Additionally, the framework integrates ergonomic energy assessment using Metabolic Equivalent of Task (MET) values, enabling the quantification of human energy waste as a distinct component of total system energy consumption. For empirical validation, the methodology was applied at a mid-sized precision-engineered material handling equipment manufacturer in the United States, focusing on the assembly process of the Industrial Wheeled Tractor (IWT) across thirteen distinct production stages. The results demonstrate that the nominal SEC at the main assembly stage was 173.24 kWh/unit, while the application of dynamic congestion and mixed-model adjustment factors revealed an adjusted SEC of 197.51 kWh/unit, uncovering an additional 24.27 kWh/unit (approximately 14%) of previously hidden energy waste. At the value-stream level, the total nominal SEC across all thirteen stages (775.42 kWh/unit) rose to 883.30 kWh/unit under the adjusted model, indicating that dynamic losses account for roughly 13.9% more energy beyond the static baseline.
These findings confirm that a substantial share of manufacturing energy demand is driven by flow and variability conditions rather than intrinsic processing requirements alone. The framework identified a mean energy gap of 13.91% across all thirteen assembly stages, confirming that WIP congestion and production variability are the dominant drivers of hidden energy waste in this manufacturing context. The proposed LEIM framework provides manufacturers with a scalable, adaptable, and data-driven methodology for achieving sustainable operational excellence by aligning lean improvements with energy performance outcomes.
Recommended Citation
Dahamah, Michael D., "INTEGRATING LEAN PRINCIPLES AND ENERGY MANAGEMENT FOR SUSTAINABLE MANUFACTURING: A PDCA-BASED FRAMEWORK WITH DYNAMIC AND ERGONOMIC ENERGY ASSESSMENT", Open Access Master's Thesis, Michigan Technological University, 2026.