This thesis examines how digital technologies have historically transformed manufacturing performance and how their integration with Lean methods can generate measurable improvements in both intralogistics and assembly-line processes. Chapter 1 traces the historical evolution of digital manufacturing technologies, from early numerical control (CNC) systems through programmable automation to contemporary sensor networks, IoT platforms, and artificial intelligence, highlighting the mechanisms by which each technological wave affected productivity. Chapter 2 investigates the relationship between Lean techniques and digitalization, arguing that successful technology adoption requires first stabilizing and standardizing processes according to Lean principles, and then layering digital solutions to amplify waste reduction, flow, and information transparency. Chapter 3 presents early stages of empirical case studies from pilot implementations in intralogistics and inline assembly, showing how sensor-driven solutions combined with Lean interventions improve key operational metrics . The thesis contributes a practical framework for sequencing Lean and digital initiatives in traditional manufacturing firms and offers evidence-based recommendations for practitioners aiming to design pilot lines and smart workstations that deliver measurable performance gains.
This thesis examines how digital technologies have historically transformed manufacturing performance and how their integration with Lean methods can generate measurable improvements in both intralogistics and assembly-line processes. Chapter 1 traces the historical evolution of digital manufacturing technologies, from early numerical control (CNC) systems through programmable automation to contemporary sensor networks, IoT platforms, and artificial intelligence, highlighting the mechanisms by which each technological wave affected productivity. Chapter 2 investigates the relationship between Lean techniques and digitalization, arguing that successful technology adoption requires first stabilizing and standardizing processes according to Lean principles, and then layering digital solutions to amplify waste reduction, flow, and information transparency. Chapter 3 presents early stages of empirical case studies from pilot implementations in intralogistics and inline assembly, showing how sensor-driven solutions combined with Lean interventions improve key operational metrics . The thesis contributes a practical framework for sequencing Lean and digital initiatives in traditional manufacturing firms and offers evidence-based recommendations for practitioners aiming to design pilot lines and smart workstations that deliver measurable performance gains.
SENSOR-DRIVEN DIGITALIZATION IN MANUFACTURING: KAIZEN KEY’S PILOT LINE FOR INTRALOGISTICS AND IN-LINE PROCESSES
SALVADOR, ALAN
2025/2026
Abstract
This thesis examines how digital technologies have historically transformed manufacturing performance and how their integration with Lean methods can generate measurable improvements in both intralogistics and assembly-line processes. Chapter 1 traces the historical evolution of digital manufacturing technologies, from early numerical control (CNC) systems through programmable automation to contemporary sensor networks, IoT platforms, and artificial intelligence, highlighting the mechanisms by which each technological wave affected productivity. Chapter 2 investigates the relationship between Lean techniques and digitalization, arguing that successful technology adoption requires first stabilizing and standardizing processes according to Lean principles, and then layering digital solutions to amplify waste reduction, flow, and information transparency. Chapter 3 presents early stages of empirical case studies from pilot implementations in intralogistics and inline assembly, showing how sensor-driven solutions combined with Lean interventions improve key operational metrics . The thesis contributes a practical framework for sequencing Lean and digital initiatives in traditional manufacturing firms and offers evidence-based recommendations for practitioners aiming to design pilot lines and smart workstations that deliver measurable performance gains.| File | Dimensione | Formato | |
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https://hdl.handle.net/20.500.12608/112779