This study explores the application of Lean principles in the service sector, addressing the complexities of translating manufacturing-focused methodologies to intangible service activities. Lean Services, a relatively recent concept, lacks a standardised definition, leading to varied interpretations ranging from customer-centric approaches to waste reduction strategies. Through a comprehensive literature review and a case study of a European scooter and motorcycle manufacturer, this research identifies a consolidated list of service-specific wastes, bridging a critical gap in Lean Services research. Additionally, the study compares two prominent methodologies—DMAIC (Define–Measure–Analyse–Improve–Control) from Six Sigma and the Cost Deployment pillar from World Class Manufacturing (WCM)—in the context of Lean Services. The analysis highlights DMAIC’s strength in advanced statistical tools and targeted problem-solving, contrasting with WCM’s systemic approach, emphasising economic feasibility and broader resource integration. By examining their individual and combined applicability, this research provides actionable insights for selecting methodologies based on specific objectives, time constraints, and resources. This work contributes to the evolving understanding of Lean Services, offering a framework for practitioners to enhance efficiency and drive continuous improvement in service-based processes.

This study addresses the scheduling problem in a single-machine system with variable processing speed. The performance of the Earliest Due Date (EDD) rule, operating at a fixed maximum speed, is compared with a Reinforcement Learning (RL)-based approach capable of dynamically adapting both processing speed and job selection. The analysis considers both temporal performance (tardiness) and sustainability (energy consumption). Simulation results, supported by statistical analysis, show that the RL approach achieves a significant reduction in total energy consumption compared to the EDD rule. Regarding temporal performance, analysis of variance reveals that the chosen policy significantly affects the number of tardy jobs, with an effect that depends on the tightness of job deadlines, whereas the overall average tardiness across all jobs does not differ significantly between the two policies. These findings highlight the potential of adaptive RL-based policies for more sustainable resource management, demonstrating the ability to achieve substantial energy savings while maintaining competitive temporal performance and dynamically managing the trade-off according to deadline pressure.