Design and Implementation of Scalable Lean Robotics for Sustainable Production in Small and Medium-Sized Enterprises

Key findings from this study

This research indicates that:

• Preparation time per unit decreased three- to four-fold following lean robotics implementation in the stone paper packaging process.
• Stone paper and adhesive consumption improved significantly, demonstrating measurable gains in material resource efficiency.
• Manual workload associated with repetitive handling operations decreased substantially, reducing ergonomic strain.
• TRIZ-based design contradictions resolution supported scalable adaptation of the lean robotics system to varied production scenarios.

Overview

This research examines the design and implementation of scalable lean robotics systems for sustainable production in small and medium-sized enterprises (SMEs). The study addresses the resource constraints SMEs face when adopting advanced automation in a structured manner. A lean robotics design approach integrating lean principles, collaborative industrial robotics, and Industrial Internet of Things (IIoT) monitoring was developed. The approach was applied to a robotic cell for stone paper packaging, where sheets are destacked, glued, and formed into cylindrical plant protectors. Key performance indicators measured cycle time, material utilization, process stability, and manual workload before and after implementation.

Methods and approach

The authors developed a lean robotics design approach that jointly incorporates lean principles, collaborative industrial robotics, and IIoT monitoring capabilities. The approach applies TRIZ (Theory of Inventive Problem Solving) to structure the resolution of design contradictions emerging when embedding lean principles into robotic systems. TRIZ also supports scalable adaptation to different production scenarios. The methodology was validated through a real-world case study of a Fold Station robotic cell processing stone paper sheets. Performance metrics captured cycle time per unit, material utilization efficiency, process stability, and manual workload reduction.

Results

The implementation achieved a three- to four-fold reduction in preparation time per unit compared to baseline operations. Stone paper and adhesive utilization improved significantly, demonstrating more efficient material consumption. Repetitive manual handling activities decreased substantially, reducing operator exposure to ergonomic risks.

The results demonstrate measurable contributions to both economic and environmental sustainability dimensions. Cycle time reductions directly improved productivity metrics, while material efficiency gains reduced waste generation and resource consumption. The scalable design approach enabled the system to accommodate different production scenarios through structured adaptation of lean principles and robotic functionality.

Implications

The findings advance understanding of how lean robotics can be systematically designed and implemented within SME contexts characterized by limited automation adoption capacity. The integration of lean principles with collaborative robotics and IIoT monitoring provides a replicable framework for resource-constrained organizations seeking sustainable production improvements. The use of TRIZ as a structured problem-solving tool demonstrates value in resolving design contradictions and scaling solutions across heterogeneous production environments.

The practical guidelines derived from this case study establish pathways for SMEs to adopt advanced automation without requiring extensive capital investment or specialized technical expertise. The demonstrated improvements in cycle time, material efficiency, and operator workload reduction provide empirical evidence supporting scaled adoption of lean robotics. The approach enables SMEs to simultaneously address economic competitiveness and environmental sustainability objectives through technology-enabled process optimization.

Scope and limitations

This summary is based on the study abstract and available metadata. It does not include a full analysis of the complete paper, supplementary materials, or underlying datasets unless explicitly stated. Findings should be interpreted in the context of the original publication.