| Abstract [eng] |
The research uses a mixed methods approach to study the impact of Automated Guided Vehicles (AGVs) on the logistics operations of a tier-1/2 automotive electronics supplier, namely enterprise “X”. Quantitative and qualitative tools are utilised to explore the impact of the implementation on the performance of plant operations. Logistics processes are mapped to show the scale of AGV implementation, and the preparations made for the robots' systematic integration and future scalability are presented. A maturity model is built based on industry practices discussed in the literature and company expectations for the automated solution. The model assesses implementation across six dimensions: system integration, autonomy, data and KPIs, workforce skills, efficiency, and AI/ML use. Expert professionals from the company are surveyed to diagnose the maturity stage of the AGV implementation. The maturity stage identified was “Standardised”. The strengths of the case company were in defining and tracking Key Performance Indicators (KPIs), while the weaknesses were in efficiency and AI/ML adoption. A quantitative statistical analysis was performed for the following KPIs: quality rate, utilisation rate, effective utilisation rate, performance rate, and total effective equipment performance (TEEP). Control charts that confirm system stability were graphed to identify that the underutilisation of the fleet was the limiting factor rather than technical performance, which was further confirmed by studying hourly average TEEP rates and performing correlation analysis. The economic section calculates the return on investment for the AGV fleet, yielding a positive IRR of 8%. The findings indicate that AGV deployment improves operational efficiency and provides scalable benefits at the operational and economic levels. The project presents a road map for industrial AGV/AMR systems implementation, using applied research methods to extract operational insights and assess maturity levels. |
