The client, an established solar panel manufacturing company, was facing inefficiencies in accurately identifying defective cells and interconnects, leading to potential performance issues and reduced panel efficiency. Additionally, the manual quality control (QC) testing procedures were time-consuming, resulting in increased production costs and delayed time-to-market. Furthermore, the absence of detailed data hindered the client’s ability to conduct thorough root cause analysis, limiting their capacity to address underlying issues and optimize manufacturing processes effectively.
Thus, the primary challenges revolved around the identification of bad cells and faulty interconnects, coupled with the need to streamline manual QC testing processes and generate comprehensive data for root cause analysis.