Lowcost statistical process control tools in the absence of automated control

The article addresses the challenge of implementing Statistical Process Control (SPC) in small and medium-sized enterprises with limited budgets and lacking automated monitoring systems. The study draws upon the experience of a domestic manufacturer of automotive air fi lters, characterized by small-batch production and low labor mechanization. The authors substantiate the necessity of implementing manual data collection methods and propose an effective approach that includes defect classifi cation, engaging frontline employees in quality management processes, and employing simple quality analysis tools such as control charts and Pareto diagrams. The developed methodology signifi cantly improved statistical data quality, enabling the identifi cation of systemic issues and prioritizing improvement directions. A comparative analysis demonstrated that the economic benefi ts of the proposed approach were comparable to those achievable with automated systems, albeit at signifi cantly lower costs. The study confi rms the feasibility and effectiveness of applying SPC without substantial fi nancial investments while enhancing employee engagement in quality management processes. The authors conclude that analog methods for data collection and SPC implementation hold signifi cant promise for enterprises with limited resources, though they emphasize the eventual need for automation as companies grow and evolve.