In this paper we'll explain how in the bus industry according to IATF 16949 we determined the appropriate use of statistical tools, and Statistical Process Control (SPC) is the usual choice. SPC is a methodology mainly used to monitor and control the behavior of the manufacturing process. The main purpose of SPC is to prevent that the non-conform products go out from the production workshop to be delivered to the final clients. Statistical Process Control, part of IATF 16949 core tools, that is an analytical decision-making tool will allow to the plant from the bus industry to determine whether the process is performing correctly or not and one main point we'll be to reduce wastes. The main advantage over the other methods used in quality control (e.g. inspection), SPC has the advantage of detecting problems before they have occurred. We'll use the most successful and widely used SPC tools: control charts to identify the wastes.

Sustainable manufacturing performance is increasingly influenced by the quality of decisions embedded in Quality Management System (QMS) activities, particularly those related to problem analysis and preventive action. In industrial environments such as welded bus chassis production, recurring quality defects—although involving small components—can generate sustainability impacts through rework, inspection effort, and energy consumption. Although artificial intelligence (AI) is increasingly adopted to support quality-related tasks, its contribution is often assessed in terms of automation rather than its effect on decision quality. This study presents an AI-supported, prompt-driven decision framework designed to strengthen preventive performance within QMS. The framework is implemented through a deterministic software application that formalizes prompt engineering as a rule-based process, transforming informal human problem descriptions into structured prompts suitable for external AI reasoning tools. The application itself does not embed AI and does not generate decisions; instead, it functions as a transparent decision interface that reduces variability in problem formulation and supports methodological consistency. The framework was validated through an industrial case study conducted in a bus chassis manufacturing plant experiencing recurring defects related to missing or incorrectly positioned welded brackets. Quantitative evaluation using Key Performance Indicators demonstrates reduced analysis cycle time, improved completeness of problem definitions, higher corrective action implementation rates, and lower defect recurrence.