• Explainable Artificial Intelligence
• AI
• XAI
• feature importance
• counterfactual
• counterfactual explanation

Industry 4.0 signifies the current industrial revolution, leveraging modern technologies to establish intelligent environments for enhanced production processes. The product quality is closely linked to the manufacturing decision system configuration. Predicting product quality could optimize parameters early in design, boosting competitiveness. While machine learning aids in prediction, its opacity hinders comprehension in critical industrial contexts. Explainable AI (XAI) emerges, with counterfactual-based explanations being effective in revealing input-output relationships. DiCE is a XAI framework generating counterfactuals to elucidate model predictions, offering diverse explanations by altering inputs. Proposed XAI doesn't directly use counterfactuals but derives feature importance using counterfactual samples for a specific instance. This leads to a global explanation termed BoCSoR 2.0, by quantifying the frequency with which a change in each feature in isolation lead to a significant change in the regressor model’s output. Comparisons between DiCE and BoCSoR 2.0 performances are organized. Experimental results on synthetic and industrial data support BoCSoR 2.0's reliability, agreement with expert knowledge, and efficiency. BoCSoR 2.0 proves promising for industry due to its effectiveness, efficiency, and robustness to feature correlation, making it a valuable XAI tool.