We present a novel counterfactual-based dashboard for explainable artificial intelligence (XAI) in process industries, aimed at enhancing the understanding and adoption of machine learning (ML) models by providing transparency, explainability, and performance evaluation. Our dashboard comprises two modules: a statistical analysis module for data visualization and model performance assessment, and an XAI module for exploring counterfactual explanations at varying levels of abstraction. Through a case study of an industrial batch process, we demonstrate the dashboard's applicability and potential to increase trust in ML models among stakeholders, paving the way for confident deployment in process industries.