Industrial applications of Artificial Intelligence (AI) can be hindered by the issues of explainability and trust from end users. Human-computer interaction and eXplainable AI (XAI) concerns become imperative in such scenarios. However, the prior evidence of applying more general principles and techniques in specialized industrial scenarios is often limited. In this case study, we focus on designing interactive interfaces of XAI solutions for operators in the pulp and paper industry. The explanation techniques supported and compared include counterfactual and feature importance explanations. We applied the user-centered design methodology, including the analysis of requirements elicited from operators during site visits and interactive interface prototype evaluation eventually conducted on site with five operators. Our results indicate that the operators preferred the combination of counterfactual and feature importance explanations. The study also provides lessons learned for researchers and practitioners.