Industry 4.0, involving technological advances such as the Internet of Things, artificial intelligence, and autonomous robotics, promises to bring great benefits and outstanding innovations to traditional manufacturing. In realizing this perspective, one critical challenge is harmonizing and adapting these new technologies to the industrial environment. There are two major obstacles. First, the technologies, divided into information technologies (IT) and operational technologies (OT), are not interoperable by default. While OT, generally under industrial-grade standards, are designed to be strict, solid, and stable, IT tend to be flexible and modular. Such remarkable differences in design and concept turn the IT and OT convergence into a complex and uneasy problem. Second, deploying an Industry 4.0 system requires the participation of experts from different domains who normally have different technical vocabularies and knowledge. Communication and collaboration between these experts can be complicated, and every misunderstanding may cause the failure of a part or the whole system. Regarding the mentioned obstacles, this paper shares our successful experiences and approaches in an industrial waste management case study. In detail, we have applied standardized modeling approaches, more specifically, the asset administration shell (AAS) digital twins models, for the interoperability between IT and OT involved in the system and used model-driven engineering (MDE) techniques to automate the translation of data models between the different stakeholders in the project.