The digital transformation of the production sector is setting the scene for a major industrial change. The need for supporting companies in this transformation is currently covered by several maturity models, generally operationalized through standardized questionnaires, which provide, as an outcome, an assessment of the current maturity stage and a set of general improvement recommendations according to it. However, to provide companies with a more tangible support, there is a need for more individual approach. In order to deal with this need, this paper proposes, following a design science research framework, a novel approach based on Problem-Based Learning for structuring the assessment procedure as a dialectic process. This approach aims at facilitating the contextualization of the assessed company and, consequently, the identification of context-specific improvement recommendations. The proposed approach, supported by a maturity model used for framing information collected during the assessment process, is tested in three industrial cases. Although these have been assessed at the same maturity stage, different improvement recommendations have been proposed according to contextual factors such as strategic goals, core processes and key performance indicators.

生产部门的数字化转型为重大工业变革奠定了基础。当前，需要通过几种成熟度模型来满足对公司的支持，这些成熟度模型通常通过标准化问卷进行操作，这些模型可以提供对当前成熟度阶段的评估，并据此提供一系列总体改进建议。但是，为了为公司提供更切实的支持，需要采用更多的个性化方法。为了满足这一需求，本文在设计科学研究框架的基础上，提出了一种基于问题学习的新方法，该方法将评估程序构建为辩证过程。这种方法旨在促进被评估公司的环境，因此，确定针对具体情况的改进建议。在三个工业案例中，对所提出的方法进行了完善，该模型由用于对评估过程中收集的信息进行框架化的成熟度模型支持。尽管这些评估是在同一成熟阶段进行的，但根据诸如战略目标，核心流程和关键绩效指标等背景因素提出了不同的改进建议。