A Fuzzy Inference System Based Digital Maturity Model for Manufacturing Enterprises

Digital maturity models are essential tools for assessing an organization's readiness, capability, and progress in adopting digital technologies. These models support strategic decision-making by identifying current digital capabilities and guiding digital transformation efforts. However, existing digital maturity models often rely on rigid and limited perspectives and linear scoring systems that fail to capture the vagueness and subjectivity inherent in real-world organizational assessments. To address this gap, this study proposes a novel digital maturity assessment framework based on a Fuzzy Inference System (FIS), which enables the incorporation of expert judgment and uncertainty in a structured and systematic way. To develop the model, key input dimensions of smart manufacturing ecosystem, Factory 4.0, Logistics 4.0, Operator 4.0, and Management 4.0 were identified based on an extensive literature review and expert consultation. The output variable was defined as the organization's overall digital maturity level, categorized into distinct stages ranging from Outsider to Top Leader. A comprehensive rule base was established using expert knowledge, and the fuzzy inference process was implemented through the Mamdani approach. The model was tested in a company engaged in wood-based panel production. The findings highlight uneven digital maturity across departments and confirm that high Factory 4.0 and Logistics 4.0 levels are necessary but not sufficient; further progress depends on advancing Operator 4.0 and Management 4.0 as well.