The decision to introduce new process equipment is always a complex and risky process. An important competitive advantage in the automotive industry is the integration of new process equipment into the production system using different decision-making models composed of measurable key indicators, scientific methods and empirical values, study defines a multi-criteria model based on various scientific sources combined with extensive manufacturing experience, which is an important tool for making quality decisions when introducing new equipment or technology. The study considers five main areas of indicators and characteristics: internal, external, financial, knowledge level, and other important indicators. By applying the Delphi method, the importance of the indicators is defined by multidisciplinary experts and the order of each characteristic is determined, which is then prioritized using MCDM methods to select the final solution. The paper presents the model definition and a practical application of the model to the castings manufacturing in the automotive industry.

The ongoing digitalization of industrial companies requires a structured, strategic integration of digital concepts into business processes. Digital transformation (DT) requires clearly defined roadmaps that align digital technologies with business objectives. Although there are many digital maturity models (DMMs), most are industry-specific and do not address the unique characteristics of individual companies. Even SME-focused models often struggle to close the gap between current and target maturity levels, hindering effective DT implementation. This study examines the existing academic and professional literature on DMMs for SMEs and assesses digital readiness in an industrial context. From these findings, the Dynamic Adaptive Maturity Assessment Model (DAMA-AHP) was developed. It comprises 66 DT elements in six dimensions: People and Expertise, Operability, Organization, Products and Production Processes, Strategy, and Technology. DAMA-AHP incorporates the Analytic Hierarchy Process (AHP), which has been enhanced with customizable weighting at both the dimension and element levels. This enables precise alignment with the company’s priorities and the definition of customized target maturity levels that form the basis for a tailored transformation roadmap. Validation through a case study confirmed the practical value of DAMA-AHP in measuring digital maturity and defining strategic DT priorities. It provides a comprehensive, adaptable, and dynamic framework that promotes continuous improvement and sustainable competitiveness of SMEs in the evolving digital economy.

The transition to digital manufacturing has become more important as the quantity and quality of the use of computer systems in manufacturing companies has increased. It has become necessary to model, simulate and analyse all machines, tools, and raw materials to optimise the manufacturing process. It is even better to determine the best possible solution at the stage of defining the manufacturing process by using technologies that analyse data from simulations to calculate an optimal design before it is even built. In this paper, Design for Six Sigma (DFSS) principles are applied to analyse different scenarios using digital twin models for simulation to determine the best configuration for the manufacturing system. The simulation results were combined with multi-criteria decision-making (MCDM) methods to define a model with the best possible overall equipment effectiveness (OEE). The OEE parameter reliability was identified as the most influential factor in the final determination of the most effective and economical manufacturing process configuration.

The aim of this paper is to introduce a multi criteria performance measurement model for newbuilding dry-docking purposes as a managerial tool. The dry-docking is presented as a process with multiple inputs and outputs, containing various technical and cost data related to the dry-docking project. The data envelopment analysis models with constant and variable returns to scale were used in this research along with the efficiency decomposition method to determine possible sources of inefficiency on the data sets available. The seven criteria to be used for dry-docking performance measurement. Research results determined the best practice/efficient vessels which create the efficient frontier. Projections to the efficient frontier, efficiency decomposition and sources of inefficiency recognition with improvement propositions were provided for the inefficient vessels. Due to the performance assessment carried out, the results represent benchmarks for dry-docking performance and a guideline to future dry-docking improvements on data set researched.

The ongoing digitalization of industrial companies requires a structured, strategic in-tegration of digital concepts into business processes. Digital transformation (DT) re-quires clearly defined roadmaps that align digital technologies with business objec-tives. Although there are many digital maturity models (DMMs), most are indus-try-specific and do not address the unique characteristics of individual companies. Even SME-focused models often struggle to close the gap between current and target maturity levels, hindering effective DT implementation. This study examines the ex-isting academic and professional literature on DMMs for SMEs and assesses digital readiness in an industrial context. From these findings, the Dynamic Adaptive Ma-turity Assessment Model (DAMA-AHP) was developed. It comprises 66 DT elements in six dimensions: People and Expertise, Operability, Organization, Products and Pro-duction Processes, Strategy and Technology. DAMA-AHP incorporates the Analytic Hierarchy Process (AHP), which has been enhanced with customizable weighting at both the dimension and element levels. This enables precise alignment with the com-pany's priorities and the definition of customized target maturity levels that form the basis for a tailored transformation roadmap. Validation through a case study con-firmed the practical value of DAMA-AHP in measuring digital maturity and defining strategic DT priorities. It provides a comprehensive, adaptable and dynamic frame-work that promotes continuous improvement and sustainable competitiveness of SMEs in the evolving digital economy.

This paper presents a concept for a Learning Factory (LF) designed for interdisciplinary engineering education. Learning factories are experiential learning environments that bridge the gap between theory and practice while supporting the demands of digital transformation. The proposed LF concept was developed using an integrated approach that assessed stakeholder needs and reviewed institutional infrastructure and capacity. These inputs were triangulated into a concept consisting of five core thematic components: Lean processes as an educational anchor, Enterprise Resource Planning (ERP) systems, Internet of Things (IoT)-based integration, simulation, and physical prototyping. Validation workshops with Small- and Medium-sized Enterprise (SME) managers, academic experts, and students confirmed the perceived relevance of this structure and its potential. The resulting concept focuses on practice-orientated, team-based learning methods that are in line with the principles of Education 4.0. The design sets goals in four key dimensions: educational integration, technological readiness, industrial relevance with SME orientation and flexibility and scalability. These design principles and practical insights can be utilized for future academic implementations of learning factories.