Early-stage production plant planning is characterised by evolving requirements, incomplete process definitions, and information distributed across documents, engineering models, and planning tools. Introducing artificial intelligence in this setting raises a fundamental question: what must be considered before a specific application, data model, or technical solution can be defined? Existing research typically starts from an already identified planning task, a selected artificial intelligence method, or an available technical infrastructure. It therefore provides limited guidance for structuring the initial need and determining how artificial intelligence should enter planning work. This paper develops a design space that addresses this early decision problem. Literature from factory planning, production planning, collaboration between humans and artificial intelligence, semantic technologies, and digital planning was compared to derive five dimensions: planning scope, information structure, authority to modify planning results, connection to planning tools and data, and functional contribution. Data quality, organisational roles, governance, and interaction are considered as additional conditions. Application to process failure mode and effects analysis shows how the appropriate configuration changes as preliminary process information develops into a maintained planning record. The design space helps planners, artificial-intelligence specialists, and system engineers specify implementation requirements before selecting a particular technical solution.
Materials are a critical factor in the functional, durability, and efficiency of robotic prostheses. In this study, an integrated multi-criteria decision-making approach is presented which integrates the three methods MEREC, MAIRCA and MARCOS for the identification of the most suitable material for the design of a robotic prosthetic palm. Twelve materials were considered and nine important criteria were evaluated based on mechanical properties, cost, deformation behavior, etc. MEREC was used to objectively calculate the criterion weights and the most influential factors were Price, Strength to Density Ratio and Tensile Yield Strength. The material ranking was then performed with the MAIRCA and MARCOS methods, after which the results were combined based on the Copeland voting principle. Low alloy steel proved to be the most suitable material because its properties were good in all aspects. The model was found to be robust by performing sensitivity analysis by rank reversal. The proposed method provides a robust and reliable decision-making tool based on data that will aid in selecting materials for prosthetic applications, ultimately leading to the creation of more efficient and user-friendly prosthetic devices.
This paper presents a performance analysis of the doubly fed induction generator (DFIG) connected to the grid. The performance of the DFIG system is analysed under a variable wind speed profile. The laboratory experiments are carried out using the LUCAS-Null wind power system. The wind speed variations are made by Lucas-Null Active-Servo software. The extent of its effect on the blade pitch angle and output power is studied. The same system of experimental work is modelled and simulated using MATLAB/Simulink. Furthermore, the simulation results are compared with the laboratory results. The results showed that the output power is directly proportional to the variation of wind speed profiles, when this variation exceeds the rated value the blade pitch angle will activate to stop the output power at the rate power limit.
This paper presents the application of non-contact 3D measurement in industry, with a particular focus on the nspection of an automotive cross member using the portable Creaform HandySCAN BLACK+™|Elite scanner. The paper covers the theoretical foundations and methods of non-contact measurement, a detailed description of the selected device, a comparison with alternative measurement systems, and its practical application on a real example. Special attention was given to the preparation of the object, the scanning process, and the data processing within the Creaform.OS software suite. The obtained results were compared with the CAD model of the cross member, and the generated measurement report enabled the detection of deviations and evaluation of the part’s quality. The analysis demonstrated that non-contact 3D measurement significantly accelerates quality control processes, reduces the likelihood of measurement control error, and enables the digitalization of complex geometries with a high level of accuracy.
The adoption of artificial intelligence (AI) in organizations is often fuelled by promises of improved efficiency and innovation, while its practical value and operational impact remain unclear. This paper presents a research framework for analysing the added value of generative AI (GenAI) in logistics organizations, with a focus on “difficult to automate” tasks and processes. It combines literature-based model development, semi-structured expert interviews, field studies, and user-cantered experiments to examine how GenAI affects task performance, user behaviour, and organizational structures. It integrates qualitative insights with experimental and modelling approaches to support a systematic assessment of value creation, efficiency gains, usability, user acceptance, and organizational impact. Preliminary results from expert interviews indicate that GenAI is primarily used for cognitive support enabling time savings in manual tasks, and less for process automatization. Eventually, the presented framework will provide a structured basis for the development of evidence-based roadmaps for user-centred AI adoption in logistics.
