Algorithmic management systems increasingly coordinate work, allocate resources, and support decisions in corporate, public sector, and research environments. Yet many such systems remain opaque: they optimize and score effectively but struggle to communicate rationales that are contextual, auditable, and defensible under emerging governance expectations. Large language models (LLMs) can help bridge this gap by translating quantitative signals into human-readable explanations and enabling interactive clarification. However, LLM integration also introduces new risks—hallucinated rationales, bias amplification, prompt-based security failures, and automation dependence—that must be governed rather than merely engineered. This article proposes a governance-oriented architecture for LLM-augmented algorithmic management. The model combines the following elements: an algorithmic decision core; an LLM-based cognitive interface for explanation and dialogue, and a verification and governance layer that enforces policy constraints, provenance, audit trails, and human-in-command oversight. The framework is developed through targeted conceptual synthesis and normative alignment with key governance instruments (e.g., the EU AI Act, GDPR, and ISO/IEC 42001). It is illustrated through cross-domain scenarios and complemented by a demonstrative synthetic-trace simulation that highlights transparency–latency trade-offs under verification controls. Using the demonstrative simulation (n = 120 decision events), the framework illustrates a mean baseline latency of 100.3 ms and a mean LLM-augmented latency of 115.8 ms (≈15.5% increase), a mean explanation validity proxy of 85.6%, and a simulated constraint-satisfaction rate of 94.2% (113/120 events), with failed cases routed to review. These values are presented as design-level indicators of operational plausibility and governance trade-offs, not empirical performance benchmarks or state-of-the-art comparisons. The paper contributes a conceptual and governance-oriented architectural blueprint for integrating generative AI into organisational decision systems without sacrificing accountability, compliance, or operational reliability.

The presented study is related to a certain problematic area in engineering analyses when using virtual prototypes—the representation of a subject with a large variation in geometry size. Especially, this is related to the possibility of including material imperfections-porosity—when a mechanical design is to be assessed—and this is the research subject. A methodology is developed, based on a preliminary solved set of simple models that allows including the influence of porosity on the elastic behavior of aluminum alloys. These simple models are created according to the requirements of a certain industrial standard, but apply to other materials and requirements as well, targeting to determine the impact of porosity changes on mechanical properties. The developed methodology is demonstrated through an example from an industrial project, where the lowest possible cost is sought while meeting a certain strength requirement. The obtained solution is based on both engineering (structural stiffness) and financial (production cost) assessments, yielding a better solution and motivating the production of the component. Future research activities are marked as related to the upgrade of the available information on the influence of porosity on material properties.

The presented study is dedicated to a “green” solution for hot beverage packaging—a biodegradable single-use cup. Product development follows a specifically elaborated methodology that involves initial material characterization, followed by adjusted product design. The research and design activities continue toward mold design development and testing. The study concludes with a cost analysis, in which an alternative material composition is also examined. The finalized product is ready for industrialization, reaching TRL 5, with assessed financial aspects. This study is a good demonstration of the application of modern technologies and tools for the development of an innovative product.

The study aims to demonstrate the role and significance of product evaluation at the concept stage of its development. It is particularly dedicated to a contemporary product—a hydropower generation system—that is directly related to green renewable energy sources. The assessment of each of the six developed design variants combines the output of the virtual prototyping system (generated power) and an analysis of capital expenditure, both used to assess the return on investment period. The project’s financial aspects highly influence final design variant selection, and this is a good example of the possibility of including financial specifics at the early project stage.