The quality of accounting information is a critical determinant of transparency, decision-making, and sustainability in small and medium-sized enterprises (SMEs), particularly in developing economies characterised by low levels of digitalisation. This study examines the impact of Accounting Information System (AIS) characteristics on accounting information quality in SMEs operating in the Democratic Republic of Congo (DRC), a context marked by limited ICT infrastructure, weak institutional support, and low AIS adoption. Using survey data collected from 133 SMEs and applying Partial Least Squares Structural Equation Modeling (PLS-SEM), the results show that AIS structure (β = 0.377, p < 0.01), system automation (β = 0.351, p < 0.05), AIS performance (β = 0.267, p < 0.01), and continuous staff training (β = 0.160, p < 0.05) have significant positive effects on accounting information quality, explaining 58.2% of its variance (R² = 0.582). In contrast, AIS integration into organisational management processes does not exhibit a significant effect. These findings suggest that, in low-digitalisation environments, foundational system attributes and human capital development are more decisive for improving accounting information quality than advanced managerial integration. The study contributes to AIS literature by providing empirical evidence from an underexplored, institutionally constrained context and offers practical insights for SME managers and policymakers seeking to enhance financial information quality through incremental and context-sensitive digitalisation strategies.
In the context of supply chain improvement, segmenting products aligns with demand patterns and contributes to strategic decisions to support better resource allocation, allowing businesses to achieve desirable service levels. This paper provides feasible strategies that strengthen sales forecasting and service-level-based inventory management, emphasizing the ABC/XYZ segmentation to improve the flexibility and responsiveness of the supply chain process in dynamic market environments. This work applies the ABC/XYZ classification to 540 finished goods, revealing that 59% fall into category C, with cluster CZ2 (27%) and CZ1 (25%) predominating. Category B accounts for 23% of items, while category A represents 18%. Based on this segmentation, service-level targets were defined at 95%, 90%, 80%, and 60%, depending on product criticality and demand variability. The results highlight that sporadic demand clusters (CZ1 and CZ2) require demand-driven replenishment, while high-value stable-demand items (AX) benefit from high service levels and statistical forecasting. These findings demonstrate potential improvements in forecast accuracy, inventory responsiveness, and cost-service trade-offs, offering a structured pathway for managers to balance service levels with inventory efficiency. Overall, the segmentation results provide actionable insights for reducing inefficiencies and improving cost-to-serve in dynamic supply chain environments.
Machining performance is strongly influenced by the selection of cutting parameters, as cutting speed, feed rate, and depth of cut directly affect both surface quality and energy consumption during material removal. In practical manufacturing environments, improving surface finish often requires operating conditions that increase power demand, while efforts to reduce energy usage may compromise surface integrity. This inherent conflict transforms machining parameter selection into a multi-objective decision problem rather than a single-criterion optimization task. To systematically investigate this tradeoff, the present study develops a simulation-based multi-objective optimization framework that simultaneously minimizes surface roughness and cutting power. Literature-validated empirical models are employed to represent the influence of cutting parameters on process responses, enabling structured performance evaluation without the need for additional experimental trials. The Non-dominated Sorting Genetic Algorithm II (NSGA-II) is implemented to generate Pareto-optimal machining conditions that capture the compromise between surface quality and energy efficiency. The obtained Pareto front clearly illustrates the opposing behavior of the two objectives: improved surface finish is generally associated with higher power consumption, whereas reduced energy demand corresponds to moderately increased surface roughness. By explicitly visualizing and analysing these trade-offs, the proposed framework functions as a decision-support tool, allowing manufacturing engineers to select machining parameters based on specific production priorities. The methodology provides a transparent and adaptable approach for balancing quality and energy considerations in machining operations.
Shared Manufacturing (SharedMfg) leverages the sharing economy principles to optimize resource utilization, reduce costs and drive innovation. Despite its potential benefits, the SharedMfg ecosystem faces challenges such as operational complexity, high-degree dynamics, uncertainty, and intellectual property concerns. This paper explores these inherent challenges within the shared manufacturing ecosystem and proposes solutions like standardization, predictive analytics, agile supply chains, and robust cybersecurity measures to address these challenges. The paper highlights the potential role of transparency, technology integration, and blockchain implementation in addressing risks associated with shared manufacturing. By addressing these difficulties, the study aims to promote collaboration, enhance efficiency, and support sustainability within the SharedMfg ecosystem. Ultimately, this research aims to contribute to shared manufacturing practices by offering a framework for addressing these challenges, potentially positioning SharedMfg as an important manufacturing approach.
Three-dimensional reconstruction of anatomical models from clinical data faces challenges in maintaining geometric fidelity during the conversion of surface models to parametric solids. Mesh processing techniques, such as smoothing, remeshing, and polygon reduction, aim to optimize mesh quality but can lead to loss of geometric information. A methodology was developed to process 3D surface meshes and convert them into solids from both faceted and organic geometries. The impact of different processing parameters on mesh topology and post-conversion geometry was evaluated using the Taguchi methodology with an L16 orthogonal design, analyzing three factors: smooth factor, remesh, and polygon reduction percentage. The smooth factor had the most significant influence (p < 0.01), followed by remesh (p < 0.05), while polygon reduction was not statistically significant (p > 0.05). Conversion techniques produced significant differences (p = 0.0012), with faceted geometries yielding results closer to the target. Geometric deviation analysis using bidirectional Hausdorff distance demonstrated sub-millimetric discrepancies in all cases. Faceted models showed RMS values between 0.067 and 0.183 mm while organic models ranged from 0.076 to 0.225 mm with minimal deviation. Volumetric analysis revealed moderate reductions averaging 4.53 ± 2.88% for faceted and 4.98 ± 3.07% for organic geometries without volumetric expansion. Despite slightly higher sensitivity in organic models, deviations remained within acceptable tolerances for anatomical reconstruction and computational biomechanics, indicating preserved morphological fidelity after processing
