Risk assessment is a critical component of the Software Development Lifecycle (SDLC) to ensure timely delivery, maintain quality, and reduce project failures. Traditional risk assessment approaches rely heavily on expert judgment and manual analysis, which can be subjective and prone to errors. This paper proposes a Machine Learning (ML)-based risk assessment framework for SDLC, leveraging historical project data and software metrics to predict potential risks at different stages of development. Several ML algorithms, including Random Forest, Support Vector Machine, and Neural Networks, are evaluated for their effectiveness in identifying high-risk components. Experimental results demonstrate that the proposed approach can enhance risk prediction accuracy, support proactive mitigation strategies, and improve overall software project success rates. The framework provides a scalable and data-driven solution for early risk detection in modern software engineering practices.
Arvind S. Menon, K. Raman· International Journal of Mac...· 0 citations
Lean Manufacturing is an important production philosophy that focuses on improving efficiency, reducing waste, and maximizing customer value. While it has been widely applied in large-scale industries, its implementation in small-scale industries (SSIs) presents unique challenges due to limited resources, low automation, skill gaps, and resistance to change. Despite these constraints, SSIs play a crucial role in economic development, employment generation, and supply-chain support. This study examines the implementation of lean manufacturing practices in small-scale industries and evaluates their impact on productivity, quality, lead time, and operational efficiency. Key lean tools such as Value Stream Mapping (VSM), 5S, Kaizen, Just-in-Time (JIT), and Total Productive Maintenance (TPM) are analyzed using empirical data from selected manufacturing firms. The research proposes a structured implementation methodology that includes organizational readiness assessment, waste identification, tool selection, pilot implementation, and performance monitoring. Quantitative analysis comparing pre-implementation and post-implementation performance indicates improvements in production efficiency, reduction in defects, better inventory turnover, and shorter lead times. The findings show that even partial adoption of lean practices can significantly improve performance in small-scale industries when supported by strong management commitment and employee participation. The study provides a practical and scalable lean implementation model for SSIs and demonstrates that lean manufacturing can be successfully applied even in resource-constrained environments.
Arvind Menon, K. Raman· International Journal of Mod...· 0 citations
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