Predictive Analytics And Machine Learning For Real-Time Detection Of Software Defects And Agile Test Management

Abstract

Supply chain agility is crucial for organizations striving to weather today's turbulent business climate. Agility requires proactive risk minimization. This paper gives a machine learning and predictive analytics strategy for real-time risk counteraction and dexterity. Conventional inventory network risk management utilizes post-occasion examination and authentic information, restricting its ability to deal with real-time interruptions. This paper proposes a modern methodology that utilizes predictive analytics to expect interruptions. Machine learning algorithms can identify patterns, correlations, and anomalies that indicate impending threats using contextual and historical data. By combining these models into a real-time monitoring system, organizations may detect and mitigate dangers. This research uses many predictive analytics methodologies to identify supply chain hazards. Natural language processing, time series analysis, and anomaly identification are examples. Risk assessment methods are also refined using machine learning techniques to ensure accuracy and flexibility. This study combines theoretical discourse with empirical data to explain the link between predictive analytics, machine learning, and supply chain agility.