3 MIN READ Operational Analytics: Insightful Analytics To Drive Operational Performance Operational Analytics involves reporting data generated from IT Systems and Operations using powerful Big Data tools. The process typically involves the analysis of large datasets in order to leverage crucial business insights that ultimately help reduce costs and increase the revenue margins. The domain of operations research has evolved in the past five decades or so, where in many operational analytics software has been extensively used in pattern recognition to adequately address business needs and streamline IT systems and operations. AIBridgeML helps firms to maximize IT efficiency and minimize complexity and process redundancy with off-the-shelf as well as proprietary Big Data tools or methodologies. Some of the prominent methodologies include Statistical Pattern Discovery and Recognition (SPDR) and Topological Analysis (TA). The team at AIBridgeML is equipped with skilled resources to leverage some of the advanced operational analysis tools in the market. The IT and Operational Analytics revolve around the following key areas and principles: Creating and storing metrics from trending data patterns on systems behavior and performance Service Impact Analysis for critical system breakdowns or failures impacting overall architecture Proactive Analysis with forward-looking figures on critical issues that could impact IT operations Ranking of impact with action by log reports and complex operations event processing (COEP) Continuous learning based on real-time reports with a focus on improving systems methodology Linear Programming: A special class of mathematical programming, that is used to depict requirements mapped in the form of a linear relationship. This methodology is also referred to as mathematical optimization. In most complex business scenarios, linear programming offers some of the most precise solutions for lowering costs or maximizing profits well within the scope of resources and timelines at disposal. Several industries such as transport and manufacturing consistently leverage linear programming models for optimizing their workflow and production. AIBridge ML helps businesses implement the most feasible linear programming models that drive their processes. Our linear programming models help in formulating well-defined 'constraints' and 'functions', and transforming the same into the relevant solution for helping businesses deploy the most feasible solution. Optimization: Optimization models are increasingly helping improve operations in many of the organizations including service-based firms. Such models rely on real-time events combined with the probability of 'external occurrences', and the degree of impact that affects the employee on the floor. A scientific and quantitative-driven model advises on the best practices and the right actions to take in specific scenarios or skewed business conditions. An optimization model also helps in taking into account unplanned events or surprise occurrences on the floor. Related Posts 06Apr 2023 AIBridge ML organized CPR training session for all our associates 08Feb 2023 AIBridge ML attended HYSEA 30th edition annual summit & awards 2023 26Aug 2022 AIBridge ML conducted general health check-up camp Latest Blogs 22Jun 2022 What are the differences between Machine Learning and Artificial Intelligence? 21Jun 2022 Artificial Intelligence in Manufacturing Industry 11Jan 2022 Document Automation and Its Benefits
3 MIN READ Operational Analytics: Insightful Analytics To Drive Operational Performance Operational Analytics involves reporting data generated from IT Systems and Operations using powerful Big Data tools. The process typically involves the analysis of large datasets in order to leverage crucial business insights that ultimately help reduce costs and increase the revenue margins. The domain of operations research has evolved in the past five decades or so, where in many operational analytics software has been extensively used in pattern recognition to adequately address business needs and streamline IT systems and operations. AIBridgeML helps firms to maximize IT efficiency and minimize complexity and process redundancy with off-the-shelf as well as proprietary Big Data tools or methodologies. Some of the prominent methodologies include Statistical Pattern Discovery and Recognition (SPDR) and Topological Analysis (TA). The team at AIBridgeML is equipped with skilled resources to leverage some of the advanced operational analysis tools in the market. The IT and Operational Analytics revolve around the following key areas and principles: Creating and storing metrics from trending data patterns on systems behavior and performance Service Impact Analysis for critical system breakdowns or failures impacting overall architecture Proactive Analysis with forward-looking figures on critical issues that could impact IT operations Ranking of impact with action by log reports and complex operations event processing (COEP) Continuous learning based on real-time reports with a focus on improving systems methodology Linear Programming: A special class of mathematical programming, that is used to depict requirements mapped in the form of a linear relationship. This methodology is also referred to as mathematical optimization. In most complex business scenarios, linear programming offers some of the most precise solutions for lowering costs or maximizing profits well within the scope of resources and timelines at disposal. Several industries such as transport and manufacturing consistently leverage linear programming models for optimizing their workflow and production. AIBridge ML helps businesses implement the most feasible linear programming models that drive their processes. Our linear programming models help in formulating well-defined 'constraints' and 'functions', and transforming the same into the relevant solution for helping businesses deploy the most feasible solution. Optimization: Optimization models are increasingly helping improve operations in many of the organizations including service-based firms. Such models rely on real-time events combined with the probability of 'external occurrences', and the degree of impact that affects the employee on the floor. A scientific and quantitative-driven model advises on the best practices and the right actions to take in specific scenarios or skewed business conditions. An optimization model also helps in taking into account unplanned events or surprise occurrences on the floor.
