Abstract
Industrial automation and control equipment are witnessing unprecedented advancements, with a multitude of technologies now available to flow through the value chain. Competing against each other, it is increasingly challenging for decision-makers to choose the most effective solutions. With so many manufacturers now offering products, industry professionals often lack the insights required to make proper comparisons. This situation results in inefficiencies and poor investments. This challenge is particularly pronounced in areas such as power generation, manufacturing, and other industries where these analytics are used, but no standardized parameter checks were taken on analytics. This study focuses on these aspects by showcasing a thorough reflection of prominent automation and control technologies, assessing their profiling parameters, and respective brand-specific advantages. This thesis employs a “mixed-methods approach” that rests on quantitative and qualitative analysis, both based on a unique dataset synthesized from publicly available data. The research assists industry professionals, procurement experts, and decision-makers in making better selections, resulting in greater efficiency, lower costs, and improved industrial automation strategies. Keywords: Industrial Automation, Control Systems, Power Industry, Modernization, Decision-Making, Technology Selection, Adaptive Automation, Power Systems