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Optimizing Complex CPQ Software Systems for Quoting Renewable Energy Products Using AI and Machine Learning
  • Thomson Alexander
Thomson Alexander

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This research study is an exploration of the application of artificial intelligence (AI) and machine learning (ML) techniques towards optimizing configured price quote (CPQ) software systems that are tailored towards quoting renewable energy products. The complexities evident within such systems are critical in that they pose some challenges related to the accurate configuration and pricing of renewable energy solutions. Through harnessing AI and ML capability, this analysis provides an analysis of effectiveness in enhancing the efficiency, accuracy, and customization capabilities of CPQ systems for renewable energy products. Through advanced algorithms and predictive modeling, this research paper discusses ways of streamlining the quoting process, improving the quote accuracy, and expediting decision-making. This will lead to the ultimate advancement of the renewable energy sector quoting efficiency.