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Bidding process : The bidding process in EPC is very elaborate and requires the evaluation and submission of numerous documents. Significant opportunities exist in this space, and many companies are working on deal assessment, comparison, and similar activities. A highly efficient technique can be developed for evaluating the bids of vendors.
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Supply chain : Purchasing from the right company at the right quality, at the optimal price, and with minimal risk is central to the EPC industry. Project costs include a significant amount of material costs. Since most EPCs have a very structured purchasing process with a wealth of past data, the supply chain could be a low-hanging fruit that offers rich ROI to the CXOs.
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Project scheduling and changes : This is an extremely challenging activity for EPC companies and depends on various controllable and uncontrollable factors. Recently, AI and other technologies have been used to model the entire work process, including the supply chain, to provide a comprehensive overview of the situation. AI can be leveraged to predict project timelines, optimize resources, predict bottlenecks, and simulate contingency scenarios based on past data. Recently, Generative AI has been used by some companies for associated process improvements.
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There is tremendous potential for applying algorithms based on past data to reduce schedules and, hence, cost escalation. There is no doubt that all the work being done in this area will provide maximum dividends to the industry.
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Engineering digitization and associated automation : A large volume of engineering design-related documentation is manually processed by EPCs at each stage. P&ID-based cost estimation, layout planning, intelligent digitization of engineering drawings, and validation and testing of initial designs are a few processes that immediately come to mind. Many repetitive processes during the design phase are very suitable for automation using AI, optimization, and other data-related technologies.
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Construction : Robots and drones can be easily deployed for various operations in the EPC industry. Many existing use cases of asset management, device operation, fleet management, and safety applications have immediate relevance in the construction of EPCs. Some implementations and adaptations have already started in the above use cases.
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Document analytics : EPCs always deal with a large number of documents such as legal contracts, specifications, design details, and SOPs. Generative AI and LLMs can significantly help EPCs by summarizing and extracting important information, correcting contradictions, and generating standard documents. The ROI for these implementations and their deployability with human-in-the-loop could be fast and effective.
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Challenges : One of the big challenges that EPCs face while deploying AI/ML technology is the sheer volume of domain knowledge in this industry. EPC companies not only deal with a huge volume of engineering knowledge but also vast lessons learned from executed projects. Implementing AI/ML or data solutions in EPC cannot be done solely by data scientists, ML, and data engineers. It requires a sound engineering background to effectively automate processes in this industry. Once the domain is understood, many existing tools and technologies can be applied with minimal or no changes.