Artificial Intelligence (AI) refers to the simulation of human intelligence in machines that are programmed to think and learn like humans. The term can also be applied to any machine that exhibits traits associated with a human mind, such as learning and problem-solving. Here are some key aspects and characteristics of AI:


  1. Learning Processes: This includes machine learning (ML), where computers can learn and adapt to new data without human intervention. Deep learning, a subset of ML, enables this learning through layers of neural networks.

  2. Reasoning and Problem-Solving: AI can process large amounts of data and make decisions based on that data. It can perform complex calculations and analyses much faster than human brains.

  3. Perception: This involves using sensors to detect or perceive a wide range of data, such as visual, auditory, thermal, or other types of inputs. Machine vision, which is the ability to see and interpret visual input, is an example of perception in AI.

  4. Language Understanding: AI can interpret and respond to human language through natural language processing (NLP). This enables AI systems to communicate with humans in a natural, human-like way.

  5. Automation: AI allows for the automation of various tasks, leading to increased efficiency, productivity, and safety in various industries. This includes the automation of mundane or dangerous tasks.

  6. Adaptability: AI systems can adapt their responses based on new data or changes in the environment, leading to more effective and optimized outcomes over time.

Types of AI Based on Technology


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  • Machine Learning: AI systems with the ability to automatically learn and improve from experience without being explicitly programmed.

  • Neural Networks: Systems that are designed to simulate the way the human brain analyses and processes information.

  • Deep Learning: A subset of machine learning involving artificial neural networks with many layers.

  • Natural Language Processing (NLP): AI that can understand, interpret, and manipulate human language.

  • Robotics: Machines able to perform a series of complex actions automatically.

  • Expert Systems: Computer systems that emulate the decision-making ability of a human expert.

Bid Evaluation: A Prerequisite for EPC Projects

architect-engineer-discussion-brainstorming-construction-concept (1)Bid evaluation typically involves two key aspects: technical bid evaluation and commercial bid evaluation.

Being one of the most critical steps, it is often complicated by various factors such as the multitude of specifications and requirements from Licensors and/or owner-operators, multiple vendor offers and close collaboration between the licensor-owner-EPC and vendors to conclude on the technical suitability.

The process is indeed meticulous, time-consuming, and prone to human errors. Here are some factors that contribute to the complexity of the technical bid evaluation process in the EPC sector:

  1. EPC projects typically have multi-layered technical requirements across various disciplines such as civil, mechanical, electrical, and instrumentation engineering. Evaluating bids involves a thorough understanding of technical specifications.
  2. The technical bid process usually includes extensive document review and comprehension which can be time-consuming and prone to human errors.
  3. Analysing and comprehending vast amounts of data increases the cognitive load on the engineers.
  4. Evaluators need to carefully weigh the potential risks and benefits of each proposal before submitting the technical acceptability of the vendor proposal.

Due diligence is essential to the success of projects. How can this be achieved?

Adopting advanced assistive tools and products based on technologies such as Artificial intelligence, large language models and machine learning can help modernize certain aspects of the evaluation process, making it more efficient without compromising accuracy.

Bid Evaluation: An AI Approach

Artificial Intelligence and large language models have emerged as a disruptive force, changing the way organizations deal with vast amounts of data.

Let us see how AI enhances accuracy, efficiency, and decision-making.

Automated Data Analysis:

AI, machine learning algorithms & advanced analytics automatically analyses bid documents & specifications (from clients), and performance data provided by vendors. It can extract relevant information, compare, and present this data in a structured and easily consumable way. This results in the following: -

Time & effort saving

Untitled (1280 x 720 px) (200 x 200 px) (2)The traditional bid evaluation process often involves manual data entry, spreadsheet management, and extensive analysis. AI automates the bid tabulation process, saving significant time and effort. As users upload enquiry documents and vendor offers, the SaaS tool extracts relevant data, compare bids based on predefined parameters, and generates comprehensive bid tabulations instantly. This automation streamlines the evaluation process, allowing engineering & procurement professionals to focus on higher-value tasks.


Accuracy & Consistency

Untitled (1280 x 720 px) (200 x 200 px) (3)Manually analyzing multiple vendors' offers can be error-prone and subjective. AI-based tools ensure accuracy and consistency by automatically generating bid tabulations based on predefined parameters. This eliminates human errors and ensures that all vendors are evaluated objectively using the same criteria. The tool's algorithmic approach ensures that the evaluation process remains consistent, fair, and transparent. 


Enhanced Vendor Comparison

Untitled (1280 x 720 px) (200 x 200 px) (4)Evaluating vendor offers manually can be challenging, especially when dealing with large quantities of data. AI-based tools enhance vendor comparison by providing a clear and structured bid tabulation that highlights key metrics and factors for evaluation. Users can easily compare vendor offers side by side, identify strengths and weaknesses, and make data-driven decisions. This comprehensive comparison enables procurement professionals to select the most suitable vendor based on their specific requirements.


Performance Prediction:

By leveraging historical performance data, the system can predict the potential performance and reliability of vendors and their offerings. It assesses factors such as vendor offer quality, vendor response time, previously supplied similar models & materials (proven track record) compliance with industry standards & project requirements (deviation lists). These predictions provide valuable insights for decision-making.

Risk and Cost Analysis:

AI-based tools can incorporate risk and cost analysis capabilities, enabling a comprehensive evaluation of not only technical aspects but also factors such as project schedule, total cost of ownership, warranty terms, and service support. This holistic assessment helps identify potential risks and cost implications associated with each vendor proposal.


Frequently Asked Questions


Is there an AI-based tool dedicated to the TBE process?

Yes, there are XAI-based tools that can assist in the bid evaluation process. The bid evaluation process for EPC projects can be enhanced by using one of Mintmesh's XAI-based platforms, RUDY.

Can AI replace procurement professionals entirely?

While AI has the potential to automate and streamline certain aspects of the procurement process, it is unlikely to replace procurement professionals entirely. Procurement involves a complex set of activities that go beyond routine tasks and require human expertise in areas such as strategic decision-making, relationship management, and negotiation. To know more about whether AI is a friend or foe, click on the link and listen from industry experts.

What are the risks and challenges of implementing AI in the EPC sector?

The implementation of Artificial Intelligence in the Engineering, Procurement, and Construction sectors brings numerous benefits, but it also comes with its share of risks and challenges such as its inability to understand complex technical descriptions and interdependencies between technical descriptions, which can lead to a less relevant outcome. Following ethical responsibilities becomes important as the EPC sector may involve sensitive data, and the decisions they make can have significant consequences.