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RT-432

Streamlining and improving the efficiency of Front End Planning

Opportunity/Problem Statement

Oftentimes, CII member organizations lack the time and resources to fully implement Front End Planning (FEP). There is an opportunity to create a more cohesive, customizable, and streamlined project planning approach. The main driver for this topic is the desire to reduce the effort and time of FEP implementation. Additionally, there is a desire to use technology, most likely Artificial Intelligence (AI) and Large Language Models, to make the FEP implementation easier. By integrating these technologies, organizations can achieve a more effective and efficient FEP process, ultimately leading to better business and project outcomes.
 

Research Questions

How can we streamline and improve the efficiency of Front End Planning?
  • What aspects of the existing FEP body of knowledge remain effective, and where are the opportunities for improved ease and simplicity in application?
  • How can we refresh the FEP Best Practice to make it more adaptable to current needs in terms of speed, cost, adaptability, and predictability?
  • Are there better approaches and tools to implement FEP fundamentals that can add efficiency to modern project planning?

Expected Outcomes

  • An FEP implementation resource that integrates new findings to replace the current FEP toolkit.
  • Standalone case studies that illustrate different use cases of the new FEP implementation resource.
  • Cases where AI can support more efficient FEP.

Notes on Scope

  • The team is encouraged to identify ways in which FEP is connected to other CII practices and tools. The idea is not to create fully integrated tools but rather to document and offer insights on the connections.
  • This project is not about creating simplified implementations of FEP. This idea focuses more on streamlining the implementation by focusing on implementation tools (FEP toolkit and the use of AI).
  • This project is not about addressing different types of projects (e.g., small projects, industry-segment specific projects)
  • This project is not about creating simplified implementations of FEP. This idea focuses more on streamlining the implementation by focusing on implementation tools (FEP toolkit and the use of AI).
  • This project may revisit the gate process and provide insights on how to improve the process.
  • RT-410 may provide some insights on current implementation of FEP and this project can leverage its findings.
  • This project may tackle the development of a new single resource to guide FEP implementation. This implementation resource may be an updated FEP Toolkit.
  • There is a desire to consider emerging technologies like AI to add value to FEP practices, making the process more efficient and effective.

Preferred Member Background

  • Expertise in Front End Planning process / implementation.

Preferred Member Background

Roster

Members

JP Bornholdt, Autodesk, Inc.

John Chen, Hatch

Yuteck Chuong, Kiewit Corporation

Margie Clow, Chevron

Felipe da Silva Costa Ramiro, PTAG, Inc.

Cathy Farina, DyCat Solutions

James Foster, Wood

Ina Greenhouse, Technip Energies

Amanda Griener, Ontario Power Generation

Nicole Johnson, McDonough Bolyard Peck, Inc.

David Koelle, Day & Zimmermann

Volodymyr Kureza, Insight-AWP Inc.

Maria Lizardo, Pathfinder, LLC

Gerald Milek, Chevron

Fellipe Morais, Verum Partners

Jason Nichols, ExxonMobil Corporation

Carter Perrier, Intel Corporation

Jimmy Richard, Shell

Erik Sella, Covestro LLC

Vijayakumar Veeramisti, Bruce Power

David Vlajic, NOVA Chemicals Corporation

Adam Wood, Wood

Faye Yang Yang, Chevron

Academic

Carlos Caldas, The University of Texas at Austin