Current Projects

Scope:

The majority of the investment made by an owner-operator during the delivery of a capital project is allocated across a complex network of product suppliers and service providers. Currently, it is often the case that owner-operators and their EPC’s look to minimize the risk of schedule delays due to late materials and parts delivery by mandating that parts and materials be delivered far in advance of when they are needed. This is in contrast to many other industries, including automotive, retail and technology, where the timing of orders and deliveries is more closely coordinated with actual needs in order to minimize system costs and better optimize overall system performance. We hypothesize that the existence of large amounts of inventory long before it is needed on capital project sites is a symptom of a sub-optimal supply chain operation.

Based on our interviews reported in [1], it seems that many of the participants in capital project supply chains believe that the supply is operating close to optimally. We hypothesize that this mindset is a consequence of, among other things, local or greedy optimization. Since there is no obvious way that small, local changes can lead to improvements, participants believe that the current approach is optimal.

Our initial conclusions from the interview responses suggest that a logical next step is to show how to build a comprehensive optimization model to coordinate project planning and scheduling with other supply chain-related decisions in order to globally optimize the supply network in terms of project delivery schedule, cost, and risk management.  A comprehensive model-based analysis could help to highlight the inefficiencies of current approaches.

Our ultimate goal is to build a comprehensive optimization/simulation model that will enable us to coordinate project planning and scheduling with other supply chain-related decisions in order to globally optimize the supply chain, in terms of project delivery time, cost, safety, and risk management. The first step in this process involves focusing on a particular specific major capital project,  conducting follow-up interviews with participants in that project supply chain to assess the current state of performance, obtaining data on parameters such as lead times, cycle times and processing times, and the throughput of individual pieces of the overall supply flow.   Using this data, we propose to build a model of the supply chain, and conduct a number of simulations to address the questions raised in this proposal.   The proposed outcome would be threefold:

• Publication of the findings – both of the current state, and recommendations from the modeling on optimizing performance – in a peer-reviewed academic journal.
• An assessment of current tools and approaches for completing this analysis, and initial development of new tools and approaches as appropriate.
• A detailed proposal for developing additional tools and techniques to facilitate future capital project supply chain optimization

Researchers:

Arman Jabbari and Dr. Philip Kaminsky, UC Berkeley, Department of Industrial Engineering and Operations Research.

[1] “Research Digest: Preliminary Investigations into Capital Projects Supply Chain Management”, Arman Jabbari and Philip Kaminsky, Journal of Project Production Management, Winter 2016.

Scope:

Optimizing project outcomes requires that current conceptual thinking and frameworks associated with project delivery be understood. This research proposes that delivery of projects can be best understood through three primary historic eras:

Era 1 – Productivity
Era 2 – Predictability
Era 3 – Profitability

These eras, which directly correlate to the development of modern operations management thinking, have had significant influence on how projects are delivered today, and form the basis of current trends in thinking about how to improve performance. Once this research is concluded, PPI envisions the development of a maturity model, which can be used to understand current and future state of a project delivery approach.

Scope:

The majority of investment made by an owner operator during the delivery of a capital project is allocated across a complex network of product suppliers and service providers. In the past, owner operators and their EPC’s have looked to minimize the risk of schedule delay resulting from not having the material and parts needed to ensure an effective flow of installation onsite. At the same time, other industries including automotive, retail, and technology have developed new  thought processes and methods to manage supply to the final assembly / installation point. To better understand the potential to improve supply performance for MCP’s, including the application of alternative models, PPI is funding research led by U.C. Berkeley’s Operations Research Department.

Partner:

U.C. Berkeley

Researcher:

Philip Kaminsky, PhD – Department Chair of Industrial Engineering and Operations Research, U.C. Berkeley

Scope:

The ability to effectively model and control well delivery project throughput, work-in-process and cycle-time is fundamental to controlling oil and gas production time and cost. This research project explores how discrete event simulation models can support modeling and optimizing system throughput, work-in-process and cycle-time. PPI is collaborating with CIFE researchers to develop and apply computational methods to rapidly search through a range of product production options defined by the project team. The goal of these methods is to help stakeholders understand performance trade-offs between production options and to find solutions that best meet project objectives and constraints, including increasing throughput and better control of time and cost. The project scope is defined in two parts. First, the product production optimization problem will be formalized in terms of inputs (variables) and outputs (objectives and constraints). Next, the analytical model and information requirements will be developed and tested using existing project data. The researchers will benchmark the performance of existing optimization algorithms to this new problem domain.

Partner:

Center for Integrated Facility Engineering (CIFE) Stanford University

Researchers:

Forest Flager, PhD, Engineering Research Associate, Stanford University
Martin Fischer, Professor of Civil and Environmental Engineering, Stanford University.

Scope:

Project cost and schedule overruns in the energy and industrial sectors have reached crisis levels. These cost and schedule overruns are impacting shareholder value and the ability for energy and industrial companies to deliver and maintain their assets. Work Packaging is one area that is gaining much interest among owners, operators and EPC firms. Recently, CII and COAA released Report 272-2 Advanced Work Packaging. Building on and expanding this recent work, this PPI research project explores how the application of operations management theory and practice can optimize the execution of work packages and in so doing support better control and predictability of project cost and duration.

Partner:

Project Production System Laboratory (P2SL) University of California Berkeley

Researchers:

Glenn Ballard, MBA, PhD Research Director P2SL, University of California Berkeley
Iris Tommelein, Professor of Engineering and Project Management, University of California Berkeley

Scope:

As the “Project as Production System” framework continues to gain interest it exposes the need for an effective framework for modeling and optimizing project production systems. This research project sets forth how specific mathematical equations can be used to model and optimize project production system behavior and performance including trade-offs between cycle-time, capacity utilization, and other variables.

Researcher:

H.J. James Choo PhD, Strategic Project Solutions, Inc.

Scope:

This research is funded and supervised by the Center for Integrated Facility Engineering at Stanford University. PPI is working closely with the research team and is providing access to project data.This research consists of two phases.The first phase consists of project case studies involving managers, subcontractors and workers. The purpose of the case studies is to understand the sources of uncertainty and variability that affect construction chains of activities. In parallel, the researchers will analyze existing project data (e.g. Percent Plan Complete) that captures specific occurrences of variability, such as delays, to compute the probability of occurrence of specific sources of variation. The second phase of the research, focuses on building a simulation model of a sequence of construction activities, incorporating the data and insights gained in the first part of the research. The simulation model and results will be evaluated and validated by a panel of industry experts.

Partner:

Center for Integrated Facility Engineering (CIFE) Stanford University

Researchers:

Nelly Garcia, PhD Candidate, Department of Civil and Environmental Engineering, Stanford University
Martin Fischer, Professor of Civil and Environmental Engineering, Stanford University
Forest Flager, PhD, Engineering Research Associate, Stanford University