Project Management Institute

Using PRISM model to improve project profitability

Dr. Gloria J. Auchey, PMP, President, The Success Institute of America, Inc., USA


This paper describes a process for improving project profitability by using the Project Risk Identification, Selection, and Management model (PRISM). The PRISM model has been refined over the past four years by working with several companies who have developed their own customized versions of the model. It was designed to help companies develop a cost effective go-no-go decision process using projects where there was historical data available for the risk analysis. Therefore, the traditional tools of risk management, such as Expected Monetary Value, Utility Theory and Pair-Wise Comparisons, were used in the development. The use of the PRISM model begins with the company creating a custom set of critical project selection questions, relative to potential project risk and opportunity. This data is then quantified using the probability of occurrence and the potential impact of the risk event. The resulting output is then plotted on a profit margin index to determine potential project profitability of one project relative to other projects being considered. This comparison prioritizes projects and provides critical information for company decision-makers to make go/no-go decisions prior to committing significant resources to create a full-scale estimate and bid on the project with the highest profit potential. In so doing, PRISM has proven to be an improved method for both qualifying and quantifying the project risk and opportunity information on one project as compared to those of another project. In addition, the company uses the output from the qualitative questions to form the basis for creating an effective prioritized list of risks and opportunities to be mitigated and managed before the contract is signed as well as after the project is underway. In this way, the model serves as a template to store quantitative and qualitative data about each project, creating an historical database to reduce the number of known-unknowns for future projects. Thus, by using PRISM to reduce overall risk and enhancing project opportunities, companies can increase profitability on present and future projects. Future models, PRISM ll and PRISM lll, will be used in conjunction with PRISM l; however, their focus will be on a) projects with significantly fewer knowns, b) projects with more known-unknowns, or c) unique projects with the potential for a high percentage of unknown-unknown risks. PRISM ll and lll components will incorporate more sophisticated risk management tools to continue the refinement of this project procurement process.

Keywords: Procurement Management, Capturing Lessons Learned, Innovation in Project Selection Methods, Risk Management, Improving Project Profitability, Assessing Project Risk and Opportunity


Too many companies are still going out of business within five years of start-up. According to Dun and Bradstreet, since 1997 approximately 56% of the companies, which generate business by responding to RFPs, have gone out of business within 5 years of start-up. There are various reasons for their demise; however, negative cash flow remains one of the prominent reasons cited. Companies are going after too much work that is either not a good match for their asset base or beyond their geographical or technical ability to control properly. (Engineering, 1997) Research indicates that the cost of resources to prepare a complete hard-bid proposal for a project runs between ¼ and ½ percent of the cost of the project. (Bajaj, Lenard & Oluwoye, 1997; De la Garza & Khalil, 1995; Duffield, Fayek & Young, 1999) Therefore, it is extremely important for companies to make quick, accurate decisions regarding which projects to go after before committing the crucial resources to obtain the contract.

Most companies encounter extremely high risk due to such factors as the uniqueness of the project and the exposure to external elements. (Kim, 2000) The majority of companies prepare cost estimates based on a traditional single figure cost estimation approach. (Leung, Mok & Tummala, 1996) Although the estimators often consider risks, those risks are rarely reflected in the final estimate in a formal, systematic way. (Dexter, 1999; Diekman, Pecosk & Songer, 1997) The most common method for protecting the company from potential risk is to add a contingency sum to the estimate. However, this approach has a number of weaknesses, including:

  • The contingency figure is usually arbitrarily arrived at and may not be appropriate for the proposed project;
  • Estimators have a tendency to double count risks;
  • A percentage addition still results in a single-figure prediction of estimated final cost, implying a degree of certainty that may not be justified by the data available;
  • This method usually assesses risk only as a negative and doesn't consider any positive potential, i.e. opportunity.

As a result, the traditional single-figure approach can be inadequate, misleading and cost inefficient. (Ranasinghe, 1997) Thus, industry requires not only a different approach to managing the risks associated with project procurement but also a tool which organizes and quantifies project selection information and stores valuable project procurement knowledge appropriately as lessons learned for future decision makers.

The PRISM Model (Exhibit 1) has been designed to approach managing procurement risks differently, more innovatively, in the rapidly changing business environment. It also provides tools to organize and quantify project selection in a format that is easily stored and retrieved. The model itself will eventually incorporate three components (PRISM l, ll, lll), which, when combined, will better predict as well as improve the potential for acquiring projects with higher profit margins. (Auchey & Auchey, 2002)


Exhibit 1: The PRISM MODEL

By developing a specific list of project selection questions, the model overcomes one of the greatest difficulties associated with project selection models, the customization to specific company operations, which, in turn, will provide a better match for company inherent abilities and assets. Further, the model provides a systematic approach in the identification and assessment of potential positive (i.e. opportunities) as well as negative risks. This, in turn, provides management with a tool to help project managers choose appropriate courses of action in managing project selection and risk. In this way, critical knowledge can be stored in a format that is easy to access, analyze and use in future project selection. That is, the PRISM Model provides an innovative approach to project risk analysis and procurement in that it marries tried and tested risk management approaches with a specific tool to give managers an at-a-glance quantitative assessment of the project as compared to other possible projects. In addition, the tool is robust enough to be used as a starting point tool for the development of customized templates for other companies. In this way, the PRISM provides the basic structure for industry-wide use in project selection and risk management.

Methodology: The PRISM Model

The PRISM Model was developed using the classic risk management process, which includes identification, quantification, response and control (Kerzner, 1998) The identification of potential risks for any project constitutes the most difficult part of this process. For purposes of this paper, negative risk will be called ‘Risk’ and positive risk will be called ‘Opportunity’. Although the PRISM l Model uses all four steps of the risk management process, this paper focuses on the tools and techniques developed for Step One (Identification) and Step Two (Analysis and Prioritization), with only brief references to the final two steps (Response and Control).

Step One: Identification

Risk is defined primarily as the potential for monetary loss (Risk) or gain (Opportunity) associated with the project. Risk analysis uses a set of techniques and tools to identify, prioritize and investigate these risks and opportunities in order to assess and quantify their cost and time impact on the project. As it relates to the PRISM Model, risk identification is heavily dependent upon the experience and perception of project managers and a quality historical database. Unfortunately, companies rarely develop and maintain good historical databases regarding project risk identification. (Leung, et al, 1996) Further, qualitative risk assessment that includes both risk and opportunity variables are often not used to develop project plans and customer proposals. (Ranasinghe, 1997; Baker, Ponniah & Smith, 1999) The PRISM Model not only assesses risk as well as opportunity it also provides a transition to valid quantitative output of qualitative issues.

The first step in risk identification requires that users of the PRISM Model develop and then qualitatively answer a series of detailed questions, approximately 40, that will identify the significant risks (20) as well as the opportunities (20) related to a potential project. One of the most difficult tasks in this process is to determine which questions should be asked. Therefore, questions are developed and reviewed by as many of the stakeholders as possible, which helps to ensure comprehensiveness as well as applicability to the company. Based on a review of stakeholder feedback, questions are revised to so that they are specific enough to reflect a single project but general enough to apply to several projects. Also, a wide range of questions can provide the basis for analyzing groups of projects, past and present, as a portfolio. Table 1 presents a sample list of questions organized by risks and opportunities:

Questions from Master List

Table 1: Questions from Master List

After several reviews and customizations to the lists of questions by various stakeholders and clients, a finalized list of questions is compiled, organized by risks and opportunities. This part of the process is similar to the method many industries, especially information technology and communications, have already adopted using risk evaluation models. Most existing models ask a series of questions that evaluate risk and a separate set of questions for opportunity. (Aspinwall, Bennett, Bohoris & Hall, 1990)

Step Two: Analysis and Prioritization

One of the greatest challenges associated with the risk quantification process is to eliminate bias when assigning the value, i.e. each person assigning a value to the question will be subject to individual prejudices and utility values. Therefore, the quantification process relies heavily on Utility Theory (Render, Stair, 2000; Bernstein, 1998; Dawood, 1997) Utility Theory allows decision-makers to incorporate their risk preference and other factors into the decision-making process. In order to measure the utility of outcomes, they are assigned a value of utility: the worst a value of zero, the best a value of one. The utility assessment is completely subjective in that the value set is not usually measured on an objective scale and it can be based on the personal preferences of the decision-maker

The next step is to begin to quantify the impact of these risks on the project. The decision maker uses utility value to convert a qualitative question of impact into a quantitative input. The following table (2) provides a sample of this part of the process:

Based upon our current or prior relationship with this customer, what is the probability that we will experience cost, schedule, or performance problems?


If problems do occur as a result of our current or prior relationship with this customer, on a scale of 1 to 10, what could be the extent of the impact on the project's success?

0 1 2 3 4 5 6 7 8 9 10

Table 2: Analysis and Prioritization

After this conversion is completed, the next step is to plot the total scores for opportunity and risk on the matrix provided within the model, with the opportunity score placed on the y-axis and the risk score on the x-axis. When using the PRISM Model, the project manager places the opportunity score on the right vertical axis and the risk score on left vertical axis. The intersection of the scores on the center profitability index determines the final profit value of the risk/opportunity assessment for a specific project. The location of this score on the index helps determine the quality of an opportunity and serves as an indicator of the level of risk to be managed for overall project success. Exhibit 2 presents a sample of this process.

Profitability Predictor Index

Exhibit 2: Profitability Predictor Index

The determination of the correct profitability predictor index constitutes the most difficult part of the development of the model for a specific company. Fine-tuning of the model must continue with each new project because the model actually helps to identify risk and opportunity events that were not predicted to be significant originally by the developers of the model. For example, the original PRISM Model predicted the correct potential profitability index for seventeen out of twenty sample projects input to the model. The predicted profit margins fell within ½ percent of actuals on the profitability predictor index scale. When analyzing the three projects where the actual profit fell outside of the accepted variance range, it was discovered that two of the projects contained an exceptional amount of earthwork. The research partner-company was exceptionally good at earthwork and there was a great deal more profit (opportunity) by taking on projects requiring earthwork. Thus, the customized model for this company needed to include two new questions that related to the relative amount of earthwork needed to be included in any project being considered for bid. After these questions were incorporated, the prediction of profitability fell to within ½ percent of the actual. The adjustment to the model for the third project that fell outside allowable variance was influenced by a business plan constraint and will be discussed later.

The final step associated with the quantification step is to create a prioritized list of risks and opportunities associated with the project. A spreadsheet program, such as Excel, can be used to rank the relative importance of each risk area. Table 3 presents a sample output from the data collected during the research.

Weighted Listing of Risks

Table 3: Weighted Listing of Risks

A simple descending order ranking can then create a prioritized list and provide an ideal foundation for developing risk mitigation strategies for each risk as part of your overall risk management plan. The company can now develop an effective risk response to each identified risk and opportunity on a cost/time effective prioritized basis. Many of these identified as critical risks need to be addressed in the contract itself and the use of this model provides the opportunity for mitigation early in the project using the actual risk/opportunity events in the contract itself.

On a final note regarding risk quantification, project estimates are often based not only on the knowledge of the estimators and schedulers as well as on the experience and data for similar projects completed previously, but also on a large number of assumptions made regarding productivity rates and materials prices. Some components of the projects are prone to variation, such as material prices. Other items such as labor productivity rates can be sensitive to many factors, including weather, temperature, state of the economy, union involvement, and project duration and cost. This model is intended to provide a logical mechanism for predicting the extent of these variations and forecasting their impact on the project. Further, the PRISM ll and lll models will continue to refine the impact of these known-unknowns through the use of more theoretical and empirical based risk tools. (Auchey & Auchey, 2002) The principal use of the output from the model is not to discourage a company from pursuing projects with high risk but to identify risk and encourage risk management in a more cost effective and timely manner. Rather, this model has been designed to express the value in dollars as well as to utilize a different approach to risk quantification using a combination of Utility Theory, Expected Monetary Value and Risk/Opportunity Assessment Models. To date the PRISM is the first published attempt at producing a risk management tool that expresses potential project profitability in monetary terms.

Step Three: Response

These responses can take the form of avoidance, transference, acceptance, or mitigation. In many situations, avoidance can be used if there is a range of risk alternatives, with a lower risk option available to the decision maker. In this way the decision maker avoids the ‘worse case scenario’. Transference, also known as deflection, shifts the risk responsibility or consequence to a third party. Acceptance, also known as retention, acknowledges the risk(s) and accepts the consequences. Mitigation, the most common of all risk handling strategies, involves taking specific courses of action to reduce the probability and/or reduce the impact of risks. With opportunity, the response strategies would increase the probability of the event occurring and improve the impact. Developing risk responses, which incorporate creativity and problem-solving, will help the project manager and his/her team control the risks as they occur in the execution of the project.

Step 4: Control and Documentation

As risks occur during the execution of the project, the strategies suggested in Step 3 are implemented. The results are then evaluated and documented for future projects. Experience has shown that the best way to prepare future risk management plans is to access and apply the historical data from past projects, the lessons-learned database. This is one of the important benefits of using the PRISM Model: the database generated is primarily graphic in format, which facilitates rapid understanding and, with additional information such as documentation of ‘assumptions’ and ‘constraints’, can become a useful lessons-learned database for future projects.

Summary: The PRISM l Model

Information from twenty (20) past projects has been used to date to provide input to this phase of the model. The research conducted using the model indicated that as more good historical data from past projects was generated using the process and templates, the accuracy of the model as a predictor of project profitability improved significantly. The participating companies recognized the value of the model as a powerful means of improving project procurement.

To summarize the results to date, the PRISM 1 Model has proven thus far to be a successful tool to assist management to:

  • Make high level go/no decisions on bidding new projects
  • Identify risks and opportunities to be addressed before going to contract and after
  • Quantify and prioritize risks and opportunities
  • Allocate risks to the party best able to handle the risk
  • Manage those risks which cannot be transferred
  • Document risk/opportunity events from past projects
  • Reduce the cost of resolving disputes
  • Place a limit on a firm's financial exposure in the event of a claim
  • Archive decision making wisdom of top management for use by future generations (Management Transition)

As modifications to the model are applied to future projects, time and experience will further validate the accuracy of the predictions of the model. In any event, the risk management process has been improved through the use of the model as a tool to help create prioritized lists of risks and opportunities to be mitigated. Rarely is there sufficient time and resources available to address all risks and opportunities; therefore, the process should be extremely helpful to management in prioritizing their risk management mitigation and control efforts. At least project managers and estimators will have invested their management time and resources on the right projects and in the risk areas that have the greatest return on investment. As Pareto stipulated, 80% of the impact on an endeavor would be realized as a result of only 20% of the possible influences. (Quality) Therefore, if we can identify and mitigate the 20% most influential risk/opportunity events, we will have gone a long way toward influencing the ultimate success of the project.


Aspinwall, E. M., Bennett J C., Bohoris G A., and Hall R C..(1996) Risk analysis techniques and their application to software development. European Journal of Operational Research,95 (3), 467-475.

Auchey, F. L. (1989) Survey: Variables influencing bid strategies. Independent surveys conducted with over 600 Associated General Contractor (AGC) members in Florida, North Carolina, South Carolina and Virginia from 1986 through 1989.

Auchey, F. L. & Auchey, G. (2002) Project selection using the PRISM model. JDS 11 (3-4), 176-194

Bajaj, D., Lenard D., & Oluwoye J. (1997) An analysis of contractors'approaches to risk identification in New South Wales, Australia. Construction Management and Economics, 15 (4), 363-369.

Baker, S, Ponniah, D. & Smith, S. (1999) Risk response techniques employed currently for major projects. Construction Management and Economics 17 (2), 205-213.

Bernstein, P L. (1998) Against The Odds: The Remarkable Story of Risk. New York: John Wiley & Sons, Inc.

Dawood, N. (1997) Estimating Project and activity duration: A risk management approach using network analysis. Division of Civil Engineering and Building, School of Science/Technology, The University of Teesside.

De la Garza, J M. & Rouhana K. G.. (1995) Neural networks versus parameter-based applications in cost estimating. Cost Engineering, 37 (2), 14-18.

Dexter, W. F. (1999) Risk management: A mine field. Consulting-Specifying Engineer, May. Retrieved from

Diekman, J, Pecosk, R S., & Songer, A. D.. (1997) Risk analysis for revenue dependent infrastructure projects. Journal of Faculty of Civil, Environmental, and Architectural Engineering, University of Colorado. January, 377-382.

Duffield, C F., Fayek, A. & Young, D. M.. (1999) A survey of tendering practices in the australian construction industry. Engineering Management Journal, December, 29-34.

Engineering News Record, (1997) 238 (14), 9.

Kerzner, H. (1998) Project Management. New York: John Wiley & Sons, Inc.

Kim, S. (2000) Risk management in construction: An approach for contractors in South Korea. Cost Engineering, 42,(1) 38-44.

Leung, H.M., Mokm C.K., & Tummala, V.M. Rao. (1996) Practices, barriers, and benefits of risk management process in building services cost estimation. Journal of Department of Manufacturing Engineering, City University of Hong Kong, April, 161-175.

Mawdesley, M. J. and Bennett, LD. (2000, November) Investigating a genetic algorithms based decision support system for the location of new major housing allocations within the local plan process. Proceedings Second International Conference on Decision-making, Lyon, France. 897-908.

Quality for Project Managers. (1998) ESI International. 2.25.

Ranasinghe, M. (1997) Risk management in the insurance industry: Insights for the engineering construction industry. Journal of School of Building and Real Estate, National University of Singapore. February.

Render, B and Stair, R. M. Jr. (2000) Quantitative analysis for management. (7th Ed), Prentice Hall.

This material has been reproduced with the permission of the copyright owner. Unauthorized reproduction of this material is strictly prohibited. For permission to reproduce this material, please contact PMI or any listed author.



Related Content

  • PM Network

    Playing with Fire

    By Jones, Tegan With the coastline of an entire continent burning, a scorched-earth urgency had teams across Australia racing to control the damage. Between September 2019 and January 2020, bushfires ravaged…

  • PM Network

    Trees of Life

    By Hendershot, Steve The world needs more trees—and a lot of them—to stem the damage wrought by mass deforestation. Brazil alone is destroying the equivalent of three football pitches per minute in the Amazon rainforest…

  • PM Network

    Rising Risks

    By Nilsson, Ryan For as long as humans have been building cities, they have migrated toward the coasts -- for food, ease of transportation and any number of ecological benefits. Today, it's estimated that more than…

  • PM Network

    From the Rubble

    By Thomas, Jennifer Puerto Rico's infrastructure woes began long ago. But a series of earthquakes this year coupled with hurricanes Irma and Maria in 2017—which racked upUS$139 billion in damage—exacerbated the U.S.…

  • PM Network

    Protection Clause

    By Parsi, Novid As harbors of sensitive client information, law firms are ripe targets for hackers. According to PwC's 2019 global survey, 100 percent of the top-10 surveyed law firms experienced a cybersecurity…