Chapter 21: Analysis of External and Internal Risks in Project Early Phase

Anne Marie Alquier, Ph.D.Universit des Sciences Sociales de Toulouse
Enrico Cagno, Ph.D.Politecnico di Milano
Franco Caron, Politecnico di Milano
V. Leopoulos, NTUA
M. A. Ridao, University of Seville

Introduction

During the early "conceptual" phase of a project life cycle—considering for instance a competitive bidding process when a request for bidding has been received by an engineering and contracting company and the decision to bid has been made—the main objective of the proposal manager is to achieve an effective trade-off between the bid competitive value on the side of the client expectations and the project baseline in terms of time/cost/performance constraints on the side of the utilization of the internal resources. Since the project's final performance depends primarily on project risk analysis and management, a "risk-driven approach" to project management appears to be necessary, particularly during the early phase when only scarce information is available and contractual obligations are to be established. In this context, both "internal" risk (e.g., probability of cost overrun) and "external" risk (e.g., probability of winning) must be taken into account.

The chapter presents the PRIMA (Project RIsk MAnagement—The European Commission Information Society Technologies [IST] Community Research Project Number 1999-10193) research project aimed at implementing a risk-driven approach to project management through the development of a risk management corporate memory (RMCM) and a decision support system (DDS), allowing for collecting, storing, sharing, and using company knowledge both in terms of data records and expert knowledge in order to improve the company capability of project risk analysis and management.

The most critical phase in the project life cycle is the conceptual phase or bidding process, since only scarce information is available and nevertheless a project baseline has to be determined, a baseline that more or less becomes a constraint for the project in terms of time, cost, and product performance. In other words, the proposal manager faces an obvious, but not easy to loose, trade-off: the more competitive the bid in terms of offered price and non-price factors, the greater may be the probability of winning, but, conversely, the higher may be the probability of deviation from the planned project baseline.

Three main problems should be addressed:

• How to estimate the competitive value of the bid and measure the probability of winning?

• How to estimate the project baseline and measure the probability of meeting the related constraints?

• How to integrate both estimates, trading off bid competitive value and project baseline constraints with the related uncertainty levels?

Knowledge Structuring

The competitive value of a bid is estimated using competitive factors (such as price, delivery time, technical assistance, process safety, training service, plant dependability, and so on), which have to be defined following a top-down approach. Since these factors may be either qualitative or quantitative it is necessary to use, together with the limited set of factors available, a suitable ranking model. The ranking is related to the influence of each competitive factor on the overall bid competitive value. Obviously, the point of view of the owner is quite different from the point of view of the contractor (in the latter case all the data should be estimated through educated guesses).

Competitive factors may be structured in different ways, depending on the availability of external information (client, competitor, market) and the possibility to identify the cognitive process of experts on the domain as a:

• Simple list

• Hierarchical structure and a taxonomy of factors

• Semantic network

• Database or knowledge base

• Variety of other approaches.

A global performance indicator for the bid competitive value is calculated using the competitive factors as parametric variables. The calculation algorithm can depend on the number of parameters, the type of ranking, or the knowledge structure complexity. This problem may be effectively approached by a multi-attribute, decision-making model, such as Saaty's Analytic Hierarchy Process, which is in fact a prioritization technique.

On the other side, when estimating the overall project baseline, a breakdown approach may be applied by choosing an appropriate level of detail and by taking into account that the more detailed the analysis the greater the amount of information required. Baseline factors may be structured in such a way to make possible a detailed knowledge capitalization and an appropriate working method to build technical solutions using information stemming from previous projects. A traditional way to estimate a project baseline is based on an analytical approach. It requires a breaking down of the project in terms of products, processes, resources, and related costs. The overall project cost is therefore estimated by summing up all the detailed cost items. A quicker way of achieving a project baseline estimate—more suitable for the bid/no bid early phase during the bid process—may be to use a parametric approach based on the identification of the main cost, time, and performance drivers and the use of rates and adjustment factors corresponding to the specific case considered.

Specific models are generally required in order to evaluate overall project main performance parameters: cost (e.g., cost breakdown structure), duration (e.g., project network), and product performance (e.g., value and functional analysis).

Obviously, maintaining a memory of the information concerning previous projects, considering both bid competitive value and project baseline, makes more efficient and effective the process of proposal preparation, since an intelligent reuse of recurrent information items can be organized. Such information can be qualitative and quantitative and should consider product/service performance, client evaluation criteria, competitors behavior, project context, and so forth.

The main information sources for the bid competitive value and project baseline estimate are data records and expert knowledge. Information sources may be internal or external to the company involved in the competitive bidding process.

Knowledge Processing

Classically, the bid process focuses on cost estimations as a final point of the technical solutions building process and a comparison with the possible price is made (DECIDE [Decision Support Optimal Bidding in a Competitive Business Environment] Project, ESPRIT [The European Union Information Technologies Program] n.22298). As previously mentioned, the early phase of the project is characterised by a high level of uncertainty, affecting both competitive factors and cost/time/performance parameters. But risk is the prime management factor.

In this context, a risk-driven approach to project management appears to be necessary, since project final performance depends primarily on project risk analysis and management along the overall project life cycle. As a consequence, project risk analysis and management tends, more and more, to become an essential requirement for project management quality.

From the contractor point of view a competitive bidding process poses two kinds of relevant decisions:

• The problem of bidding (bid/no bid decision), i.e., the choice whether to take part in the auction or not, which primarily depends on a preliminary evaluation of the contractor strengths and weaknesses and must be viewed in the light of a project portfolio strategy and some assumptions about the competitors' behavior.

• The bidding problem, i.e., the choice of the bid profile, pursuing the objective of winning the competition without overbidding.

During the bidding phase the contractor has to decide whether to accept an external risk (described by the probability of winning) on the basis of the presumable judgment given by the client on his bid compared to those prepared by competitors, simultaneously incurring an internal risk (described by the probability of deviation from the project baseline representing a constraint for the project). Such a decision can be supported by the above mentioned knowledge organization. But a new way to define and take into account risk should be applied at the project management level.

The concept of risk is normally associated with an adverse event and described in terms of probability of occurrence and severity of consequences. But for managers, risk is a threat as well as an opportunity, which could affect adversely or favorably the achievement of project objectives.

Risk sources may be considered classically, as internal sources (i.e., related to industrial risk subject to company control) or external sources (i.e., related to market risk not subject to company control). For example, current company overall workload causing a possible slippage in project completion date, or currency fluctuation causing a possible financial loss.

The knowledge process organization needs an identification of risks as soon as possible during the proposal preparation process. For each project, it is possible to identify a list of "risk sources" from which a set of "risk events" may stem. For instance, an increase in the purchase price for an equipment item may stem from current market conditions and a loss of competitive value of the bid may stem from local safety rules and standards that have not been correctly considered.