Investing decisions may be broadly classified into two types:

• Strategic Decisions – Those that change the direction of the firm. Examples include acquisitions of assets and Capital Expenditure (CAPEX) in a different region or different sector.
• Tactical Decisions – Those, which do not fundamentally change the direction of the firm. Examples include replacement, maintenance and expansion projects.

The economic analytical tools used in CAPEX decisions should vary by the nature of decision. For example, replacement, maintenance or expansion projects may be analyzed by Net Present Value (NPV) alone, but projects involving development of new technologies should be analyzed using decision tree analysis. Similarly, real options provide better risk analysis when acquiring assets of other companies.The mechanics of making an investing decision in traditional capital budgeting is quite straightforward. Future cash flows resulting from the investing decision are forecasted and discounted to weigh them against the cash outflows required for investment and the resulting NPV is compared against other investment projects available to the firm. However, this simplicity masks an extremely subtle decision making problem. Investment environment and human characteristics predispose companies to short-termism while making CAPEX decisions.

Firms usually have a number of options for investing which must be compared. Size and timing of investing, response by other companies, future trends about technology and government regulations are some of the factors that need to be considered when making these decisions.

Cost engineers often play a vital role in providing an unbiased forecast of these investments in various projects to the management. They are akin to a car’s dashboard, providing vital health signs of the project to decision makers. A knowledgeable cost engineer provides complete information of the projects, suggests various courses of action for the future, their respective probabilities, and attached consequences. Typically, decision makers are assumed to assimilate and understand this information, implicitly calculate the advantages and disadvantages of each alternative, and choose a course of action that maximizes the expected utility.

However, a look at the history of major projects provides enough evidence that clearly, decision makers do not operate this way. Their perception is highly selective and memory is often fraught with biases. The consequences attached to various alternatives are often misunderstood and decision makers do not necessarily compare all the available alternatives. So, at best, this is a normative model of decision making (a model about how rational decision makers would behave) and not how they actually make decisions.

One of the earliest alternative explanations to expected utility theory was suggested by Nobel laureate Herbert Simon (1956), who proposed that people ‘Satisfice’ rather than ‘Optimize’ while making decisions. To satisfice is to choose a path that satisfies one’s most important needs, even though the choice may not be ideal or optimal. Simon noted, “However adaptive the behavior of organisms in learning and choice situations, this addictiveness falls short of the ideal of maximizing in economic theory. Evidently, organisms adapt well enough to satisfice, they do not in general optimize”.

Once the key projects that align with the strategic objectives of the company get approved, project management office takes control. It is well recognized that the greatest portion of CAPEX is controlled at this stage. Practices like value engineering, modularization, contract negotiations, equipment plans are all part of this phase. Proletariats can play a critical role with their experience, but to share it effectively with the top management, information needs to be collected and analyzed effectively. Cost engineers can help their companies in identifying non value added activities and can propose more efficient and productive alternatives with their experience in data collection and analysis. Cost engineer’s understanding of the company’s strategic objectives can also help in bringing forward the hidden constraints and suggest the right kind of incentives during contract negotiations. It is not uncommon to meet Construction and Project Managers who will try and predict the construction schedules. They often judge probabilities “by the degree to which A is representative of B, i.e. the degree to which A resembles B”. The psychologists call it “representative heuristics”. GE’s former CEO and Chairman Jack Welch called this ‘gut feeling’. It is interesting to analyze how people generally make their decisions. How do they choose among different options on a project? How do they form judgments of the value or likelihood of events or outcomes? Decision makers use “Heuristics” or general rules of thumb to arrive at their judgments. Normally, with experience, the decision makers develop this hunch – Heuristics often yield fairly good estimates. However, there can be instances in which heuristics also lead to systematic biases (i.e. deviations from a normatively derived answer to maximize expected utility). Many alternatives to expected utility theory have been proposed since the time of Simon’s paper alluded earlier, but the most widely accepted (and applicable in projects) is ‘Prospect Theory’ developed by Nobel Laureate Daniel Kahnemann and Amos Tversky. It replaces the notion of ‘utility’ with ‘value’. Value is defined in terms of gains and losses from the reference point (consider project budget). Prof. Kahnemann and Tversky proposed that value function for losses is different than the value functions for gains. Value function for losses is convex and relatively steep. In contrast, the value function for gains is concave and not so steep. This difference leads to noteworthy results. Because the value function for losses is steeper than that for gains, losses “loom larger” than gains. This “loss aversion” may complicate bargaining and negotiations for projects because each party may view its own concessions as losses that loom larger than the gains achieved by the concessions of the adversary.

Consider the following pair of problems posed to project managers –

Problem 1: Consider that a project manager is asked to choose between alternative A and B

Alternative A: A 50 percent chance of gaining $1.0M
Alternative B: A sure gain of $500,000

This question, when posed to respondents, 84% chose sure gain (Alternative B), because the value function raises more from $0 to $500k than from $500k to $1.0 M.

Now consider the second variant of the problem

Problem 2 : Consider that a project manager is asked to choose between Alternative C and D
Alternative C: A 50 percent chance of losing $1.0M
Alternative D: A sure loss of $500,000

In this case, nearly 70% of the respondents chose the risky alternative (Alternative C). Risk seeking makes sense when losses are at stake because more value is lost from $0 to $500,000 than from $500K to $1.0M. Thus, even though the two problems are numerically equivalent, they lead to very different choices. Project managers are generally risk averse when it comes to gains and risk seeking when it comes to losses, and even though the probability of a project exceeding its budget may be 50 percent, the impact is often much more than the projects finishing under budget.

Prospect theory provides enough evidence to show that the decision makers are often prone to a range of biases in judgment and choice behavior, but in many cases, these biases are systematic and can be managed by cost engineers with good understanding of cognitive biases. Large projects require strong leadership and proactive management. At many companies, project management efforts often fail for several reasons. Leaders may not completely understand complex projects, or they may lack the preparation and determination necessary to oversee their development effectively. Some companies approach projects as a series of individual initiatives executed independently, without the required upfront coordination and planning.

Our cost engineers are adept in collecting, analyzing and presenting data in a meaningful framework. They have the ability to change the culture of organizations from that of advocacy to a culture of evidence.