Generating revenue forecasts for project financing involves some kind of accepted qualitative or quantitative models or a combination of forecasts from both types of models. While forecasting models vary from simple deterministic relationships to qualitative and quantitative models to system dynamic models, our research findings suggest that roughly only a quarter of business decisions are based on analysis,
evaluations, and substance (Triantis, 2013).This is a major factor in project failures to achieve expected performance and value creation.
Simple deterministic models are functions such as y = a*x and are based on engineering studies, past experience, and financial/accounting relationships and are useful in
developing cost component projections. Also, for small projects, deterministic models using good assumptions may be appropriate to forecast costs and revenues, but they are wholly inappropriate and inadequate to use for large, greenfield, complex infrastructure project revenue forecasting.
Greenfield projects are new investments to construct new facilities for infrastructure
projects. For our discussion, brownfield projects are projects outside a sponsor's or developer's home country; they are existing operational projects in need of
investments to upgrade equipment and facilities to increase output.
Quantitative methods rely on sufficient past data to create relationships and models to forecast project revenue. They are statistical models based on internal company, industry association, and the host government authority's historical data and are appropriate to use in brownfield projects with adequate observations. Quantitative forecasting methods include the following types of models: Univariate time series, multivariate time series, various exponential smoothing models, and causal statistical models of high levels of complexity. Quantitative models do not use expert opinions and try to remove subjective judgement from forecasts.
For greenfield projects, the lack of historical data necessitates the use of qualitative methods, which includes:
1. Industry studies and market research 2. Delphi method and its variants
3. Sales force polling 4. Life cycle analogy
5. Panel of experts meeting face to face 6. Scenario building
7. System dynamics 8. Foresight maturity
The qualitative forecasting models of demand and project revenue use expert judgment for medium and long term planning purposes. Industry studies provide insights about upper demand limits and market research is conducted to identify consumer or user needs and preferences, their ability and willingness to pay, and yield additional data on the size of the market, potential demand, and project revenue. Sales force and regional company office personnel polling is another method of generating useful information and insights on demand and pricing developments and forecasts. Also, face to face expert
discussions are used to create and validate assumptions and project revenue forecasts.
When a project team expects that demand and revenue of a PPP project is likely to follow the typical growth, maturity, and eventual decline, life cycle analogy models are used, often with little consideration of underlying demand factors. Occasionally, these S curve kind of models can have their basic structure modified to incorporate external influences and judgment to generate revenue forecasts. S curve models are widely used in
forecasting demand and revenue for new product or service introductions and do have some applicability in availability projects.
Availability projects are private finance initiative (PFI) model projects under which the project company is paid for making the project available for the contracting authority's use and include seaports, airports, toll roads, bridges, etc.
The Delphi method was introduced in the 1950s and is a commonly used method to create assumptions, relationships, and forecasts and to build consensus. It is an
interactive and iterative method of forecasting based on the views and opinions of panel experts. Panel participants can provide their individual inputs on data, assumptions, and forecasts and after each iteration, these experts revise their judgments toward the mean responses. The Delphi steps end when a process stopper is reached; usually when the responses converge to a consensus view.
The scenario building and planning method is related to contingency planning and is used when there is uncertainty concerning pricing and demand for a new infrastructure
project. The purpose of scenario building is to identify a few possible scenarios for revenue outcomes and to plan and prepare for responding when negative cases
materialize. This method of forecasting is used not only in the early stages of project development, but also when project risks are not fully identified and mitigated or when there is uncertainty about external factors impacting project revenue. Because of the importance of scenario building and planning in forecasting infrastructure project demand, this topic is discussed in more detail later in this section.
System dynamics models have been widely used in engineering, social sciences, and the military and recently in forecasting for infrastructure projects (Bala, Arshad, and Noh, 2016). But what are system dynamics models? They are a modeling technique that is capable of building a replica of the complex structure of a project's demand and revenue using assumptions, data, mathematical functions, and diagrams. Their structure allows for introducing quantitative and qualitative new factors, changes, and shocks to the project company operations and its revenue structure. In our judgment, they are well suited for large infrastructure project finance forecasting needs and evaluations.
System dynamics models are well suited for forecasting demand and revenue, especially in PPP projects because of their ability to integrate many different factors, external
influences, assumptions, data, and relationships into one place. Their structure requires expertise in handling the interactions between the multitude of demand, pricing, and supply influence factors and the risks associated with these factors. The scenario
development and simulation capabilities of system dynamics models shed light on the impact of each factor involved and their unique sensitivity analysis and simulation capabilities to generate project revenue forecast ranges for senior managers to have a higher comfort level in making decisions. They are also useful in determining the costs and benefits associated with a risk factor and whether to insure against it or absorb its impact.
The development of system dynamics involves the creation of conceptual representations
of demand and revenue, which includes the following key elements:
1. Identifying the key influencing factors using any of the techniques mentioned earlier to anchor the model to a sound foundation
2. Creating causal loop diagrams that require knowledge of the host country's
environment, the project's industry structure, and the project company's customers or users to assign direction of influence and feedbacks
3. Validating causal loop diagrams by expert analysis of the system dynamics model and forecasts generated by expert analysis and knowledge
Another approach to generate forecasts and make decisions in complex project structures that is beginning to get traction is foresight maturity models, which are particularly
useful in guiding project teams when applied in the early stages of project development.
Foresight maturity models help project teams to define desired, probable, planned, and creatable futures using data, qualitative inputs, and scenario building. In the presence of uncertainty, the foresight maturity technique helps to create strategies and plans to
enhance project success rates by generating a baseline and plausible scenarios and limits.
In its simplest form, the basic structure of the foresight method consists of the following elements:
1. Gathering inputs from the situational analysis, expert views, and the project feasibility study
2. Assessing the inputs assembled, which helps in understanding what seems to be taking place and determines the project's future and the revenue stream
3. Interpreting the results of the assessment to determine what factors are indeed influencing project revenue
4. Exploring or prospecting what alternative project revenue outcomes may materialize 5. The actions the project team may need to be taking at this stage for the planned future
revenues to be realized
6. Evaluating the outputs and conclusions and then developing a strategy and
recommendations of what the project team needs to do and how to do it to ensure that revenue forecasts materialize
Companies with broad experience in strategic decision forecasting, which includes infrastructure revenue forecasting, usually employ more than one of the forecasting
techniques and models discussed. However, better project revenue predictions are created by combining forecasts that come out of both qualitative and quantitative methods. The advantage of this practice is that it incorporates elements from different knowledgeable perspectives, which usually produce more easily accepted forecasts.
Every sound forecasting methodology, whether quantitative or qualitative, involves scenario building and the development of project forecasts. Scenarios are schemes, concepts, sketches, outlines, representations or plans of the sequence of events, their
timing, and what happens when decisions are made and begin to get implemented. They are models of assumed or expected sequences of decisions, inputs, actions, reactions, and events constructed for the purpose of capturing their effect on a target variable. Namely, they show how a hypothesized chain of events leads to future states in a structured way of seeing beyond the current state, creating descriptions of future states, and describing how they unfold.
Scenarios are also used to explore wild card possibilities and black swans and quantify their impacts. In defining possible futures, scenarios help project team members to understand the time ordered events and causation from the current stage to the project implementation, and to create strategies and options to deal with uncertainties. They build “flight simulators” to create learning and sound project implementation strategies by clearly articulating the events and processes generating the future states, simulating them, and answering critical questions. Good scenarios are stories of plausible, divergent but deterministic futures, and they capture project team biases and different points of view. Nevertheless, they can help to see vividly what drives the business and what is required to achieve the objectives of a project, to stimulate discussion, to question assumptions and the model of business operations, and to increase project team effectiveness.
Scenario building is a structured approach to predict future project revenue by assuming a series of alternative possibilities instead of forecasting the future on the basis of
extrapolated historical or analog data alone. Scenario planning, also called scenario thinking or scenario analysis, is a corporate planning method employed in making strategic decisions and long term plans. It is an adaptation of the methods used by military intelligence and strategic planning that relies on model simulation tools and controllable factors to manage the future.
When other methods of forecasting are not appropriate, scenario development and planning is a practical tool used to solve strategic decision problems and create future state forecasts. It is a method to learn about the future by understanding the impact of the most uncertain and important forces driving the business. Scenario development is based on the belief that strategic decision forecasters are not at the mercy of fate; instead, they use this method of envisioning future states to incorporate them into scenario
models to be simulated. The common steps involved in part one of scenario development include:
1. Starting with an accurate description of the project attributes and defining the
project's environment, the context of the decision to be made, and its major objectives 2. Defining the scope of each scenario and brainstorming on the megatrends and the
host country's external environment driving forces surrounding the project 3. Developing major assumptions about timing, causality, and the strength of
relationships, gathering information, and evaluating industry and market trends and structural changes
4. Engaging an independent consultant to screen and provide suggestions on the driving forces and events and ensure objectivity and reasonableness of scenario building 5. Determining the extent to which scenario driving forces can be predetermined,
projected, and fixed and how steady their influence is on the projected project revenue 6. Creating distinct and convincing stories based on the effect of driving forces and
critical uncertainties and eliminating competing narratives
7. Weaving hypotheses and plots to the stories to fit the identified events and forces and creating, at the most, three or four plausible scenarios
Key Takeaway
The most valuable application of scenario development, analysis, and planning is that scenarios can be used as a flight simulator that:
1. Enables project teams and senior management to visualize how to get to the desired future state of the project company and what is required to achieve it 2. Helps create the conditions, commitment, risk mitigation and support levels that
have to be in place to obtain financing and achieve the desired project revenue objective
3. Allows the creation of the desired project state by influencing drivers and inputs in model form today before decisions are made and are implemented
In part two of scenario building, the forecasting team conducts sanity checks on the assumptions, actions, reactions, events and their timing, and on the processes that generate the future project states and their logic and performs the following activities:
1. Developing decision trees and influence diagrams based on modeling, simulations, and the Delphi technique and performing forecast analysis
2. Focusing on analysis of demand discontinuities of trends, cross impact analysis, and analog experiences to identify unexpected driving factors and uncontrollable events 3. Assessing the financial, human resource, competitive, operational, and strategic
implications of each scenario and evaluating differences with project expectations 4. Comparing the scenario generated forecasts against the baseline projection and
estimating the contribution of each driving factor to the additional value created by the project
5. Creating a system of early warning indicators to monitor each scenario's performance as it unfolds through time and adapting the scenario to fit circumstances that
approximate reality
6. Using the decision selection matrix to identify, evaluate, and rate the economic value of each future state scenario and the likelihood of achieving them