The types of risks ports are exposed to force them to adopt means of coping with uncertainty. Increased volatility and major external and internal shocks require a focus on risk analysis and mitigation and avoiding actions and situations that entail more than moderate degrees of risk.
Tools can provide port authorities and port-related companies with a set of actions and alterations to their risk approach. In the long term, any actor wishing to survive and thrive should become resilient. This means elevating risk management to a strategic level and implementing a mindful corporate culture where the organization can recognize, take, and rapidly and effectively adapt to changes and the resulting risks.
A. Modelling tools to deal with uncertainty
Sensitivity analysis is a method of determining how the variation in the output of a model (numerical or otherwise) can be apportioned, qualitatively or quantitatively, to different sources of variation. The given model responds to the information it uses. The goal of sensitivity analysis is to understand the quantitative sources of uncertainty and identify the sources providing the greatest uncertainty. Sensitivity testing does not encourage the analyst to consider dependencies between parameters and probabilities that certain values will occur together.
Error propagation equations were originally developed by The Intergovernmental Panel on Climate Change (IPCC) to estimate error propagation in calculations. The goal of the error propagation equations is to assess how the quantified uncertainties in model inputs propagate in model calculations to produce an uncertainty range in a given model outcome of interest. This addresses statistical uncertainty (inexactness) in inputs and parameters. The error propagation equations require no specific hardware or software and can typically be applied using a spreadsheet. Therefore, it is suitable as a quick check tool since it requires very little resources and skills.
Monte Carlo simulation is a statistical numerical technique for stochastic model calculations and error propagation analysis in (model) calculations. The goal of Monte Carlo analysis is to trace the structure of model output that results from the uncertainty of model inputs. A number of software packages are available to do Monte Carlo analysis. Widely used are the commercial packages @Risk and Crystal Ball. Monte Carlo analysis typically addresses statistical uncertainty, and although it is rarely used this way, it is possible to use Monte Carlo analysis also for assessing model structure uncertainty, by introducing one or more “switch parameter” to switch between different model structures with probabilities attached for each position of the switch. Monte Carlo assessment is limited to those uncertainties that can be quantified and expressed as probabilities. Moreover, the interpretation of a probability distribution of the model output by decision-makers is not always straightforward.
B. Forecasting tools to deal with uncertainty
Scenario analysis is a method that tries to describe logical and internally consistent sequences of events to explore how the future might, could, or should evolve from the past and present. Through scenario analysis, different alternative futures can be explored, and uncertainties thus addressed. Scenario analysis creates awareness of alternative development paths, risks, opportunities, and possibilities for policies or decision-making. The two main methods used when developing scenarios are scenario writing (qualitative scenarios) or basic policy exercises and modeling analysis (quantitative scenarios). Quantitative scenario models are, for example, forecast scenarios for traffic throughput within a port. Scenario Analysis typically addresses ignorance, the impact of choices (assumptions), and “what-if” questions (scenario uncertainty) concerning both the context of the (environmental) system considered in the assessment and assumptions about the environmental processes involved.
PRIMA is an acronym for Pluralistic Framework of Integrated uncertainty Management and risk Analysis. PRIMA is not a tool in the classic sense, but a framework for structuring the process of uncertainty management. The guiding principle is that uncertainty legitimates different perspectives on policy issues and that, as a consequence, uncertainty management should explicitly take these different perspectives into account. Central to the PRIMA approach is the determination of the most policy-relevant uncertainties that play a role in controversies surrounding complex issues. Various legitimate and consistent narratives are developed to serve as a basis for perspective-based model routes. The methodology is best applicable to scenario-based planning. PRIMA is the only approach so far that advances and provides structure to the systematic use of multiple values, paradigms, perceptions, and judgments in assessment processes. The PRIMA approach is, by definition, a group process approach.
A near miss is an unplanned event that did not result in injury or damage but could have under different circumstances. A near miss analysis is an approach to identifying and analyzing these events and creating the appropriate corrective actions in order to avoid such events in the future. The purpose of this analysis is to identify systemic or latent errors and hazards and alert the port or enterprise about them. Although near-miss events are much more common than adverse events, reporting systems for such events are much less common. Near miss management consists of seven steps: (1) Identification of the incident, (2) Disclosure/ reporting of the incident; (3) Distribution of the incident data; (4) Root cause analysis; (5) Solution/improvement recommendation; (6) Dissemination; (7) Follow up. The method is scalable and applicable to all industries, and easy to implement and act upon. However, it remains a reactive approach, not a proactive one.
Adaptive forecasting allows a port or port-related company to plug in various variables to gauge the potential outcomes of a single course of action from multiple angles. While including more variability in the forecast, this method also forces the port or port company to think in terms of uncertain outcomes and take new variables from their external environment into account. However, the abundance of data and variables makes selection cumbersome. Therefore, it might be more difficult to implement and create than historical trend forecasting. Adaptive port planning can serve as a complement to more traditional port planning approaches.
A more detailed discussion on forecasting methods and tools (using traffic forecasting as an example) is provided in Chapter 7.3. Port planning and development.
C. Project tools to deal with uncertainty
A SWOT analysis is a study undertaken by an organization to identify internal strengths and weaknesses, as well as external opportunities and threats. This technique examines a project or plan from each of the strengths, weaknesses, opportunities, and threats (SWOT) perspectives to increase the breadth of identified risks by including internally generated risks. The technique starts with identifying the strengths and weaknesses of the organization, focusing on either the project, organization or the business area in general. SWOT analysis then identifies any opportunities for the project that arise from organizational strengths and any threats arising from organizational weaknesses. The analysis also examines the degree to which organizational strengths offset threats and identifies opportunities that may serve to overcome weaknesses. SWOT highlights both the positive and negative aspects of a company and its risks and explicitly the areas where danger lurks or improvement is possible.
Diagramming Techniques consist of a set of techniques, methods, and tools aimed at graphically representing variables and outcomes of certain decisions or risks. Examples include:
- Cause and effect diagrams. These are also known as fishbone diagrams and are useful for identifying causes of risks.
- System or process flow charts. These show how various elements of a system interrelate and the mechanism of causation.
- Influence diagrams. These are graphical representations of situations showing causal influences, time ordering of events, and other relationships among variables and outcomes.