Forecasting is a crucial skill for any business, but it also involves uncertainty and risk. How can you account for the possible outcomes and scenarios that may affect your projections? In this article, you will learn some methods and tools to incorporate risk into your forecasting models and improve your decision-making.
Risk in forecasting is the probability and impact of events or factors that may deviate your actual results from your expected results. Risk can be internal, such as operational issues, or external, such as market changes, competitors, or regulations. Risk can also be positive, creating opportunities, or negative, creating threats. To incorporate risk into your forecasting models, you need to identify, measure, and manage the sources and impacts of risk.
Every forecast is an informed prediction, by selecting the most probable outcome from a range of plausible scenarios. Consider a client who has just received a forecast. They might be keen to understand its reliability, yet they're unaware of the underlying scenarios and are able to see only the projected outcome. A skilled forecaster's duty extends beyond just predicting; they must articulate the inherent uncertainty in their forecast and the subsequent risk to the client. It's crucial to distinguish between uncertainty (the range of possible outcomes) and risk (the potential impact of that uncertainty). Drawing an analogy to the world of gambling, bettors should always be informed of the odds before placing their bets.
The first step to incorporate risk into your forecasting models is to identify the potential risks that may affect your assumptions and variables. You can use different techniques to generate and prioritize a list of risks, such as brainstorming, SWOT analysis, PESTEL analysis, or risk registers. You should consider both qualitative and quantitative aspects of risk, such as likelihood, severity, timing, and interdependence. You should also review historical data, trends, benchmarks, and best practices to identify common or emerging risks.
Risk is pinpointed by charting uncertainties and their risk drivers. Uncertainties can be categorized into three main groups: 1. Volatility in the model's input variables; 2. Vulnerability to rare or unexpected events like disasters, economic downturns, pandemics, or wars; 3. Lack of knowledge about the reality the model aims to depict. A portion of a model's uncertainty is "normal" and stems from the first category. If forecasters have historical data on the input variables, they can simulate various combinations and quantify the uncertainty using standard deviation. Events can be identified using historical data, past experiences, or forward-thinking ("futurizing"), while lack of knowledge demands self-criticism from the forecasters.
The next step to incorporate risk into your forecasting models is to measure the probability and impact of each risk. You can use different methods and tools to quantify and visualize risk, such as probability distributions, sensitivity analysis, scenario analysis, or Monte Carlo simulation. These methods allow you to estimate the range and variability of your forecast outcomes, as well as the key drivers and assumptions behind them. You can also use risk indicators, such as variance, standard deviation, or confidence intervals, to monitor and communicate risk.
The final step to incorporate risk into your forecasting models is to manage the risk exposure and response. You can use different strategies to mitigate or exploit risk, such as avoidance, reduction, transfer, or acceptance. You should also update and revise your forecasts regularly, based on new information, feedback, or changes. You should also document and report your risk assumptions, methods, and results, as well as the actions and recommendations you take to address risk.
One way to simplify and automate the process of incorporating risk into your forecasting models is to use software tools that can handle complex calculations, data analysis, and visualization. Some examples of software tools that can help you with risk forecasting are Excel, @RISK, Crystal Ball, or Risk Solver. These tools allow you to create and test different forecasting models, apply probability distributions and simulations, generate scenarios and sensitivity charts, and produce risk reports and dashboards.
By incorporating risk into your forecasting models, you can improve your accuracy, reliability, and confidence in your projections. You can also enhance your decision-making, planning, and budgeting processes, as you can account for uncertainty and contingency. You can also identify and seize opportunities, as well as prevent or reduce threats, that may arise from risk. You can also communicate and justify your forecasts more effectively, as you can show the underlying assumptions and scenarios behind them.