The uncertainties of future business trends in different industries leave business leaders searching for strategies to understand and anticipate possible disruptions to business operations and prepare to successfully sail through uncertainties in the future. Thus, to determine the future, most organizations use scenario planning and forecasting models to predict and prepare for what may happen in the future. In the article ‘How (In)accurate Are Demand Forecasts in Public Works Projects: The Case of Transportation’, the author emphasized that even though most transport organizations spend enormous money on transportation infrastructure, they have very little systemic knowledge about the cost, benefits, and risks involved (Flyvbjerg et al., 2005). Thus, it is hard to determine whether transportation projects perform as forecasted, given that underperformance is often explained as an isolated instance of unfortunate events and not as a pattern of underperformance in transportation infrastructure projects, which are often based solely on traffic forecasts. This paper explains the problems of misleading forecasts on projects carried out in the rail and transportation industries where planners use current and historical data on traffic patterns to forecast the success of future transportation projects.

While Business forecasting gathers information from historical data and the current business climate to make predictions, scenario planning involves the identification of different possible paths that could lead to the future, taking into consideration the changing market conditions. Scenario-based planning is a process of decision-making widely used by professionals and decision-makers to plan and respond dynamically to an unknown future. Thus, scenario planning provides a coherent and credible alternative to projecting the future (Cornelius et al., 2005). With scenarios, decision-makers can think and plan on the different ways the future could unfold and how they can respond effectively and be resilient as the future becomes a reality. The forces driving scenario-based planning include the flexibility in dealing with risk and uncertainties towards multiple possible futures. Thus, in order for organizations to determine the impact of risks and future uncertainties, they develop different scenarios to study the uncertainties that surround decisions made about the future.

To adequately predict future trends, various scenario planning models use qualitative or quantitative methods to make predictions for the future (Dean, 2019). The qualitative methodologies employed in scenario planning expose participant assumptions about a divergent and challenging future by bringing out their perspectives on their future expectations. On the other hand, the quantitative models use mathematically based forecasting and financial models to understand and project future outcomes for a business, thereby making forecasts in mathematical terms like revenue projections, return on investment, and market share, amongst others (Kennedy & Avila, 2013). Quantitative scenarios use financial models to present the best and worst-case versions of model output by altering variables based on the risk and uncertainties envisaged in the future. Organizations could use several scenario planning techniques to predict the future while considering risks and uncertainties. These techniques include probability-based, operational, interactive, normative, and strategic management scenarios. These techniques could be based on models like the Bayesian hierarchical model, which uses historical data and prior information to predict future outcomes in a series (Gastelu et al., 2018). However, this approach considers risks and uncertainties by updating and re-estimating model parameters, thereby ensuring forecasts can be updated.

The use of various scenario planning models requires enormous time and resources. It is a collaborative process involving different groups of people within an organization with creative thinking abilities to determine the possible routes the future could lead to, thereby creating multiple long-term futures based on unknown risks and uncertainties. Using scenario planning for future innovative efforts starts with brainstorming expectations for the future, then identifying trends and the various forces that may drive the future innovation while considering numerical data and people’s perspectives, cultures, beliefs, and expectations for the future. From there, various scenarios could be developed with multiple paths to the future while considering uncertainties of the future.

Also, given that scenario-based planning considers risks and uncertainties in determining multiple paths for the future, it may influence the social impact of change as different scenarios could be used to alter human interactions, relationships, behavior patterns, and cultural changes over time. These changes could transform people’s perspectives, leading to a more permanent change in ideologies and transforming cultures and social institutions in the long run. The value of scenario planning is realizable in a range of features or possible outcomes rather than the ability to predict a particular future outcome (Dean, 2019). Even though scenario planning and standard forecasting have advantages and disadvantages, scenario planning is a superior and more strategic technique for predicting the future. This is because scenario planning considers possible risks and uncertainties and offers flexibility and preparedness with different quantitative and qualitative methodologies to predict the future, making it a preferred strategy for planning future innovations, unlike traditional forecasting models that depend solely on quantitative data.


Cornelius, P., Van de Putte, A., & Romani, M. (2005). Three Decades of Scenario Planning in Shell [Article]. California Management Review, 48(1), 92-109.     

Dean, M. (2019). Scenario planning: A literature review. A report of project(769276-2).    

Flyvbjerg, B., Holm, M. K. S., & Buhl, S. L. (2005). How (In)accurate Are Demand Forecasts in Public Works Projects? [Article]. Journal of the American Planning Association, 71(2), 131-146.           

Gastelu, J. V., Trujillo, J. D. M., & Padilha-Feltrin, A. (2018). Hierarchical Bayesian Model for Estimating Spatial-Temporal Photovoltaic Potential in Residential Areas. IEEE Transactions on Sustainable Energy, 9(2), 971-979.            

Kennedy, P. J., & Avila, R. J. (2013). Decision making under extreme uncertainty: blending quantitative modeling and scenario planning. Strategy & Leadership    

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