Scenario Planning over Forecasting

 

There are moments in history where traditional forecasting methods have failed (Ademmer & Boysen-Hogrefe, 2022). While it is not possible to completely what will occur in the future. Organizations can attempt to predict what may occur based on historical precedent and accumulated data (Klimberg et al., 2010). While some errors do not have severe impacts, there are times in history where forecasting errors can have consequences. In this individual project, one of these forecasting errors will be discussed. Following the summary will be a discussion on how scenario-planning supports futuring and innovation. The forces that support scenario-planning will be identified as well. The project will conclude with the student considering how they will use scenario-planning for future innovation efforts.

Forecast Failure: The Great Recession

One of the more recent forecast errors that resulted in far reaching consequences was the economic crisis of 2007 – 2009 (Gadea Rivas & Pérez-Quirós, 2012). This event is also known as the Great Recession. This event occurred due to a long period of rapid expansion of the US housing market from the 1990s to the mid-2000s (Gadea Rivas & Pérez-Quirós, 2012). With this expansion, there was a large issuance of high-risk mortgages being approved and repackaged into securities. This resulted in an increased demand for homes and increased the prices of homes. Unfortunately, this bubble would burst. Causing a dramatic decrease in home prices and banks risking closure.

The primary issue of forecasting is that the methods used by most economist’s did not foresee the crisis occurring. (Gadea Rivas & Pérez-Quirós, 2012). Despite the issuance of high-risk mortgages at a high-rate, most forecasters did not predict the Great Recession (Gadea Rivas & Pérez-Quirós, 2012). While scenario-planning may not have been feasible at such a large scale, individual organizations may have been able to minimize the impact. Including the banks that issued the high-risk loans (Gadea Rivas & Pérez-Quirós, 2012).

Scenario-Planning Discussion

Scenario-Planning is a method of futuring that can be used to make long-term plans based on unknown certainties that an organization may face (Chermack et al., 2001). An organization identifies the driving forces of society, economics, technology, and politics that could impact the company in the future. Once the forces are determined, the uncertainties from these forces are identified. The organization will then develop scenarios that could be a result of the uncertainties. The final step will be to determine the impact and implications that could arise from the different scenarios (Amer et al., 2013).

Support for Innovation and Change

Scenario-planning provides multiple scenarios that could impact and organization in the future. An example of this would be the continuing effort to reduce carbon emissions and increase the efforts of clean energy (Chakraborty & Mazzanti, 2020). Forces are shifting to reduce the carbon footprint that humanity has been inflicting on the planet. Scenario-planning by organizations looking at these forces could determine scenarios that could heavily impact the business. To reduce the carbon footprint of airline travel, there have been innovations in fuel that airplanes can use. Sustainable aviation fuel in one innovation that produces a fraction of the carbon compared to standard fuel (Ng et al., 2021). Figure 1 demonstrates the climate impact of sustainable aviation fuel (Teoh et al., 2022). Further innovations in sustainable energy could come from scenario-planning.

Figure 1

Targeted Use of Sustainable Aviation Fuel to Maximize Climate Benefits



Environ. Sci. Technol. 2022, 56, 23, 17246-17255

 

Forces Contributing to Scenario-Planning Innovation

The 2020s has seen an increase in societal and political forces of climate activism (Tillotson et al., 2023). These forces have been increasing pressure on organizations to make plans to reduce their climate footprint. While there is still a debate on the degree of necessary alarmism for these forces (Tillotson et al., 2023). Innovation from organizations that contribute to climate change is still necessary to continue business. New forms of sustainable energy from hydrogen, sustainable aviation fuel, graphene, and fusion could be more prevalent in the future (Naber et al., 2017).


 

Futuring the Future of Phishing

The question now is how can scenario-planning be used to predict futures scenarios of phishing? While there are organizations that work in the information security sector, there are few that specialize in phishing. Scenario-planning could be used to determine how phishing will be used in the future. One particular force that is current is the rapid evolution of artificial intelligence, or AI (Zhou et al., 2023). It was not long before the time of this writing that AI art was barely recognizable and AI voice production was barely intelligible. In a short time, AI can now rapidly produce works of art, translate text-to-speech to sound like any celebrity, and even be used for homework (Mageira et al., 2022).

This driving force in technology could see a more effective method of social engineering that is not expected. AI will be able to draft letters that are specific to any person based on a few prompts (Caulfield, 2023). Grammatical and spelling errors are becoming minimal with the help of AI. Scenario-planning could allow the few counter-phishing organizations to begin planning mitigations now. The student using this same scenario-planning could also begin research into how AI will impact the future of phishing.

 

 

 

 

           


 

References

Ademmer, M., & Boysen-Hogrefe, J. (2022). The impact of forecast errors on fiscal planning and debt accumulation. Jahrbücher für Nationalökonomie und Statistik, 242(2), 171-190.

 

Amer, M., Daim, T. U., & Jetter, A. (2013). A review of scenario planning. Futures, 46, 23-40.

 

Caulfield, M. (2023). ChatGPT is changing the phishing game. Retrieved May 31 from https://www.securityinfowatch.com/cybersecurity/information-security/breach-detection/article/53057705/chatgpt-is-changing-the-phishing-game

 

Chakraborty, S. K., & Mazzanti, M. (2020). Energy intensity and green energy innovation: Checking heterogeneous country effects in the OECD. Structural Change and Economic Dynamics, 52, 328-343.

 

Chermack, T. J., Lynham, S. A., & Ruona, W. E. (2001). A review of scenario planning literature. Futures research quarterly, 17(2), 7-32.

 

Gadea Rivas, M. D., & Pérez-Quirós, G. (2012). The Failure to Predict the Great Recession-The Failure of Academic Economics? A View Focusing on the Role of Credit.

 

Klimberg, R. K., Sillup, G. P., Boyle, K. J., & Tavva, V. (2010). Forecasting performance measures–what are their practical meaning? In Advances in business and management forecasting (pp. 137-147). Emerald Group Publishing Limited.

 

Mageira, K., Pittou, D., Papasalouros, A., Kotis, K., Zangogianni, P., & Daradoumis, A. (2022, 2022

2023-05-01). Educational AI Chatbots for Content and Language Integrated Learning. Applied Sciences, 12(7), 3239. https://doi.org/https://doi.org/10.3390/app12073239

 

Naber, R., Raven, R., Kouw, M., & Dassen, T. (2017). Scaling up sustainable energy innovations. Energy Policy, 110, 342-354.

 

Ng, K. S., Farooq, D., & Yang, A. (2021). Global biorenewable development strategies for sustainable aviation fuel production. Renewable and Sustainable Energy Reviews, 150, 111502.

 

Teoh, R., Schumann, U., Voigt, C., Schripp, T., Shapiro, M., Engberg, Z., Molloy, J., Koudis, G., & Stettler, M. E. (2022). Targeted use of sustainable aviation fuel to maximize climate benefits. Environmental Science & Technology, 56(23), 17246-17255.

 

Tillotson, P., Slade, R., Staffell, I., & Halttunen, K. (2023). Deactivating climate activism? The seven strategies oil and gas majors use to counter rising shareholder action. Energy Research & Social Science, 103, 103190.

 

Zhou, L., Rudin, C., Gombolay, M., Spohrer, J., Zhou, M., & Souren, P. (2023). From Artificial Intelligence (AI) to Intelligence Augmentation (IA): Design Principles, Potential Risks, and Emerging Issues. AIS Transactions on Human-Computer Interactions, 15(1), 111-135. https://doi.org/https://doi.org/10.17705/1thci.00185

 

 

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