Scenario Planning versus Traditional Forecasting

Scenario Planning versus Traditional Forecasting

Introduction

The blog will compare and contrast scenario planning versus traditional forecasting. First, an explanation of scenario planning, along with its advantages and disadvantages, will be presented, followed by an explanation of traditional forecasting and its advantages and disadvantages to differentiate between the two concepts. Next, a summary and conclusion will follow on how to best apply scenario planning and traditional forecasting.

Scenario Planning

Organizational leaders must direct their organizations to a sustainable competitive advantage and must plan and implement business strategies five to ten years in the future. If there is one thing for certain about the future, it is that the future is not absolute; consequently, no one can predict it with absolute certainty. There is no methodology to see the future; therefore, leaders must plan for different educated scenarios that would be the most likely to occur or to emerge (Wade, 2012; Wade 2014). The scenario planning strategy is advantageous to strengthen the ability to cope strategically with volatility and uncertainty strategically. The Oxford scenario approach uses the probabilistic approaches of best- or worst-case scenarios, or a normative (what the future should look like) view. 

The Oxford approach focuses on immediate business transactions and environments beyond direct influence. For example, the immediate environment of the supply chain, competition, customers, and stakeholders. Both scenario planning and traditional forecasting follow no exact cookie-cutter method. Scenario planning differentiates from traditional forecasting by involving a cross-section of inside and outside management to invest time and involve planning processes to understand plausible future versus probable future (Ramirez, 2017). Traditional forecasting focuses on the quantitative probability of future outcomes, and the Oxford scenario assigns a probability to plausible future outcomes.

For this reason, the Oxford scenario planning approach has an advantage over the traditional planning approach by relating challenges to a larger contextual system thinking organizational framework. The contextual environment includes, but is not limited to, international finance, commerce, legislation, exchange rates, energy prices, technology, social values, geopolitical trends, demographics, and macroeconomic conditions. Transactional environments include, but are not limited to, employees, customers, suppliers, investors, NGOs, lobbies, regulators, and competitors. 

The strengths of scenario planning are that it combines a broad range of contextual environments with transactional environments and then assigns a probability factor with unique combinations of organizational elements to those plausible outcomes. The primary weakness of scenario planning is that it is an iterative process that typically requires additional insights with more rounds of iteration. Furthermore, it requires organizations to define what is plausible and suitably obtainable. In juxtaposition, traditional forecasting focuses on past data analysis and assigns a higher probability of plausible outcomes. Finally, scenario planning may benefit from quantum computing, AI (artificial intelligence), and machine learning with neuro-networks to better assign suitability to plausible and obtainable outcomes in the future to increase accuracy over traditional forecasting

Traditional Forecasting

Traditional forecasting mostly uses qualitative data analysis with less than 10% of traditional forecasting using the qualitative methods approach (Treiblmaier, 2015). Treiblmaier (2015) notes some exceptions with a mixed-methods approach taking into account socio-economic, technological, and market developments. An advantage of traditional forecasting is that frequently management judgments are used for adjustments to improve accuracy as a form of triangulation.   Traditional forecasting methodology advantages in research by Burger et al. (2001) showed simple traditional forecasting models outperform more complex ones in tourism demand forecasting. Ellero (2014) suggests that traditional forecasting models based on historical data are suitable for many markets, and the comparison of different forecasting models is needed to adjust for geography with differencing data parameters. Geographical and lag of industry data are a disadvantage of traditional forecasting. Another disadvantage is that industry gaps in research exist between the practitioner and academic research to produce successful outcomes. Decision-makers need to focus on data generation, range, time, and the target will help decision-makers triangulate to adjust and refine results for recent and significant market changes and the Bullwhip effect.

 Traditional forecasting strength lies in its simplicity with a qualitative data analysis approach, especially when combined with management judgments that have years of experience in industries and organizations. Decision-making is both an art and a science, and knowing the past trends and how the industry, competitors, customers, and employees have responded in the past can offer great insight to the organization's ability to accurately find a suitable forecast. Transactional environment experience and knowledge will help leadership deduce a strategic action plan to potentially use mergers and acquisitions or partnerships for future endeavors to increase market share and competitive advantage. 

The weakness of traditional forecasting is that it is prone to bias, disruptive technology, underestimation of environmental factors, lack of creativity, and strategic diversification. When contrasted with multiple rounds of scenario planning and potential machine learning, traditional forecasting could be left behind with an over-simplistic model. Traditional forecasting also is not utilizing an RBV (Resource Based View) to fully utilize internal expertise in forecasting. Traditional forecasting is at the mercy of upper management for the decision-making process which could amplify biases and conflict with culture, thus sabotaging innovation and change.

Summary

Both scenario planning and traditional forecasting have their strengths and weaknesses and potentially have strategic advantages depending on the organization, industry, and external environments. I recommend organizations first define their vision and mission and then evaluate which process will align with the goals and help differentiate the organization from the competition. Additionally, the organizational strategy might need to be slightly adjusted to open up strategic conditions for collaborative strategies depending upon the results. I would suggest organizations use a combination of both traditional forecasting and scenario forecasting to create a sustainable competitive advantage consistent with the board and executive team. My recommendation is to first start with the big picture scenario planning because it is easily aligned with the board and vision of the organization (Ramirez, 2017). I then recommend also incorporating multiple traditional forecasting methods to help get short-term wins that align with a long-term strategy. 

References

Burger, C. J. S. C., Dohnal, M., Kathrada, M., & Law, R. (2001). A practitioners guide to time-series methods for tourism demand forecasting—a case study of Durban, South Africa. Tourism management, 22(4), 403-409. doi:10.1016/S0261-5177(00)00068-6

Ellero, A., & Pellegrini, P. (2014). Are traditional forecasting models suitable for hotels in italian cities? International Journal of Contemporary Hospitality Management, 26(3), 383-400. doi:10.1108/IJCHM-02-2013-0107

Ramirez, R., Churchhouse, S., Hoffman, J., & Palermo, A. (2017). Using scenario planning to reshape strategy. MIT Sloan Management Review, 58(4), 31-37. Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1916720878?accountid=144789

Treiblmaier, H. (2015). A classification framework for supply chain forecasting literature. Acta Technica Corviniensis - Bulletin of Engineering, 8(1), 49-52. Retrieved from https://proxy.cecybrary.com/login?url=https://search-proquest-com.proxy.cecybrary.com/docview/1646396272?accountid=144789

Wade, W. (2012). Scenario planning: A field guide to the future. John Wiley & Sons.

Wade, W. (2014). Scenario Planning – Thinking differently about future innovation. Globis Insights.  Retrieved from http://e.globis.jp/article/343