Traditional forecasting is looking at where you are currently leveraging historical observations to estimate the best case scenario in the future. Zuma-Netshiukhwi et al. (2013) discuss how farmers in South Africa leverage historical experience for weather and climate changes to influence the farming decision. Stock market predictions are another real-world example of traditional forecasting. Brokers and investors leverage a 52-week high and low, three year and five-year performance in an effort to shape decisions on buying more stock or selling stock. Another example could include analyzing how many pizzas were ordered over the last 5 Superbowls in order to gauge how many pizza, cooks and delivery personnel would be needed for the next Superbowl. One of the advantages of using traditional forecasting is the use of percentages. The more information collected over time provides an organization to leverage percentages to make decisions and assess possible outcomes based on data (Metcalf, 2019). The disadvantages include fluctuations in the data utilizing the Superbowl scenario, the historical data may not provide an accurate depiction of the data (Posadas, 2017). For example, a Pizza shop located in Los Angeles, CA and a Superbowl featuring the Los Angeles Chargers and Los Angeles Rams may skew the numbers with a higher demand for pizza due to the proximity of the teams playing.
Scenario planning is a framework organization use to support the decision-making process in an attempt to analyze different variables that affect decision or project outcome. For example, leveraging our Superbowl and Pizza scenario the owners could introduce weather as a potential external factor. Bad weather in Los Angeles such as rain could affect delivery time and could potentially delay deliveries. By analyzing the potential for rain, the owners could require more drivers than store personnel to keep up with the demand for pizza while dealing with the weather elements. Pros include Strengths, weaknesses, Opportunities and Threat (SWOT) methodology to analyze different scenarios (Wade, 2014). Pre-staging of the labor force to move quickly to meet different situations. Cons include resource and time management, with each scenario needing analysis the process becomes more time consuming and requires more planning and resources to ensure all aspect are covered in the planning phase.
Metcalf, T. (2019). The Pros & Cons of Trend Analysis in Forecasting. Retrieved from https://smallbusiness.chron.com/pros-cons-trend-analysis-forecasting-58786.html
Posadas, S. (2017). When traditional forecasting doesn’t fit - Clockwork Solutions. Retrieved from https://clockwork-solutions.com/traditional-forecasting-doesnt-fit/
Wade, W. (2014). Woody Wade: “Scenario Planning” - Thinking Differently about Future Innovation | GLOBIS Insights - Watch. Retrieved from https://e.globis.jp/article/343
Zuma-Netshiukhwi, G., Stigter, K., & Walker, S. (2013). Use of traditional weather/climate knowledge by farmers in the South-western Free State of South Africa: Agrometeorological learning by scientists. Atmosphere, 4(4), 383-410.