Scenario analysis (SA) is one of the methodologies utilized for forecasting issues within long-term events. Past researches have typically used SA to attain foresight into future issues that focus on a short-term time frame. Nevertheless, SA implies some weaknesses in that it is unable to define the transition between time states clearly and it cannot meaningfully explain how to forecast long-term uncertainty effectively and how to link present and future situations. Based on these weaknesses, we established a Markov SA (MASA) model that integrates the concept of vision, linking analysis planning, Markov chain, and SA so that we can improve the existing model for SA. The MASA model not only solves our insufficient information problem for a complete SA model but also classifies four categories for forecasting future events. The four categories that can confirm a future trend are (1) state of constancy, (2) state of disappearance, (3) state of change, and (4) state of uncertainty. This paper introduces the principles and application of the MASA model. A sample case study is given to explain how the MASA model can be applied.
