This topic addressed various problems that arise in the business industry and how to resolve problems through choosing the appropriate forecasting model and techniques for a given situation. One major problem could be determining which forecasting model should a new startup company use when they have insufficient data. Another common problem that is faced by large companies involves separating out critical information from other irrelevant data to generate profitable forecasts. Also, deciding on what ways a company could produce short term forecasts when values of observation occur in an identifiable pattern.
Forecasting plays a fundamental role in the operations of a company because it informs the company what demand level, product lifecycle, …show more content…
A new local bakery shop is likely to use the Delphi Method by inviting a panel of experts, such as Chief Operating Officer from reputable bakeries and gourmands to forecast the future revenue of the bakery. These experts might be asked to estimate the annual revenue of the bakery and reach a general consensus after several rounds of surveys. We often use another qualitative model called Cross-Impact Analysis when estimating the likelihood of a future event occurring in relation to an earlier event. For example, we could forecast conditional probability of doubling ridership on Mass Transit 20 years from now given that the gasoline price rises to $3 per gallon in seven years. Historical Analogy helps companies to forecast the life cycles of new product or services. An example could be predicting the market penetration of newly launched 4D TV based on historical data of 3D TV. The Regression Model is a quantitative method that finds explanatory factors of an event. A daily example would be to forecast the future economic growth of our country by selecting numbers of independent variables, such as oil price (x) that can contribute to economic growth (dependent variable y). McDonald uses Simple Moving Average (SMA) or Weighted Moving Average (WMA) to estimate the demand for its hamburger in the next hour. While both methods remove random variations in demand, WMA assigns more weight to recent data to make the forecast more responsive to changes in demand. Assume the demand for hamburger becomes relatively unstable; McDonald would switch to the Exponential Smoothing Model that can gradually correct forecast errors and systematically ages the data. When a commuter airline observes a steady increase in percentages of seats sold each week, they might use the Exponential Smoothing with Trend Adjustment, because it not only helps them to predict seat sales for the next week, but
Forecasting plays a fundamental role in the operations of a company because it informs the company what demand level, product lifecycle, …show more content…
A new local bakery shop is likely to use the Delphi Method by inviting a panel of experts, such as Chief Operating Officer from reputable bakeries and gourmands to forecast the future revenue of the bakery. These experts might be asked to estimate the annual revenue of the bakery and reach a general consensus after several rounds of surveys. We often use another qualitative model called Cross-Impact Analysis when estimating the likelihood of a future event occurring in relation to an earlier event. For example, we could forecast conditional probability of doubling ridership on Mass Transit 20 years from now given that the gasoline price rises to $3 per gallon in seven years. Historical Analogy helps companies to forecast the life cycles of new product or services. An example could be predicting the market penetration of newly launched 4D TV based on historical data of 3D TV. The Regression Model is a quantitative method that finds explanatory factors of an event. A daily example would be to forecast the future economic growth of our country by selecting numbers of independent variables, such as oil price (x) that can contribute to economic growth (dependent variable y). McDonald uses Simple Moving Average (SMA) or Weighted Moving Average (WMA) to estimate the demand for its hamburger in the next hour. While both methods remove random variations in demand, WMA assigns more weight to recent data to make the forecast more responsive to changes in demand. Assume the demand for hamburger becomes relatively unstable; McDonald would switch to the Exponential Smoothing Model that can gradually correct forecast errors and systematically ages the data. When a commuter airline observes a steady increase in percentages of seats sold each week, they might use the Exponential Smoothing with Trend Adjustment, because it not only helps them to predict seat sales for the next week, but