3.11 Key Terms
Key Terms
- Associative Model Forecasting Methods, also known as causal or econometric forecasting methods, are quantitative techniques used in operations management to predict future values of a variable by analyzing its relationship with other related variables.
- Correlation analysis is often used with regression analysis to measure the strength and direction of the linear relationship between the dependent and independent variables.
- Demand forecasts, also known as sales forecasts, estimate consumers’ future demand for a company’s products or services.
- Economic forecasts address the overall business cycle and predict economic indicators such as housing starts, inflation rates, money supply, and others.
- Exponential Smoothing Method integrates the most recent actual demand and the previous forecast to generate the forecast for the upcoming period.
- Forecasting is the process of predicting future events or trends by analyzing historical and contemporary data.
- Long-term forecasting, typically encompassing a timeframe exceeding two years, is often undertaken at the strategic level of an organization.
- Mean Absolute Deviation (MAD) measures the average magnitude of the forecast errors without considering their direction (positive or negative.
- Mean Absolute Percentage Error (MAPE) is a relative measure that expresses the forecast errors as a percentage of the actual values.
- Mean Squared Error (MSE) is a measure that squares the forecast errors before averaging them.
- Medium-term forecasting, also referred to as intermediate-term forecasting, typically spans from several months to two years into the future.
- Naive Approach, also known as naive forecasting or naïve method, is one of the simplest forecasting techniques used in operations management. It involves using the actual value from the previous period as the forecast for the next period without considering any other factors or patterns in the data.
- Qualitative forecasting techniques, unlike their quantitative counterparts, rely on subjective judgments and opinions rather than historical data.
- Quantitative forecasting models are used to forecast future data as a function of past data. They are appropriate to use when past numerical data is available and when it is reasonable to assume that some of the patterns in the data are expected to continue into the future.
- Regression analysis involves constructing a mathematical equation that relates the dependent variable to one or more independent variables.
- Seasonal Index is a statistical tool used in forecasting to quantify and account for recurring seasonal patterns or demand fluctuations over a specific period.
- Short-term forecasting focuses on a timeframe ranging from daily to a few months. These forecasts are primarily employed for operational decision-making processes, such as inventory management, production scheduling, and workforce allocation.
- Simple Moving Average Method is fundamental for forecasting future values based on historical data. It operates by calculating the average of the data points from the most recent n periods.
- Technological forecasts monitor the rates of technological progress and trends.
