Questions and Answers
What is a forecast?
An estimate of the future level of some variable.
Forecasts are always correct.
False
Which factors tend to make forecasts more accurate?
The simple forecasting model that uses demand for the current period is called the ______.
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What is a moving average model?
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What does the Weighted Moving Average Model allow?
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How is the Exponential Smoothing Model calculated?
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What is the purpose of Adjusted Exponential Smoothing?
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What does seasonal adjustment involve?
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Linear regression is used to calculate the ______ and ______ of a trend.
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Study Notes
Forecasting Overview
- Forecasting is the process of estimating the future level of a variable.
- Essential for assessing long-term capacity needs, developing budgets, hiring plans, and planning production or ordering materials.
Laws of Forecasting
- Forecasts are typically inaccurate by some margin but remain valuable.
- Short-term forecasts are usually more accurate than long-term ones.
- Aggregated forecasts for groups of products or services are generally more reliable.
Forecasting Methods
- Qualitative Techniques: Based on intuition or informed opinion, useful when data is scarce or irrelevant.
- Quantitative Models: Utilize measurable historical data to create forecasts, including time series and causal models.
Demand Movement
- Randomness: Unpredictable changes in demand from one period to the next.
- Trend: Long-term directional movements upward or downward in a time series.
- Seasonality: Consistent patterns of spikes or drops associated with specific times of the year.
Time Series Models
- Last Period Model: Forecast for the next period is based solely on the demand of the current period (Ft+1 = Dt).
Moving Average Model
- Moving Average Model: Averages recent demand to forecast future demand.
- Formula: Ft+1 = (∑D_t+1−i) / n, where n is the number of periods.
Weighted Moving Average Model
- A variation of the moving average where different weights are applied to past observations.
- Example for Period 8: Forecast calculated as [(0.5 × 14) + (0.3 × 8) + (0.2 × 10)] / 1.
Exponential Smoothing Model
- Forecast calculated as a weighted average of the current period’s actual value and forecast.
- Weighting factor (α) determines the influence of prior forecasts.
Adjusted Exponential Smoothing Model
- Includes a trend adjustment factor alongside the unadjusted forecast.
- Components: AFt+1 = Ft+1 + Tt+1, where Tt+1 factors in trend adjustments.
Linear Regression
- Establishes a linear relationship between variables and is used to predict outcomes.
- Involves calculating coefficients 'a' (intercept) and 'b' (slope) to form the regression equation.
Seasonal Adjustments
- Addresses repeated demand patterns linked to specific times of the year.
- Four-step adjustment process:
- Calculate forecasts using unadjusted models.
- Compute the ratio (Demand/Forecast).
- If spanning multiple years, average the ratios for corresponding periods to establish a seasonal index.
- Multiply unadjusted forecasts by the seasonal index for adjusted forecasts.
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Description
This quiz explores the essential concepts and methodologies of forecasting, focusing on how it is used to estimate future levels of various variables. Participants will learn about the importance of forecasts in capacity planning, budgeting, production, and other strategic decisions. Additionally, the quiz will touch on the accuracy of forecasts based on timing and aggregation.
