Podcast
Questions and Answers
What is the goal of forecasting in business?
What is the goal of forecasting in business?
Why are qualitative forecasting methods considered subjective?
Why are qualitative forecasting methods considered subjective?
What is a weakness of the Delphi method in qualitative forecasting?
What is a weakness of the Delphi method in qualitative forecasting?
Why are forecasts more accurate for grouped data than for individual items?
Why are forecasts more accurate for grouped data than for individual items?
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What determines the effectiveness of quantitative forecasting methods?
What determines the effectiveness of quantitative forecasting methods?
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Why is forecasting considered an ongoing process?
Why is forecasting considered an ongoing process?
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What is the main characteristic of Naïve Forecasts?
What is the main characteristic of Naïve Forecasts?
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Which aspect of forecasts is related to the potential size of forecast error?
Which aspect of forecasts is related to the potential size of forecast error?
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What is a key assumption of Time Series Models?
What is a key assumption of Time Series Models?
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Which type of forecasting method uses judgmental approaches and educated guesses?
Which type of forecasting method uses judgmental approaches and educated guesses?
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What type of models predict future outcomes through cause-and-effect relationships?
What type of models predict future outcomes through cause-and-effect relationships?
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What does MAD, MSE, and TS measure in forecasting?
What does MAD, MSE, and TS measure in forecasting?
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Study Notes
Forecasting Steps
- Decide what needs to be forecast, including the level of detail, units of analysis, and time horizon required.
- Evaluate and analyze appropriate data, identifying what data is needed and if it's available.
- Select and test a forecasting model, considering cost, ease of use, and accuracy.
- Generate the forecast and monitor forecast accuracy over time.
Principles of Forecasting
- Forecasts are rarely perfect.
- Forecasts are more accurate for grouped data than for individual items.
- Forecasts are more accurate for shorter time periods than longer ones.
- The goal of forecasting is to generate good forecasts on average over time and keep errors low.
Types of Forecasting
- Classified into two groups: qualitative and quantitative methods.
Qualitative Methods
- Based on human judgment and opinions, subjective and non-mathematical.
- Can incorporate latest changes in the environment and "inside information."
- Can be biased and reduce forecast accuracy.
Quantitative Methods
- Based on mathematics, consistent and objective.
- Can consider much information and data at once.
- Often, quantifiable data are not available, and the method is only as good as the data.
Types of Qualitative Methods
Executive Opinion
- A group of managers meet to generate a forecast.
- Good for strategic or new-product forecasting.
- One person's opinion can dominate the forecast.
Market Research Delphi Method
- Seeks to develop a consensus among a group of experts.
- Good for identifying customer preferences and forecasting long-term product demand, technological changes, and scientific advances.
- Can be time-consuming to develop.
Naïve Forecasts
- The forecast for any period equals the previous period's actual value.
- Simple to use, virtually no cost, quick, and easy to prepare.
- Data analysis is nonexistent, easily understandable, but cannot provide high accuracy.
Time Series Models
- Forecaster looks for data patterns, including level, trend, seasonality, cycle, and random variation.
- Historic pattern to be forecasted, including level, trend, and seasonality.
Forecast Accuracy Methods
- Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Tracking Signal (TS).
- Mean Absolute Percentage Error (MAPE).
Forecasting Models
- Time Series Models: assumes information needed to generate a forecast is contained in a time series of data.
- Causal Models or Associative Models: explores cause-and-effect relationships, using leading indicators to predict the future.
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Description
Learn about the uses and main characteristics of Naïve Forecasts in time series data analysis. Explore the two important aspects of forecasts related to the expected level of demand.
