Forecasting Methods Quiz

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Questions and Answers

What is the forecasted tonnage for quarter 9 using an alpha ($\alpha$) of 0.10?

179.5

180.22

178.6 (correct)

182

Which quarter's forecast for $\alpha = 0.50$ was the lowest?

4 (correct)

What is the absolute deviation for quarter 5 using an alpha ($\alpha$) of 0.10?

6.77 (correct)

6.64

What alpha ($\alpha$) value resulted in a higher forecast for quarter 3?

0.5 Signup and view all the answers

What is the forecast for quarter 2 using an alpha ($\alpha$) of 0.10?

175.5 Signup and view all the answers

What is the total sum of the weights used in the weighted moving average model?

6 Signup and view all the answers

What is the weighted moving average for April, using the sales data provided?

12.17 Signup and view all the answers

How would the weighted moving average for May change if the sales for April were 15 instead of 16?

It would decrease. Signup and view all the answers

What was the actual sales number in March?

13 Signup and view all the answers

What would the weighted moving average for June be if the sales for June were 25 instead of 23?

22.67 Signup and view all the answers

Which month had the highest sales figure recorded?

August Signup and view all the answers

What was the weighted moving average for September based on the available data?

28.33 Signup and view all the answers

How is the moving average calculated using the weighted scheme?

Multiplying by appropriate weights and dividing by the sum of the weights. Signup and view all the answers

What is the primary reason for using trend adjustment in exponential smoothing?

To respond effectively to trends Signup and view all the answers

Which equation represents the trend correction in exponential smoothing?

$T_{t+1} = (1 - eta)T_t + eta (F_{t+1} - F_t)$ Signup and view all the answers

How does a high value of the trend smoothing constant, $eta$, affect forecasts?

It increases responsiveness to changes in trend. Signup and view all the answers

What type of method is often referred to as first-order smoothing?

Simple exponential smoothing Signup and view all the answers

What is the purpose of using trial-and-error when selecting a smoothing constant?

To minimize errors in forecasting Signup and view all the answers

Which method utilizes a linear regression equation for forecasting time-series data?

Trend projection Signup and view all the answers

What is a characteristic of time-series data that trend projections are particularly suited for?

Data that follows a linear trend Signup and view all the answers

In trend projection, which equation is sometimes used to derive future forecasts?

$y = mx + b$ Signup and view all the answers

What is the purpose of using the Centered Moving Average (CMA) approach in seasonal variation analysis?

To isolate seasonal variations from trend effects. Signup and view all the answers

How should seasonal indices be computed when both trend and seasonal components are present?

Using a Centered Moving Average (CMA) approach. Signup and view all the answers

If the sum of seasonal indices is not equal to the number of seasons, what should be done?

An adjustment should be made. Signup and view all the answers

In the provided data for monthly sales, what does a seasonal ratio greater than 1 indicate?

Sales are higher than the average for that time. Signup and view all the answers

What is indicated by averaging the seasonal ratios to obtain the seasonal index?

It identifies the overall seasonal effect across different quarters. Signup and view all the answers

Which months in the provided data have a sales factor of greater than 1?

May and August. Signup and view all the answers

Why is it incorrect to interpret random fluctuations as seasonal variations?

Seasonal variations follow a predictable pattern. Signup and view all the answers

When calculating CMA, which quarters are involved for a specific quarter in an annual time series?

One-half of the previous quarter and the entire next two quarters. Signup and view all the answers

What is the purpose of multiplying seasonal indices by (Number of seasons)/(Sum of the indices)?

To ensure the seasonal indices sum to the number of seasons Signup and view all the answers

What is the correct formula for calculating the Tracking Signal?

RSFE / MAD Signup and view all the answers

In the regression model for forecasting sales, what does X1 represent?

The time period Signup and view all the answers

What does a Tracking Signal value of +2.5 indicate?

The forecasts are performing adequately. Signup and view all the answers

What is the formula for the regression equation given in the content?

Ŷ = 104.1 + 2.3X1 + 15.7X2 + 38.7X3 + 30.1X4 Signup and view all the answers

What does using dummy independent variables in regression allow for?

Generating distinct coefficient values for different seasons Signup and view all the answers

In the Kimball’s Bakery example, what was the cumulative RSFE after the 6th period?

+35 Signup and view all the answers

Which of the following is NOT a step in computing seasonal indices based on CMAs?

Divide each seasonal ratio by the number of seasons Signup and view all the answers

Which measure is expected to be close to zero for adequate forecast performance at Disney?

Mean Absolute Percentage Error (MAPE) Signup and view all the answers

What is the Mean Absolute Deviation (MAD) for the first 6 periods in the Kimball’s Bakery example?

14.2 Signup and view all the answers

What do the terms MAD and MSE refer to in the context of evaluating the model?

Mean Absolute Deviation and Mean Squared Error Signup and view all the answers

What are the results from using the regression model mentioned in the content for the first quarter?

134 Signup and view all the answers

Which software is NOT mentioned as a tool that can be used for forecasting?

Excel Signup and view all the answers

What type of forecasting model can be automatically selected by dedicated forecasting software?

Time-series and Causal Models Signup and view all the answers

How does the regression model handle seasonal components?

By incorporating dummy variables for distinct seasons Signup and view all the answers

How many periods are included in the tracking signal calculation for Kimball's Bakery?

6 Signup and view all the answers

Study Notes

Introduction to Forecasting

Managers constantly strive to reduce uncertainty and enhance future estimations.
Forecasting serves as the primary tool for achieving this objective.
Subjective methods, like intuition and experience, are sometimes used by firms.
Several quantitative techniques, including moving averages, exponential smoothing, trend projections, and least squares regression analysis, are employed by businesses.
Forecasting involves methodical steps:
- Defining the forecast's objective
- Selecting the items to be forecasted
- Determining the forecast's time horizon
- Choosing the appropriate forecasting model or multiple models
- Gathering necessary data
- Validating the forecasting model
- Making the forecast
- Implementing the forecast results

What is Forecasting?

Forecasting is a process of estimating or predicting future demand through past and present events.
It provides insights into potential future events and their impact on businesses.
Forecasting is considered a blend of art and science in estimating future events (Heizer and Render, 2010).
It involves a systematic approach to inferring the future based on known facts (Louis Allen).

Strategic Importance of Forecasting

Supply Chain Management involves all activities that ensure the right product, at the right price, and in the right place.
Apple Inc. exemplifies effective supply chain management by controlling diverse aspects of its global system.
Human resources (HR) demand forecasting predicts future human resource needs to ensure the right number and quality of personnel. HR functions, such as hiring, training, promotions, and lay-offs, rely on this forecast.
Capacity from an organizational perspective encompasses all elements—physical resources, ideas, and people—required to achieve the organization's mission and meet demand.

Benefits of Forecasting

Aids in inventory and material management.
Improves employee relations.
Optimizes the utilization of available resources.
Enhances customer satisfaction.

Forecasting Models

Models are classified into qualitative and quantitative categories.
Qualitative methods include Delphi Methods, Jury of Executive Opinion, Sales Force Composite, and Consumer Market Surveys.
Quantitative methods are further categorized into time-series and causal techniques.
Time-series methods, such as moving averages and exponential smoothing, identify trends and predict future values based on historical data.
Causal models employ variables or factors potentially affecting a forecasted quantity. Regression analysis serves as the standard quantitative approach for causal modeling.

Time-Series Models

Time-series models anticipate future values based exclusively on historical patterns.
Methods including moving averages, exponential smoothing, trend projections, and decomposition utilize past data to make predictions.
Regression analysis underpins trend projections and some decomposition models.

Causal Models

Causal models identify and model the relationships between factors influencing the forecasted quantity.
The goal is to establish a statistically sound relationship between the variable being forecast and relevant influencing factors.
Regression analysis is the most prevalent technique employed in causal modeling.

Qualitative Models

Qualitative models incorporate judgment or subjective factors, proving valuable when accurate quantitative data acquisition is challenging.
Qualitative methods like Delphi, Jury of Executive Opinion, and Sales Force Composite incorporate expert judgment.
Consumer market surveys gather input from potential or current customers about purchasing plans.

Scatter Diagrams

Scatter diagrams visually represent the relationship between variables in a time series, aiding forecast construction.
Wacker Distributors utilized scatter data to forecast sales for various products.
By examining plotted data points, patterns can be identified; if the relationship between variables appears linear, a trend projection might be employed to predict future outcomes.

Measures of Forecast Accuracy

Forecasts are evaluated against actual values.
Common error measures include Mean Absolute Deviation (MAD), Mean Squared Error (MSE), and Mean Absolute Percent Error (MAPE).
Bias, representing the average error, indicates whether forecasts tend towards high or low values.
Naïve forecasting models serve as baseline comparisons for evaluating the accuracy of more sophisticated methods.

Hospital Days Forecast Example

Ms. Smith, forecasting total hospital inpatient days, reevaluates her model using provided actual data.
MAD, MSE, and MAPE are used for model evaluation.

Time-Series Forecasting Models

A time series compiles evenly-spaced events.
Time-series forecasts solely utilize past data values.

Moving Averages

Moving averages produce forecasts when demand remains relatively stable over time.
The forecast is the average of recent data values from the time series, replacing the oldest data point as new values become available.

Weighted Moving Averages

Weighted moving averages assign varying weights to past data points, emphasizing recent data.
The technique is particularly beneficial when a trend or other pattern is emerging in the data.

Exponential Smoothing

Exponential smoothing is a data-smoothing technique that places greater weight on the most recent data points.
The forecast for a period consists of the previous forecast adjusted for the error.
A smoothing constant (0 ≤ α ≤ 1) controls the relative importance given to recent versus prior data in the forecast.

Selecting the Smoothing Constant

Choosing the proper smoothing constant (α) is critical in creating a reliable forecast.
Experimentation to find the smoothing constant (α) with the lowest mean absolute deviation (MAD) is commonly employed.

Trend Projections

Linear regression generates a trend line from observed data.
Future forecasts are calculated by projecting this line into the future.
The least squares method determines the trend line that minimizes errors between predicted and actual values.

Midwestern Manufacturing Company Example

The data shows a trend in electrical generator sales.
Using simple linear regression, forecasts for the next two quarters can readily be made.
Data analysis helps to decide on the best model and its forecasting accuracy.

Decomposition of a Time-Series

Time series consist of components including trend, seasonality, cycles, and random variations.
Multiplicative models express demand as the product of trend, seasonal, cyclical, and random factors.
Additive models represent demand as the sum of these components.

Seasonal variations

Recurring fluctuations suggest the need for seasonal adjustments within trend lines.
A seasonal index quantifies how a particular season compares with the average.
This index calculates the ratio of the average value for a certain season to the average across all data points when no trend is present.

Regression with Trend and Seasonal Components

Multiple regression methods forecast a trend or seasonal pattern by using time period and dummy variables to capture seasonal effects.
This approach builds a model for forecasting.

Monitoring and Controlling Forecasts

Tracking signals monitor forecast accuracy.
A tracking signal exceeding predefined limits indicates a need for analysis or adjustments to the forecasting model or procedure.
A standardized measure like Mean Absolute Deviation (MAD) clarifies the scale of forecast errors.

Forecasting at Disney

Disney's daily reports summarize yesterday's attendance forecasts and current attendance.
Error measures, like MAPE, are expected to approach zero.
An annual forecast for 2000 achieved a MAPE of 0.

Using The Computer to Forecast

Computer programs, including specialized forecasting software, aid in handling intricate time series or causal models.
These tools automate the selection of optimal model parameters.
Integrating the process within inventory planning and control systems improves efficiency.

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Description

Test your understanding of forecasting methods with this quiz focused on alpha values in quarterly forecasting. You'll answer questions regarding tonnage predictions, absolute deviation, and comparisons of different alpha rates for better accuracy in forecasts.