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Management and Production 3-3
Terms in this set (39)
a statement about the future value of a variable of interest. Forecasts are an important element in making informed decisions.
What forecasts do you use?
Two Important Aspects of Forecasts
1. Expected LEVEL of demand
---The level of demand may be a function of some STRUCTURAL VARIATION such as trend or seasonal variation
---Related to the potential size of forecast error
Features common to all Forecasts (4)
1. Techniques assume some underlying causal system that existed in the past will persist into the future.
2. Forecasts are not perfect.
3. Forecasts for groups of items are more accurate than those for individual items.
4. Forecast accuracy decreases as the forecasting horizon increases.
Elements of a GOOD forecasts
--should be TIMELY
--should be ACCURATE
--should be RELIABLE
--should be expressed in MEANINGFUL UNITS
--should be IN WRITING
--technique should be SIMPLE TO UNDERSTAND AND USE
--should be COST EFFECTIVE
Steps in the Forecasting process
1.Determine the purpose of the forecast
2. Establish a time horizon
3. Select a forecasting technique
4. Obtain, clean, and analyze appropriate data
5. Make the forecast
6. Monitor the forecast
Forecast Accuracy and Control
-Forecasters want to minimize forecast errors
---It is nearly impossible to correctly forecast real-world variable values on a regular basis
---So, it is important to provide an indication of the extent to which the forecast might deviate from the value of the variable that actually occurs
-Forecast accuracy should be an important forecasting technique selection criterion
-Forecast errors should be monitored
Error = Actual - Forecast
---If errors fall beyond acceptable bounds, corrective action may be necessary.
----------When does a weather forecast fall outside of acceptable bounds? Why?
1. Qualitative Forecasting
2. Quantitative Forecasting
-Qualitative techniques permit the inclusion of soft information such as:
-These factors are difficult, or impossible, to quantify
-Quantitative techniques involve either the projection of historical data or the development of associative methods that attempt to use causal variables to make a forecast
-These techniques rely on hard data
Types of Forecasts
2. Time series
3. Associative models
uses subjective inputs
Forecasts that use subjective inputs such as opinions from consumer surveys, sales staff, managers, executives, and experts
uses historical data assuming the future will be like the past
-Forecasts that project patterns identified in recent time-series observations
-----Time-series - a time-ordered sequence of observations taken at regular time intervals
-Assume that future values of the time-series can be estimated from past values of the time-series
uses explanatory variables to predict the future
Associative Forecasting Techniques
-Home values may be related to such factors as home and property size, location, number of bedrooms, and number of bathrooms
---________ ________ are based on the development of an equation that summarizes the effects of predictor variables
variables that can be used to predict values of the variable of interest
a technique for fitting a line to a set of data points
Simple Linear Regression
- the simplest form of regression that involves a linear relationship between two variables
----------The object of _____ ______ _______ is to obtain an equation of a straight line that minimizes the sum of squared vertical deviations from the line (i.e., the least squares criterion)
Simple Linear Regression Assumptions
1. Variations around the line are random
2. Devaiations around the average value (the line) should be normally distributed
3. Predictions are made only within the range of observed values
--A measure of the scatter of points around a regression line
--If the ____ _____ is relatively small, the predictions using the linear equation will tend to be more accurate than if the _____ ____ is larger
-Always plot the line to verify that a linear relationships is appropriate
-The data may be time-dependent.
--If they are
-------use analysis of time series
-------use time as an independent variable in a multiple regression analysis
-A small correlation may indicate that other variables are important
---A measure of the strength and direction of relationship between two variables
---Ranges between -1.00 and +1.00
r^2, square of the correlation coefficient
---A measure of the percentage of variability in the values of y that is "explained" by the independent variable
---Ranges between 0 and 1.00
Time-Series Behaviors - Understanding demand patterns
Before a forecasting technique is selected, you MUST understand the underlying pattern of demand for your organization. Your selected forecasting technique MUST be able to mimic and predict that pattern of demand.
A long-term upward or downward movement in data
Short-term, fairly regular variations related to the calendar or time of day. A pattern of demand that repeats itself in the short run.
Restaurants, service call centers, and theaters all experience seasonal demand
Wavelike variations lasting more than one year
---These are often related to a variety of economic, political, or even agricultural conditions
Residual variation that remains after all other behaviors have been accounted for
Due to unusual circumstances that do not reflect typical behavior
1. Naive Forecast
2. Moving Average
3. Weighted Moving Average
4. Exponential Smoothing
Uses a single previous value of a time series as the basis for a forecast
----The forecast for a time period is equal to the previous time period's value
Can be used when
---The time series is stable
---There is a trend
---There is seasonality
Time-Series Forecasting - Averaging
These Techniques work best when a series tends to vary about an average
---Averaging techniques smooth variations in the data
---They can handle step changes or gradual changes in the level of a series
1. Moving average
2. Weighted moving average
3. Exponential smoothing
Technique that averages a number of the most recent actual values in generating a forecast.
As new data become available, the forecast is updated by adding the newest value and dropping the oldest and then recomputing the the average.
The number of data points included in the average determines the model's sensitivity
------> Fewer data points used-- more responsive
------> More data points used-- less responsive
Weighted Moving Average
The most recent values in a time series are given more weight in computing a forecast
----The choice of weights, w, is somewhat arbitrary and involves some trial and error
A weighted averaging method that is based on the previous forecast plus a percentage of the forecast error.
Regardless of the forecasting technique selected, accuracy must be measured and evaluated.
Two common measures of ______ _____ are MAD and MSE.
weights all errors evenly.
weight errors according to their squared values.
Using Forecast Information
1. Reactive Approach
2. Proactive Approach
--View forecasts as probable future demand
--React to meet that demand
Seeks to actively influence demand
Generally requires either and explanatory model or a subjective assessment of the influence on demand
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