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It is a statement about the future value of a variable of interest
Forecast
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Two Important Aspects of Forecast
- OExpected level of demand
- OAccuracy
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Forecast Uses
- 1. Plan the system- generally involves long-range plans
- 2. Plan the use of the system – generally involves short- and medium-range plans
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Forecast Uses in Operations
ODemand Planning: Forecasts help predict future demand, allowing for better inventory management and production scheduling.
OResource Allocation: They aid in allocating resources efficiently, including labor, materials, and equipment.
OCost Control: Accurate forecasts enable cost-effective production and reduce overproduction or underproduction costs. Customer
ORisk Mitigation: Helps identify potential supply chain disruptions and plan for contingencies.
OStrategic Decision Making: Guides long-term strategies and investments based on anticipated market trends.
OEfficient Supply Chain: Enhances the efficiency of the supply chain by aligning procurement and distribution with expected demand.
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Forecasts are not perfect because
Because random variation is always present, there will always be some residual error, even if all other factors have been accounted for.
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Elements of a Good Forecast
- OShould be timely
- OShould be accurate
- OShould be reliable
- OShould be expressed in meaningful units
- OShould be in writing
- OTechnique should be simple to understand and use
- OShould be cost effective
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Steps in the Forecasting Process
- 1.Determine the purpose of the forecast
- 2.Establish a time horizon
- 3.Obtain, clean, and analyze appropriate data
- 4.Select a forecasting technique
- 5.Make the forecast
- 6.Monitor the forecast errors
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is consistent upward or downward movement of the demand. This may be related to the product’s life cycle.
Trend
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is a pattern in the data that tends to last more than one year in duration. Often, they are related to events such as interest rates, the political climate, consumer confidence or other market factors.
Cycle
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Many products have a seasonal pattern, generally predictable changes in demand that are recurring every year. Fashion products and sporting goods are heavily influenced by seasonality.
Seasonal
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use historical data as the basis of estimating future outcomes
Time series methods
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a series of data points indexed (or listed or graphed) in time order. Most commonly, it is a sequence taken at successive equally spaced points in time.
Time series
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The simplest forecasting method is the_________. In this case, the forecast for the next period is set at the actual demand for the previous period.
naïve method
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In this method, we take the average of the last “n” periods and use that as the forecast for the next period. The value of “n” can be defined by the management in order to achieve a more accurate forecast.
Simple Moving Average
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Similar to a moving average, except that it assigns more weight to the most recent values in a time series.
Weighted Moving Average
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In its simplest form, an___________ of time series data allocates the exponentially decaying weights from newest to oldest observations, ie. analyzing data from a specific period of time via providing more importance to recent data and less importance to former data. This method produces “smoothed data”, the data that has a noise removed, and allows trends and patterns to be more clearly visible.
exponential smoothing
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helps to define this linear relation that is present between the two quantities and how they are interdependent.
Linear regression formula
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Linear Regression Types
- Simple Linear Regression
- Multiple Linear Regression
- Logistic Regression
- Ordinal Regression
- Multinomial Regression
- Discriminant Analysis
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•One is the dependent variable (that is interval or ratio).
•One is the independent variable (that is interval or ratio or dichotomous).
Simple Linear Regression
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•One is the dependent variable (that is interval or ratio).
•Two or more independent variables ( that is interval or ratio or dichotomous).
Multiple Linear Regression
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•One is the dependent variable (that is binary).
•Two or more independent variable(s) ( that is interval or ratio or dichotomous).
Logistic Regression
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•One is the dependent variable (that is ordinal).
•One or more independent variable(s) (that is nominal or dichotomous).
Ordinal Regression
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•One is the dependent variable (that is nominal).
•One or more independent variable(s) (that is interval or ratio or dichotomous).
Multinomial Regression
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•One is the dependent variable (that is nominal).
•One or more independent variable(s) (that is interval or ratio).
Discriminant Analysis
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Linear Regression Formula
Y= a+bx
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