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Welcome to POM 320 - Production and 

Operations Analysis - Web page.

In this session, we will be covering key and basic concepts about Production and

Operations analysis

A Brief Introduction to Forecasting. 

Through this video, we wanna give you a brief and general idea about Forecasting. Don't worry if you think that

the video covers a lot of concepts. Actually it does, and it does it in a perfectly organized way. Later in our

development of this course, we will go through each one of these concepts and discuss them in depth. 

Subjective Vs Objective forecasting methods

Subjective forecasting methods refers to methods that measure either individual or group opinion. The better known subjective forecasting methods include:

Objective forecasting methods (time series methods and regression). Using objective forecasting methods, one makes forecasts based on past history.
Time series forecasting uses only the past history of the series to be forecasted, while regression models often incorporate the past history of other series.
In time series forecasting, the goal is to find predictable and repeatable patterns in past data. Based on the identified pattern, different methods are appropriate. Repeatable patterns that we consider include increasing or decreasing linear trend, curvilinear trend (including exponential growth), and seasonal fluctuation.
When using regression, one constructs a causal model that predicts one phenomenon (the dependent variable) based on the evolution of one or more other phenomenon (the independent variables).

The Evaluation of forecasting methods

Three common measures of forecast error are: 
  • MAD (Average of the absolute errors over n periods)
  • MSE(The average of the sum of the squared errors over n periods)
  • MAPE(The average of the percentage errors over n periods)
Methods for forecasting stationary time series:  we consider two forecasting methods when the underlying pattern of the series is stationary over time: 
  • Moving Average: is the arithmetic average of the N most recent observations.
  • Exponential smoothing forecasts rely on a weighted average of the most recent observation and the previous forecast. The weight applied to the most recent observation is "X", where 0<X<1, and the weight applied to the last forecast is 1-X.
     Both methods are commonly used in practice, but the exponential smooothing method is favored in inventory controle applications-especially in large systems-because it requires much less data storage than does moving average.
Methods for forecasting series with trend. When there is an upward or downward linear trend in the data, two common forecasting methods are:
  • Linear regression: is used to fit a straight line to past data based on the method of least squares.
  • Holt's method: uses seperate exponential smoothing equations to forecast the intercept and the slope of the series each period.
Methods for forecasting seasonal series. A seasonal time series is one that has a regular repeating pattern over the same time frame. Typically, the time frame would be a year and the periods would be weeks or months. The simplest approach for forecasting seasonal series is based on multiplicative seasonal factors. A multiplicative seasonal factor is a number that indicates the relative value of the seriesin any period compared to the average value over a year.