JIT, lean and forecasting - Operations Management assignment
Chapter 8
Niccole Hyatt, PhD
Chapter overview
The principles and steps in forecasting are described. The various types of forecasting methods are presented and discussed.
Qualitative and quantitative methods are compared. Choosing the appropriate method based on the demand pattern is discussed.
Methods for determining the accuracy of the forecasts are presented.
objectives
Identify principles of forecasting.
Explain the steps involved in the forecasting process.
Identify types of forecasting methods and their characteristics.
Describe time series models.
Describe causal modeling using linear regression.
Compute forecast accuracy.
Explain the factors that should be considered when selecting a forecasting model.
Explain the nine-step process of CPFR.
Forecasting introduction
Forecasts are needed as inputs to other operations decisions such as inventory planning, production scheduling, and staff scheduling and hiring.
Example: Sub Shop
Demand for the amount of food
Scheduling employees
Determining schedule for supplies, trash pick up
Forecasting steps
The first step is to decide what to forecast in terms of the data to forecast and the level of detail required.
The second step is to evaluate and analyze the appropriate data. In this step, we identify the data needed and its availability.
The third step is to select and test the forecasting model. We must consider different factors when selecting the model such as ease of use, cost, and accuracy.
In the fourth step, the forecasts are generated using the model.
During the last step, we monitor the accuracy of the forecasts since we may need to change the model if the environment has changed.
Qualitative and quantitative forecasting
Qualitative methods are subjective since they rely on the educated guesses of the forecaster.
Quantitative forecasting methods are based on using calculations to make forecasts. These calculations require past data. They are more objective.
I think that qualitative methods are better because we are in a rapidly changing environment where the past is not a good predictor of the future.
Types of data patterns
Level patterns show a stable demand that fluctuates around the mean. The demand for some food items is relatively stable.
A trend is where the data is either increasing or decreasing rather steadily over time. For example, the current trend in computer sales has been decreasing in the U.S. because less people need to upgrade their computers.
Seasonality occurs when the season affects the level of demand. For example, computer sales increase during the Christmas season dramatically.
Cycles are movements in the data over longer periods of time. The demand for housing tends to follow a cyclical pattern based on the interest rates and other economic factors.
Time series and casual models
Time series models assume that the demand is only related to its own past demand patterns.
Causal models assume that the some other factors affect the variable we are trying to predict. Causal models measure the relationship between the other factor(s) and the data we are trying to forecast.
When thinking of level, trend, and seasonality, the same set of models can be used in most cases.
The key difference is that an additional feature or calculation is added to the model to adjust for the effect of the trend or seasonality.
Questions?
Niccole Hyatt, PhD