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SATURDAY LECTURE
Forecasting introduction
Forecasts are needed as inputs to other operations decisions such as inventory planning, production scheduling, and staff scheduling and hiring.
Example: Sandwich Shop
Demand for the amount of food
Scheduling employees
Determining schedule for supplies, trash pick-up, etc.
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 cauSal 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?
SATURDAY ASSIGNMENT
The following must be submitted by the end of the day (11:59pm local time) on Saturday. No credit is available for submissions not made by this time. The system will not accept submissions made after this time. This is not a group assignment, so no collaboration or discussion is allowed; it must be worked on individually. Submit your responses to all three questions in a single document. There should be a single title page for the document. However, each question should have its own separate references page. The total length of the document should be between 13 and 16 pages.
Question 1: In 750-1000 words, identify and explain the five steps of forecasting, and then come up with an original example taken from your own professional experiences to illustrate these steps. Your response must be original. You must incorporate at least three reliable sources, one of which must be the class text, both as references and corresponding in-text citations. APA format is expected. Question 2: In 750-1000 words, explain qualitative and quantitative forecasting, and then come up with an original example of each taken from your own professional experiences to illustrate these two forecasting types. Your response must be original. You must incorporate at least three reliable sources, one of which must be the class text, both as references and corresponding in-text citations. APA format is expected. Question 3: In 750-1000 words, identify and explain the types of data patterns, and then come up with an original example of each (strive to make it based on your own professional experiences) to illustrate each data pattern type. Your response must be original. You must incorporate at least three reliable sources, one of which must be the class text, both as references and corresponding in-text citations. APA format is expected.