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Forecasting model 15 04-31

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    Forecasting model 15 04-31 Forecasting model 15 04-31 Presentation Transcript

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    • What Comes next??
    • Forecasting Models
      • Chindy Lien
      • MKTG188
      • T1- Prof. William Rice
    • Forecasting In forecasting, we want to predict future events Forecasting underlines basics of business decisions (Production, Inventory, Personnel, Marketing, and Facilities)
    • Now….
      • There are numerous Forecasting Methods.
      • Below is the list of 10 Forecasting Models that will be
      • discussed:
      • Times Series
      • Moving Average
      • Market Research
      • Exponential Smoothing
      • Jury of Executive
      6. Naïve 7. Correlation-Regression 8. Sales Force Composite 9. Delphi Technique 10. Econometric Models
    • Times series 1
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    • Times Series
    • It is the most commonly used forecasting method and it works best when the time series fluctuates around the constant base level. There are 2 types of Moving Average methods: 1. Simple Moving Average Uses the average demand for a fixed sequence of periods. 2. Weighted Moving Average Adjust the moving average method by assigning weights to the most recent data Moving Average 2
    • This is an example of simple moving average, using a 3 period moving average Moving Average This is an example of weighted moving average, using a 3 period moving average
    • Moving Average
    • Qualitative method that based on the consumer’s buying intentions. Normally is conducted through surveys, test markets, questionnaire, and consumer panels. Market Research 3
    • Market Research
      • Bias from the buyers
      • Difference between desires and reality
      • Can be time consuming and expensive
      Advantages
      • The forecasting is according to the actual buyers
      • Suitable to forecast new products
      • It’s relatively simple and easy to conduct
      Disadvantages
    • Moving average technique that requires little record keeping of past data. Exponential Smoothing Uses a smoothing constant α with a value between 0 and 1 (normally range from 0.1-0.5) It reacts quickly with the changes in demand. 4
    • Exponential Smoothing
    • Jury of executive opinion Qualitative method that based on the opinions of a group of executives. Often used in combination with statistical models. Value as most important method by marketing managers. 5
      • It is opinions based. Not supported by data
      • Not all the executives have knowledge on forecasting
      Jury of executive opinion Advantages
      • Inexpensive
      • Opinions from all executives are integrated
      • Easy, Quick, and not much math required
      Disadvantages
    • Time-series method that make forecast based on purely on historical patterns in the data. With naïve approach, forecast for the next period is the same as the last period. For example: if October sales were 1500, then November sales will be 1500 as well. Naive 6
      • Not suitable for long run forecasting
      • Does not work with data that is trended
      Naive Advantages
      • The model is simple and flexible
      • Works best in the short term
      • Provides a baseline to measure other models
      Disadvantages
    • Method of forecasting that shows the nature of relationship between two or more variables Step 1: Determine independent variable Step 2: Measure correlation Step 3: Compute slope and intercept Obtain forecast of independent variables Correlation-regression 7
    • Correlation-regression
    • Sales Force Composite Forecasting method that based on feelings, ideas, and personal experiences of the company’s sales people. It is recommended to include the opinions of management and the higher levels to reach an overall forecast. 8
    • Sales Force Composite
    • Delphi Technique Delphi technique is qualitative method of forecasting that relies on group consensus Normally, a panel of experienced people are asked to respond to a series of questionnaires. It is conducted in writing and does not require a face-to-face meeting. 9
    • Delphi Method
    • Econometric Models It is one of the tools used to forecast future developments in the economy. Econometric model measures two or more independent variable. Forecast of one dependent variable might influence other variables 10
    • Econometric Models
      • Expensive
      • Experts are required for this model
      • Need constant monitor
      • Need of data
      Advantages
      • Discover the relationships and interrelationships
      • Major changes can be anticipated
      • A model for simulation
      Disadvantages
    • Application Apricot production in the central California valley is an important field crop. People wait for the first fruits with great anticipation. The first apricots to reach the store shelf are quite expensive and usually get purchased quickly. There is a reason to try to get those first apricots on the shelf. The problem is the weather and varieties. In selling to stores on the east coast they want dates for advertising, shipping, handling etc about 2 to 3 months before. We in California have to continue to produce earlier varieties by cross breeding. But these varieties are not as sweet or juicy as later varieties.
    • Solution
      • Forecasting method: Times Series-Seasonal
      • Since the taste of the Apricots varies over time, weather, and location, I think the farmers should make a wise decision on the distribution of the apricots.
      • I will store and stock all the first high quality produced apricots to California market only due the convenience distribution.
    • Solution 3. The later- produced apricots will be distributed to East Coast and continually distributed throughout California and other states. 4. By applying this strategy, I want to emphasize on “ONLY IN CALIFORNIA” image for the apricots. So, people from all over the countries know that the best apricots are produced in California. This can add value for apricots growth and distributed in California.
    • References: Presentation template: www.presentationmagazine.com http://www.uoguelph.ca/~dsparlin/forecast.htm http://www.referenceforbusiness.com/small/Eq-Inc/Forecasting.html http://www.smetoolkit.org/smetoolkit/en/content/en/416/Demand-Forecasting http://www.docviewer.org/view.php?url=http://www.umsl.edu/~campbelljf/252/chapter4a.ppt&t=Chapter%201,%20Heizer/Render,%205th%20edition http://www.uoguelph.ca/~dsparlin/forecast.htm