1. The document discusses 10 different forecasting models: time series moving average, market research, exponential smoothing, jury of executive opinion, naive method, correlation-regression, sales force composite, Delphi technique, and econometric models. 2. It provides examples and explanations of simple and weighted moving averages as well as exponential smoothing. It also outlines advantages and disadvantages of various qualitative forecasting methods. 3. The document concludes with an application example of forecasting apricot production and distribution using a times series seasonal model.

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9. What Comes next??

10. Forecasting Models Chindy Lien MKTG188 T1- Prof. William Rice

11. Forecasting In forecasting, we want to predict future events Forecasting underlines basics of business decisions (Production, Inventory, Personnel, Marketing, and Facilities)

12. 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

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16. Times Series

17. 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

18. 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

19. Moving Average

20. Qualitative method that based on the consumer’s buying intentions. Normally is conducted through surveys, test markets, questionnaire, and consumer panels. Market Research 3

21. 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

22. 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

23. Exponential Smoothing

24. 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

25. 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

26. 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

27. 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

28. 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

29. Correlation-regression

30. 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

31. Sales Force Composite

32. 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

33. Delphi Method

34. 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

35. 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

36. 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.

37. 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.

38. 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.

39. 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