Forecasting K.Prasanthi. Forecasting Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, ? b) 2.5, 4.5, 6.5, 8.5, 10.5, ? c) 5.0, 7.5, - презентация

1 Forecasting K.Prasanthi

2 Forecasting Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, ? b) 2.5, 4.5, 6.5, 8.5, 10.5, ? c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, ?

3 Forecasting Predict the next number in the pattern: a) 3.7, 3.7, 3.7, 3.7, 3.7, b) 2.5, 4.5, 6.5, 8.5, 10.5, c) 5.0, 7.5, 6.0, 4.5, 7.0, 9.5, 8.0, 6.5, Process of predicting a future event based on historical data

4 What is Forecasting? Process of predicting a future event based on historical data Educated Guessing On the basis of all business decisions Production Inventory Personnel Facilities

5 Departments throughout the organization depend on forecasts to formulate and execute their plans. Finance needs forecasts to project cash flows and capital requirements. Human resources need forecasts to anticipate hiring needs. Production needs forecasts to plan production levels, workforce, material requirements, inventories, etc. Importance of Forecasting

6 Short-range forecast Usually < 3 months Job scheduling, worker assignments Medium-range forecast 3 months to 2 years Sales/production planning Long-range forecast > 2 years New product planning Period of forecasting Design of system Detailed use of system

7 Qualitative Forecasting Methods Qualitative Forecasting Models Market Research/ Survey Sales Force Composite Executive Judgement Delphi Method Smoothing

8 Briefly, the qualitative methods are: Executive Judgment: Opinion of a group of high level experts or managers Sales Force Composite: Each regional salesperson provides his/her sales estimates. Those forecasts are then reviewed to make sure they are realistic. All regional forecasts are then pooled at the district and national levels to obtain an overall forecast. Market Research/Survey: Solicits input from customers pertaining to their future purchasing plans. It involves the use of questionnaires, consumer panels and tests of new products and services.. Qualitative Methods

9 Delphi Method: As opposed to regular panels where the individuals involved are in direct communication, this method eliminates the effects of group potential dominance of the most vocal members. The group involves individuals from inside as well as outside the organization. Typically, the procedure consists of the following steps: Each expert in the group makes his/her own forecasts in form of statements The coordinator collects all group statements and summarizes them The coordinator provides this summary and gives another set of questions to each group member including feedback as to the input of other experts. The above steps are repeated until a consensus is reached.. Qualitative Methods

10 Quantitative Forecasting Methods Quantitative Forecasting Regression Models 2. Moving Average 1. Naive Time Series Models 3. Exponential Smoothing a) simple b) weighted a) level b) trend c) seasonality

11 Quantitative Forecasting Methods Quantitative Models 2. Moving Average 1. Naive Time Series Models 3. Exponential Smoothing a) simple b) weighted a) level b) trend c) seasonality

12 Time Series Models Try to predict the future based on past data Assume that factors influencing the past will continue to influence the future

13 1. Naive Approach Demand in next period is the same as demand in most recent period May sales = 48 Usually not good June forecast = 48

14 2a. Simple Moving Average Assumes an average is a good estimator of future behavior Used if little or no trend Used for smoothing F t+1 = Forecast for the upcoming period, t+1 n = Number of periods to be averaged A t = Actual occurrence in period t

15 2a. Simple Moving Average Youre manager in Amazons electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average. Month Sales (000) ? 5? 6 ?

16 2a. Simple Moving Average Month Sales (000) Moving Average (n=3) 14 NA ? 5? (4+6+5)/3=5 6 ? Youre manager in Amazons electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average.

17 What if ipod sales were actually 3 in month 4 Month Sales (000) Moving Average (n=3) 14 NA ? 5 6 ? 2a. Simple Moving Average ?

18 Forecast for Month 5? Month Sales (000) Moving Average (n=3) 14 NA ? 5 6 ? (6+5+3)/3= a. Simple Moving Average

19 Actual Demand for Month 5 = 7 Month Sales (000) Moving Average (n=3) 14 NA ? a. Simple Moving Average ?

20 Forecast for Month 6? Month Sales (000) Moving Average (n=3) 14 NA ? (5+3+7)/3=5 2a. Simple Moving Average

21 Gives more emphasis to recent data Weights decrease for older data sum to 1.0 2b. Weighted Moving Average Simple moving average models weight all previous periods equally Simple moving average models weight all previous periods equally

22 2b. Weighted Moving Average: 3/6, 2/6, 1/6 Month Weighted Moving Average 14 NA /6 = ? ? ? Sales (000)

23 2b. Weighted Moving Average: 3/6, 2/6, 1/6 MonthSales (000) Weighted Moving Average 14 NA /6 = /6 = /6 = 5.333

24 3a. Exponential Smoothing Assumes the most recent observations have the highest predictive value gives more weight to recent time periods F t+1 = F t + (A t - F t ) etet F t+1 = Forecast value for time t+1 A t = Actual value at time t = Smoothing constant Need initial forecast F t to start. Need initial forecast F t to start.

25 3a. Exponential Smoothing – Example 1 Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using =0.10? Assume F 1 =D 1 Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using =0.10? Assume F 1 =D 1 F t+1 = F t + (A t - F t ) iAi

26 F t+1 = F t + (A t - F t ) 3a. Exponential Smoothing – Example 1 = F 2 = F 1 + (A 1 –F 1 ) =820+ (820–820) =820 iAiFi

27 F t+1 = F t + (A t - F t ) 3a. Exponential Smoothing – Example 1 = F 3 = F 2 + (A 2 –F 2 ) =820+ (775–820) =815.5 iAiFi

28 F t+1 = F t + (A t - F t ) This process continues through week 10 3a. Exponential Smoothing – Example 1 = iAiFi

29 F t+1 = F t + (A t - F t ) What if the constant equals 0.6 3a. Exponential Smoothing – Example 1 = = iAiFi

30 F t+1 = F t + (A t - F t ) What if the constant equals 0.6 3a. Exponential Smoothing – Example 2 = = iAiFi

31 Company A, a personal computer producer purchases generic parts and assembles them to final product. Even though most of the orders require customization, they have many common components. Thus, managers of Company A need a good forecast of demand so that they can purchase computer parts accordingly to minimize inventory cost while meeting acceptable service level. Demand data for its computers for the past 5 months is given in the following table. 3a. Exponential Smoothing – Example 3

32 F t+1 = F t + (A t - F t ) What if the constant equals 0.5 3a. Exponential Smoothing – Example 3 = = iAiFi

33 α How to choose α depends on the emphasis you want to place on the most recent data depends on the emphasis you want to place on the most recent data α Increasing α makes forecast more sensitive to recent data 3a. Exponential Smoothing

34 F t+1 = A t + (1- ) A t (1- ) 2 A t Forecast Effects of Smoothing Constant Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - ) 2 = = 0.10 = % 9% 8.1% 90%9%0.9% F t+1 = F t + (A t - F t ) or w1w1 w2w2 w3w3

35 Collect historical data Select a model Moving average methods Select n (number of periods) For weighted moving average: select weights Exponential smoothing Select Selections should produce a good forecast To Use a Forecasting Method …but what is a good forecast?

36 A Good Forecast Has a small error Error = Demand - Forecast

37 Measures of Forecast Error b.MSE = Mean Squared Error etet Ideal values =0 (i.e., no forecasting error) c.RMSE = Root Mean Squared Error a. MAD = Mean Absolute Deviation

38 MAD Example MonthSalesForecast 1220n/a What is the MAD value given the forecast values in the table below? |A t – F t | FtFt AtAt = 40 4 =10

39 MSE/RMSE Example MonthSalesForecast 1220n/a What is the MSE value? |A t – F t | FtFt AtAt = =137.5 (A t – F t ) = 550 RMSE = =11.73

40 Measures of Error tAtAt FtFt etet |e t |et2et2 Jan Feb Mar April May11598 June Mean Absolute Deviation (MAD) 2a. Mean Squared Error (MSE) 2b. Root Mean Squared Error (RMSE) , = 14 1,446 6 = 241 = SQRT(241) =15.52 An accurate forecasting system will have small MAD, MSE and RMSE; ideally equal to zero. A large error may indicate that either the forecasting method used or the parameters such as α used in the method are wrong. Note: In the above, n is the number of periods, which is 6 in our example

41 30 How can we tell if a forecast has a positive or negative bias? TS = Tracking Signal Good tracking signal has low values Forecast Bias MAD

42 Quantitative Forecasting Methods Quantitative Forecasting Regression Models 2. Moving Average 1. Naive Time Series Models 3. Exponential Smoothing a) simple b) weighted a) level b) trend c) seasonality

43 We looked at using exponential smoothing to forecast demand with only random variations Exponential Smoothing (continued) F t+1 = F t + (A t - F t ) F t+1 = F t + A t – F t F t+1 = A t + (1- ) F t

44 Exponential Smoothing (continued) We looked at using exponential smoothing to forecast demand with only random variations What if demand varies due to randomness and trend? What if we have trend and seasonality in the data?

45 Regression Analysis as a Method for Forecasting Regression analysis takes advantage of the relationship between two variables. Demand is then forecasted based on the knowledge of this relationship and for the given value of the related variable. Ex: Sale of Tires (Y), Sale of Autos (X) are obviously related If we analyze the past data of these two variables and establish a relationship between them, we may use that relationship to forecast the sales of tires given the sales of automobiles. The simplest form of the relationship is, of course, linear, hence it is referred to as a regression line.

46 Formulas y = a + b x where, y = a + b x where,

47 y = a + b X Regression – Example

48 General Guiding Principles for Forecasting 1.Forecasts are more accurate for larger groups of items. 2.Forecasts are more accurate for shorter periods of time. 3.Every forecast should include an estimate of error. 4.Before applying any forecasting method, the total system should be understood. 5.Before applying any forecasting method, the method should be tested and evaluated. 6.Be aware of people; they can prove you wrong very easily in forecasting

49 FOR JULY 2 nd MONDAY READ THE CHAPTERS ON Forecasting Product and service design