1. Forecasting MKA/13 1 Meaning Elements Steps Types of forecasting

2. Forecasting MKA/13 2 FORECAST: A statement about the future Used to help managers Plan the system Plan the use of the system

3. Common Features MKA/13 3 Assumes causal system past ==> future Forecasts rarely perfect because of randomness Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases I see that you will get an A this quarter

4. Elements of a Good Forecast MKA/13 4 Timely Accurate Reliable Meaningful Written Easy to use

5. Steps in the Forecasting Process MKA/13 5 Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”

6. Types of forecast MKA/13 6 i. Qualitative ii. Time series analysis iii. Causal relationship iv. Simulation

7. Qualitative MKA/13 7 subjective; Judgmental, based on estimates and opinions Can be of many types such as: i. Grass roots ii. Market research iii. Panel consensus iv. Historical analogy v. Delphi method

8. Grass roots MKA/13 8 Forecast by adding successively from the bottom Derives a forecast by compiling input from those at the end of hierarchy who deal with what is being forecast. As for example: An overall sales forecast may be derived by combining inputs from each sales person who is closest to his territory.

9. Market research MKA/13 9 Firms often hire outside companies that specialize in market research to conduct this type of forecast. Typically used to forecast long range and new product sales.

10. Panel consensus MKA/13 10 Free open exchange at meeting. Idea is that two heads are better than one Group discussion will produce better forecast than any one individual Participants may be executive, salespeople and customers

11. Historical analogy MKA/13 11 Where a forecast may be derived by using the history of a similar product Where an existing product or generic product could be used as a model. Example can be complementary or substitute product. Demand for CD is caused by DVD players.

12. Delphi method MKA/13 12 Group of experts responds to questionnaires A moderator compiles results and formulates a new questionnaire and submitted again to the respondents As a results initiate learning process and no influence of group pressure.

13. Time series analysis MKA/13 13 Tries to predict the future based on past data Such as collected six weeks sales data can be used to predict 7 th week sales Can be of i. Simple moving average ii. Weighted moving average iii. Simple exponential smoothing iv. Exponential smoothing with trend v. Linear regression

14. Guide to select appropriate method FMAmt of historical dataData patternForecast horizon Simple moving average6 to 12 monthsstationaryShort- medium Weighted moving average5-10 observationsdoshort Simple exponential smoothingdoStationary and trend short Exponential smoothing with trend do Linear regression10-20 observations at least 5 observations/season Stationary, seasonality, trend Short to medium MKA/13 14

15. Which model you choose? MKA/13 15 Depends on  Time horizon to forecast  Data availability  Accuracy required  Size of forecasting budget  Availability of qualified personnel

16. Simple moving average MKA/13 16 A time period containing a number of data points is averaged by dividing the sum of the points values by the number of points When demand fro product is neither growing nor declining and if it does not have seasonal characteristics, this model can be used. F t =A t-1 +A t-2 +A t-3 ……+A t-n /n F t = forecast for the coming period A t-1 = Actual occurrence for the past period A t-2 =Actual occurrence two periods ago n= no of periods to be averaged

17. Weighted moving average MKA/13 17 Moving average allows any weight to be placed on each element The sum of all weights equal 1 F t =w 1 A t-1 + w 2 A t-2 + w 3 A t-3 ……+ w n A t-n F 5 =.40*95+.3*105+.20*90+.1*100 =97.5 M1m2m3m4 1009010595?

18. Exponential smoothing MKA/13 19 Only three pieces of data are used such as The most recent forecast The actual demand that occurred for that forecast period Smoothing constant α F t =F t-1 + α (A t-1 –F t-1 ) F t = the exponential smooth forecast for period t F t-1= Exponentially smoothed forecast made for the prior period. A t-1 = The actual demand in the prior period α = the desired response rate or smoothing constant

19. Why exponential smoothing MKA/13 20 Because of four reasons  Are surprisingly accurate  Formulating the model is relatively easy  Little computation is required  The user can understand how the model works.

20. Linear regression analysis MKA/13 24 The past data and future projections are assumed to fall about a straight line Linear regression line is of the form Y is the dependent variable, a is the y intercept b is the slope t is the independent variable Y t = a + bt 0 1 2 3 4 5 t Y

21. Calculating a and b MKA/13 25 B( Slope ) = n(ty) - ty nt 2 - ( t) 2 A Intercept = y - bt n   

22. Linear Trend Equation Example MKA/13 26

23. Linear Trend Calculation MKA/13 27 y = 143.5 + 6.3t a= 812- 6.3(15) 5 = b= 5 (2499)- 15(812) 5(55)- 225 = 12495-12180 275-225 = 6.3 143.5

24. Example MKA/13 28 Sunrise baking company markets cakes through a chain of food stores. It has been experiencing over and underproduction because of forecasting errors. The following data are its demand in dozens of cakes for the past four weeks. Cakes are made for the following day; for example Sunday's cake production is for Monday’s sale….the bakery is closed Saturday, so Friday’s production must satisfy demand for both Saturday and Sunday

25. Example Day4weeks ago 3weeks ago 2 weeks ago Last week Monday2200240023002400 Tuesday220021002200 Wednesday2300240023002500 Thursday1800190018002000 Friday1900180021002000 Saturday Sunday2800270030002900 MKA/13 29

26. Example MKA/13 30 Make a forecast for this week on the following basis i. Daily using a simple four week moving average ii. Daily using a weighted average of.4,.3,.2,.1 for the past four weeks iii. Sun rise is also planning its purchases of ingredients for bread production. If bread demand had been forecast for last week at 22000 loaves and only 21000 loaves were actually demanded, what would sunrise’s forecast be for this week using exponential smoothing with a=.10 iv. suppose with the forecast made in c this week’s demand actually turns out to be 22500.what would be the new forecast be for the next week

27. Causal relationship forecasting One occurrence causes another The rain causes the sale of rain gear If housing starts are known then sale of carpet forecasting is possible MKA/13

28. Forecasting using a causal relationship YearHousing Permits x Sales sqr mtr y 19981813000 19991512000 20001211000 20011014000 20022816000 20033519000 20043017000 MKA/13

29. Y=a+bx a is y intercept b is slope= y2-y1/x2-x1 = 17000-1000/30-10 y = 7000+350x if house permit is 26 y= 7000+350*26 is the forecast of next year. Book1.xlsx MKA/13

30. Simulation Dynamic model usually computer based Allow the forecaster to make assumptions about the internal variables and external environment in the model Depending on the variable in the model forecaster may ask such question as what would happen to my forecast if price increased by 10% MKA/13