2. © 2011 Pearson Education, Inc. publishing as Prentice Hall What is Forecasting?  Process of predicting a future event  Underlying basis of all business decisions  Production  Inventory  Personnel  Facilities ??

3. © 2011 Pearson Education, Inc. publishing as Prentice Hall  Short-range forecast  Up to 1 year, generally less than 3 months  Purchasing, job scheduling, workforce levels, job assignments, production levels  Medium-range forecast  3 months to 3 years  Sales and production planning, budgeting  Long-range forecast  3 + years  New product planning, facility location, research and development Forecasting Time Horizons

4. © 2011 Pearson Education, Inc. publishing as Prentice Hall Distinguishing Differences  Medium/long range  Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes  Short-term  Short-term forecasting usually employs different methodologies than longer-term forecasting  Short-term  Short-term forecasts tend to be more accurate than longer-term forecasts

5. © 2011 Pearson Education, Inc. publishing as Prentice Hall Influence of Product Life Cycle  Introduction and growth require longer forecasts than maturity and decline  As product passes through life cycle, forecasts are useful in projecting  Staffing levels  Inventory levels  Factory capacity Introduction – Growth – Maturity – Decline

6. © 2011 Pearson Education, Inc. publishing as Prentice Hall Product Life Cycle Best period to increase market share R&D engineering is critical Practical to change price or quality image Strengthen niche Poor time to change image, price, or quality Competitive costs become critical Defend market position Cost control critical IntroductionGrowthMaturityDecline Company Strategy/Issues Figure 2.5 Internet search engines Sales Drive-through restaurants CD-ROMs Analog TVs iPods Boeing 787 LCD & plasma TVs Twitter Avatars Xbox 360

7. © 2011 Pearson Education, Inc. publishing as Prentice Hall Product Life Cycle Product design and development critical Frequent product and process design changes Short production runs High production costs Limited models Attention to quality IntroductionGrowthMaturityDecline OM Strategy/Issues Forecasting critical Product and process reliability Competitive product improvements and options Increase capacity Shift toward product focus Enhance distribution Standardization Fewer product changes, more minor changes Optimum capacity Increasing stability of process Long production runs Product improvement and cost cutting Little product differentiation Cost minimization Overcapacity in the industry Prune line to eliminate items not returning good margin Reduce capacity Figure 2.5

8. © 2011 Pearson Education, Inc. publishing as Prentice Hall Types of Forecasts  Economic forecasts  Address business cycle – inflation rate, money supply, housing starts, etc.  Technological forecasts  Predict rate of technological progress  Impacts development of new products  Demand forecasts  Predict sales of existing products and services

9. © 2011 Pearson Education, Inc. publishing as Prentice Hall Strategic Importance of Forecasting  Human Resources – Hiring, training, laying off workers  Capacity – Capacity shortages can result in undependable delivery, loss of customers, loss of market share  Supply Chain Management – Good supplier relations and price advantages

10. © 2011 Pearson Education, Inc. publishing as Prentice Hall Seven Steps in Forecasting 1.Determine the use of the forecast 2.Select the items to be forecasted 3.Determine the time horizon of the forecast 4.Select the forecasting model(s) 5.Gather the data 6.Make the forecast 7.Validate and implement results

11. © 2011 Pearson Education, Inc. publishing as Prentice Hall The Realities!  Forecasts are seldom perfect  Most techniques assume an underlying stability in the system  Product family and aggregated forecasts are more accurate than individual product forecasts

12. © 2011 Pearson Education, Inc. publishing as Prentice Hall Forecasting Approaches  Used when situation is vague and little data exist  New products  New technology  Involves intuition, experience  e.g., forecasting sales on Internet Qualitative Methods

13. © 2011 Pearson Education, Inc. publishing as Prentice Hall Forecasting Approaches  Used when situation is ‘stable’ and historical data exist  Existing products  Current technology  Involves mathematical techniques  e.g., forecasting sales of color televisions Quantitative Methods

14. © 2011 Pearson Education, Inc. publishing as Prentice Hall Overview of Qualitative Methods 1.Jury of executive opinion  Pool opinions of high-level experts, sometimes augment by statistical models 2.Delphi method  Panel of experts, queried iteratively

15. © 2011 Pearson Education, Inc. publishing as Prentice Hall Overview of Qualitative Methods 3.Sales force composite  Estimates from individual salespersons are reviewed for reasonableness, then aggregated 4.Consumer Market Survey  Ask the customer

16. © 2011 Pearson Education, Inc. publishing as Prentice Hall Overview of Quantitative Approaches 1.Naive approach 2.Moving averages 3.Exponential smoothing 4.Trend projection 5.Linear regression time-series models associative model

17. © 2011 Pearson Education, Inc. publishing as Prentice Hall  Set of evenly spaced numerical data  Obtained by observing response variable at regular time periods  Forecast based only on past values, no other variables important  Assumes that factors influencing past and present will continue influence in future Time Series Forecasting

18. © 2011 Pearson Education, Inc. publishing as Prentice Hall Trend Seasonal Cyclical Random Time Series Components

19. © 2011 Pearson Education, Inc. publishing as Prentice Hall Components of Demand Demand for product or service |||| 1234 Time (years) Average demand over 4 years Trend component Actual demand line Random variation Figure 4.1 Seasonal peaks

20. © 2011 Pearson Education, Inc. publishing as Prentice Hall Naive Approach  Assumes demand in next period is the same as demand in most recent period  e.g., If January sales were 68, then February sales will be 68  Sometimes cost effective and efficient  Can be good starting point

21. © 2011 Pearson Education, Inc. publishing as Prentice Hall  MA is a series of arithmetic means  Used if little or no trend  Used often for smoothing  Provides overall impression of data over time Moving Average Method Moving average = ∑ demand in previous n periods n

22. © 2011 Pearson Education, Inc. publishing as Prentice Hall January10 February12 March13 April16 May19 June23 July26 Actual3-Month MonthShed SalesMoving Average (12 + 13 + 16)/3 = 13 2 / 3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1 / 3 Moving Average Example 101213 (10 + 12 + 13)/3 = 11 2 / 3

23. © 2011 Pearson Education, Inc. publishing as Prentice Hall Graph of Moving Average ||||||||||||JFMAMJJASOND||||||||||||JFMAMJJASOND Shed Sales 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales Moving Average Forecast

24. © 2011 Pearson Education, Inc. publishing as Prentice Hall  Used when some trend might be present  Older data usually less important  Weights based on experience and intuition Weighted Moving Average Weighted moving average = ∑ (weight for period n) x (demand in period n) ∑ weights

25. © 2011 Pearson Education, Inc. publishing as Prentice Hall January10 February12 March13 April16 May19 June23 July26 Actual3-Month Weighted MonthShed SalesMoving Average [(3 x 16) + (2 x 13) + (12)]/6 = 14 1 / 3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 20 1 / 2 Weighted Moving Average101213 131210 [(3 x 13) + (2 x 12) + (10)]/6 = 12 1 / 6 Weights AppliedPeriod 3 3Last month 2 2Two months ago 1 1Three months ago 6Sum of weights

26. © 2011 Pearson Education, Inc. publishing as Prentice Hall Moving Average And Weighted Moving Average 30 – 25 – 20 – 15 – 10 – 5 – Sales demand ||||||||||||JFMAMJJASOND||||||||||||JFMAMJJASOND Actual sales Moving average Weighted moving average Figure 4.2

27. © 2011 Pearson Education, Inc. publishing as Prentice Hall  Increasing n smooths the forecast but makes it less sensitive to changes  Do not forecast trends well  Require extensive historical data Potential Problems With Moving Average

28. © 2011 Pearson Education, Inc. publishing as Prentice Hall

29.  Form of weighted moving average  Weights decline exponentially  Most recent data weighted most (depending on alpha value)  Involves little record keeping of past data  Many forecasts can be made with and then tested by finding forecast error – choose most accurate (in hindsight). Exponential Smoothing

30. Choosing the smoothing constant (   Requires smoothing constant (  )  Ranges from 0 to 1  Subjectively chosen  High  if data are extremely variable  (moving average changes a lot)  Low  is data are steady  (moving average fairly steady)

31. © 2011 Pearson Education, Inc. publishing as Prentice Hall Exponential Smoothing New period forecast = Last period’s forecast +  (Last period’s demand – Last period’s forecast) F t = F t – 1 +  (A t – 1 - F t – 1 ) whereF t =new forecast F t – 1 =previous forecast  =smoothing (or weighting) constant (0 ≤  ≤ 1)

32. © 2011 Pearson Education, Inc. publishing as Prentice Hall Effect of Smoothing Constants Big alpha = Little interest in past periods Little alpha = more interest in past periods Weight Assigned to Most2nd Most3rd Most4th Most5th Most RecentRecentRecentRecentRecent SmoothingPeriodPeriodPeriodPeriodPeriod Constant(  )  (1 -  )  (1 -  ) 2  (1 -  ) 3  (1 -  ) 4  =.1.1.09.081.073.066  =.5.5.25.125.063.031

33. © 2011 Pearson Education, Inc. publishing as Prentice Hall Impact of Different  225 – 200 – 175 – 150 – |||||||||123456789|||||||||123456789 Quarter Demand  =.1 Actual demand  =.5

34. © 2011 Pearson Education, Inc. publishing as Prentice Hall Exponential Smoothing Example Predicted demand(Jan) = 142 Ford Mustangs Actual demand(Jan) = 153 Smoothing constant  =.20

35. © 2011 Pearson Education, Inc. publishing as Prentice Hall Exponential Smoothing Example Predicted demand(Jan) = 142 Ford Mustangs Actual demand(Jan) = 153 Smoothing constant  =.20 New forecast(Feb)= 142 +.2(153 – 142)

36. © 2011 Pearson Education, Inc. publishing as Prentice Hall Exponential Smoothing Example Predicted demand(Jan) = 142 Ford Mustangs Actual demand(Jan) = 153 Smoothing constant  = 0.20 New forecast(Feb)= 142 +.2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars

37. © 2011 Pearson Education, Inc. publishing as Prentice Hall Choosing  The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting the model that gives us the lowest forecast error Forecast error= Actual demand - Forecast value = A t - F t

38. © 2011 Pearson Education, Inc. publishing as Prentice Hall Common Measures of Error Mean Absolute Deviation (MAD) MAD = ∑ |Actual - Forecast| n Mean Squared Error (MSE) MSE = ∑ (Forecast Errors) 2 n

39. © 2011 Pearson Education, Inc. publishing as Prentice Hall Common Measures of Error Mean Absolute Percent Error (MAPE) MAPE = ∑ 100|Actual i - Forecast i |/Actual i n i = 1

40. © 2011 Pearson Education, Inc. publishing as Prentice Hall Comparison of Forecast Error RoundedAbsoluteRoundedAbsolute ActualForecastDeviationForecastDeviation Tonnagewithforwithfor QuarterUnloaded  =.10  =.10  =.50  =.50 11801755.001755.00 2168175.57.50177.509.50 3159174.7515.75172.7513.75 4175173.181.82165.889.12 5190173.3616.64170.4419.56 6205175.0229.98180.2224.78 7180178.021.98192.6112.61 8182178.223.78186.304.30 82.4598.62

41. © 2011 Pearson Education, Inc. publishing as Prentice Hall Comparison of Forecast Error RoundedAbsoluteRoundedAbsolute ActualForecastDeviationForecastDeviation Tonnagewithforwithfor QuarterUnloaded  =.10  =.10  =.50  =.50 11801755.001755.00 2168175.57.50177.509.50 3159174.7515.75172.7513.75 4175173.181.82165.889.12 5190173.3616.64170.4419.56 6205175.0229.98180.2224.78 7180178.021.98192.6112.61 8182178.223.78186.304.30 82.4598.62

42. MAD = ∑ |deviations| n = 82.45/8 = 10.31 For  =.10 = 98.62/8 = 12.33 For  =.50

43. © 2011 Pearson Education, Inc. publishing as Prentice Hall Comparison of Forecast Error RoundedAbsoluteRoundedAbsolute ActualForecastDeviationForecastDeviation Tonnagewithforwithfor QuarterUnloaded  =.10  =.10  =.50  =.50 11801755.001755.00 2168175.57.50177.509.50 3159174.7515.75172.7513.75 4175173.181.82165.889.12 5190173.3616.64170.4419.56 6205175.0229.98180.2224.78 7180178.021.98192.6112.61 8182178.223.78186.304.30 82.4598.62 MAD10.3112.33

44. = 1,526.54/8 = 190.82 For  =.10 = 1,561.91/8 = 195.24 For  =.50 MSE = ∑ (forecast errors) 2 n

45. © 2011 Pearson Education, Inc. publishing as Prentice Hall Comparison of Forecast Error RoundedAbsoluteRoundedAbsolute ActualForecastDeviationForecastDeviation Tonnagewithforwithfor QuarterUnloaded  =.10  =.10  =.50  =.50 11801755.001755.00 2168175.57.50177.509.50 3159174.7515.75172.7513.75 4175173.181.82165.889.12 5190173.3616.64170.4419.56 6205175.0229.98180.2224.78 7180178.021.98192.6112.61 8182178.223.78186.304.30 82.4598.62 MAD10.3112.33 MSE190.82195.24

46. = 44.75/8 = 5.59% For  =.10 = 54.05/8 = 6.76% For  =.50 MAPE = ∑ 100|deviation i |/actual i n i = 1

47. © 2011 Pearson Education, Inc. publishing as Prentice Hall Comparison of Forecast Error RoundedAbsoluteRoundedAbsolute ActualForecastDeviationForecastDeviation Tonnagewithforwithfor QuarterUnloaded  =.10  =.10  =.50  =.50 11801755.001755.00 2168175.57.50177.509.50 3159174.7515.75172.7513.75 4175173.181.82165.889.12 5190173.3616.64170.4419.56 6205175.0229.98180.2224.78 7180178.021.98192.6112.61 8182178.223.78186.304.30 82.4598.62 MAD10.3112.33 MSE190.82195.24 MAPE5.59%6.76%

48. © 2011 Pearson Education, Inc. publishing as Prentice Hall

49. Seasonal Variations In Data The seasonal index model can adjust trend data for seasonal variations in demand

50. © 2011 Pearson Education, Inc. publishing as Prentice Hall Seasonal Variations In Data 1.Find average historical demand for each season 2.Compute the average demand over all seasons 3.Compute a seasonal index for each season 4.Estimate next year’s total demand 5.Divide this estimate of total demand by the number of seasons, then multiply it by the seasonal index for that season Steps in the process:

51. © 2011 Pearson Education, Inc. publishing as Prentice Hall Seasonal Index Example Jan80851059094 Feb7085858094 Mar8093828594 Apr909511510094 May11312513112394 Jun11011512011594 Jul10010211310594 Aug8810211010094 Sept8590959094 Oct7778858094 Nov7572838094 Dec8278808094 DemandAverageAverage Seasonal Month2007200820092007-2009MonthlyIndex

52. © 2011 Pearson Education, Inc. publishing as Prentice Hall Seasonal Index Example Jan80851059094 Feb7085858094 Mar8093828594 Apr909511510094 May11312513112394 Jun11011512011594 Jul10010211310594 Aug8810211010094 Sept8590959094 Oct7778858094 Nov7572838094 Dec8278808094 DemandAverageAverage Seasonal Month2007200820092007-2009MonthlyIndex 0.957 Seasonal index = Average 2007-2009 monthly demand Average monthly demand = 90/94 =.957

53. © 2011 Pearson Education, Inc. publishing as Prentice Hall Seasonal Index Example Jan808510590940.957 Feb70858580940.851 Mar80938285940.904 Apr9095115100941.064 May113125131123941.309 Jun110115120115941.223 Jul100102113105941.117 Aug88102110100941.064 Sept85909590940.957 Oct77788580940.851 Nov75728380940.851 Dec82788080940.851 DemandAverageAverage Seasonal Month2007200820092007-2009MonthlyIndex

54. © 2011 Pearson Education, Inc. publishing as Prentice Hall Seasonal Index Example 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – ||||||||||||JFMAMJJASOND||||||||||||JFMAMJJASOND Time Demand 2010 Forecast 2009 Demand 2008 Demand 2007 Demand

55. © 2011 Pearson Education, Inc. publishing as Prentice Hall Seasonal Index Example Jan808510590940.957 Feb70858580940.851 Mar80938285940.904 Apr9095115100941.064 May113125131123941.309 Jun110115120115941.223 Jul100102113105941.117 Aug88102110100941.064 Sept85909590940.957 Oct77788580940.851 Nov75728380940.851 Dec82788080940.851 DemandAverageAverage Seasonal Month2007200820092007-2009MonthlyIndex Expected annual demand = 1,200 Janx.957 = 96 1,200 12 Febx.851 = 85 1,200 12 Forecast for 2010

56. © 2011 Pearson Education, Inc. publishing as Prentice Hall Forecasting in the Service Sector  Presents unusual challenges  Special need for short term records  Needs differ greatly as function of industry and product  Holidays and other calendar events  Unusual events

57. © 2011 Pearson Education, Inc. publishing as Prentice Hall