Mba ii pmom_unit-1.3 forecasting a
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This document discusses forecasting methods used in production and operations management. It defines forecasting as predicting future values based on historical data. The key types of forecasts discussed are judgmental forecasts using subjective inputs, time series forecasts using historical data patterns, and associative models using explanatory variables. Time series methods covered include simple moving averages, weighted moving averages, and exponential smoothing. Exponential smoothing gives more weight to recent periods to generate forecasts. Quantitative forecasting methods are chosen based on the forecast horizon and data available.
Mba ii pmom_unit-1.3 forecasting a
- 1. Course: MBA Subject: Production & Operation Management Unit:1.3 Forecasting
- 2. Forecast • Nature • Types • Factors affecting forecasting • Models
- 3. What is Forecasting Process of predicting a future event based on historical data Educated Guessing Underlying basis of all business decisions Production Inventory Personnel Facilities
- 4. Importance of Forecasting in PM &OM • 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.
- 5. Importance of Forecasting in PM &OM • Demand is not the only variable of interest to forecasters. • Manufacturers also forecast worker absenteeism, machine availability, material costs, transportation and production lead times, etc. • Besides demand, service providers are also interested in forecasts of population, of other demographic variables, of weather, etc.
- 6. Nature FORECAST: • A statement about the future value of a variable of interest such as demand. • Forecasts affect decisions and activities throughout an organization – Accounting, finance – Human resources – Marketing – MIS – Operations – Product / service design
- 7. Uses of Forecasts Accounting Cost/profit estimates Finance Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy MIS IT/IS systems, services Operations Schedules, MRP, workloads Product/service design New products and services
- 8. • 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 semester.
- 9. Elements of a Good Forecast Timely AccurateReliable Written
- 10. Steps in the Forecasting Process 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”
- 11. Types of Forecasts • Judgmental - uses subjective inputs • Time series - uses historical data assuming the future will be like the past • Associative models - uses explanatory variables to predict the future
- 12. Judgmental Forecasts • Executive opinions • Sales force opinions • Consumer surveys • Outside opinion • Delphi method – Opinions of managers and staff – Achieves a consensus forecast
- 13. Time Series Forecasts • Trend - long-term movement in data • Seasonality - short-term regular variations in data • Cycle – wavelike variations of more than one year’s duration • Irregular variations - caused by unusual circumstances • Random variations - caused by chance
- 15. Types of Forecasts by Time Horizon • 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 Quantitative methods Qualitative Methods Detailed use of system Design of system
- 16. Forecasting During the Life Cycle Introduction Growth Maturity Decline Sales Time Quantitative models - Time series analysis - Regression analysis Qualitative models - Executive judgment - Market research -Survey of sales force -Delphi method
- 18. Qualitative Methods Briefly, the qualitative methods are: Executive Judgment: Opinion of a group of high level experts or managers is pooled 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. • .
- 19. Delphi Method 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. • .
- 20. 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
- 21. Time Series Models • Try to predict the future based on past data – Assume that factors influencing the past will continue to influence the future
- 22. Time Series Models: Components Random Seasonal Trend Composite
- 23. Product Demand over Time Year 1 Year 2 Year 3 Year 4 Demandforproductorservice
- 24. Product Demand over Time Year 1 Year 2 Year 3 Year 4 Demandforproductorservice Trend component Actual demand line Seasonal peaks Random variation Now let’s look at some time series approaches to forecasting… Borrowed from Heizer/Render - Principles of Operations Management, 5e, and Operations Management, 7e
- 25. 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
- 26. 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
- 27. Naïve Approach • Simple to use • Virtually no cost • Quick and easy to prepare • Data analysis is nonexistent • Easily understandable • Cannot provide high accuracy • Can be a standard for accuracy
- 28. 2a. Simple Moving Average n A+...+A+A+A =F 1n-t2-t1-tt 1t • Assumes an average is a good estimator of future behavior – Used if little or no trend – Used for smoothing Ft+1 = Forecast for the upcoming period, t+1 n = Number of periods to be averaged A t = Actual occurrence in period t
- 29. 2a. Simple Moving Average You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average. n A+...+A+A+A =F 1n-t2-t1-tt 1t Month Sales (000) 1 4 2 6 3 5 4 ? 5 ? 6 ?
- 30. 2a. Simple Moving Average Month Sales (000) Moving Average (n=3) 1 4 NA 2 6 NA 3 5 NA 4 ? 5 ? (4+6+5)/3=5 6 ? n A+...+A+A+A =F 1n-t2-t1-tt 1t You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average.
- 31. What if ipod sales were actually 3 in month 4 Month Sales (000) Moving Average (n=3) 1 4 NA 2 6 NA 3 5 NA 4 3 5 ? 5 6 ? 2a. Simple Moving Average ?
- 32. Forecast for Month 5? Month Sales (000) Moving Average (n=3) 1 4 NA 2 6 NA 3 5 NA 4 3 5 ? 5 6 ? (6+5+3)/3=4.667 2a. Simple Moving Average
- 33. Actual Demand for Month 5 = 7 Month Sales (000) Moving Average (n=3) 1 4 NA 2 6 NA 3 5 NA 4 3 5 7 5 6 ? 4.667 2a. Simple Moving Average ?
- 34. Forecast for Month 6? Month Sales (000) Moving Average (n=3) 1 4 NA 2 6 NA 3 5 NA 4 3 5 7 5 6 ? 4.667 (5+3+7)/3=5 2a. Simple Moving Average
- 35. • Gives more emphasis to recent data • Weights – decrease for older data – sum to 1.0 2b. Weighted Moving Average 1n-tn2-t31-t2t11t Aw+...+Aw+Aw+Aw=F Simple moving average models weight all previous periods equally
- 36. 2b. Weighted Moving Average: 3/6, 2/6, 1/6 Month Weighted Moving Average 1 4 NA 2 6 NA 3 5 NA 4 31/6 = 5.167 5 6 ? ? ? 1n-tn2-t31-t2t11t Aw+...+Aw+Aw+Aw=F Sales (000)
- 37. 2b. Weighted Moving Average: 3/6, 2/6, 1/6 Month Sales (000) Weighted Moving Average 1 4 NA 2 6 NA 3 5 NA 4 3 31/6 = 5.167 5 7 6 25/6 = 4.167 32/6 = 5.333 1n-tn2-t31-t2t11t Aw+...+Aw+Aw+Aw=F
- 38. 3a. Exponential Smoothing • Assumes the most recent observations have the highest predictive value – gives more weight to recent time periods Ft+1 = Ft + a(At - Ft) et Ft+1 = Forecast value for time t+1 At = Actual value at time t a = Smoothing constant Need initial forecast Ft to start.
- 39. 3a. Exponential Smoothing – Example 1 Week Demand 1 820 2 775 3 680 4 655 5 750 6 802 7 798 8 689 9 775 10 Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using a=0.10? Assume F1=D1 Ft+1 = Ft + a(At - Ft) i Ai
- 40. Week Demand 0.1 0.6 1 820 820.00 820.00 2 775 820.00 820.00 3 680 815.50 793.00 4 655 801.95 725.20 5 750 787.26 683.08 6 802 783.53 723.23 7 798 785.38 770.49 8 689 786.64 787.00 9 775 776.88 728.20 10 776.69 756.28 Ft+1 = Ft + a(At - Ft) 3a. Exponential Smoothing – Example 1 a = F2 = F1+ a(A1–F1) =820+.1(820–820) =820 i Ai Fi
- 41. Week Demand 0.1 0.6 1 820 820.00 820.00 2 775 820.00 820.00 3 680 815.50 793.00 4 655 801.95 725.20 5 750 787.26 683.08 6 802 783.53 723.23 7 798 785.38 770.49 8 689 786.64 787.00 9 775 776.88 728.20 10 776.69 756.28 Ft+1 = Ft + a(At - Ft) 3a. Exponential Smoothing – Example 1 a = F3 = F2+ a(A2–F2) =820+.1(775–820) =815.5 i Ai Fi
- 42. Week Demand 0.1 0.6 1 820 820.00 820.00 2 775 820.00 820.00 3 680 815.50 793.00 4 655 801.95 725.20 5 750 787.26 683.08 6 802 783.53 723.23 7 798 785.38 770.49 8 689 786.64 787.00 9 775 776.88 728.20 10 776.69 756.28 Ft+1 = Ft + a(At - Ft) This process continues through week 10 3a. Exponential Smoothing – Example 1 a = i Ai Fi
- 43. Week Demand 0.1 0.6 1 820 820.00 820.00 2 775 820.00 820.00 3 680 815.50 793.00 4 655 801.95 725.20 5 750 787.26 683.08 6 802 783.53 723.23 7 798 785.38 770.49 8 689 786.64 787.00 9 775 776.88 728.20 10 776.69 756.28 Ft+1 = Ft + a(At - Ft) What if the a constant equals 0.6 3a. Exponential Smoothing – Example 1 a = a = i Ai Fi
- 44. Month Demand 0.3 0.6 January 120 100.00 100.00 February 90 106.00 112.00 March 101 101.20 98.80 April 91 101.14 100.12 May 115 98.10 94.65 June 83 103.17 106.86 July 97.12 92.54 August September Ft+1 = Ft + a(At - Ft) What if the a constant equals 0.6 3a. Exponential Smoothing – Example 2 a = a = i Ai Fi
- 45. 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
- 46. Month Demand 0.3 0.5 January 80 84.00 84.00 February 84 82.80 82.00 March 82 83.16 83.00 April 85 82.81 82.50 May 89 83.47 83.75 June 85.13 86.38 July ?? ?? Ft+1 = Ft + a(At - Ft) What if the a constant equals 0.5 3a. Exponential Smoothing – Example 3 a = a = i Ai Fi
- 47. • How to choose α – depends on the emphasis you want to place on the most recent data • Increasing α makes forecast more sensitive to recent data 3a. Exponential Smoothing
- 48. Ft+1 = a At + a(1- a) At - 1 + a(1- a)2At - 2 + ... Forecast Effects of Smoothing Constant a Weights Prior Period a 2 periods ago a(1 - a) 3 periods ago a(1 - a)2 a= a= 0.10 a= 0.90 10% 9% 8.1% 90% 9% 0.9% Ft+1 = Ft + a (At - Ft) or w1 w2 w3
- 49. • Collect historical data • Select a model – Moving average methods • Select n (number of periods) • For weighted moving average: select weights – Exponential smoothing • Select a • Selections should produce a good forecast To Use a Forecasting Method …but what is a good forecast?
- 50. A Good Forecast Has a small error Error = Demand - Forecast
- 51. Measures of Forecast Error b. MSE = Mean Squared Error n F-A =MSE n 1=t 2 tt MAD = A - F n t t t=1 n et Ideal values =0 (i.e., no forecasting error) MSE=RMSEc. RMSE = Root Mean Squared Error a. MAD = Mean Absolute Deviation
- 52. MAD Example Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300 320 5 325 315 What is the MAD value given the forecast values in the table below? MAD = A - F n t t t=1 n 5 5 20 10 |At – Ft| FtAt = 40 = 40 4 =10
- 53. MSE/RMSE Example Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300 320 5 325 315 What is the MSE value? 5 5 20 10 |At – Ft| FtAt = 550 4 =137.5 (At – Ft)2 25 25 400 100 = 550 n F-A =MSE n 1=t 2 tt RMSE = √137.5 =11.73
- 54. Measures of Error t At Ft et |et| et 2 Jan 120 100 20 20 400 Feb 90 106 256 Mar 101 102 April 91 101 May 115 98 June 83 103 1. Mean Absolute Deviation (MAD) n e MAD n t 1 2a. Mean Squared Error (MSE) MSE e n t n 2 1 2b. Root Mean Squared Error (RMSE) RMSE MSE -16 16 -1 1 -10 17 -20 10 17 20 1 100 289 400 -10 84 1,446 84 6 = 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
- 55. deviationabsoluteMean )( = MAD RSFE =TS t tt forecastactual 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
- 56. 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
- 57. • We looked at using exponential smoothing to forecast demand with only random variations Exponential Smoothing (continued) Ft+1 = Ft + a (At - Ft) Ft+1 = Ft + a At – a Ft Ft+1 = a At + (1-a) Ft
- 58. 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?
- 59. 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. Sales of Autos (100,000)
- 60. Formulas xbya 22 xnx yxnxy b x y x y y = a + b x where,
- 61. MonthAdvertising Sales X 2 XY January 3 1 9.00 3.00 February 4 2 16.00 8.00 March 2 1 4.00 2.00 April 5 3 25.00 15.00 May 4 2 16.00 8.00 June 2 1 4.00 2.00 July TOTAL 20 10 74 38 y = a + b X Regression – Example 22 xnx yxnxy b xbya
- 62. 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
- 63. Reference • Forecasting-Mcgraw hill/irwin • Operation management 8th edition by-William J • POM-J.GALVAN