Lec-3 Forecasting.pdf Data science college
- 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, 3.7 12.5 9.0
- 4. Outline • What is forecasting? • Types of forecasts • Time-Series forecasting • Good forecasts • Monitoring forecasts
- 5. Forecasting • What is Forecasting? • Determining Future Events Based on Historical Facts and Data • Some Thoughts on Forecasts • Forecasts Tend to Be Wrong! • Forecasts Can Be Biased! (Marketing, Sales, etc.) • Forecasts Tend to Be Better for Near Future • So, Why Forecast? • Better to Have “Educated Guess” About Future Than to Not Forecast At All!
- 6. 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 Sales will be $200 Million!
- 7. Realities of Forecasting • Forecasts are seldom perfect • Most forecasting methods assume that there is some underlying stability in the system • Both product family and aggregated product forecasts are more accurate than individual product forecasts
- 8. In general, forecasts are almost always wrong. So, Why do we need to forecast? Throughout the day we forecast very different things such as weather, traffic, stock market, state of our company from different perspectives. Virtually every business attempt is based on forecasting. Not all of them are derived from sophisticated methods. However, “Best" educated guesses about future are more valuable for purpose of Planning than no forecasts and hence no planning. Hochschule Bremen forecasts? hjjkllkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkk bvjlllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllllv jkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkkjkkl
- 9. 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. • What departments in your university needs to forecast? Importance of Forecasting in OM
- 10. 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. Importance of Forecasting in OM
- 11. • 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 Types of Forecasts by Time Horizon Design of system Detailed use of system Quantitative methods Qualitative Methods
- 12. Short vs. Long Term • Medium/long range forecasts • More comprehensive issues • Support management decisions • Short-term forecasting usually employs different methodologies than longer-term forecasting • Short-term forecasts tend to be more accurate than longer- term forecasts
- 13. How to Forecast? • Qualitative Methods • Based On Educated Opinion & Judgment (Subjective) • Particularly Useful When Lacking Numerical Data (Example: Design and Introduction Phases of a Product’s Life Cycle) • Quantitative Methods • Based On Data (Objective)
- 14. Forecasting Approaches Qualitative • Used when situation is vague & little data exist • New products • New technology • Involves intuition, experience • e.g., forecasting sales on Internet Quantitative • Used when situation is ‘stable’ & historical data exist • Existing products • Current technology • Involves mathematical techniques • e.g., forecasting sales of color televisions
- 15. Qualitative Methods • Executive Judgment • Sales Force Composite • Market Research/Survey • Delphi Method
- 16. Jury of Executive Opinion • Involves small group of high-level managers • Group estimates demand by working together • Combines managerial experience with statistical models • Relatively quick • ‘Group-think’ disadvantage
- 17. Sales Force Composite • Each salesperson projects his or her sales • Combined at district & national levels • Sales reps know customers’ wants • Tends to be overly optimistic Sales © 1995 Corel Corp.
- 18. Consumer Market Survey • Ask customers about purchasing plans • What consumers say, and what they actually do are often different • Sometimes difficult to answer 18 How many hours will you use the Internet next week? © 1995 Corel Corp.
- 19. Delphi Method • Iterative group process • 3 types of people • Decision makers • Staff • Respondents • Reduces ‘group-think (Sales?) (What will sales be? survey) (Sales will be 45, 50, 55) Respondents Staff (Sales will be 50!) Decision Makers
- 20. 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. . Delphi Method
- 21. Quantitative Methods • Time Series & Regression • Time Series Popular Forecasting Approach in Operations Management • Assumption: • “Patterns” That Occurred in the Past Will Continue to Occur In the Future • Patterns • Random Variation • Trend • Seasonality • Composite
- 22. What is a Time Series? • Obtained by observing response variable at regular time periods • Set of evenly spaced numerical data • Forecast based only on past values • Assumes that factors influencing past and present will continue influence in future • Assumes that factors influencing the past will continue to influence the future
- 23. Monthly Champagne Sales 0 200 400 600 800 1000 1200 1400 1600 0 12 24 36 48 60 72 84 Time (t)
- 24. Trend Component • Persistent, overall upward or downward pattern • Due to population, technology etc. • Several years duration Mo., Qtr., Yr. Response © 1984-1994 T/Maker Co.
- 25. Seasonal Component • Regular pattern of up & down fluctuations • Due to weather, customs etc. • Occurs within 1 year Mo., Qtr. Response Summer © 1984-1994 T/Maker Co.
- 26. U.K. Airline Miles 0 2000 4000 6000 8000 10000 12000 14000 16000 18000 1 4 7 10 13 16 19 22 25 28 31 34 37 40 43 46 49 52 55 58 61 64 67 70 73 76 79 82 85 88 91 94 Month Thousands of Miles U.K. Airline Miles UK Airline Miles Thousands of Miles Observe: Increasing trend, Seasonal component. Random variation.
- 27. Common Seasonal Patterns Period of Pattern “Season” Length Number of “Seasons” in Pattern Week Day 7 Month Week 4 – 4 ½ Month Day 28 – 31 Year Quarter 4 Year Month 12 Year Week 52 27
- 28. Cyclical Component • Repeating up & down movements • Due to interactions of factors influencing economy • Usually 2-10 years duration Mo., Qtr., Yr. Response Cycle
- 29. Random Component • Erratic, unsystematic, ‘residual’ fluctuations • Due to random variation or unforeseen events • Union strike • Hurricane/Cyclone • Short duration & non-repeating
- 30. Forecasting Steps Data Collection Data Analysis Model Selection Monitoring Collect Relevant/Reliable Data Be Aware of “Garbage-In, Garbage Out”
- 31. Forecasting Steps Data Collection Data Analysis Model Selection Monitoring Plot the Data Identify Patterns
- 32. Forecasting Steps Data Collection Data Analysis Model Selection Monitoring Choose Model Appropriate for Data Consider Complexity Trade-Offs Perform Forecast(s) Select Model Based on Performance Measure(s)
- 33. Forecasting Steps Data Collection Data Analysis Model Selection Monitoring Track Forecast Performance (Conditions May and Often Do Change)
- 34. Time Series Models • Short Term • Naïve • Simple Moving Average • Weighted Moving Average • Exponential Smoothing
- 35. Forecasting Example • L&F Bakery has been forecasting by “gut feel.” They would like to use a formal (i.e., quantitative) forecasting technique.
- 36. Forecasting Methods - Naïve • Forecast for July = Actual for June • Ft+1 = At • FJul = AJun = 600 • Forecast Very Sensitive to Demand Changes; Good for stable demand
- 37. Forecasting Methods - Naïve =C4 =C5
- 38. Forecasting Methods – Moving Avg • Forecast for July = Average of June, May, and April • Ft+1 = (At+At-1+…)/n • FJul = (600+500+400)/3 = 500 • Values Equally Weighted; Good for stable demand; Sensitive to fluctuation; Lags
- 39. Forecasting Methods – Moving Avg =AVERAGE(C4:C6) = AVERAGE(C5:C7)
- 40. 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 1 n - t 2 - t 1 - t t 1 t You’re manager in Amazon’s electronics department. You want to forecast ipod sales for months 4-6 using a 3-period moving average.
- 41. 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 ? ?
- 42. 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
- 43. 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 ?
- 44. 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
- 45. Weighted MovingAverage Method • Used when trend is present • Older data usually less important • Weights based on intuition • Often lay between 0 & 1, & sum to 1.0 • Equation WMA = Σ(Weight for period n) (Demand in period n) ΣWeights
- 46. 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 ? ? ? 1 n - t n 2 - t 3 1 - t 2 t 1 1 t A w + ... + A w + A w + A w = F Sales (000)
- 47. 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 1 n - t n 2 - t 3 1 - t 2 t 1 1 t A w + ... + A w + A w + A w = F
- 48. 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.
- 49. Exponential Smoothing Equations • Premise--The most recent observations might have the highest predictive value • Therefore, we should give more weight to the more recent time periods when forecasting 49 Ft+1 = Ft + a(At - Ft)
- 50. 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
- 51. 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
- 52. 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
- 53. 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
- 54. 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
- 55. • 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 • Small alpha Less importance on recent results (Good for products with stable demand) • Large alpha Recent forecast results more important (Good for product with varying demands) Exponential Smoothing
- 56. Determining Forecast Quality • How Well Did a Forecast Perform? • Determine Forecast Error Error = Actual Demand – Forecasted Demand Month Actual Forecast Error Jan 200 200 0 Feb 300 200 100 Mar 200 230 -30 Apr 400 221 179 May 500 275 225 Jun 600 343 257 Average Error 121.8
- 57. 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
- 58. 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
- 59. Summary • What is forecasting • How does it help a firm? • What is the difference between potential tools one may use if the time frame is short term versus long term? • Describe the four qualitative forecasting approaches • Describe the quantitative forecasting approaches • Calculate a simple moving average • What approach will let you weight more recent data versus older data?