9. administración y pronóstico de la demanda

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  • 9. administración y pronóstico de la demanda

    1. 1. 1Demand Management and Forecasting 1-1
    2. 2. 2OBJECTIVES • Demand Management • Qualitative Forecasting Methods • Simple & Weighted Moving Average Forecasts • Exponential Smoothing • Simple Linear Regression • Web-Based Forecasting 1-2
    3. 3. 3Demand Management Independent Demand: Finished Goods A Dependent Demand: Raw Materials, Component parts, B(4) C(2) Sub-assemblies, etc. D(2) E(1) D(3) F(2) 1-3
    4. 4. 4Independent Demand:What a firm can do to manage it? • Can take an active role to influence demand • Can take a passive role and simply respond to demand 1-4
    5. 5. 5Types of Forecasts • Qualitative (Judgmental) • Quantitative – Time Series Analysis – Causal Relationships – Simulation 1-5
    6. 6. 6Components of Demand • Average demand for a period of time • Trend • Seasonal element • Cyclical elements • Random variation • Autocorrelation 1-6
    7. 7. 7 Finding Components of Demand Seasonal variation Seasonal variation x x x Linear Linear x x x x Trend x x TrendSales x x x x x xx x xx x x x x x x x x x x x x xxx x x x x x xxxxx x x x 1 2 3 4 Year 1-7
    8. 8. 8 Qualitative MethodsExecutive Judgment Grass Roots Qualitative Market ResearchHistorical analogy MethodsDelphi Method Panel Consensus 1-8
    9. 9. 9Delphi Method l. Choose the experts to participate representing a variety of knowledgeable people in different areas 2. Through a questionnaire (or E-mail), obtain forecasts (and any premises or qualifications for the forecasts) from all participants 3. Summarize the results and redistribute them to the participants along with appropriate new questions 4. Summarize again, refining forecasts and conditions, and again develop new questions 5. Repeat Step 4 as necessary and distribute the final results to all participants 1-9
    10. 10. 10Time Series Analysis • Time series forecasting models try to predict the future based on past data • You can pick models based on: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel 1-10
    11. 11. 11Simple Moving Average Formula • The simple moving average model assumes an average is a good estimator of future behavior • The formula for the simple moving average is: A t-1 + A t-2 + A t-3 +...+A t- n Ft = n Ft = Forecast for the coming period N = Number of periods to be averaged A t-1 = Actual occurrence in the past period for up to “n” periods 1-11
    12. 12. 12 Simple Moving Average Problem (1) A t-1 + A t-2 + A t-3 +...+A t- n Ft =Week Demand n 1 650 Question: What are the 3- Question: What are the 3- 2 678 week and 6-week moving week and 6-week moving 3 720 average forecasts for average forecasts for 4 785 demand? demand? 5 859 6 920 Assume you only have 3 Assume you only have 3 7 850 weeks and 6 weeks of weeks and 6 weeks of 8 758 actual demand data for the actual demand data for the 9 892 respective forecasts 10 920 respective forecasts 11 789 12 844 1-12
    13. 13. 13Calculating the moving averages gives us: Week Demand 3-Week 6-Week 1 650 F4=(650+678+720)/3 2 678 =682.67 3 720 F7=(650+678+720 4 785 682.67 +785+859+920)/6 5 859 727.67 =768.67 6 920 788.00 7 850 854.67 768.67 8 758 876.33 802.00 9 892 842.67 815.33 10 920 833.33 844.00 11 789 856.67 866.50 12 844 867.00 854.83 ©The McGraw-Hill Companies, Inc., 2004
    14. 14. 14 Plotting the moving averages and comparing Plotting the moving averages and comparing them shows how the lines smooth out to reveal them shows how the lines smooth out to reveal the overall upward trend in this example the overall upward trend in this example 1000 900 Demand 800Demand 3-Week 700 6-Week 600 500 Note how the Note how the 1 2 3 4 5 6 7 8 9 10 11 12 3-Week is 3-Week is Week smoother than smoother than the Demand, the Demand, and 6-Week is and 6-Week is even smoother even smoother 1-14
    15. 15. 15Simple Moving Average Problem (2) Data Question: What is the Question: What is the 3 week moving 3 week moving Week Demand average forecast average forecast 1 820 for this data? 2 775 for this data? 3 680 Assume you only Assume you only 4 655 have 3 weeks and have 3 weeks and 5 620 5 weeks of actual 5 weeks of actual 6 600 demand data for demand data for 7 575 the respective the respective forecasts forecasts 1-15
    16. 16. 16Simple Moving Average Problem (2) Solution Week Demand 3-Week 5-Week 1 820 F4=(820+775+680)/3 2 775 =758.33 3 680 F6=(820+775+680 +655+620)/5 4 655 758.33 =710.00 5 620 703.33 6 600 651.67 710.00 7 575 625.00 666.00 1-16
    17. 17. 17 Weighted Moving Average FormulaWhile the moving average formula implies an equal While the moving average formula implies an equalweight being placed on each value that is being averaged, weight being placed on each value that is being averaged,the weighted moving average permits an unequal the weighted moving average permits an unequalweighting on prior time periods weighting on prior time periodsThe formula for the moving average is:The formula for the moving average is: Ft = w1A t-1 + w 2 A t-2 + w 3A t-3 +...+w n A t-n n wt = weight given to time period “t” wt = weight given to time period “t” occurrence (weights must add to one) ∑w i =1 occurrence (weights must add to one) i=1 1-17
    18. 18. 18Weighted Moving Average Problem (1) Data Question: Given the weekly demand and weights, what is Question: Given the weekly demand and weights, what is the forecast for the 4th period or Week 4? the forecast for the 4th period or Week 4? Week Demand Weights: 1 650 t-1 .5 2 678 3 720 t-2 .3 4 t-3 .2 Note that the weights place more emphasis on the Note that the weights place more emphasis on the most recent data, that is time period “t-1” most recent data, that is time period “t-1” 1-18
    19. 19. 19Weighted Moving Average Problem (1) Solution Week Demand Forecast 1 650 2 678 3 720 4 693.4F4 = 0.5(720)+0.3(678)+0.2(650)=693.4 1-19
    20. 20. 20Weighted Moving Average Problem (2) Data Question: Given the weekly demand information and Question: Given the weekly demand information and weights, what is the weighted moving average forecast weights, what is the weighted moving average forecast of the 5th period or week? of the 5th period or week? Week Demand Weights: 1 820 t-1 .7 2 775 t-2 .2 3 680 t-3 .1 4 655 1-20
    21. 21. 21Weighted Moving Average Problem (2) Solution Week Demand Forecast 1 820 2 775 3 680 4 655 5 672 F5 = (0.1)(755)+(0.2)(680)+(0.7)(655)= 672 1-21
    22. 22. 22Exponential Smoothing Model Ftt = Ft-1 + α(At-1 - Ft-1) F = Ft-1 + α(At-1 - Ft-1) Where : Ft = Forcast value for the coming t time period Ft - 1 = Forecast value in 1 past time period At - 1 = Actual occurance in the past t time period α = Alpha smoothing constant • 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 1-22
    23. 23. 23Exponential Smoothing Problem (1) Data Week Demand 1 820 Question: Given the Question: Given the 2 775 weekly demand weekly demand 3 680 data, what are the data, what are the 4 655 exponential exponential 5 750 smoothing smoothing 6 802 forecasts for forecasts for 7 798 periods 2-10 using periods 2-10 using 8 689 α=0.10 and α=0.60? α=0.10 and α=0.60? 9 775 Assume F1=D11 Assume F1=D 10 1-23
    24. 24. 24Answer: The respective alphas columns denote the forecast values. Note Answer: The respective alphas columns denote the forecast values. Notethat you can only forecast one time period into the future. that you can only forecast one time period into the future. 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 1-24
    25. 25. 25Exponential Smoothing Problem (1) PlottingNote how that the smaller alpha results in a smoother line Note how that the smaller alpha results in a smoother linein this example in this example 900 800 Demand Demand 700 0.1 600 0.6 500 1 2 3 4 5 6 7 8 9 10 Week 1-25
    26. 26. 26Exponential Smoothing Problem (2) Data Question: What are the Question: What are the Week Demand exponential smoothing exponential smoothing 1 820 forecasts for periods 2-5 forecasts for periods 2-5 2 775 using a =0.5? using a =0.5? 3 680 4 655 Assume F11=D11 Assume F =D 5 1-26
    27. 27. 27 Exponential Smoothing Problem (2) SolutionF1=820+(0.5)(820-820)=820 F3=820+(0.5)(775-820)=797.75 Week Demand 0.5 1 820 820.00 2 775 820.00 3 680 797.50 4 655 738.75 5 696.88 1-27
    28. 28. 28The MAD Statistic to Determine Forecasting Error n 1 MAD ≈ 0.8 standard deviation ∑ A t - Ft 1 standard deviation ≈ 1.25 MAD t=1 MAD = n • The ideal MAD is zero which would mean there is no forecasting error • The larger the MAD, the less the accurate the resulting model 1-28
    29. 29. 29MAD Problem Data Question: What is the MAD value given Question: What is the MAD value given the forecast values in the table below? the forecast values in the table below? Month Sales Forecast 1 220 n/a 2 250 255 3 210 205 4 300 320 5 325 315 1-29
    30. 30. 30MAD Problem Solution Month Sales Forecast Abs Error 1 220 n/a 2 250 255 5 3 210 205 5 4 300 320 20 5 325 315 10 40 n Note that by itself, the MAD ∑A t=1 t - Ft 40 Note that by itself, the MAD only lets us know the mean only lets us know the mean MAD = = = 10 error in a set of forecasts error in a set of forecasts n 4 1-30
    31. 31. 31 Tracking Signal Formula • The Tracking Signal or TS is a measure that indicates whether the forecast average is keeping pace with any genuine upward or downward changes in demand. • Depending on the number of MAD’s selected, the TS can be used like a quality control chart indicating when the model is generating too much error in its forecasts. • The TS formula is: RSFE Running sum of forecast errorsTS = = MAD Mean absolute deviation 1-31
    32. 32. 32 Simple Linear Regression ModelThe simple linear regression The simple linear regression Ymodel seeks to fit a line model seeks to fit a linethrough various data over through various data overtime a time 0 1 2 3 4 5 x (Time) Yt = a + bx Is the linear regression model Is the linear regression model Yt is the regressed forecast value or dependent variable in the model, a is the intercept value of the the regression line, and b is similar to the slope of the regression line. However, since it is calculated with the variability of the data in mind, its formulation is not as straight forward as our usual notion of slope. 1-32
    33. 33. 33Simple Linear Regression Formulas for Calculating “a” and “b” a = y - bx ∑ xy - n(y)(x) b= 2 2 ∑ x - n(x ) 1-33
    34. 34. 34 Simple Linear Regression Problem DataQuestion: Given the data below, what is the simple linear Question: Given the data below, what is the simple linearregression model that can be used to predict sales in future regression model that can be used to predict sales in futureweeks? weeks? Week Sales 1 150 2 157 3 162 4 166 5 177 1-34
    35. 35. 35Answer: First, using the linear regression formulas, weAnswer: First, using the linear regression formulas, wecan compute “a” and “b”can compute “a” and “b” Week Week*Week Sales Week*Sales 1 1 150 150 2 4 157 314 3 9 162 486 4 16 166 664 5 25 177 885 3 55 162.4 2499 Average Sum Average Sum b= ∑ xy - n(y)(x) = 2499 - 5(162.4)(3) = 63 = 6.3 ∑ x 2 - n(x )2 55 − 5(9) 10 a = y - bx = 162.4 - (6.3)(3) = 143.5
    36. 36. 36The resulting regression modelis: Yt = 143.5 + 6.3xNow if we plot the regression generated forecasts against theactual sales we obtain the following chart: 180 175 170 165 160 Sales Sales 155 Forecast 150 145 140 135 1 2 3 4 5 Period
    37. 37. 37Web-Based Forecasting: CPFR • Collaborative Planning, Forecasting, and Replenishment (CPFR) a Web- based tool used to coordinate demand forecasting, production and purchase planning, and inventory replenishment between supply chain trading partners. • Used to integrate the multi-tier or n- Tier supply chain, including manufacturers, distributors and retailers. • CPFR’s objective is to exchange selected internal information to provide for a reliable, longer term future views of demand in the supply chain. • CPFR uses a cyclic and iterative approach to derive consensus forecasts. 1-37
    38. 38. 38Web-Based Forecasting:Steps in CPFR • 1. Creation of a front-end partnership agreement • 2. Joint business planning • 3. Development of demand forecasts • 4. Sharing forecasts • 5. Inventory replenishment 1-38

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