Demand forecasting methods 1 gp
- 1. Demand Forecasting PowerPoint Presentation by R.B. Clough - UNH Professor & Lawyer. Puttu Guru Prasad, M.Com. M.B.A., L.L.B., M.Phil, PGDFTM, APSET. ICFAI TMF, (PhD) at JNTUK, Expert Resource person at APHRDI, Bapatla, Senior Faculty for Management Studies, Certified NSS Program Officer, Coordinator – College Beautification S&H Department, VVIT, Nambur, 93 94 96 98 98, 807 444 95 39, My Blog: puttuguru.blogspot.in
- 2. Decisions that Need Forecasts Which markets to pursue? What products to produce? How many people to hire? How many units to purchase? How many units to produce? And so on……
- 3. Common Characteristics of Forecasting Forecasts are rarely perfect Forecasts are more accurate for aggregated data than for individual items Forecast are more accurate for shorter than longer time periods
- 4. Forecasting Steps What needs to be forecast? Level of detail, units of analysis & time horizon required What data is available to evaluate? Identify needed data & whether it’s available Select and test the forecasting model Cost, ease of use & accuracy Generate the forecast Monitor forecast accuracy over time
- 5. Types of Forecasting Models Qualitative (technological) methods: Forecasts generated subjectively by the forecaster Quantitative (statistical) methods: Forecasts generated through mathematical modeling
- 6. Qualitative Methods Type Characteristics Strengths Weaknesses Executive opinion A group of managers meet & come up with a forecast Good for strategic or new-product forecasting One person's opinion can dominate the forecast Market research Uses surveys & interviews to identify customer preferences Good determinant of customer preferences It can be difficult to develop a good questionnaire Delphi method Seeks to develop a consensus among a group of experts Excellent for forecasting long-term product demand, technological changes, and Time consuming to develop
- 7. Statistical Forecasting Time Series Models: Assumes the future will follow same patterns as the past Causal Models: Explores cause-and-effect relationships Uses leading indicators to predict the future E.g. housing starts and appliance sales
- 8. Composition of Time Series Data Data = historic pattern + random variation Historic pattern may include: Level (long-term average) Trend Seasonality Cycle
- 10. Methods of Forecasting the Level Naïve Forecasting Simple Mean Moving Average Weighted Moving Average Exponential Smoothing
- 11. Time Series Problem Determine forecast for periods 11 Naïve forecast Simple average 3- and 5-period moving average 3-period weighted moving average with weights 0.5, 0.3, and 0.2 Exponential smoothing with alpha=0.2 and 0.5 Period Orders 1 122 2 91 3 100 4 77 5 115 6 58 7 75 8 128 9 111 10 88 11
- 12. Time Chart of Orders Data 0 20 40 60 80 100 120 140 1 2 3 4 5 6 7 8 9 10
- 13. Naïve Forecasting Next period forecast = Last Period’s actual: tt AF =+1
- 14. Simple Average (Mean) Next period’s forecast = average of all historical data n AAA F ttt t .............21 1 +++ = −− +
- 15. Moving Average Next period’s forecast = simple average of the last N periods N AAA F Nttt t 11 1 ......... +−− + +++ =
- 16. The Effect of the Parameter N A smaller N makes the forecast more responsive A larger N makes the forecast more stable
- 17. Weighted Moving Average 1......... ......... 21 11211 =++ +++= +−−+ N NtNttt CCC where ACACACF
- 19. The Effect of the Parameter α A smaller α makes the forecast more stable A larger α makes the forecast more responsive
- 20. Time Series Problem Solution Simple Simple Weighted Exponential Exponential Naïve Simple Moving Moving Moving Smoothing Smoothing Period Orders (A) Forecast Average Average (N=3) Average(N=5) Average (N=3) (α = 0.2) (α = 0.5) 1 122 122 122 2 91 122 122 122 122 3 100 91 107 116 107 4 77 100 104 104 102 113 104 5 115 77 98 89 87 106 91 6 58 115 101 97 101 101 108 103 7 75 58 94 83 88 79 98 81 8 128 75 91 83 85 78 93 78 9 111 128 96 87 91 98 100 103 10 88 111 97 105 97 109 102 107 11 88 97 109 92 103 99 98
- 21. Forecast Accuracy Forecasts are rarely perfect Need to know how much we should rely on our chosen forecasting method Measuring forecast error: Note that over-forecasts = negative errors and under-forecasts = positive errors ttt FAE −=
- 22. Tracking Forecast Error Over Time Mean Absolute Deviation (MAD): A good measure of the actual error in a forecast Mean Square Error (MSE): Penalizes extreme errors Tracking Signal Exposes bias (positive or negative) n forecastactual MAD ∑ − = ( ) 2 actual - forecast MSE n = ∑ ( ) MAD TS ∑= forecast-actual
- 23. Accuracy & Tracking Signal Problem: A company is comparing the accuracy of two forecasting methods. Forecasts using both methods are shown below along with the actual values for January through May. The company also uses a tracking signal with ±4 limits to decide when a forecast should be reviewed. Which forecasting method is best? Month Actua l sales Method A Method B F’cast Error Cum. Error Tracking Signal F’cast Error Cum. Error Tracking Signal Jan. 30 28 2 2 2 28 2 2 1 Feb. 26 25 1 3 3 25 1 3 1.5 March 32 32 0 3 3 29 3 6 3 April 29 30 -1 2 2 27 2 8 4 May 31 30 1 3 3 29 2 10 5 MAD 1 2 MSE 1.4 4.4
- 24. Forecasting Trends Trend-adjusted exponential smoothing Three step process: Smooth the level of the series: Smooth the trend: Calculate the forecast including trend: ))(1( 11 −− +−+= tttt TSAS αα 11 )1()( −− ++−= tttt TSST ββ ttt TSFIT +=+1
- 25. Forecasting trend problem: a company uses exponential smoothing with trend to forecast usage of its lawn care products. At the end of July the company wishes to forecast sales for August. July demand was 62. The trend through June has been 15 additional gallons of product sold per month. Average sales have been 57 gallons per month. The company uses alpha+0.2 and beta +0.10. Forecast for August. Smooth the level of the series: Smooth the trend: Forecast including trend: ( )( ) ( )( ) 14.8150.957700.1β)T(1)Sβ(ST 1t1ttJuly =+−=−+−= −− ( )( ) ( )( ) 7015570.8620.2)Tα)(S(1αAS 1t1ttJuly =++=+−+= −− gallons84.814.870TSFIT ttAugust =+=+=
- 26. Adjusting for Seasonality Calculate the average demand per season E.g.: average quarterly demand Calculate a seasonal index for each season of each year: Divide the actual demand of each season by the average demand per season for that year Average the indexes by season E.g.: take the average of all Spring indexes, then of all Summer indexes, ...
- 27. Adjusting for Seasonality Forecast demand for the next year & divide by the number of seasons Use regular forecasting method & divide by four for average quarterly demand Multiply next year’s average seasonal demand by each average seasonal index Result is a forecast of demand for each season of next year
- 28. Seasonality problem: a university wants to develop forecasts for the next year’s quarterly enrollments. It has collected quarterly enrollments for the past two years. It has also forecast total enrollment for next year to be 90,000 students. What is the forecast for each quarter of next year? Quarter Year 1 Seasonal Index Year 2 Seasonal Index Avg. Index Year3 Fall 24000 26000 Winter 23000 22000 Spring 19000 19000 Summer 14000 17000 Total 90000 Average
- 29. Seasonality Problem: Solution Quarter Year 1 Seasonal Index Year 2 Seasonal Index Avg. Index Year3 Fall 24000 1.20 26000 1.24 1.22 27450 Winter 23000 1.15 22000 1.05 1.10 24750 Spring 19000 0.95 19000 0.90 0.93 20925 Summer 14000 0.70 17000 0.81 0.76 17100 Total 80000 4.00 84000 4.00 4.01 90000 Average 20000 21000 22500
- 30. Casual Models Often, leading indicators hint can help predict changes in demand Causal models build on these cause- and-effect relationships A common tool of causal modeling is linear regression: bxaY +=
- 31. Linear Regression ( )( ) ( )( )∑ ∑ ∑ ∑ − − = XXX YXXY b 2 Identify dependent (y) and independent (x) variables Solve for the slope of the line Solve for the y intercept Develop your equation for the trend line Y=a + bX XbYa −= ∑ ∑ − − = 22 XnX YXnXY b
- 32. Linear Regression Problem: A maker of golf shirts has been tracking the relationship between sales and advertising dollars. Use linear regression to find out what sales might be if the company invested $53,000 in advertising next year. ∑ ∑ − − = 22 XnX YXnXY bSales $ (Y) Adv.$ (X) XY X^2 Y^2 1 130 48 4240 2304 16,900 2 151 52 7852 2704 22,801 3 150 50 7500 2500 22,500 4 158 55 8690 3025 24964 5 153.85 53 Tot 589 205 30282 10533 87165 Av g 147.25 51.2 5 ( )( ) ( ) ( ) ( ) 153.54533.58-36.20Y 3.58x-36.20bXaY -36.20a 51.253.58147.25XbYa 3.58 51.25410533 147.2551.25430282 b 5 2 =+= +=+= = −=−= = − − =
- 33. How Good is the Fit? Correlation coefficient (r) measures the direction and strength of the linear relationship between two variables. The closer the r value is to 1.0 the better the regression line fits the data points. Coefficient of determination ( ) measures the amount of variation in the dependent variable about its mean that is explained by the regression line. Values of ( ) close to 1.0 are desirable. ( ) ( )( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) .788.982r .888 58987,1654*(205)-4(10,533) 589205)(30,282(4) r YYn*XXn YXXYn r 22 22 2 2 2 2 == = − − = −− − = ∑∑∑∑ ∑∑∑ 2 r 2 r
- 34. Factors for Selecting a Forecasting Model The amount & type of available data Degree of accuracy required Length of forecast horizon Presence of data patterns
- 35. Forecasting Software Spreadsheets Microsoft Excel, Quattro Pro, Lotus 1-2-3 Limited statistical analysis of forecast data Statistical packages SPSS, SAS, NCSS, Minitab Forecasting plus statistical and graphics Specialty forecasting packages Forecast Master, Forecast Pro, Autobox, SCA
- 36. Homework- Aryasri MEFA TEXT Book Least squares methods- page no 4.9, chapter --Demand Forecasting, example 1. Moving Averages Method- page no 4.12. Exponential Smoothing Method- page no 4.13. Flow Chart of Demand Forecasting Methods. Practice the above problems.