1 forecasting SHORT NOTES FOR ESE AND GATE
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Time horizons in forecasting can be short term (1-3 months), intermediate (2-3 years), or long term (2+ years). Qualitative techniques like market surveys and Delphi method are used for long term forecasting while quantitative techniques like time series analysis, exponential smoothing, and regression analysis are used for short and medium term forecasting. Moving average and exponential smoothing methods smooth random fluctuations in demand data and represent significant variations. Exponential smoothing assigns higher weight to recent demand data compared to older data.
1 forecasting SHORT NOTES FOR ESE AND GATE
- 2. FORCASTING
- 4. DEMAND forecast is basically concerned with the estimation of DEMAND.
- 5. Time Horizons in forecasting Short term forecast Intermediate forecast Long term forecast • Time period btw 1 to 3 month. • They are calculated for specific product. a. Purchasing, overtime decisions b. machine maintenance, etc., • They are calculated for aggregate product. • Application: • sales planning & sales force decisions, inventory planning, etc,. • Time period 2,3……years. • new type of product, major capital investment. • QUALITATIVE techniques of forecasting are used • Application: Capital planning, plant location, new product planning, etc,.
- 6. IES 2007 Which one of the following is not a purpose of long-term forecasting? (a) To plan for the new unit of production (b) To plan the long-term financial requirement. (c) To make the proper arrangement for training the personnel. (d) To decide the purchase programme.
- 7. Methods and types of forecasting Qualitative Quantitative Survey Group averaging Group Consensus Delphi
- 8. Group averaging • This involves averaging of individual forecast to obtain one value. • This method is fast simple and inexpensive
- 9. Group consensus • This method involves all the subject expert meeting together at one place for obtaining one unanimous forecast.
- 10. Delphi • This is one of the most widely used technique in the industry. The coordinating industry asked the subject expert to give their individual forecast along with supporting information. Which is used to arrive at those forecast value. • , pooling of expert opinions
- 11. IES-1995 Which one of the following methods can be used for forecasting the sales potential of a new product? (a) Time series analysis (b) Jury of executive opinion method (c) Sales force composite method (d) Direct survey method
- 12. IAS-1996 For sales forecasting, pooling of expert opinions is made use of in (a)Statistical correlation (b) Delphi technique (c) Moving average method (d) Exponential smoothing
- 13. Quantitative forecasting • This method is generally used for short and medium term forecasting. Time series model :-In this method the variable that is being forecast is decomposed into its components (N) Associative model:-
- 14. IES-2001 Assertion (A): Time series analysis technique of sales-forecasting can be applied to only medium and short-range forecasting. Reason (R): Qualitative information about the market is necessary for long-range forecasting. (a) Both A and R are individually true and R is the correct explanation of A (b) Both A and R are individually true but R is not the correct explanation of A (c) A is true but R is false (d) A is false but R is true
- 16. Trend Component Seasonal Component 0 5 10 15 20 Cyclical Component M T W T F Random Component
- 17. IES-1997 Given T = Underlying trend, C = Cyclic variations within the trend, S = Seasonal variation within the trend and R = Residual, remaining or random variation, as per the time series analysis of sales forecasting, the demand will be a function of: (a) T and C (b) R and S (c) T, C and S (d) T, C, S and R
- 18. Rolling horizon in forecast is used for easy updating of changes and maintaining same length of forecast horizon by adding a new period when one period is over
- 19. IES 2012 Rolling horizon in forecast is used for (A) Allowing same length of forecast horizon by easily adding a new period when one period is over (B) Easy updating of changes and maintaining same length of forecast horizon by adding a new period when one period is over (C) Easy updating of changes and there is no addition of a new period (D) Different reasons other than the above
- 20. Time series analysis technique of sales-forecasting can be applied to only medium and short-range forecasting. Qualitative information about the market is necessary for long-range forecasting.
- 21. Simple moving average • Simple moving average: -Moving Average obtained by adding and averaging the value from a given number of period repeatedly, each time deleting the oldest value and adding a new value.
- 22. Simple moving average S.No Demand Ft (for N=3) 1 100 2 120 3 110 4 130 5 140 6 160 7 150 8 140 9 If N is the period of moving average
- 23. IES 2013 If N is the period of moving average the number of demand data to be stored for calculating the moving average for a period is the demand of last________periods. (A)(N+1) periods (B) (N-1) periods (C) (N) periods (D) (N-2) periods
- 24. IES 2011 Consider the following characteristics. (1) It is very sensitive to small movements in the data (2) The technique is simple (3) The method is affected by the personal prejudice of the people Which of these characteristics of moving average method of forecasting are correct? (A) 1, 2 and 3 (B) 1 and 2 only (C) 2 and 3 only (D) 1 and 3 only
- 25. Weighted moving average • In this method the weights are given to the values vary, The highest weight is given to the latest value and the weight decreases as the values become old.
- 26. Weighted moving average S.No Demand Ft (for N=4) 1 100 2 120 3 110 4 130 5 140 6 160 7 150 8 140 9 SOD method to find the weight N/SMN, N-1/SMN, N-2/SMN, N-3/SMN, ----------- SUM OF NATURAL NUMBER=SMN
- 27. Exponential smoothing Ft+1 = Dt + (1- ) Dt - 1 + (1- )2Dt - 2 + ...
- 28. Exponential Smoothing • Assumes the most recent observations have the highest predictive value • The weightage of the data diminishes exponentially as the data become older • gives more weight to recent time periods Ft+1 = Ft + (Dt - Ft) et Ft+1 = Forecast value for time t+1 Dt = Actual value at time t = Smoothing constant
- 29. α α(1- α) t t-1 t-2 t-3 3 2 1 α(1- α)2 α(1- α)3 α(1- α)t-3 α(1- α)t-2 α(1- α)t-1 Weightage Of Past data period
- 30. Ft+1 = Dt + (1- ) Dt - 1 + (1- )2Dt - 2 + ... Forecast Effects of Smoothing Constant Weights Prior Period 2 periods ago (1 - ) 3 periods ago (1 - )2 = = 0.10 = 0.90 10% 9% 8.1% 90% 9% 0.9% Ft+1 = Ft + (Dt - Ft) or w1 w2 w3
- 31. IES 2012 In an exponentially weighted moving average, the weight of the demand of past periods (A)Increases as age of the data increases (B) Increases as age of the data deceases (C) Decreases as age of the data increases (D) Has no relationship with age of the data
- 32. If α is not given then α=2/N+1 F1 value will be given. If value is not given, take F1=D1 Another case average of all demand=F1. Values of α varies 0 to 1 Exponential smoothening methods are best suited under conditions when forecasting horizon is relatively large
- 33. Exponential Smoothing – Example 1 Given the weekly demand data what are the exponential smoothing forecasts for periods 2-10 using =0.10? Assume F1=D1 Week Demand 1 820 2 775 3 680 4 655 5 750 6 802 7 798 8 689 9 775 10 Ft+1 = Ft + (Dt - Ft) i Di
- 34. 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 Ft+1 = Ft + (Dt - Ft) i Di
- 35. IES 2014 Exponential smoothening methods are best suited under conditions when (A) forecasting horizon is relatively large (B) forecasting for large number of items (C) available outside information is more (D) All of the above
- 36. IES-1999 A company intends to use exponential smoothing technique for making a forecast for one of its products. The previous year's forecast has been 78 units and the actual demand for the corresponding period turned out to be 73 units. If the value of the smoothening constant α is 0.2, the forecast for the next period will be: (a) 73 units (b) 75 units (c) 77 units (d) 78 units
- 37. IES-2005 For a product, the forecast for the month of January was 500 units. The actual demand turned out to be 450 units. What is the forecast for the month of February using exponential smoothing method with a smoothing coefficient = 0.1? (a) 455 (b) 495 (c) 500 (d) 545
- 39. • Responsiveness indicates that forecast as calculated have a fluctuating or swinging pattern. Stability means that the forecast show a leveled or flat character as the value of N increases the forecast become stable. Lower value of N results in forecast being more responsive
- 40. Ft+1 = Ft + (Dt - Ft) If =0 Ft+1 = Ft ---------------- STABLE If =1 Ft+1 = Dt ---------------- RESPONSIVE
- 41. • Higher the value of α, more responsive the forecast will be and this is desirable for forecasting of new products. Whereas lower value of α makes the forecast more stable and this desirable for old and stable products.
- 42. IES-2008 Using the exponential smoothing method of forecasting, what will be the forecast for the fourth week if the actual and forecasted demand for the third week is 480 and 500 respectively and α = 0·2? (a) 400 (b) 496 (c) 500 (d) 504 F4 = α d3 + 1 −α F3 = 0.2 480) + (0.8)500 = 96 + 400 = 496
- 43. Error analysis • It is assumed that the forecasting model should over estimate and under estimate with equal magnitude so that the errors produces by forecasting model will fit into a Normal distribution curve
- 44. Measuring Forecast Error • Forecasts are never 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 Et = Dt − Ft
- 45. Measuring Forecasting Accuracy • Mean Absolute Deviation (MAD) • measures the total error in a forecast without regard to sign • Cumulative Forecast Error (CFE) • Measures any bias in the forecast • Mean Square Error (MSE) • Penalizes larger errors • Tracking Signal • Measures if your model is working • Good tracking signal has low values
- 46. Measuring Forecasting Accuracy Mean Error or BIAS
- 47. IES-2009 Which of the following is the measure of forecast error? (a) Mean absolute deviation (b) Trend value (c) Moving average (d) Price fluctuation
- 48. IES-2004 It is given that the actual demand is 59 units, a previous forecast 64 units and smoothening factor 0.3. What will be the forecast for next period, using exponential smoothing? (a) 36.9 units (b) 57.5 units (c) 60.5 units (d) 62.5 units
- 49. IES 2007 Consider the following statements: Exponential smoothing 1. Is a modification of moving average method 2. Is a weighted average of past observations 3. Assigns the highest weight age to the most recent observation Which of the statements given above are correct? (a) 1, 2 and 3 (b) 1 and 2 only (c) 2 and 3 only (d) 1 and 3 only
- 50. Associative Forecasting Used when changes in one or more independent variables can be used to predict the changes in the dependent variable Most common technique is linear regression analysis We apply this technique just as we did in the time series example
- 51. Linear regression in forecasting Linear regression is based on 1. Fitting a straight line to data 2. Explaining the change in one variable through changes in other variables. dependent variable = a + b (independent variable)
- 52. Example: do people drink more when it’s cold? Alcohol Sales Average Monthly Temperature Which line best fits the data?
- 53. IES-2008 Which one of the following is not a technique of Long Range Forecasting? (a)Market Research and Market Survey (b)Delphi (c) Collective Opinion (d) Correlation and Regression
- 54. NOTE:- • Regression will forecast a higher value compared to moving average method.
- 55. IES-2005 Which one of the following forecasting techniques is most suitable for making long range forecasts? (a) Time series analysis (b) Regression analysis (c) Exponential smoothing (d) Market Surveys
- 56. IES-2005 Which one of the following methods can be used for forecasting when a demand pattern is consistently increasing or decreasing? (a) Regression analysis (b) Moving average (c) Variance analysis (d) Weighted moving average
- 57. IES-2003 Which one of the following statements is correct? (a) Time series analysis technique of forecasting is used for very long range forecasting (b) Qualitative techniques are used for long range forecasting and quantitative techniques for short and medium range forecasting (c) Coefficient of correlation is calculated in case of time series technique (d) Market survey and Delphi techniques are used for short range forecasting
- 58. IES-2009 • Assertion (A): Moving average method of forecasting demand gives an account of the trends in fluctuations and suppresses day-to-day insignificant fluctuations. • Reason (R): Working out moving averages of the demand data smoothens the random day-to-day fluctuations and represents only significant variations. (a) Both A and R are true and R is the correct explanation of A (b) Both A and R are true but R is NOT the correct explanation of A (c) A is true but R is false (d) A is false but R is true
- 59. IES-2006 Which one of the following is a qualitative technique of demand forecasting? (a) Correlation and regression analysis (b) Moving average method (c) Delphi technique (d) Exponential smoothing
- 60. IES-2006 Which one of the following statements is not correct for the exponential smoothing method of demand forecasting? (a) Demand for the most recent data is given more weightage (b) This method requires only the current demand and forecast demand (c) This method assigns weight to all the previous data (d) This method gives equal weightage to all the periods
- 61. TYPES OF FORECASTS PASSIVE FORECASTS Where the factors being forecasted are assumed to be constant over a period of time and changes are ignored. ACTIVE FORECASTS Where factors being forecasted are taken as flexible and are subject to changes.
- 62. IES 2013 Forecasting which assumes a static environment in the future is: (A)Passive forecasting (B) Active forecasting (C) Long term forecasting (D) Short term forecasting