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FORECASTING

FORECASTING. FORECASTING TECHNIQUES. There are three types of techniques Quantitative Methods ( JUDGEMENTAL ) Time-Series Method ( NUMERICAL) Casual Methods. Quantitative Methods. It includes: Customer Survey Method Executive Opinion Method

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FORECASTING

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  1. WWW.PPTMART.COM FORECASTING

  2. WWW.PPTMART.COM FORECASTING TECHNIQUES There are three types of techniques • Quantitative Methods (JUDGEMENTAL) • Time-Series Method (NUMERICAL) • Casual Methods

  3. WWW.PPTMART.COM Quantitative Methods It includes: • Customer Survey Method • Executive Opinion Method • Delphi Method It is based on intuition, estimate & opinions • Sales Force Composite Method • Past Analysis

  4. WWW.PPTMART.COM Time-Series Method It includes: • Simple Moving Average Ft = Dt-1 + Dt-2 + Dt-3 + ------ + Dt-n / n where, Ft = Forecast for the period t n = No. of preceding periods taken for average Dt = Actual demand in immediately preceding time-periods Eg. Let sales for Jan, Feb & March be: 20, 23 & 19 respectively Demand for April= 20+23+19/3 =20.67 =21

  5. WWW.PPTMART.COM • Weighted Moving Average When fluctuations in demand are there, more weightage is given to recent data E.g. Let forecasters assign weights for the sales data for past 3 months: 50% for most recent month 33% for past 2 months 17% for past 3 months Actual sales are: 90,110 &130 So, WMA for 4th month=.50*90 + .33*110 + .17*130 = 103.4

  6. WWW.PPTMART.COM • Exponential Smoothing • First Order Exponential Smoothing Demand forecast for the next period is given by: Ft = αDt-1 + (1- α) Ft-1 Where, Ft-1= Forecast for period t-1 Dt-1= Actual demand for period t-1 α= Smoothing constant, 0≤ α ≤ 1. It shows effect of past demand on future.

  7. WWW.PPTMART.COM Q. A firm achieved actual sale of 1000 units in the month of May when the forecast was for 900 units. Calculate the sales for 900 units. Calculate the sales for the month of June by using a smoothing constant of 0.1 Sol. FJune = αDMay + (1- α) FMay FJune = 0.1*1000 + (1-0.1)*900 = 910

  8. WWW.PPTMART.COM • Trend Adjusted Exponential Smoothing Single Moving Average & First order Exponential Smoothing lag behind actual data which has a steady trend upwards or downwards At = α Dt + (1- α) (At-1+ Tt-1) Tt = β (At - At-1) + (1- β) (Tt-1) Ft+1 = At + Tt Where, Dt =Demand in period t At = Exponential smoothed average for period t Tt = Exponential smoothed trend for period t Tt-1 = Trend estimate for t-1 period At-1 = Actual demand for t-1 period Ft+1 = Forecast for period t+1 α = Smoothing constant (0≤ α ≤ 1) β = Smoothing constant (0≤ β ≤ 1)

  9. WWW.PPTMART.COM Q. For a car dealer sales for last 5 months averaged 30 units. Average increase was 4 units/month. In the 5th month, 31 units were sold. Forecast sales for next month using trend adjusted exponential smoothing method. Given: α= .2, β= .17 Sol. A0 = 30, T0 = 4 (Given) D1 = 31 For 6th Month: At = αDt + (1- α) (At-1+ Tt-1) Tt = β (At - At-1) + (1- β) (Tt-1) Ft+1 = At + Tt Thus, A5 = .2(31)+(1-.2)(30+4) = 33.4 T5 = .17(33.4-30) + .83(4) = 3.898 Ft = 33.4 + 3.898 = 37.298

  10. WWW.PPTMART.COM Casual Methods • Linear Regression Least Square Method ---Do----

  11. WWW.PPTMART.COM Accuracy of Forecast The different measures of forecast accuracy are:- Mean Absolute Deviation (MAD) It is themean of errors made over time period without considering direction of error i.e. over or under estimation. n MAD = 1∑ At – Ft n t=1 Where, At = Actual demand in period t Ft = Forecasted demand in period t n = No. of periods At – Ft = Indicates absolute value of deviation

  12. WWW.PPTMART.COM Mean Square Error (MSE) Here, large errors are penalized more than small ones because of squaring. n MSE = 1∑ (At – Ft)² n t=1 Mean Forecast Error (MFE) Here, absolute values are not taken into consideration n MFE = 1∑ (At – Ft) n t=1

  13. WWW.PPTMART.COM Mean Absolute Percentage Error (MAPE) It is indicative of relative error n MAPE = 100∑ (At – Ft) n t=1 At Tracking Signal (TS) Here, absolute values are not taken into consideration n TS = ∑ ( Actual Demand– Forecasted Demand ) i=1 MAD + ve TS = Underestimation - ve Ts = Overestimation

  14. Q. Calculate MAD, MSE, MFE, MAPE,& TS for the given forecast & actual demand WWW.PPTMART.COM

  15. Sol. MAD = 28/6 = 4.67 MSE = 230/6 = 38.34 MFE = -2 / 6 = -.33 MAPE = 33.50 TS = -2/4.67 WWW.PPTMART.COM

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