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Forecasting Techniques and Approaches in Operations Management

Understand forecasting methods and approaches, including time series forecasting, exponential smoothing, and correlation techniques. Learn about qualitative and quantitative forecasts, and their impact on business decisions.

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Forecasting Techniques and Approaches in Operations Management

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  1. ForecastingLecture 25Dr. Arshad ZaheerSource: Operations Management, 9e, by Heizer/Render, PEARSON, Prentice Hall and online sources

  2. RECAP Assignment Model (Unbalanced problems) Hungarian Method Steps Involved Illustrations Optimal Assignment

  3. Outline • What Is Forecasting • Forecasting Time Horizons • Steps in Forecasting • Forecasting Types • Time Series Forecasting • Exponential Smoothing • Correlation and Regression

  4. Forecasting A technique to predict future Process of predicting the future value of variable of interest Forecasting is the basis of all business decisions in different areas Helps to predict demand, supply, profit, cost, inflation, price of stocks and bonds

  5. Forecasting Time Horizons • Short-range forecast • Up to 1 year • Purchasing, job scheduling, workforce levels, job assignments, production levels • Medium-range forecast • 1 to 3 years • Sales and production planning, budgeting • Long-range forecast • 3+ years • New product planning, facility location, research and development

  6. Seven Steps in Forecasting • Define purpose of forecast • Define the time horizon of the forecast • Select the appropriate forecasting technique • Gather the data • Analyze the data • Make the forecast • Validate, implement and monitor results

  7. Forecasting Types Qualitative Forecasts • Subjective and personal opinion, inputs and experience • No numerical data involved, no statistical calculation • Used when situation is vague and little data exist

  8. Forecasting Approaches Quantitative Forecasts • Used when situation is ‘stable’ and historical data exist • Involves mathematical techniques and statistical calculations • e.g., forecasting sales of product

  9. Qualitative Forecasting Methods Expert Opinion • Judgment and opinion of different experts • Managerial experience • Statistical models may be used

  10. Qualitative Forecasting Methods Nominal Group Technique and Delphi Method • Iterative process to reach consensus • Select experts (decision makers, staff and respondents) • Send questionnaire to obtain forecasts • Summarize, redistribute questionnaire • Repeat process till consensus achieved

  11. Time Series Forecasting • Quantitative Forecasting Method • Forecasting based on past and historical data values, other factors are not important • Assumes that factors influencing past and present will continue influence in future

  12. Time Series Forecasting • Trend: Long term upward or downward movement in data. Counts several year changes due to population, culture, age etc. • Seasonal: Short term regular movement in data due to weather, vacations, customs etc. • Cyclical: Repeating up and down wave like movement in data after long term duration due to business cycle, political or economical conditions

  13. Time Series Forecasting • Irregular: Unsystematic, short term and non repeating fluctuations in data due to unforeseen events like earthquake, strikes, major changes in products etc.

  14. Naive Approach • Assumes demand in next period is the same as demand in most recent period • e.g., If January sales were 68, then February sales will be 68 • Sometimes cost effective and efficient • Can be good starting point

  15. ∑ demand in previous n periods n Moving average = Moving Average Method • Moving average method is the average of a series of past actual values of data • Used if little or no trend • Used often for smoothing • Provides overall impression of data over time

  16. Actual 3-Month Month Shed Sales Moving Average January 10 February 12 March 13 April 16 May 19 June 23 July 26 10 12 13 (10 + 12 + 13)/3 = 11 2/3 Moving Average Example (12 + 13 + 16)/3 = 13 2/3 (13 + 16 + 19)/3 = 16 (16 + 19 + 23)/3 = 19 1/3

  17. Moving Average Forecast 30 – 28 – 26 – 24 – 22 – 20 – 18 – 16 – 14 – 12 – 10 – Actual Sales Shed Sales | | | | | | | | | | | | J F M A M J J A S O N D Graph of Moving Average

  18. Weightedmoving average ∑ (weight for period n) x (demand in period n) ∑ weights = Weighted Moving Average • Used when trend is present • Older data usually less important • Weights based on experience and intuition

  19. Weights Applied Period 3 Last month 2 Two months ago 1 Three months ago 6 Sum of weights Actual 3-Month Weighted Month Shed Sales Moving Average January 10 February 12 March 13 April 16 May 19 June 23 July 26 10 12 13 [(3 x 13) + (2 x 12) + (10)]/6 = 121/6 Weighted Moving Average [(3 x 16) + (2 x 13) + (12)]/6 = 141/3 [(3 x 19) + (2 x 16) + (13)]/6 = 17 [(3 x 23) + (2 x 19) + (16)]/6 = 201/2

  20. Exponential Smoothing • Form of weighted moving average • Weights decline exponentially • Most recent data weighted most • Requires smoothing constant () • Ranges from 0 to 1 • Subjectively chosen • Involves little record keeping of past data

  21. Exponential Smoothing New forecast = Last period’s forecast + a (Last period’s actual demand – Last period’s forecast) Ft = Ft – 1 + a(At – 1 - Ft – 1) where Ft = new forecast Ft – 1 = previous forecast At – 1 = Last period actual demand a = smoothing (or weighting) constant (0 ≤a≤ 1) Higher the value of a higher the weightage given to recent observation

  22. Exponential Smoothing Example Predicted demand = 142 Actual demand = 153 Smoothing constant a = .20 Ft = Ft – 1 + a(At – 1 - Ft – 1) New forecast = 142 + .2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144

  23. Correlation • How strong is the linear relationship between the variables? • Correlation does not necessarily imply causality! • Coefficient of correlation, r, measures degree of association • Values range from -1 to +1

  24. nSxy - SxSy [nSx2 - (Sx)2][nSy2 - (Sy)2] r = Correlation Coefficient

  25. y nSxy - SxSy [nSx2 - (Sx)2][nSy2 - (Sy)2] r = x (a) Perfect positive correlation: r = +1 (b) Positive correlation: 0 < r < 1 y y y x x (d) Perfect negative correlation: r = -1 (c) No correlation: r = 0 x Correlation Coefficient

  26. Correlation • Coefficient of Determination, r2, measures the percent of change in y predicted by the change in x • Values range from 0 to 1 • Easy to interpret r = .901 r2 = .81

  27. ^ y = a + bx ^ where y = computed value of the variable to be predicted (dependent variable) a = y-axis intercept b = slope of the regression line x = the independent variable Trend Projections-Associative Forecasting Fitting a trend line to historical data points to project into the medium to long-range Linear trends can be found using the least squares technique

  28. Actual observation (y value) Deviation7 Deviation5 Deviation6 Deviation3 Values of Dependent Variable Deviation4 Deviation1 (error) Deviation2 ^ Trend line, y = a + bx Time period Least Squares Method Figure 4.4

  29. Actual observation (y value) Deviation7 Deviation5 Deviation6 Deviation3 Values of Dependent Variable Deviation4 Deviation1 (error) Deviation2 ^ Trend line, y = a + bx Time period Least Squares Method Least squares method minimizes the sum of the squared errors (deviations) Figure 4.4

  30. Sxy - nxy Sx2 - nx2 b = ^ y = a + bx a = y - bx Least Squares Method Equations to calculate the regression variables

  31. Time Electrical Power Year Period (x) Demand x2 xy 2001 1 74 1 74 2002 2 79 4 158 2003 3 80 9 240 2004 4 90 16 360 2005 5 105 25 525 2005 6 142 36 852 2007 7 122 49 854 ∑x = 28 ∑y = 692 ∑x2 = 140 ∑xy = 3,063 x = 4 y = 98.86 3,063 - (7)(4)(98.86) 140 - (7)(42) a = y - bx = 98.86 - 10.54(4) = 56.70 ∑xy - nxy ∑x2 - nx2 b = = = 10.54 Least Squares Example

  32. Time Electrical Power Year Period (x) Demand x2 xy 1999 1 74 1 74 2000 2 79 4 158 2001 3 80 9 240 2002 4 90 16 360 2003 5 105 25 525 2004 6 142 36 852 2005 7 122 49 854 Sx = 28 Sy = 692 Sx2 = 140 Sxy = 3,063 x = 4 y = 98.86 The trend line is ^ y = 56.70 + 10.54x 3,063 - (7)(4)(98.86) 140 - (7)(42) a = y - bx = 98.86 - 10.54(4) = 56.70 Sxy - nxy Sx2 - nx2 b = = = 10.54 Least Squares Example

  33. Trend line, y = 56.70 + 10.54x 160 – 150 – 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – 60 – 50 – ^ Power demand | | | | | | | | | 2001 2002 2003 2004 2005 2006 2007 2008 2009 Year Least Squares Example

  34. ^ y = a + b1x1 + b2x2 … Multiple Regression Analysis If more than one independent variable is to be used in the model, linear regression can be extended to multiple regression to accommodate several independent variables

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