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Operations Management

Operations Management

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Operations Management

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  1. Operations Management Chapter 4 - Forecasting PowerPoint presentation to accompany Heizer/Render Principles of Operations Management, 6e Operations Management, 8e © 2006 Prentice Hall, Inc.

  2. ?? What is Forecasting? • Process of predicting a future event • Underlying basis of all business decisions • Production • Inventory • Personnel • Facilities

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

  4. Influence of Product Life Cycle • Introduction and growth require longer forecasts than maturity and decline • As product passes through life cycle, forecasts are useful in projecting • Staffing levels • Inventory levels • Factory capacity Introduction – Growth – Maturity – Decline

  5. Types of Forecasts • Economic forecasts • Address business cycle – inflation rate, money supply, housing starts, etc. • Technological forecasts • Predict rate of technological progress • Impacts development of new products • Demand forecasts • Predict sales of existing product

  6. The Realities! • Forecasts are seldom perfect • Most techniques assume an underlying stability in the system • Product family and aggregated forecasts are more accurate than individual product forecasts

  7. Forecasting Approaches Qualitative Methods • Used when situation is vague and little data exist • New products • New technology • Involves intuition, experience • e.g., forecasting sales on Internet

  8. Forecasting Approaches Quantitative Methods • Used when situation is ‘stable’ and historical data exist • Existing products • Current technology • Involves mathematical techniques • e.g., forecasting sales of color televisions

  9. Overview of Qualitative Methods • Jury of executive opinion • Delphi method • Sales force composite • Consumer Market Survey

  10. Jury of Executive Opinion • Involves small group of high-level managers • Group estimates demand by working together • Combines managerial experience with statistical models • Relatively quick • ‘Group-think’disadvantage

  11. Decision Makers (Evaluate responses and make decisions) Staff (Administering survey) Respondents (People who can make valuable judgments) Delphi Method • Iterative group process, continues until consensus is reached • 3 types of participants • Decision makers • Staff • Respondents

  12. Sales Force Composite • Each salesperson projects his or her sales • Combined at district and national levels • Sales reps know customers’ wants • Tends to be overly optimistic

  13. Consumer Market Survey • Ask customers about purchasing plans • What consumers say, and what they actually do are often different • Sometimes difficult to answer

  14. Time-Series Models Associative Model Overview of Quantitative Approaches • Naive approach • Moving averages • Exponential smoothing • Trend projection • Linear regression

  15. Time Series Forecasting • Set of evenly spaced numerical data • Obtained by observing response variable at regular time periods • Forecast based only on past values • Assumes that factors influencing past and present will continue influence in future

  16. Trend Cyclical Seasonal Random Time Series Components

  17. Trend Component • Persistent, overall upward or downward pattern • Changes due to population, technology, age, culture, etc. • Typically several years duration

  18. Number of Period Length Seasons Week Day 7 Month Week 4-4.5 Month Day 28-31 Year Quarter 4 Year Month 12 Year Week 52 Seasonal Component • Regular pattern of up and down fluctuations • Due to weather, customs, etc. • Occurs within a single year

  19. 0 5 10 15 20 Cyclical Component • Repeating up and down movements • Affected by business cycle, political, and economic factors • Multiple years duration

  20. M T W T F Random Component • Erratic, unsystematic, ‘residual’ fluctuations • Due to random variation or unforeseen events • Short duration and nonrepeating

  21. Naive Approach • Assumes demand in next period is the same as demand in most recent period • e.g., If May sales were 48, then June sales will be 48 • Sometimes cost effective and efficient

  22. ∑ demand in previous n periods n Moving average = Moving Average Method • MA is a series of arithmetic means • Used if little or no trend • Used often for smoothing • Provides overall impression of data over time

  23. 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

  24. 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

  25. 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

  26. 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

  27. Potential Problems With Moving Average • Increasing n smooths the forecast but makes it less sensitive to changes • Do not forecast trends well • Require extensive historical data

  28. Weighted moving average 30 – 25 – 20 – 15 – 10 – 5 – Actual sales Sales demand Moving average | | | | | | | | | | | | J F M A M J J A S O N D Moving Average And Weighted Moving Average Figure 4.2

  29. 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

  30. 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 a = smoothing (or weighting) constant (0  a  1)

  31. Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20

  32. New forecast = 142 + .2(153 – 142) Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20

  33. Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant a = .20 New forecast = 142 + .2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars

  34. Weight Assigned to Most 2nd Most 3rd Most 4th Most 5th Most Recent Recent Recent Recent Recent Smoothing Period Period Period Period Period Constant (a) a(1 - a) a(1 - a)2a(1 - a)3a(1 - a)4 a = .1 .1 .09 .081 .073 .066 a = .5 .5 .25 .125 .063 .031 Effect of Smoothing Constants

  35. 225 – 200 – 175 – 150 – Actual demand a = .5 Demand a = .1 | | | | | | | | | 1 2 3 4 5 6 7 8 9 Quarter Impact of Different 

  36. Choosing  The objective is to obtain the most accurate forecast no matter the technique We generally do this by selecting the model that gives us the lowest forecast error Forecast error = Actual demand - Forecast value = At - Ft

  37. Mean Absolute Deviation (MAD) Mean Squared Error (MSE) MAD = ∑ |actual - forecast| n ∑(forecast errors)2 n MSE = Common Measures of Error

  38. Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloadeda = .10 a = .10 a = .50 a = .50 1 180 175 5 175 5 2 168 176 8 178 10 3 159 175 16 173 14 4 175 173 2 166 9 5 190 173 17 170 20 6 205 175 30 180 25 7 180 178 2 193 13 8 182 178 4 186 4 84 100 Comparison of Forecast Error

  39. Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonage with for with for Quarter Unloadeda = .10 a = .10 a = .50 a = .50 ∑ |deviations| n MAD = For a = .10 1 180 175 5 175 5 2 168 176 8 178 10 3 159 175 16 173 14 4 175 173 2 166 9 5 190 173 17 170 20 6 205 175 30 180 25 7 180 178 2 193 13 8 182 178 4 186 4 84 100 = 84/8 = 10.50 For a = .50 = 100/8 = 12.50 Comparison of Forecast Error

  40. Forecast including (FITt) = trend exponentially exponentially smoothed (Ft) + (Tt) smoothed forecast trend Exponential Smoothing with Trend Adjustment When a trend is present, exponential smoothing must be modified

  41. Exponential Smoothing with Trend Adjustment Ft = a(At - 1) + (1 - a)(Ft - 1 + Tt - 1) Tt = b(Ft - Ft - 1) + (1 - b)Tt - 1 Step 1: Compute Ft Step 2: Compute Tt Step 3: Calculate the forecast FITt= Ft+ Tt

  42. Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Exponential Smoothing with Trend Adjustment Example Table 4.1

  43. Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Exponential Smoothing with Trend Adjustment Example Step 1: Forecast for Month 2 F2 = aA1 + (1 - a)(F1 + T1) F2 = (.2)(12) + (1 - .2)(11 + 2) = 2.4 + 10.4 = 12.8 units Table 4.1

  44. Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 12.80 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Exponential Smoothing with Trend Adjustment Example Step 2: Trend for Month 2 T2 = b(F2 - F1) + (1 - b)T1 T2 = (.4)(12.8 - 11) + (1 - .4)(2) = .72 + 1.2 = 1.92 units Table 4.1

  45. Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 12.80 1.92 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Exponential Smoothing with Trend Adjustment Example Step 3: Calculate FIT for Month 2 FIT2 = F2 + T1 FIT2 = 12.8 + 1.92 = 14.72 units Table 4.1

  46. Forecast Actual Smoothed Smoothed Including Month(t) Demand (At) Forecast, Ft Trend, Tt Trend, FITt 1 12 11 2 13.00 2 17 12.80 1.92 14.72 3 20 4 19 5 24 6 21 7 31 8 28 9 36 10 Exponential Smoothing with Trend Adjustment Example 15.18 2.10 17.28 17.82 2.32 20.14 19.91 2.23 22.14 22.51 2.38 24.89 24.11 2.07 26.18 27.14 2.45 29.59 29.28 2.32 31.60 32.48 2.68 35.16 Table 4.1

  47. 35 – 30 – 25 – 20 – 15 – 10 – 5 – 0 – Actual demand (At) Product demand Forecast including trend (FITt) | | | | | | | | | 1 2 3 4 5 6 7 8 9 Time (month) Exponential Smoothing with Trend Adjustment Example Figure 4.3

  48. ^ 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 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

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

  50. Actual observation (y value) Deviation7 Deviation5 Deviation6 Deviation3 Values of Dependent Variable Deviation4 Deviation1 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