Production & Operations Management IME 316 Forecasting (Spring 2024) PDF

Production & Operations Management IME 316 Forecasting Zakaria Yahia, PhD Associate Professor – Industrial Engineering and Systems Management Innovative Design Engineering Course Outline and Plan Week...

Production & Operations Management IME 316 Forecasting Zakaria Yahia, PhD Associate Professor – Industrial Engineering and Systems Management Innovative Design Engineering Course Outline and Plan Week No. Date Lecture Quizzes 1 30-SEP Course Overview and Introduction to Operations Management 2 07-OCT Strategies, Competitiveness and Productivity Analysis 3 14-OCT Forecasting 4 21-OCT Product and Service Design Quiz_1 5 28-OCT Process Design 6 04-NOV Product and Process Layout Design Quiz_2 7 11-NOV Location Planning and Analysis and Revision 8 18-NOV Mid-term Exams 9 25-NOV Global Location Planning and Analysis 10 02-DEC Capacity and Aggregate Planning Techniques 11 09-DEC Aggregate Planning Optimization Quiz_3 12 16-DEC Master Production Planning and Production Scheduling 13 23-DEC Material Requirement Planning 14 30-DEC Inventory Management 15 06-JAN Inventory Management Stochastic Models and Simulation 16 13-JAN Final Term Exams 13-10-24 Production & Operations Management IME 316 2 Outline What Is Forecasting? Seven Steps in the Forecasting System Forecasting Approaches Time-Series Forecasting Associative Forecasting Methods: Regression and Correlation Analysis Monitoring and Controlling Forecasts 13-10-24 Production & Operations Management IME 316 3 Learning Objectives 1. Understand the three-time horizons and which models apply for each use 2. Apply the naive, moving average, exponential smoothing, and trend methods 3. Conduct a regression and correlation analysis 4. Compute three measures of forecast accuracy 13-10-24 Production & Operations Management IME 316 4 What is Forecasting? Is the art and science of predicting a future event Underlying basis of all business decisions Production Inventory Personnel Facilities 13-10-24 Production & Operations Management IME 316 5 Forecasting Time Horizons Short-range forecast Quantitative methods Generally less than 3 months Purchasing, workforce levels, production levels Details Medium-range forecast Accuracy 3 months to 3 years Sales and production planning Long-range forecast 3+ years New product planning, facility location 13-10-24 Production & Operations Management IME 316 6 Distinguishing Differences Medium/long range forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes Short-term forecasting usually employs different methodologies than longer-term forecasting Short-term forecasts tend to be more accurate than longer-term forecasts 13-10-24 Production & Operations Management IME 316 7 Seven Steps in Forecasting 1 Determine the use of the forecast 2 Select the items to be forecasted 3 Determine the time horizon of the forecast 4 Select the forecasting model(s) 5 Gather the data needed to make the forecast 6 Make the forecast 7 Validate and implement results 13-10-24 Production & Operations Management IME 316 8 Forecasting Approaches Forecasting Used when situation Approaches Used when situation is vague and little is ‘stable’ and data exist historical data exist Qualitative Quantitative New products Existing products New technology methods methods Decision maker Need historical data Intuition + personal and/or associative In practice, a combination of the two experience. variables methods is usually more effective 13-10-24 Production & Operations Management IME 316 9 Overview of Qualitative Methods Jury of executive opinion Sales force Delphi composite Methods method Consumer market survey 13-10-24 Production & Operations Management IME 316 10 Overview of Quantitative Approaches Time-Series models Associative models Assumption: the future is a Incorporate the variables or factors that function of the past. might influence the variable being forecast. Trend Cyclical Seasonal Random 13-10-24 Production & Operations Management IME 316 11 Components of Demand Trend component Demand for product or service Seasonal peaks Actual demand line Average demand over 4 years Random variation | | | | 1 2 3 4 Time (years) 13-10-24 Production & Operations Management IME 316 12 Forecasting and Competitiveness of Disney! ► Global portfolio includes: Shanghai Disney, Hong Kong Disneyland, Disneyland Paris, Tokyo Disneyland, Disneyland Resort (in California), and Walt Disney World Resort (in Florida) 13-10-24 Production & Operations Management IME 316 13 Forecasting and Competitiveness of Disney! ► Revenues are derived from people – how many visitors and how they spend their money ► Daily management report contains only the forecast and actual attendance at each park 13-10-24 Production & Operations Management IME 316 14 Forecasting and Competitiveness of Disney! ► Disney generates daily, weekly, monthly, annual, and 5-year forecasts ► Forecast used by labor management, maintenance, operations, finance, and park scheduling ► Forecast used to adjust opening times, rides, shows, staffing levels, and guests admitted 13-10-24 Production & Operations Management IME 316 15 Forecasting and Competitiveness of Disney! ► A staff of 35 analysts and 70 field people survey 1 million park guests, employees, and travel professionals each year ► Inputs to the forecasting model include airline specials, Federal Reserve policies, Wall Street trends, vacation/holiday schedules around the world 13-10-24 Production & Operations Management IME 316 16 Forecasting and Competitiveness of Disney! ► Average forecast error for the 5-year forecast is 5% ► Average forecast error for annual forecasts is between 0% and 3% 13-10-24 Production & Operations Management IME 316 17 Overview of Quantitative Approaches Quantitative methods Naive Moving Exponential Trend Linear approach averages smoothing projection regression Time-series models Associative model 13-10-24 Production & Operations Management IME 316 18 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 13-10-24 Production & Operations Management IME 316 19 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 13-10-24 Production & Operations Management IME 316 20 Moving Average Example Three-periods moving average MONTH ACTUAL SHED SALES 3-MONTH MOVING AVERAGE January 10 February 12 March 13 April 16 (10 + 12 + 13)/3 = 11 2/3 May 19 (12 + 13 + 16)/3 = 13 2/3 June 23 (13 + 16 + 19)/3 = 16 July 26 (16 + 19 + 23)/3 = 19 1/3 August 30 (19 + 23 + 26)/3 = 22 2/3 September 28 (23 + 26 + 30)/3 = 26 1/3 October 18 (29 + 30 + 28)/3 = 28 November 16 (30 + 28 + 18)/3 = 25 1/3 December 14 (28 + 18 + 16)/3 = 20 2/3 13-10-24 Production & Operations Management IME 316 21 Weighted Moving Average Used when some trend might be present Older data usually less important Weights based on experience and intuition Weighted moving average 13-10-24 Production & Operations Management IME 316 22 Weighted Moving Average Three-periods weighted moving average MONTH ACTUAL SHED SALES 3-MONTH WEIGHTED MOVING AVERAGE January 10 February 12 March 13 April 16 [(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6 May 19 June WEIGHTS 23 APPLIED PERIOD July 26 3 Last month August 30 2 Two months ago September 28 1 Three months ago October 18 6 Sum of the weights November Forecast for 16this month = December 3 x14 Sales last mo. + 2 x Sales 2 mos. ago + 1 x Sales 3 mos. ago Sum of the weights 13-10-24 Production & Operations Management IME 316 23 Weighted Moving Average Three-periods weighted moving average MONTH ACTUAL SHED SALES 3-MONTH WEIGHTED MOVING AVERAGE January 10 February 12 March 13 April 16 [(3 x 13) + (2 x 12) + (10)]/6 = 12 1/6 May 19 [(3 x 16) + (2 x 13) + (12)]/6 = 14 1/3 June 23 [(3 x 19) + (2 x 16) + (13)]/6 = 17 July 26 [(3 x 23) + (2 x 19) + (16)]/6 = 20 1/2 August 30 [(3 x 26) + (2 x 23) + (19)]/6 = 23 5/6 September 28 [(3 x 30) + (2 x 26) + (23)]/6 = 27 1/2 October 18 [(3 x 28) + (2 x 30) + (26)]/6 = 28 1/3 November 16 [(3 x 18) + (2 x 28) + (30)]/6 = 23 1/3 December 14 [(3 x 16) + (2 x 18) + (28)]/6 = 18 2/3 13-10-24 Production & Operations Management IME 316 24 Potential Problems With Moving Average Increasing (n) smooths the forecast but makes it less sensitive to changes Does not forecast trends well Requires extensive historical data 13-10-24 Production & Operations Management IME 316 25 Exponential Smoothing A weighted-moving-average forecasting in which data points are weighted by an exponential function. Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant (α) Smoothing constant generally.05 ≤ α ≤.50 Generally, ranges from 0.05 to 0.5 As α increases, older values become less Subjectively chosen significant Involves little record keeping of past data 13-10-24 Production & Operations Management IME 316 26 Exponential Smoothing New forecast = Last period’s forecast + α (Last period’s actual demand – Last period’s forecast) The latest estimate of demand is equal to the old estimate adjusted by a fraction of the difference between the last period Ft = Ft – 1 + α(At – 1 - Ft – 1) actual demand and the old estimate where Ft = new forecast Ft – 1 = previous period’s forecast α = smoothing (or weighting) constant (0 ≤ α ≤ 1) At – 1 = previous period’s actual demand 13-10-24 Production & Operations Management IME 316 27 Exponential Smoothing Example Predicted demand = 142 Ford Mustangs Actual demand = 153 Smoothing constant α =.20 New forecast = 142 +.2(153 – 142) = 142 + 2.2 = 144.2 ≈ 144 cars 13-10-24 Production & Operations Management IME 316 28 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 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 13-10-24 Production & Operations Management IME 316 29 Least Squares Method Values of Dependent Variable (y-values) Actual observation Deviation7 (y-value) Deviation5 Deviation6 Deviation3 Least squares method Deviation4 minimizes the sum of the squared errors (deviations) Deviation1 (error) Deviation2 Trend line, y^ = a + bx | | | | | | | 1 2 3 4 5 6 7 Time period 13-10-24 Production & Operations Management IME 316 30 Least Squares Method Equations to calculate the regression variables 13-10-24 Production & Operations Management IME 316 31  Least Squares Example ELECTRICAL POWER YEAR (x) DEMAND (y) x2 xy 1 74 1 74 2 79 4 158 3 80 9 240 4 90 16 360 5 105 25 525 6 142 36 852 7 122 49 854 Σx = 28 Σy = 692 Σx2 = 140 Σxy = 3,063 13-10-24 Production & Operations Management IME 316 32  Least Squares Example ELECTRICAL POWER YEAR (x) DEMAND (y) x2 xy 1 74 1 74 2 79 4 158 3 80 9 240 4 90 16 360 5 105 25 525 6 142 36 852 7 122 49 854 Σx = 28 Σy = 692 Σx2 = 140 Σxy = 3,063 Demand in year 8 = 56.70 + 10.54(8) = 141.02, or 141 megawatts 13-10-24 Production & Operations Management IME 316 33 Least Squares Example Trend line, 160 – y^ = 56.70 + 10.54x 150 – Power demand (megawatts) 140 – 130 – 120 – 110 – 100 – 90 – 80 – 70 – 60 – 50 – | | | | | | | | | 1 2 3 4 5 6 7 8 9 Year 13-10-24 Production & Operations Management IME 316 34 Least Squares Requirements We always plot the data to insure a linear relationship We do not predict time periods far beyond the database Deviations around the least squares line are assumed to be random 13-10-24 Production & Operations Management IME 316 35 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 13-10-24 Production & Operations Management IME 316 36 Associative Forecasting Forecasting an outcome based on predictor variables using the least squares technique y^ = a + bx where y^ = value of the dependent variable (in our example, sales) a = y-axis intercept b = slope of the regression line x = the independent variable 13-10-24 Production & Operations Management IME 316 37 Associative Forecasting Example NODEL’S SALES AREA PAYROLL NODEL’S SALES AREA PAYROLL (IN $ MILLIONS), y (IN $ BILLIONS), x (IN $ MILLIONS), y (IN $ BILLIONS), x 2.0 1 2.0 2 3.0 3 2.0 1 2.5 4 3.5 7 Nodel’s sales 4.0 – (in$ millions) 3.0 – 2.0 – 1.0 – | | | | | | | 0 1 2 3 4 5 6 7 Area payroll (in $ billions) 13-10-24 Production & Operations Management IME 316 38 Associative Forecasting Example SALES, y PAYROLL, x x2 xy 2.0 1 1 2.0 3.0 3 9 9.0 2.5 4 16 10.0 2.0 2 4 4.0 2.0 1 1 2.0 3.5 7 49 24.5 Σy = 15.0 Σx = 18 Σx2 = 80 Σxy = 51.5 13-10-24 Production & Operations Management IME 316 39 Associative Forecasting Example SALES, y PAYROLL, x x2 xy 2.0 1 1 2.0 3.0 3 9 9.0 2.5 4 16 10.0 2.0 2 4 4.0 2.0 1 1 2.0 3.5 7 49 24.5 Σy = 15.0 Σx = 18 Σx2 = 80 Σxy = 51.5 13-10-24 Production & Operations Management IME 316 40 Associative Forecasting Example 4.0 – Nodel’s sales (in$ millions) 3.0 – 2.0 – 1.0 – | | | | | | | 0 1 2 3 4 5 6 7 Area payroll (in $ billions) 13-10-24 Production & Operations Management IME 316 41 Associative Forecasting Example If payroll is estimated to be $6 billion, then: Sales (in $ millions) = 1.75 +.25(6) = 1.75 + 1.5 = 3.25 Sales = $3,250,000 13-10-24 Production & Operations Management IME 316 42 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 13-10-24 Production & Operations Management IME 316 43 Correlation Coefficient y y x x (a) Perfect negative (e) Perfect positive correlation y y correlation y x x (b) Negative correlation (d) Positive correlation x (c) No correlation High Moderate Low Low Moderate High | | | | | | | | | –1.0 –0.8 –0.6 –0.4 –0.2 0 0.2 0.4 0.6 0.8 1.0 Correlation coefficient values 13-10-24 Production & Operations Management IME 316 44 Correlation Coefficient y x x2 xy y2 2.0 1 1 2.0 4.0 3.0 3 9 9.0 9.0 2.5 4 16 10.0 6.25 2.0 2 4 4.0 4.0 2.0 1 1 2.0 4.0 3.5 7 49 24.5 12.25 Σy = 15.0 Σx = 18 Σx2 = 80 Σxy = 51.5 Σy2 = 39.5 (6)(51.5) – (18)(15.0) r= [(6)(80) – (18) ][(6)(39.5) – (15.0) ] 2 2 Perfect positive correlation! 13-10-24 Production & Operations Management IME 316 45 Which technique to use? 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 13-10-24 Production & Operations Management IME 316 46 Common Measures of Error Mean Absolute Deviation (MAD) 13-10-24 Production & Operations Management IME 316 47 Determining the MAD FORECAST ABSOLUTE FORECAST ABSOLUTE ACTUAL WITH DEVIATION WITH DEVIATION QUARTER VALUE α =.10 FOR a =.10 α =.50 FOR a =.50 1 180 175 5.00 175 5.00 2 168 175.50 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 Sum of absolute deviations: 82.45 98.62 Σ|Deviations| MAD = 10.31 12.33 n 13-10-24 Production & Operations Management IME 316 48 Common Measures of Error Mean Squared Error (MSE) 13-10-24 Production & Operations Management IME 316 49 Determining the MSE FORECAST FOR QUARTER ACTUAL VALUE α =.10 (ERROR)2 1 180 175 52 = 25 2 168 175.50 (–7.5)2 = 56.25 3 159 174.75 (–15.75)2 = 248.06 4 175 173.18 (1.82)2 = 3.31 5 190 173.36 (16.64)2 = 276.89 6 205 175.02 (29.98)2 = 898.80 7 180 178.02 (1.98)2 = 3.92 8 182 178.22 (3.78)2 = 14.29 Sum of errors squared = 1,526.52 13-10-24 Production & Operations Management IME 316 50 Common Measures of Error Mean Absolute Percent Error (MAPE) 13-10-24 Production & Operations Management IME 316 51 Determining the MAPE FORECAST FOR ABSOLUTE PERCENT ERROR QUARTER ACTUAL VALUE α =.10 100(ERROR/ACTUAL) 1 180 175.00 100(5/180) = 2.78% 2 168 175.50 100(7.5/168) = 4.46% 3 159 174.75 100(15.75/159) = 9.90% 4 175 173.18 100(1.82/175) = 1.05% 5 190 173.36 100(16.64/190) = 8.76% 6 205 175.02 100(29.98/205) = 14.62% 7 180 178.02 100(1.98/180) = 1.10% 8 182 178.22 100(3.78/182) = 2.08% Sum of % errors = 44.75% 13-10-24 Production & Operations Management IME 316 52 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α =.10 α =.10 α =.50 α =.50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 13-10-24 Production & Operations Management IME 316 53 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage with for with for Quarter Unloaded α =.10 α =.10 α =.50 α =.50 1 180 175 5.00 175 5.00 2 168 175.5 7.50 177.50 9.50 3 159 174.75 15.75 172.75 13.75 4 175 173.18 1.82 165.88 9.12 5 190 173.36 16.64 170.44 19.56 6 205 175.02 29.98 180.22 24.78 7 180 178.02 1.98 192.61 12.61 8 182 178.22 3.78 186.30 4.30 82.45 98.62 MAD 10.31 12.33 MSE 190.82 195.24 MAPE 5.59% 6.76% 13-10-24 Production & Operations Management IME 316 54  What Is Forecasting? Summary: Seven Steps in the Forecasting System Discussion Forecasting Approaches &Questions! Time-Series Forecasting Associative Forecasting Methods: Regression and Correlation Analysis Monitoring and Controlling Forecasts 13-10-24 Production & Operations Management IME 316 55  Activity  Practice the following exercises:  SOLVED PROBLEMS: 1, 3, 6 and 7.  PROBLEMS: 2, 4 , 7, 20 and 21.  Using Excel in applying Forecasting quantitative techniques 13-10-24 Production & Operations Management IME 316 56