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Chapter 4 - Forecasting

Chapter 4 - Forecasting. Mr. David P. Blain. C.Q.E. Management Department UNLV. Outline. Global Company Profile: Tupperware What is Forecasting? Types of Forecasts Seven Steps in the Forecasting System Forecasting Approaches Overview of Qualitative & Quantitative Methods

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Chapter 4 - Forecasting

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  1. Chapter 4 - Forecasting Mr. David P. Blain. C.Q.E. Management Department UNLV

  2. Outline • Global Company Profile: Tupperware • What is Forecasting? • Types of Forecasts • Seven Steps in the Forecasting System • Forecasting Approaches • Overview of Qualitative & Quantitative Methods • Time-Series Forecasting • Monitoring and Controlling Forecasts

  3. Forecasting at Tupperware • Each of 50 profit centers around the world is responsible for computerized monthly, quarterly, and 12-month sales projections • These projections are aggregated by region, then globally, at Tupperware’s World Headquarters • Tupperware uses techniques discussed in text

  4. Three Key Factors for Tupperware • The number of registered “consultants” or sales representatives • The percentage of currently “active” dealers (this number changes each week and month) • Sales per active dealer, on a weekly basis

  5. Tupperware - Forecast by Consensus • Although inputs come from sales, marketing, finance, and production, final forecasts are the consensus of all participating managers. • The final step is Tupperware’s version of the “jury of executive opinion”

  6. Sales will be $200 Million! What is Forecasting? • Process of predicting a future event • Underlying basis of all business decisions • Production • Inventory • Personnel • Facilities

  7. Forecasting Time Horizons • Process of predicting a future event • Short-range forecast • Job scheduling, worker assignments • Medium-range forecast • Sales & production planning, budgeting • Long-range forecast • New product planning, facility location

  8. Types of Forecasts • Economic forecasts • Address business cycle, e.g., inflation rate, money supply, etc. • Technological forecasts • Predict technological change • Predict new product sales • Demand forecasts • Predict existing product sales

  9. Forecasting Approaches Qualitative Methods Quantitative Methods • Used when situation is vague & little data exist • New products • New technology • Involves intuition, experience • e.g., forecasting sales on Internet • Used when situation is stable & historical data exist • Existing products • Current technology • Involves mathematical techniques • e.g., forecasting sales of color televisions

  10. Overview of Qualitative Methods • Jury of executive opinion • Pool opinions of high-level executives, sometimes augment by statistical models • Sales force composite • Estimates from individual salespersons are reviewed for reasonableness, then aggregated • Delphi method • Panel of experts, queried iteratively • Consumer Market Survey • Ask the customer

  11. Overview of Quantitative Approaches • Naïve approach • Moving averages • Exponential smoothing • Trend projection • Linear regression Time-series models Associative models

  12. Trend Cyclical Seasonal Random Time Series Forecasting

  13. Naive Approach • Assumes demand in nextperiod is the same as demand in most recentperiod • If May sales were 48, then June sales will be 48 • Sometimes can be cost effective & efficient

  14. Demand in previous n periods MA  n 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 • Equation

  15. Weighted Moving Average Method • Used when trend is present • Older data usually less important • Weights based on intuition • Ranges between 0 & 1, & sum to 1.0 • Equation Σ(Weight for periodn) (Demand in periodn) WMA = ΣWeights

  16. Exponential Smoothing Method • 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

  17. Exponential Smoothing Equations • Ft= Ft-1 + (At-1 - Ft-1) • Use for computing forecast • Ft = At - 1 + (1-)At - 2 + (1- )2·At - 3 + (1- )3At - 4 + ... + (1- )t-1·A0 • Ft = Forecast value • At = Actual value •  = Smoothing constant

  18. Trend & Linear Regression Model • Shows linear relationship between dependent & explanatory variables • Example: Sales & advertising (not time) Y-intercept Slope ^ Y a + b X = i i Dependent (response) variable Independent (explanatory) variable

  19. Linear Regression Equations Equation: Slope: Y-Intercept:

  20. Interpretation of Coefficients • Slope (b) • Estimated Y changes by b for each 1 unit increase in X • If b = 2, then sales (Y) is expected to increase by 2 for each 1 unit increase in advertising (X) • Y-intercept (a) • Average value of Y when X = 0 • If a = 4, then average sales (Y) is expected to be 4 when advertising (X) is 0

  21. Selecting a Forecasting Model • You want to achieve: • No pattern or direction in forecast error • Error = (Yi - Yi) = (Actual - Forecast) • Seen in plots of errors over time • Smallest forecast error • Mean square error (MSE) • Mean absolute deviation (MAD)

  22. Forecast Error Equations • Mean Square Error (MSE) • Mean Absolute Deviation (MAD)

  23. Tracking Signal • Measures how well the forecast is predicting actual values • Ratio of running sum of forecast errors (RSFE) to mean absolute deviation (MAD) • Good tracking signal has low values • Should be within upper and lower control limits

  24. Chap 4 ForecastingLearning Objectives • Ability to Identify or Define: • Forecasting and types of forecasts • Time horizons • Approaches to forecasts • Ability to Describe or Explain: • Moving averages • Exponential smoothing • Trend projections and regression • Measures of forecast accuracy • Tracking signal

  25. Ch 4 Forecasting • 4.6, 4.7

  26. Ch 4 Forecasting Problem 4.6 Forecasting methods Monthly Sales for Telco Batteries were as follows: MonthSales (y) January 20 February 21 March 15 April 14 May 13 June 16 July 17 August 16 September 20 October 20 November 21 December 23 Period (x) x 2 xy 1 1 20 2 4 42 3 9 45 4 16 56 5 25 65 6 36 96 7 49 119 8 64 144 9 81 180 10 100 200 11 121 231 12 144 276 Sum 218 78 650 1474 Average 18.17 6.5

  27. Ch 4 Forecasting P 4.6 a) Plotting this data:

  28. Ch 4 Forecasting P 4.6 b) Forecast January Sales using each of the following : 1) Naive Method Demand in next period same as last period Therefore January will be 23 since those were December’s sales 2) 3 month Moving Average Using the last 3 months Oct, Nov, Dec = (20 + 21 + 23)/3 =21 .33 We would forecast January at 21 based on smoothing of 3 month average

  29. Ch 4 Forecasting 3) 6-month weighted average Using weighting of .1, .1, .1, .2, .2 and .3; with the heavier weights applied to the most recent months ( Oct, Nov, Dec) (0.1 * 17) + (0.1 * 18) + (0.1 * 20) + (0.2 * 20) + ( 0.2 * 21) + (0.3 * 23) = 21.33 which gives us a January forecast of 21

  30. Ch 4Forecasting 4) Exponential smoothing sophisticated weighted moving average forecasting method New forecast = last periods forecast + alpha * (last period's actual demand - last period's forecast) Therefore with alpha of .3 and September’s forecast of 18 October forecast = Sept forecast + alpha times (Sept. actual - Sept. forecast) October forecast = 18 + 0.3 (20-18) = 18.6 November forecast = 18.6 + 0.3 (20-18.6) = 19.02 December forecast = 19.02 + 0.3 (21-19.02) = 19.6 January forecast = 19.6 + 0.3 (23-19.6) = 20.6

  31. Ch 4 Forecasting 5) Trend projection is a forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasting: Ŷ = a + b*x (equation of the line) Calculate slopeand y-intecept b = ∑xy – n x y a = y – bx ∑x² - nx² ∑ = summation sign Ŷ = (“y hat”) computed value of the variable to be predicted (dependant variable) a = y-axis intercept b = slope of regression line (rate of change in y given change in x) x = the independent variable X = known values of the independent variable Y = known values of the dependant variable x = average of value of x’s y = average of value of y’s n = number of data points or observations

  32. Ch 4 Forecasting 5) Trend projection: Based on earlier data supplied: ∑ x = 78 X = 6.5 ∑ y = 218 Ŷ = 18.2 Calculate slope b = ∑xy – n x y ∑x² - nx² b = 1474-(12*6.5*18.2) = 54.4 = 0.38 650 – (12*(6.5)2 )143 and y-intercept a = y – bx a = 18.2 – (0.38*6.5) = 15.73 Forecast is Ŷ = a + b*x = 15.73 + ( .38*13) = 20.76 , where January is 13th month

  33. Ch 4 Forecasting P 4.7Doug Moodie is the president of Garden Products Limited. Over the last 5 tears, he has asked both his vice president of marketing and his vice president of operations to provide sales forecasts. The actual sales and the forecasts are given below. Mkt VP Oper VP ErrorError 2,675 7,325 5,362 10,322 7,464 2,536 23,268 18,268 11,32511,325 Totals 50,094 49,816 VP VP YEAR SALES Marketing Operations 1 167,325 170,000 160,000 2 167,325 170,000 160,000 3 167,325 170,000 160,000 4 167,325 170,000 160,000 5 176,325 165,000 165,000 Using MAD which vice president is better at forecasting? MAD VP Marketing = 50,094 /5 = 10,019 MAD VP Operations = 49,816 /5 = 9.963 Therefore, based on past data, the VP of operations has been presenting better forecasts.

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