Mr. David P. Blain. C.Q.E. Management Department UNLV 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 Time-Series Forecasting Monitoring and Controlling Forecasts

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 This slide introduces the topic of forecasting at Tupperware. The next several slides elaborate. One might ask students several questions: - “What does a useful forecast consist of at Tupperware? - What problems might a company such as Tupperware experience in developing a useful forecast. As you move through the following two slides, you could point out the application of multiple forecasting techniques to help solve some of the problems identified.

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 Ask students: “Why does the number of ‘active’ dealers change so often?” and “If the number of ‘active’ dealers changes so often, should not this problem be addressed before attempting to forecast sales?” This question raises the issue of the impact of the distribution chain on one’s ability to forecast.

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” You might take the notion of “problems” one step further and ask students why Tupperware uses a “jury of executive opinion” as part of its forecasting process.

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

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 At this point, it may be useful to point out the “time horizons” considered by different industries. For example, some colleges and universities look 30 to fifty years ahead, industries engaged in long distance transportation (steam ship, railroad) or provision of basic power (electrical and gas utilities, etc.) also look far ahead (20 to 100 years). Ask them to give examples of industries having much shorter long-range horizons. Short-range forecast Up to 1 year; usually less than 3 months Job scheduling, worker assignments Medium-range forecast 3 months to 3 years Sales & production planning, budgeting Long-range forecast 3+ years New product planning, facility location 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.

Types of Forecasts Economic forecasts Technological 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 One can use an example based upon one’s college or university. Students can be asked why each of these forecast types is important to the college. Once they begin to appreciate the importance, one can then begin to discuss the problems. For example, is predicting “demand” merely as simple as predicting the number of students who will graduate from high school next year (i.e., a simple counting exercise)?

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 This slide distinguishes between Quantitative and Qualitative forecasting. If you accept the argument that the future is one of perpetual, and perhaps significant change, you may wish to ask students to consider whether quantitative forecasting will ever be sufficient in the future - or will we always need to employ qualitative forecasting also. (Consider Tupperware’s ‘jury of executive opinion.’)

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 This slide outlines several qualitative methods of forecasting. Ask students to give examples of occasions when each might be appropriate. 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 Sales Force Composite Each salesperson projects their sales Combined at district & national levels Sales rep’s know customers’ wants Tends to be overly optimistic Delphi Method Iterative group process 3 types of people Decision makers Staff Respondents Reduces ‘group-think’ Consumer Market Survey Ask customers about purchasing plans What consumers say, and what they actually do are often different Sometimes difficult to answer

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

Time Series Forecasting Trend Seasonal Cyclical Random Trend Component Persistent, overall upward or downward pattern Due to population, technology etc. Several years duration Seasonal Component Regular pattern of up & down fluctuations Due to weather, customs etc. Occurs within 1 year Cyclical Component Repeating up & down movements Due to interactions of factors influencing economy Usually 2-10 years duration Random Component Erratic, unsystematic, ‘residual’ fluctuations Due to random variation or unforeseen events Short duration & nonrepeating

Naive Approach Assumes demand in next period is the same as demand in most recent period If May sales were 48, then June sales will be 48 Sometimes can be cost effective & efficient This slide introduces the naïve approach. Subsequent slides introduce other methodologies.

 Moving Average Method MA 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 At this point, you might discuss the impact of the number of periods included in the calculation. The more periods you include, the closer you come to the overall average; the fewer, the closer you come to the value in the previous period. What is the tradeoff? MA n   Demand in previous periods

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 This slide introduces the “weighted moving average” method. It is probably most important to discuss choice of the weights. Disadvantages of Moving Average Methods Increasing n makes forecast less sensitive to changes Do not forecast trend well Require much historical data Σ(Weight for period n) (Demand in period n) WMA = ΣWeights

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 This slide introduces the exponential smoothing method of time series forecasting. The following slide contains the equations, and an example follows.

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 You may wish to discuss several points: - this is just a moving average wherein every point in included in the forecast, but the weights of the points continuously decrease as they extend further back in time. - the equation actually used to calculate the forecast is convenient for programming on the computer since it requires as data only the actual and forecast values from the previous time point. - we need a formal process and criteria for choosing the “best” smoothing constant (based on lowest error).

Trend & Linear Regression Model Shows linear relationship between dependent & explanatory variables Example: Sales & advertising (not time) Y-intercept Slope ^ Y = a + b X This slide introduces the linear regression model. This can be approached as simply a generalization of the linear trend model where the variable is something other than time and the values do not necessarily occur a t equal intervals. i i Dependent (response) variable Independent (explanatory) variable

Linear Regression Model Y a Y b X i =  Error +  + i Error Regression line ^ Y = a  b X + i i X Observed value

Linear Regression Equations Slope: Again, this is basically a repeat of the slide for the linear trend problem. Y-Intercept:

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 This slide probably merits discussion - additional to that for the linear trend model. You might make the point here that the dependent and independent variable are not necessarily of the same nature - they need not both be dollars, for example. You might also wish to note that setting x = 0 may not have a useful physical interpretation.

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) This slide introduces overall guideline for selecting a forecasting model. You may also wish to re-emphasize the role of scatter plots, and discuss the role of “understanding what is going on” (especially in limiting one’s choice of model).

Forecast Error Equations Mean Square Error (MSE) Mean Absolute Deviation (MAD) This slide illustrates the equations for two measures of forecast error. Students might be asked if there is an occasion when one method might be preferred over the other.

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

Chap 4 Forecasting Learning 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

Ch 4 Forecasting 4.6, 4.7

Ch 4 Forecasting Problem 4.6 Forecasting methods Monthly Sales for Telco Batteries were as follows: Month Sales (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

Ch 4 Forecasting P 4.6 a) Plotting this data:

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

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

4) Exponential smoothing Ch 4 Forecasting 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

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 slope and 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

Ch 4 Forecasting 5) Trend projection: X = 6.5 ∑ y = 218 Ŷ = 18.2 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

Ch 4 Forecasting Mkt VP Oper VP P 4.7 Doug 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 Error Error 2,675 7,325 5,362 10,322 7,464 2,536 23,268 18,268 11,325 11,325 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.