Forecasting SCM Pearson Education, Inc. publishing as Prentice Hall

Transcription

1 4 Forecasting SCM 352

2 Outline Global Company Profile: Disney World What is Forecasting? Types of Forecasts Forecasting Approaches Overview of Qualitative & Quantitative Methods Time-Series Forecasting Monitoring and Controlling Forecasts

3 Famous Forecasting Quotes "Those who have knowledge, don't predict. Those who predict, don't have knowledge. " -- Lao Tzu, 6th Century BC Chinese Poet "It is often said there are two types of forecasts... lucky or wrong!!!! " -- "Control" magazine (Inst. of Ops. Mgmt.) (

4 Forecasting at Disney World Global portfolio includes parks in Hong Kong, Paris, Tokyo, Orlando, and Anaheim 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 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

5 Forecasting at Disney World 20% of customers come from outside the USA Economic model includes gross domestic product, cross-exchange rates, arrivals into the USA 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 for 3,000 school districts around the world Average forecast error for the 5-year forecast is 5% Average forecast error for annual forecasts is between 0% and 3%

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

7 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

8 Types of Forecasts Economic forecasts Address business cycle, e.g., 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 products and services

9 Strategic Importance of Forecasting Human Resources Hiring, training, laying off workers Capacity Capacity shortages can result in undependable delivery, loss of customers, loss of market share Supply Chain Management Good supplier relations and price advantages

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

11 Overview of Qualitative Methods Jury of executive opinion Pool opinions of high-level executives, sometimes augment by statistical models Group-think disadvantage Sales force composite Estimates from individual salespersons are reviewed for reasonableness, then aggregated Sales reps know customers wants Delphi method Panel of experts, queried iteratively Consumer market survey Ask the customer

12 Quantitative Approaches 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection 5. Linear regression Time-Series Models Associative Model

13 Time Series Forecasting Set of evenly spaced numerical data Obtained by observing response variable at regular time periods Forecast based only on past values, no other variables important Assumes that factors influencing past and present will continue influence in future

14 Time Series Forecasting Trend Cyclical Seasonal Random

15 Components of Demand Trend component Demand for product or service Seasonal peaks Random variation Actual demand line Average demand over 4 years Time (years)

16 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 and efficient Can be good starting point

17 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 Moving average = demand in previous n periods n

18 Potential Problems With MA Increasing n smooths the forecast but makes it less sensitive to changes Do not forecast trends well Require extensive historical data

19 Moving Average Example Actual 3-Month Month Shed Sales Moving Average January February March April 16 May 18 June 23 July 26 ( )/3 = 12 ( )/3 = 14 ( )/3 = 16 ( )/3 = 19

20 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 WMA = Σ(Weight for period n) (Demand in period n) ΣWeights

21 Weighted Moving Average Example Actual 3-Month Month Shed Sales Moving Average January February March April 16 May 18 June 23 July 26 (10* * *0.5) = 12.6 (12* * *0.5) = 14.6 (14* * *0.5) = 16.6 (16* * *0.5) = 20.1 Weights: heaviest weights applied to most recent month 0.5, 0.3, 0.2

22 Exponential Smoothing Method Form of weighted moving average Weights decline exponentially Most recent data weighted most Requires smoothing constant (α) Ranges from 0 to 1 Select the value of α that gives us the lowest forecast error (MAD or MSE) Involves little record keeping of past data

23 Exponential Smoothing Equations F t = F t-1 + α(a t-1 - F t-1 ) Use for computing forecast F t = αa t-1 + α(1-α)a t-2 + α(1- α) 2 A t-3 + α(1- α) 3 A t α(1- α) t-1 A 0 F t = Forecast value A t = Actual value α = Smoothing constant What happens when α = 1?

24 Problem 4.6, Page 140 (b) What is the forecast for January? [iv] Exponential smoothing, α = 0.3 F Sep = 18 F Oct = (20-18) = 18.6 F Nov = ( ) = F Dec = ( ) = 19.6 F Jan = ( ) = Month Sales January 20 February 21 March 15 April 14 May 13 June 16 July 17 August 18 September 20 October 20 November 21 December 23

25 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

26 Least Squares Method Equations to calculate the regression variables y ^ = a + bx b = Σxy - nxy Σx 2 -nx 2 a = y - bx

27 Interpretation of Coefficients Example: Sales (y) ^ & advertising (x) 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

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

29 Measuring Forecast Error Mean Absolute Deviation (MAD) MAD = actual - forecast n Mean Squared Error (MSE) MSE = (forecast error)2 n

30 Comparison of Forecast Error Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage using for using for Quarter Unloaded Model A Model A Model B Model B

31 Forecast Error - MAD Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnage n with for with for Quarter Unloaded Model A Model A Model B Model B MAD = For Model A deviation = 159 ( )/ = /4 = For Model B = ( )/4 = 12/4 = 3 Model A and Model B have the same MAD values.

32 Forecast Error - MSE Rounded Absolute Rounded Absolute Actual Forecast Deviation Forecast Deviation Tonnagen with for with for Quarter Unloaded Model A Model A Model B Model B MSE = = ( )/ = 84/4 175= For Model A For Model B (forecast error) 2 = ( )/4 = 36/4 = 9 Model B has a smaller MSE (=9) than Model A (=21)

33 Monitoring & Controlling Forecasts 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 If forecasts are continually high or low, the forecast has a bias error

34 Monitoring & Controlling Forecasts Tracking signal Tracking signal = = RSFE MAD (actual demand in period i - forecast demand in period i) ( actual - forecast /n) What s the interpretation of a positive or negative RSFE?

35 Tracking Signal + Signal exceeding limit Upper control limit Tracking signal 0 MADs Acceptable range Lower control limit Time

36 Thank You Questions??