MBA.782.ForecastingCAJ Demand Management Qualitative Methods of Forecasting Quantitative Methods of Forecasting Causal Relationship Forecasting Focus Forecasting Development of a Forecasting System Operations Management Forecasting

MBA.782.ForecastingCAJ Forecasts are seldom __________ - find the best method Forecasting methods assume there is some underlying stability in the system _______________ product forecasts are more accurate than individual product forecasts Basis of long-run planning –budget planning and cost control Marketing - sale forecast Operations - capacity, scheduling, inventory Forecasting Forecasting in Business

MBA.782.ForecastingCAJ Forecasting Demand Management Independent demand –demand for item is independent of demand for _____ other item Dependent demand –demand for item is dependent upon the demand for ______ _______ item

MBA.782.ForecastingCAJ The greater the ability to react, the less accurate the forecast has to be A __________ between the cost of doing the forecast and the opportunity cost of proceeding with misleading numbers Factors: 1. Time horizon to forecast 2. Data availability 3. Accuracy required 4. Size of forecasting budget 5. Availability of qualified personnel Forecasting Choice of Forecasting Model

MBA.782.ForecastingCAJ Qualitative (Judgmental) Quantitative – Time Series Analysis > past data – Causal Relationships > related to some other factors – Simulation > test assumptions Forecasting Types of Forecasting

MBA.782.ForecastingCAJ Choose the participants - never meeting as a ________ 2.Through a questionnaire, obtain forecasts from all participants 3.Summarize the results and redistribute them to the participants along with appropriate new questions 4.Summarize again, refining forecasts and conditions, and develop new questions. 5.Repeat Step 4 if necessary. Distribute the final results to all participants. Qualitative Methods Delphi Method

MBA.782.ForecastingCAJ Components of Demand –Trend, Seasonal, Cyclic, Random Time Series Analysis Causal Relationships Simulation Forecasting Quantitative Methods

MBA.782.ForecastingCAJ _______________, overall upward or downward pattern Due to population, technology etc. Linear; S-curve; asymptotic; exponential Mo., Qtr., Yr. Response Components of Demand Trend Component

MBA.782.ForecastingCAJ Regular pattern of ____ & ________ fluctuations Due to weather, customs etc. Occurs within __ _______ Mo., Qtr. Response Summer Components of Demand Seasonal Component

MBA.782.ForecastingCAJ Repeating up & down movements Due to interactions of factors influencing economy Non-annual; __________ ; Mo., Qtr., Yr. Response Cycle Components of Demand Cyclical Component

MBA.782.ForecastingCAJ Erratic, unsystematic, ‘residual’ fluctuations Unexplained portion Components of Demand Random Component

MBA.782.ForecastingCAJ Set of ________ spaced numerical data –Obtained by observing response variable at regular time periods Forecast based only on _______ values –Assumes that factors influencing past, present, & future will continue Example Year: Sales: Quantitative Methods What is a Time Series?

MBA.782.ForecastingCAJ Used if demand is ____ growing nor declining rapidly Used often for smoothing –Remove ____________ fluctuations Equation where: F t = forecast for period t, A t = actual demand realized in period t, Time Series Analysis Simple Moving Average

MBA.782.ForecastingCAJ Let’s develop 3-week moving average forecasts for demand. Assume you only have 3 weeks of actual demand data for the respective forecasts Time Series Analysis Simple Moving Average

MBA.782.ForecastingCAJ Allows different ________ to be assigned to past observations –Older data usually ______ important Weights based on experience, trial-and-error Equation Time Series Analysis Weighted Moving Average

MBA.782.ForecastingCAJ Determine the 3-period weighted moving average forecast for period 4. Weights:t-10.5 t-20.3 t-30.2 Time Series Analysis Weighted Moving Average

MBA.782.ForecastingCAJ Increasing n makes forecast ______ sensitive to changes Do not forecast _______ well Require ______ historical data Time Series Analysis Disadvantages of M.A. Methods

MBA.782.ForecastingCAJ To ramp changes of demand Demand High weight n = Ramp Shift T Low weight n = 6 Time Series Analysis Responsiveness of M.A. Methods Forecast _____ with increasing demand, and _______ with decreasing demand

MBA.782.ForecastingCAJ Premise--The most ________ observations might have the highest predictive value. Therefore, we should give _______ weight to the more recent time periods when forecasting Requires smoothing constant (  ) –Ranges from 0 to 1 –Subjectively chosen Involves _______ record keeping of past data Time Series Analysis Exponential Smoothing

MBA.782.ForecastingCAJ The equation used to compute the forecast is... F t = F t-1 +  ·(A t-1 - F t-1 ) where.... F t = forecast demand A t = actual demand realized  = smoothing constant Exponential because each increment in the past is decreased by (1 -  ): Time Series Analysis Exponential Smoothing

MBA.782.ForecastingCAJ Determine exponential smoothing forecasts for periods 2-10 using  =0.20 (Let F 1 =D 1 ) Time Series Analysis Exponential Smoothing

MBA.782.ForecastingCAJ Actual demand alpha =.1 alpha =.5 alpha =.9 Exponential Smoothing Responsiveness to Different Values of 

MBA.782.ForecastingCAJ Attempts to __________ (somewhat) the lag in the exponential smoothing method Trend equation with a smoothing constant, ___ (delta) formulae…… FIT t = Forecast including trend FIT t = F t + T t F t = FIT t-1 +  (A t-1 - FIT t-1 ) T t = T t-1 +   (A t-1 - FIT t-1 ) Time Series Analysis Exponential Smoothing with Trend

MBA.782.ForecastingCAJ Error = Actual - Forecast E t = A t - F t RSFE = running sum of the forecast errors RSFE =  E t Bias = Average Error –occurs when a _______________ mistake is made Bias = RSFE / n Random errors –cannot be explained by the forecast model being used. Time Series Analysis Forecast Accuracy

MBA.782.ForecastingCAJ Mean Absolute Deviation is the sum of each error’s magnitude divided by the number of error--so we get the ___________ magnitude of the forecast error Time Series Analysis Forecast Errors If the errors are normally distributed, the standard deviation, _________________

MBA.782.ForecastingCAJ Time Series Analysis Forecast Errors

MBA.782.ForecastingCAJ TS measures the ________ of MADs that the forecast is above or below the actual value of the variable –Good tracking signal has _____ values In the usual statistical manner, if control limits were set at plus or minus 3 standard deviations (or MADs), then _____ percent of the points would fall within these limits. Measures how _____ the forecast is predicting actual values Time Series Analysis Tracking Signal

MBA.782.ForecastingCAJ Describe functional relationship between two or more correlated variables. Equation of the form: Y = a + bx –used to predict Y for some _________ value of x Useful for long-run decisions and aggregate planning Assumes a straight-line (linear) relationship Use in _____ _______ and _______ forecasting Time Series Analysis Linear Regression

MBA.782.ForecastingCAJ Seasonality ….. A seasonal factor (index) is the amount of the correction necessary to _________ for the season of the year Decomposition ….. To ___________ the basic components of trend and seasonality Forecasting Integrative Example

MBA.782.ForecastingCAJ Given three years of quarterly data: Determine the seasonal factors. Forecasting Integrative Example

MBA.782.ForecastingCAJ Deseasonalize the actual demand data by _________ by the appropriate seasonal factor: Forecasting Integrative Example

MBA.782.ForecastingCAJ The perform a linear regression, least squares approximation of the relationship between quarter (x) and ___ - seasonalized sales (y): y = a + b x Forecasting Integrative Example

MBA.782.ForecastingCAJ Project the ________ using the predictive equation for each quarter of year 4: Forecasting Integrative Example Quarter 13: F 13 = ( ___ ) = _____ Quarter 14: F 14 = ( ___ ) = _____ Quarter 15: F 15 = ( ___ ) = _____ Quarter 16: F 16 = ( ___ ) = _____

MBA.782.ForecastingCAJ Adjust for seasonality by multiplying by the seasonal factors for the appropriate quarters: Forecasting Integrative Example

MBA.782.ForecastingCAJ Forecasting Integrative Example

MBA.782.ForecastingCAJ One occurrence causes another If the causing element if _____ enough in advance, it can be used as a basis for forecasting The independent variable must be a _________ indicator Challenge is to find those occurrences that are ________ the causes Forecasting Causal Relationship Forecasting

MBA.782.ForecastingCAJ Uses simulation –____________ to test various forecasting models Pick the model that produces the smallest error Illustrate…. Forecasting Focus Forecasting

MBA.782.ForecastingCAJ Choice depends _________ on –the type of business, and who is using the forecast No pattern or direction in forecast error –Seen in plots of errors over time ____________ forecast error –Mean absolute deviation (MAD) Focus Forecasting –has merit –computer time is not an issue –component of many business systems Forecasting Developing a Forecasting System

MBA.782.ForecastingCAJ Forecasting

MBA.782.ForecastingCAJ Forecasting Chapter Wrap-Up Read Chapter 11 Concepts and Terminology Review Lecture Notes Recommended Problems