CHAPTER 3 FORECASTING

FORECASTING Forecasts serve as a basis for planning--capacity, budgeting, sales, production, inventory, personnel Successful forecasting requires a skillful blending of both art and science Two uses of forecasts: Planning the system--Long Range Planning the use of the system--Short Range

Forecasting Assumes causal system past ==> future Forecasts rarely perfect because of randomness Forecasts more accurate for groups vs. individuals Forecast accuracy decreases as time horizon increases I see that you will get an A this semester.

Elements of a Good Forecast Timely Accurate Reliable Written Easy to use Meaningful

Steps in the Forecasting Process Step 1 Determine purpose of forecast Step 2 Establish a time horizon Step 3 Select a forecasting technique Step 4 Gather and analyze data Step 5 Prepare the forecast Step 6 Monitor the forecast “The forecast”

APPROACHES TO FORECASTING QUALITATIVE--based on subjective inputs, soft data judgmental forecasts, opinions, hunches, experience, etc. QUANTITATIVE--based on historical data --project past experience into the future --uncover relationships between variables that can be used to predict the future

Types of Forecasts Judgmental - uses subjective inputs Time series - uses historical data assuming the future will be like the past Associative models - uses explanatory variables to predict the future

Judgmental Forecasts Executive opinions Sales force composite Consumer surveys Outside opinion Opinions of managers and staff Delphi technique

QUANTITATIVE FORECASTS Time-Series techniques --Naïve --Moving Average models --Exponential Smoothing models --Classical Decomposition --Box-Jenkins ARIMA models --Neural Networks

QUANTITATIVE FORECASTS Causal or Associative techniques --Simple linear regression --Multiple linear regression --Nonlinear regression

FORECASTING DATA “time-series” --time-ordered sequence of observations taken at regular intervals over a period of time Annual, Quarterly, Monthly, Weekly, Daily, Hourly, etc.

UNDERLYING BEHAVIOR Trend - long-term movement in data Seasonality - short-term, regular, periodic variations in data Cycles - wave-like variations of more than one year’s duration Irregular variations - caused by unusual circumstances Random variations - caused by chance

Forecast Variations Trend Cycles Irregular variation 90 89 88 Seasonal variations

Naive Forecasts Uh, give me a minute.... We sold 250 wheels last week.... Now, next week we should sell.… “the latest observation in a sequence is used as the forecast for the next period” Ft = At-1

MAn Ft = n Ai “an average that is repeatedly updated” å Simple Moving Average MAn Ft = n Ai “an average that is repeatedly updated” i = 1 å

Exponential Smoothing Ft = Ft-1 + a(At-1 - Ft-1) Premise--The most recent observations might have the highest predictive value. Therefore, we should give more weight to the more recent time periods when forecasting.

Forecast Accuracy Error – difference between actual value and predicted value Mean absolute deviation (MAD) - Average absolute error Mean squared error (MSE) - Average of squared error Mean absolute percent error (MAPE) - Average absolute percent error Tracking Signal - Ratio of cumulative error and MAD

 - forecast Actual n MAD = Actual forecast - å n MSE = Actual MAD,MSE, & MAPE MAD = Actual forecast - å n MSE = Actual forecast) - 1 2 å n ( Actual - forecast  X 100 MAPE = Actual n

å å Tracking Signal Tracking signal = (Actual - forecast) MAD