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Lecture 3 Forecasting CT – Chapter 3

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Forecast A statement about the future value of a variable of interest such as demand. Forecasts affect decisions and activities throughout an organization Accounting, finance Human resources Marketing Operations Product / service design

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**Uses of Forecasts Accounting Cost/profit estimates Finance**

Cash flow and funding Human Resources Hiring/recruiting/training Marketing Pricing, promotion, strategy Operations Schedules, MRP, workloads Product/service design New products and services

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**Elements of a Good Forecast**

Timely Accurate Reliable Meaningful Written Easy to use

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**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”

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**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

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**Judgmental Forecasts Executive opinions Sales force opinions**

Consumer surveys Outside opinion Delphi method Opinions of managers and staff Achieves a consensus forecast

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**Time Series Forecasts Trend - long-term movement in data**

Seasonality - short-term regular variations in data Cycle – wavelike variations of more than one year’s duration Irregular variations - caused by unusual circumstances

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**Forecast Variations Figure 3.1 Trend Cycles Irregular variation 90 89**

88 Seasonal variations

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**Smoothing/Averaging Methods**

Used in cases in which the time series is fairly stable and has no significant trend, seasonal, or cyclical effects Purpose of averaging - to smooth out the irregular components of the time series. Four common smoothing/averaging methods are: Moving averages Weighted moving averages Exponential smoothing

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**Example of Moving Average**

Sales of gasoline for the past 12 weeks at your local Chevron (in ‘000 gallons). If the dealer uses a 3-period moving average to forecast sales, what is the forecast for Week 13? Past Sales Week Sales Week Sales

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**Management Scientist Solutions**

MA(3) for period 4 = ( )/3 = 19 Forecast error for period 3 = Actual – Forecast = 23 – 19 = 4

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MA(5) versus MA(3)

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**Review of last class Forecasting Smoothing/averaging method**

What is a forecast? Organizational functions that use forecasts Desirable characteristics of a forecast Types of forecasts Types of time series Smoothing/averaging method Moving averages Weighted moving averages Advantage

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**Exponential Smoothing**

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.

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**Exponential Smoothing**

Ft+1 = Ft + (At - Ft) Weighted averaging method based on previous forecast plus a percentage of the forecast error A-F is the error term, is the % feedback

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**Picking a Smoothing Constant**

.1 .4 Actual

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**Suitable for time series data that exhibit a long term linear trend**

Linear Trend Equation Suitable for time series data that exhibit a long term linear trend Ft Ft = a + bt a Ft = Forecast for period t t = Specified number of time periods a = Value of Ft at t = 0 b = Slope of the line t

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**Sale increases every time period @ 1.1 units**

Linear Trend Example Linear trend equation F11 = (11) = 32.5 Sale increases every time 1.1 units

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**Actual/Forecasted sales**

Actual vs Forecast Linear Trend Example 35 30 25 Actual/Forecasted sales 20 Actual 15 Forecast 10 5 1 2 3 4 5 6 7 8 9 10 Week F(t) = t

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**Forecasting with Trends and Seasonal Components – An Example**

Business at Terry's Tie Shop can be viewed as falling into three distinct seasons: (1) Christmas (November-December); (2) Father's Day (late May - mid-June); and (3) all other times. Average weekly sales ($) during each of the three seasons during the past four years are known and given below. Determine a forecast for the average weekly sales in year 5 for each of the three seasons. Year Season

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**Management Scientist Solutions**

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**Interpretation of Seasonal Indices**

Seasonal index for season 2 (Father’s Day) = 1.236 Means that the sale value of ties during season 2 is 23.6% higher than the average sale value over the year Seasonal index for season 3 (all other times) = 0.586 Means that the sale value of ties during season 3 is 41.4% lower than the average sale value over the year

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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

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**MAD and MSE Actual forecast MAD = n MSE = Actual forecast) n (**

2 n (

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**Measure of Forecast Accuracy**

MSE = Mean Squared Error

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**Forecasting Accuracy Estimates Example 10 of textbook**

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**Sources of Forecast errors**

Model may be inadequate Irregular variations Incorrect use of forecasting technique

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**Characteristics of Forecasts**

They are usually wrong A good forecast is more than a single number Aggregate forecasts are more accurate The longer the forecast horizon, the less accurate the forecast will be Forecasts should not be used to the exclusion of known information

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**Choosing a Forecasting Technique**

No single technique works in every situation Two most important factors Cost Accuracy Other factors include the availability of: Historical data Computers Time needed to gather and analyze the data Forecast horizon

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