Qualitative Forecasting

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Qualitative forecasting methods are based on educated opinions of appropriate persons 1. Delphi method: forecast is developed by a panel of experts who anonymously answer a series of questions; responses are fed back to panel members who then may change their original responses a- very time consuming and expensive

b- new groupware makes this process much more feasible
2. Market research: panels, questionnaires, test markets, surveys, etc. 3. Product life-cycle analogy: forecasts based on life-cycles of similar products, services, or processes 4. Expert judgement: by management, sales force, or other knowledgeable persons [pic]



Time series forecasting methods are based on analysis of historical data (time series: a set of observations measured at successive times or over successive periods). They make the assumption that past patterns in data can be used to forecast future data points. 1. Moving averages (simple moving average, weighted moving average): forecast is based on arithmetic average of a given number of past data points 2. Exponential smoothing (single exponential smoothing, double exponential smoothing) - a type of weighted moving average that allows inclusion of trends, etc. 3. Mathematical models (trend lines, log-linear models, Fourier series, etc.) - linear or non-linear models fitted to time-series data, usually by regression methods 4. Box-Jenkins methods: autocorrelation methods used to identify underlying time series and to fit the "best" model COMPONENTS OF TIME SERIES DEMAND

1. Average: the mean of the observations over time
2. Trend: a gradual increase or decrease in the average over time 3. Seasonal influence: predictable short-term cycling behaviour due to time of day, week, month, season, year, etc. 4. Cyclical movement: unpredictable long-term cycling behaviour due to business cycle or product/service life cycle 5. Random error: remaining variation that cannot be explained by the other four components [pic]


Moving average techniques forecast demand by calculating an average of actual demands from a specified number of prior periods Each new forecast drops the demand in the oldest period and replaces it with the demand in the most recent period; thus, the data in the calculation "moves" over time simple moving average :At = Dt + Dt-1 + Dt-2 + ... + Dt-N+1N where N = total number of periods in the average

forecast for period t+1: Ft+1 = At
Key Decision: N - How many periods should be considered in the forecast Tradeoff: Higher value of N - greater smoothing, lower responsiveness Lower value of N - less smoothing, more responsiveness

- the more periods (N) over which the moving average is calculated, the less susceptible the forecast is to random variations, but the less responsive it is to changes - a large value of N is appropriate if the underlying pattern of demand is stable - a smaller value of N is appropriate if the underlying pattern is changing or if it is important to identify short-term fluctuations [pic]

A weighted moving average is a moving average where each historical demand may be weighted differently average: At = W1 Dt + W2 Dt-1 + W3 Dt-2 + ... + WN Dt-N+1 where:
N = total number of periods in the average
Wt = weight applied to period t's demand
Sum of all the weights = 1
Forecast: Ft+1 = At = forecast for period t+1
exponential smoothing gives greater weight to demand in more recent periods, and less weight to demand in earlier periods average: At = a Dt +...
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