[POM-TQM] Finals - (2) Forecasting

Forecast

An estimate about the future value of a variable of interest

Expected Level of Demand

The level of demand may be a function of some structural variation such as trend or seasonal variation

Accuracy

Related to the potential size of forecast error (ability of the forecaster to correctly model demand)

Two Important Aspects of Forecasts

• Expected level of demand

• Accuracy

Two Uses of Forecast

• Plan the system

• Plan the use of the system

Elements of a Good Forecast

The forecast

• Should be timely

• Should be accurate

• Should be reliable

• Should be expressed in meaningful units

• Should be in writing

• Technique should be simple to understand and use

• Should be cost-effective

Steps in the Forecasting Process

1. Determine the purpose of the forecast

2. Establish a time horizon

3. Obtain, clean, and analyze appropriate data

4. Select a forecasting technique

5. Make the forecast

6. Monitor the forecast errors

Qualitative Forecasting

This permits the inclusion of soft information such as:

• Human factors

• Personal opinions

• Hunches

• These factors are difficult, or impossible, to quantify

Quantitative Forecasting

These techniques rely on hard data and involve either the projection of historical data or the development of associative methods that attempt to use causal (explanatory) variables to make a forecast

Four Qualitative Forecasting Techniques

• Executive opinions

• Salesforce opinions

• Consumer surveys

• Other approaches

Executive Opinions

A small group of upper-level managers may meet and collectively develop a forecast

Salesforce Opinions

Members of the sales or customer service staff can be good sources of information due to their direct contact with customers and may be aware of plans customers may be considering for the future

Consumer Surveys

Since consumers ultimately determine demand, it makes sense to solicit input from them. These typically represent a sample of consumer opinions.

Delphi Method

It is an iterative process intended to achieve a consensus

Time-series Forecasts

These project patterns identified in recent time-series observations and assume that future values of the time-series can be estimated from past values of the time-series

Time-series

A time-ordered sequence of observations taken at regular time intervals

Time-Series Behaviors

Analysis of time series data requires the analyst to identify the underlying behavior of the series. This behavior can be described as follows:

• Trend

• Seasonality

• cycles

• Irregular variations

• Random variation

Trend

This refers to long-term upward or downward movement in data (due to:)

• Population shifts

• Changing income

• Cultural changes

Seasonality

This referes to short-term, fairly regular variations related to the calendar or time of day

Cycle

These are wavelike variations lasting more than one year. These are often related to a variety of economic, political, or even agricultural conditions

Irregular Variation

These are due to unusual circumstances that do not reflect typical behavior

Random Variation

These are residual variation that remains after all other behaviors have been accounted for