Operations Management CH 8 Forecasting

Seasonal index

the percentage amount by which data for each season are above or below the mean.

Linear regression

the procedure that models a straight-line relationship between two variables.

Multiplicative seasonality

the seasonality expressed as a percentage of the average.

Dependent variable

the variable being forecast

Delphi weaknesses

time-consuming to develop.

Types of forecasting trends

trend adjusted exponential smoothing, linear trend line,

Random variation

unexplained variation that cannot be predicted.

Exponential smoothing

uses a sophisticated weighted average procedure to generate a forecast.

Collaborative planning, forecasting, and replenishment (CPFR)

a collaborative process between two trading partners that establishes formal guidelines for joint forecasting and planning.

Weighted moving average method

a forecasting method in which n of the most recent observations are averaged and past observations may be weighted differently, provided that all the weights add up to 1.

Forecast bias

a persistent tendency for a forecast to be over or under the actual value of the data.

Correlation coefficient

a statistic that measures the direction and strength of the linear relationship between two variables.

Linear trend line

a time series technique that computes a forecast with trend by drawing a straight line through a set of data.

Tracking signal

a tool used to monitor the quality of a forecast.

Selecting the right model

amout and type of available data, degree of accuracy required, length of forecast horizon, and data patterns present.

Market research

an approach that uses surveys and interviews to determine customer likes, dislikes, and preferences and to identify new-product ideas.

Seasonality

any data pattern that regularly repeats itself and is of a constant length.

Time series models characteristics

assume that all the information needed to generate a forecast is contained in the time series of data

Casual models

assume that the variable we wish to forecast is somehow related to other variables in the environment.

Naïve method

assumes that the next period's forecast is equal to the current periods actual.

Qualitative characteristics

based on human judgment, opinions; subjective and nonmathematical.

Quantitative forecasting method

based on mathematical modeling.

Quantitative characteristics

based on mathematics; quantitiative in nature.

Qualitative weaknesses

can bias the forecast and reduce forecast accuracy.

Qualitative strengths

can incorporate latest changes in the enviornment and "inside information"

Quantitative strengths

consistent and objective; able to consider much infomration and data at one time.

Trend

data exhibit an increasing or decreasing pattern over time.

Cycles

data patterns created by economic fluctuations.

Level or horizontal

data values fluctuate around a constant mean. This is the simplest pattern and the easiest to predict.

Steps in the process

decide what to forecast, evaluate and analyze appropriate data, select and test the forecasting model, generate the forecast, monitor forecast accuracy.

Multiple regression

develops a relationship between a variable and multiple independent variables.

Delphi strengths

excellent for forecasting long-term product demand, technological changes, and scientific advances.

Features of forecasting models

forecasts are rarely perfect, forecasts are more accurate for groups or families rather than for individual items, forecasts are more accurate for shorter than longer time horizons.

Market strengths

good determinant of customer preferences.

Executive strengths

good for strategic or new-product forecasting.

Overforecasting

having too much of an expensive product that will quickly become obsolete.

Simple moving average method

including only n of the most recent periods in the average.

Executive opinion

is a forecasting method in which a group of managers meet and collectively develop a forecast.

Delphi method

is a forecasting method in which the objective is to reach a consensus among a group of experts while maintaining their anonymity.

Time series

is a series of observations taken at regular intervals over a specified period of time.

Trend adjusted exponential smoothing

is the exponential smoothing model that is suited to data that exhibit a trend.

Market weaknesses

it can be difficult to develop a good questionnaire.

Time series models

level or horizontal, trend, seasonality, and cycles

Types of casual models

linear regression, correlation coefficeint, multiple regression

Types of level or horizontal patterns

naïve method, simple mean or average method, simple moving average method, weighted moving average method, exponential smoothing model

r = -1

negative relationship

r = 0

no relationship

Qualitative forecasting methods

often called judgmental methods, are methods in which the forecast is made subjectively by the forecaster.

Quantitative weaknesses

often quantifiable data are not available. Only as good as the data on which they are based.

Executive weaknesses

one person's opinion can dominate the forecast.

r = +1

perfect relationship

Forecasting

predicting the future

Level pattern models

simple mean, simple moving average, weighted moving average.

Independent variable

some other variable

Linear trend

straight line

Simple mean or average method

the average of a set of data.

Forecast error

the difference between forecast and actual value for a given period.

Lagging data

the forecasts are trailing behind the actual data, which happens when you apply a model that is good only for a level pattern to data that have a trend.

Mean squared error (MSE)

the measure of forecast error that computes error as the average of the squared error.

Mean absolute deviation (MAD)

the measure of forecast error that computes error as the average of the sum of the absolute error.