OM 303 - Chapter 4

Technological forecasts

are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment.

Demand forecasts

are projections of demand for a company's products or services. Forecasts drive decisions, so managers need immediate and accurate information about real demand.

Mean Absolute Percent Error (MAPE)

the average of the absolute differences between the forecast and actual values, expressed as a percent of actual values

Mean squared error (MSE)

the average of the squared differences between the forecasted and observed values

Forecast error

the difference found by subtracting the forecast from actual demand for a given period

smoothing constant

the weighting factor used in an exponential smoothing forecast, a number greater than or equal to 0 and less than or equal to 1

Quantitative forecasts

use a variety of mathematical models that rely on historical data and/or associative variables to forecast demand.

Medium- and long-range forecasts are distinguished from short-range forecasts by three features:

- intermediate and long-range forecasts deal with more comprehensive issues supporting management decisions regarding planning and products, plants, and processes. - short-term forecasting usually employs different methodologies than longer-term forecasting. Mathematical techniques, such as moving averages, exponential smoothing, and trend extrapolation (all of which we shall examine shortly), are common to short-run projections. Broader, less quantitative methods are useful in predicting such issues as whether a new product, like the optical disk recorder, should be introduced into a company's product line. - short-range forecasts tend to be more accurate than longer-range forecasts.

Seven Steps in the Forecasting System

1. Determine the use of the forecast 2. Select the items to be forecasted 3. Determine the time horizon of the forecast 4. Select the forecasting model(s) 5. Gather the data needed to make the forecast 6. Make the forecast 7. Validate and implement results

future time horizon

A forecast is usually classified by the ___ ___ ___ that it covers. Time horizons fall into three categories: 1. Short-range forecast: This forecast has a time span of up to 1 year but is generally less than 3 months. It is used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels. 2. Medium-range forecast: A medium-range, or intermediate, forecast generally spans from 3 months to 3 years. It is useful in sales planning, production planning and budgeting, cash budgeting, and analysis of various operating plans. 3. Long-range forecast: Generally 3 years or more in time span, long-range forecasts are used in planning for new products, capital expenditures, facility location or expansion, and research and development.

Exponential Smoothing

A weighted-moving-average forecasting technique in which data points are weighted by an exponential function.It involves very little record keeping of past data and is fairly easy to use.

four components

Analyzing time series means breaking down past data into components and then projecting them forward. A time series has ___ ___: 1. Trend 2. Seasonality 3. Cycles 4. Random variations

Long-range forecast

Generally 3 years or more in time span, long-range forecasts are used in planning for new products, capital expenditures, facility location or expansion, and research and development.

Sales force composite

In this approach, each salesperson estimates what sales will be in his or her region. These forecasts are then reviewed to ensure that they are realistic. Then they are combined at the district and national levels to reach an overall forecast.

first-order smoothing

Simple exponential smoothing is often referred to as ______ smoothing, and trend-adjusted smoothing is called second-order smoothing or double smoothing. Other advanced exponential-smoothing models are also used, including seasonal-adjusted and triple smoothing.

five

There are ___ quantitative forecasting methods, all of which use historical data.

Delphi method

There are three different types of participants in the Delphi method: decision makers, staff personnel, and respondents. Decision makers usually consist of a group of 5 to 10 experts who will be making the actual forecast. Staff personnel assist decision makers by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results. The respondents are a group of people, often located in different places, whose judgments are valued. This group provides inputs to the decision makers before the forecast is made.

Medium-range forecast

This forecast generally spans from 3 months to 3 years. It is useful in sales planning, production planning and budgeting, cash budgeting, and analysis of various operating plans.

Market survey

This method solicits input from customers or potential customers regarding future purchasing plans. It can help not only in preparing a forecast but also in improving product design and planning for new products. The consumer market survey and sales force composite methods can, however, suffer from overly optimistic forecasts that arise from customer input.

Jury of executive opinion

Under this method, the opinions of a group of high-level experts or managers, often in combination with statistical models, are pooled to arrive at a group estimate of demand.

peak loads

Understanding seasonal variations is important for capacity planning in organizations that handle ___ ___. These include electric power companies during extreme cold and warm periods, banks on Friday afternoons, and buses and subways during the morning and evening rush hours.

Using the least-squares method implies that we have met three requirements:

Using the _____ _____ implies that we have met three requirements: 1. We always plot the data because least-squares data assume a linear relationship. If a curve appears to be present, curvilinear analysis is probably needed. 2. We do not predict time periods far beyond our given database. For example, if we have 20 months' worth of average prices of Microsoft stock, we can forecast only 3 or 4 months into the future. Forecasts beyond that have little statistical validity. Thus, you cannot take 5 years' worth of sales data and project 5 years into the future. The world is too uncertain. 3. Deviations around the least-squares line (see Figure 4.4) are assumed to be random and normally distributed, with most observations close to the line and only a smaller number farther out.

weights

When a detectable trend or pattern is present, weights can be used to place more emphasis on recent values. This practice makes forecasting techniques more responsive to changes because more recent periods may be more heavily weighted. Choice of weights is somewhat arbitrary because there is no set formula to determine them. Therefore, deciding which weights to use requires some experience.

Random variations

___ ___ are "blips" in the data caused by chance and unusual situations. They follow no discernible pattern, so they cannot be predicted.

Moving averages

___ ___ do present three problems: 1. Increasing the size of n (the number of periods averaged) does smooth out fluctuations better, but it makes the method less sensitive to changes in the data. 2. ___ ___cannot pick up trends very well. Because they are averages, they will always stay within past levels and will not predict changes to either higher or lower levels. That is, they lag the actual values. 3. ___ ___ require extensive records of past data.

Time series

___ ___ models predict on the assumption that the future is a function of the past. In other words, they look at what has happened over a period of time and use a series of past data to make a forecast.

Forecasting time-series data

___ ___-___ ___ implies that future values are predicted only from past values and that other variables, no matter how potentially valuable, may be ignored.

Cycles

___ are patterns in the data that occur every several years. They are usually tied into the business cycle and are of major importance in short-term business analysis and planning. Predicting business cycles is difficult because they may be affected by political events or by international turmoil.

Seasonality

___ is a data pattern that repeats itself after a period of days, weeks, months, or quarters. There are six common seasonality patterns:

Forecasting

___ is the art and science of predicting future events. Forecasting may involve taking historical data (such as past sales) and projecting them into the future with a mathematical model.

Trend

___ is the gradual upward or downward movement of the data over time. Changes in income, population, age distribution, or cultural views may account for movement in trend.

Associative

___ models, such as linear regression, incorporate the variables or factors that might influence the quantity being forecast.

moving-average forecast

a forecasting method that uses an average of the n most recent periods of data to forecast the next period; useful if we can assume that market demands will stay fairly steady over time.

Naive Approach

a forecasting technique which assumes that demand in the next period is equal to demand in the most recent period

Mean absolute deviation (MAD)

a measure of the overall forecast error for a model

trend projection

a time-series forecasting method that fits a trend line to a series of historical data points and then projects the line into the future for forecasts

Economic forecasts

address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators

Subjective or qualitative forecasts

incorporate such factors as the decision maker's intuition, emotions, personal experiences, and value system in reaching a forecast.

Cycles

patterns in the data that occur every several years

seasonal variations

regular upward or downward movements in a time series that tie to recurring events

multiplicative seasonal model

seasonal factors are multiplied by an estimate of average demand to produce a seasonal forecast.

sequence of evenly spaced

A time series is based on a ___ ___ ___ ___ (weekly, monthly, quarterly, and so on) data points.

Forecasts tend to be more accurate

Forecasts tend to be more ___ as they become shorter. Therefore, forecast error also tends to drop with shorter forecasts.

.05 to .50

The smoothing constant is generally in the range from ______ for business applications

two

There are ___ general approaches to forecasting, just as there are two ways to tackle all decision modeling.

Short-range forecast

This forecast has a time span of up to 1 year but is generally less than 3 months. It is used for planning purchasing, job scheduling, workforce levels, job assignments, and production levels.