Operations Management - Chapter 4 Review

Time Series: Random Variations

"Blips" in the data caused by chance or unusual situations. They follow no discernible pattern, so they cannot be predicted.

Time-series: Seasonality

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

Bias

A forecast that is consistently higher or consistently lower than actual values of a time series

Moving averages

A forecasting method that uses an average of the n most recent period of data to forecast the next period

Naive Approach

A forecasting technique that assumes that demand in he next period is equal to demand in the most recent period

Jury of executive opinion

A forecasting technique that uses the opinion of a small group of high-level managers to form a group estimate of demand

Delphi Method

A forecasting technique using a group process that allows experts to make forecasts

Respondents

A group of people, often located in different places, who judgments are valued. This group provides inputs to the decision makers before the forecast is made

Least-squares method

A least-squares line is described in terms of its y-intercept (the height at which it intercepts the y-axis) and its expected change (slope). If we can compute the y-intercept and slope, we can express the line with the following equation: ŷ = a + bx where ŷ = computed value of the variable to be predicted (dependent variable) a = y-axis intercept b = slope of the regression line (or the rate of change in y for given changes in x) x - the independent variable (which in this case is time)

Coefficient of determination

A measure of the amount of the variation in the dependent variable about its mean that is explained by the regression equation

Mean absolute deviation (MAD)

A measure of the overall forecast error for a model

Coefficient of Correlation

A measure of the strength of the relationship between two variables must be between -1 and +1

Standard error of the estimate

A measure of variability around the regression line—its standard deviation

Tracking signal

A measurement of how well a forecast is predicting actual values

Mean Absolute Percent Error (MAPE)

A problem with MAD and MSE is that their values depend on the magnitude of the item being forecasted. If the forecast item is measured in thousands, MAD and MSE values can be very large. MAPE is used to address this issue. Computed as the average of the absolute difference between the forecasted and actual values, expressed as a percentage of the actual values.

Staff personnel

Assist decision makers by preparing, distributing, collecting, and summarizing a series of questionnaires and survey results.

Time-series forecasting

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

Medium-range forecast

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

Focus forecasting

Forecasting that tries a variety of computer models and selects the best one for a particular application

Cyclical variations in data

Forecasting these variations in a time series can be difficult. This is because these variations include a wide variety of factors that cause the economy to go from recession to expansion to recession over a period of years. These factors include national or industry-wide over-expansion in times of euphoria and contraction in times of concern. Forecasting demand for individual products can also be driven by product life cycles—the stages products go through from introduction through decline.

Quantitative forecasts

Forecasts that employ mathematical modeling to forecast demand

Qualitative forecasts

Forecasts that incorporate such factors as the decision maker's intuition, emotions, personal experiences, and value system

Forecasting system - Step 5

Gather data needed to make the forecast: Disney's forecasting team employs 35 analysts and 70 field personnel to survey 1 million people/businesses every year. Disney also uses a firm called Global Insights for travel industry forecasts and gather data on exchange rates, arrivals into the U.S., airline specials, Wall Street trends, and school vacation schedules

Long-range forecast

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

Benefits of forecasting relative to supply-chain management

Good supplier relations and the ensuing advantages in product innovation, cost, and speed to market depend on accurate forecasts.

Benefits of forecasting relative to human resources

Hiring, training, and laying off workers all depend on anticipated demand. If the human resources department must hire additional workers without warning, the amount of training declines, and the quality of the workforce suffers.

Basic exponential smoothing formula

New forecast = Last period's forecast + α (last period's actual demand - last period's forecast) α is a weight, or smoothing constant, that has a value greater than or equal to 0 and less than or equal to 1

Jury of executive opinion

Opinions of a group of high-level experts or managers, often in combination with statistical models, are pooled to arrive at a group of estimate demand

Cycles

Patterns in the data that occur every several years

Time-series: Cycles

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

Economic forecasts

Planning indicators that are valuable in helping organizations prepare medium- to long-range forecasts

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.

Demand forecasts

Projections of a company's sales for each time period in the planning horizon

Demand forecasts

Projections of demand for a company's products or services. Forecasts drive decisions, so managers need immediate and accurate information about real demand. They need demand-driven forecasts, where the focus is on rapidly identifying and tracking customer desires. These forecasts may use recent point-of-sale (POS) data, retailer-generated reports of customer preferences, and any other information that will help to forecast with the most current data possible. Demand-driven forecasts drive a company's production, capacity, and scheduling systems and serve as inputs to financial, marketing, and personnel planning. In addition, the payoff in reduced inventory and obsolescence can be huge.

Associative Forecasting Methods:

Regression and Correlation Analysis: usually consider several variables that are related to the quantity being predicted. Once these related variables have been found, a statistical model is built and used to forecast the item of interest.

Seasonal variations in Data

Regular movements in a time series that relate to recurring events such as weather or holidays. Seasonality may be applied to hourly, daily, weekly, monthly, or other recurring patterns.

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.

Forecasting system - Step 4

Select the forecasting model(s): Disney uses a variety of statistical models, which include: moving averages, econometrics, and regression analysis. It also employs judgmental, or non-quantitative, models

Forecasting system - Step 2

Select the items to be forecasted: For Disney World, there are six main parks. A forecast of daily attendance at each is the main number that determines labor, maintenance, and scheduling

Cyclical variations in data

Similar to seasonal variations but occur every several years, not weeks, months, or quarters.

Economic and technological forecasting

Specialized techniques that may fall outside the role of the operations manager.

Coefficient of determination

The square of the coefficient of correlation. The percent of variation in the dependent variable that is explained by the regression equation.

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

Forecasting Approaches

There are two general approaches to forecasting, just as there are two ways to tackle all decision modeling. One is a quantitative analysis; the other is a qualitative approach. In practice, a combination of the two is usually most effective.

Measures to calculate overall forecast error

These measures can be used to compare different forecasting models, as well as to monitor forecasts to ensure they are performing well. Three of the most popular measures are as follows: Mean absolute deviation (MAD) Mean squared error (MSE) Mean absolute percent error (MAPE)

Least-squares method

This approach results in a straight line that minimizes the sum of the squares of the vertical differences or deviations from the line to each of the actual observations.

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

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.

Trend projection

This technique fits a trend line to a series of historical data points and then projects the slope of the line into the future for medium- to long-range forecasts. Several mathematical trend equations can be developed.

Delphi Method

Three different types of participants in the Delphi method: decision makers, staff personnel, and respondents.

Tracking signal formula

Tracking signal = Cumulative Error / MAD = [Σ(Actual demand in period i - Forecast demand in period i)] / MAD where MAD = (Σ | Actual - Forecast |) / n

What are five quantitative forecasting methods?

Two categories of quantitative forecasting: Time-series models and Associative model Time-series: 1. Naive approach 2. Moving averages 3. Exponential smoothing 4. Trend projection Associative: 1. Linear regression

Standard error of the estimate

Used to measure the accuracy of the regression estimates. This computation is called the standard deviation of the regression: It measures the error from the dependent variable, y, to the regression line, rather than to the mean.

Moving Averages

Useful if we can assume that market demands will stay fairly steady over time. A 4-month moving average is found by simply summing the demand during the past 4 months and dividing by 4.

Decision makers

Usually consist of a group of 5 to 10 experts who will be making the actual forecast.

Forecasting system - Step 7

Validate and implement the results: At Disney, forecasts are reviewed daily at the highest levels to make sure that the model, assumptions, and data are valid. Error measures are applied; then the forecasts are used to schedule personnel down to 15-minute intervals

Weighted moving average

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.

Benefits of forecasting relative to capacity

When capacity is inadequate, the resulting shortages can lead to loss of customers and market share. Actual demand exceeding forecasted demand can result in lost customers

Naive Approach

The simplest way to forecast is to assume that demand in the next period will be equal to demand in the most recent period.

Realities of forecasting

-Outside factors that we cannot predict or control often impact the forecast -Most forecasting techniques assume that there is some underlying stability in the system. Consequently, some firms automate their predictions using computerized forecasting software, then closely monitor only the product items whose demand is erratic. -Both product family and aggregated forecasts are more accurate than individual product forecasts. Disney, for example, aggregates daily attendance forecasts by park. This approach helps balance the over- and under-predictions for each of the six attractions

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 the results

Types of forecasts

1. Economic forecasts 2. Technological forecasts 3. Demand forecasts

Three distinguishing features of forecast ranges

1. First, intermediate and long-range forecasts deal with more comprehensive issues supporting management decisions regarding planning and products, plants, and processes. Implementing some facility decisions, such as GM's decision to open a new Brazilian manufacturing plant, can take 5 to 8 years from inception to completion 2. Second, short-term forecasting usually employs different methodologies than longer-term forecasting. Mathematical techniques, such as moving averages, exponential smoothing, and trend extrapolation, 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 recorded, should be introduced into a company's product line 3. Finally, as you would expect, short-range forecasts tend to be more accurate than longer-range forecasts. Factors that influence demand change every day. Thus, as the time horizon lengthens, it is likely that forecast accuracy will diminish. It almost goes without saying, then, that sales forecasts must be updated regularly to maintain their value and integrity. After each sales period, forecasts should be reviewed and revised.

Problems related to moving averages

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. Moving averages 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. Moving averages require extensive records of past data

What are four different qualitative forecasting techniques?

1. Jury of executive opinion 2. Delphi method 3. Sales force composite 4. Market survey

Forecasting Time Horizons

1. Short-range forecast 2. Medium-range forecast 3. Long-range forecast

Focus forecasting Principles

1. Sophisticated forecasting models are not always better than simple ones. 2. There is no single technique that should be used for all products or services

Four components of a time series

1. Trend 2. Seasonality 3. Cycles 4. Random variations

Notes on the use of the Least-squares method

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 10 years into the future. The world is too uncertain. 3. Deviations around the least-squares line are assumed to be random and normally distributed, with most observations close to the line and only a smaller number farther out.

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

Exponential smoothing

A weighted-moving-average forecasting technique in which data points are weighted by an exponential function

Forecast error

Actual demand - forecast value

Economic forecasts

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

Multiple regression analysis

Allows us to build a model with several independent variables instead of just one variable.

Adaptive smoothing

An approach to exponential smooth forecasting in which the smoothing constant is automatically changed to keep errors to a minimum

Multiple regression

An associative forecasting method with more than one independent variable.

Mean absolute deviation (MAD)

Computed by taking the sum of the absolute values of the individual forecast errors (deviations) and dividing by the number of periods of data (n): MAD = (Σ | Actual - Forecast | ) / n

Technological forecasts

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

Forecasting system - Step 3

Determine the time horizon of the forecast: Is it short, medium, or long term? Disney develops daily, weekly, monthly, annual, and 5-year forecasts

Forecasting system - Step 1

Determine the use of the forecast: Disney uses park attendance forecasts to drive decisions about staffing, opening times, ride availability and food supplies

Sales force composite

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. A variation of this approach occurs at Lexus, where every quarter Lexus dealers have a "make meeting." At this meeting, they talk about what is selling, in what colors, and with what options, so the factory knows what to build

Importance of understanding seasonal variations

Important for capacity planning in organizations that handle peak loads. 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.

Associative Models

Incorporate the variables or factors that might influence the quantity being forecast. For example, an associative model for lawn mower sales might use factors such as new housing starts, advertising budget, and competitors' prices

Bernard Smith

Inventory manager for American Hardware Supply, coined the term focus forecasting.

Technological forecasts

Long-term forecasts concerned with the rates of technological progress

Forecasting system - Step 6

Make the forecast

What is forecasting?

Managers are always trying to make better estimates of what will happen in the future in the face of uncertainty. Making good estimates is the main purpose of forecasting.

Mean Squared Error (MSE)

Tends to accentuate large deviations due to the squared term. For example, if the forecast error for period 1 is twice as large as the error for period 2, the squared error in period 1 is four times as large as that for period 2. Hence, using MSE as the measure of forecast error typically indicates that we prefer to have several smaller deviations rather than even one large deviation.

Selecting the Smoothing Constant

The appropriate value of the smoothing constant, α, can make the difference between an accurate forecast and an inaccurate forecast. High values of α are chosen when the underlying average is likely to change. Low values of α are used when the underlying average is fairly stable. In picking a value for the smoothing constant, the objective is to obtain the most accurate forecast

Forecasting

The art and science of predicting future events

Mean squared error (MSE)

The average of the squared differences between the forecasted and observed values. Its formula is: MSE = (Σ(forecase errors) ^ 2) / n

The strategic importance of forecasting

The forecast is the only estimate of demand until actual demand becomes known. Forecasts of demand therefore drive decisions in many areas.

Time-series: Trend

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

Measuring forecast error

The overall accuracy of any forecasting model—moving average, exponential smoothing or other—can be determined by comparing the forecasted values with the actual or observed values.

Correlation Coefficients for Regression Lines

The regression equation is one way of expressing the nature of the relationship between two variables. Regression lines are not "cause-and-effect" relationships. They merely describe the relationships among variables. The regression equation shows how one variable relates to the value and changes in another variable.

Weighted moving average formula

[Σ((weight for period n)(demand in period n))]/ Σ(weights)

Moving average formula

[Σ(demand in previous n periods)]/n