SCM 3650 Chapter 4

cycles

patterns in the data that occur every several years

Forecast error

=Actual demand-forecast value

Forecast including trend

=Exponentially smoothed forecast average + exponentially smoothed trend

coefficient of correlation

A measure of the strength of the relationship between two variables

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.

Seven Steps in the Forecasting System

1) Determine the use of the forecast: Disney uses park attendance forecasts to drive decisions about staffing, opening times, ride availability, and food supplies. 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. 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. 4) Select the forecasting model(s): Disney uses a variety of statistical models that we shall discuss, including moving averages, econometrics, and regression analysis. It also employs judgmental, or nonquantitative, models. 5) Gather the 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 gathers data on exchange rates, arrivals into the U.S., airline specials, Wall Street trends, and school vacation schedules. 6) Make the Forecast 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.

Three major types of forecasts:

1) Economic forecasts address the business cycle by predicting inflation rates, money supplies, housing starts, and other planning indicators. 2) Technological forecasts are concerned with rates of technological progress, which can result in the birth of exciting new products, requiring new plants and equipment. 3) 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. 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.

Moving averages 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) 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.

Future time horizon...

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) 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.

A time series has four components:

1) 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. 2) Seasonality is a data pattern that repeats itself after a period of days, weeks, months, or quarters. There are six common seasonality patterns: 3) 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. 4) Random variations are "blips" in the data caused by chance and unusual situations. They follow no discernible pattern, so they cannot be predicted.

Naive approach

Demand in the next period is equal to demand in the most recent period

Economic and technological forecasting are...

Economic and technological forecasting are specialized techniques that may fall outside the role of the operations manager

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

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. Second, 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. 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.

Forecasting time-series data implies...

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.

Ft= new forecast Ft-1=previous periods forecast a=smoothing constant At-1=previous periods actual demand

Ft=Ft−1+α(At−1−Ft−1)

How does forecasting impact 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. A large Louisiana chemical firm almost lost its biggest customer when a quick expansion to around-the-clock shifts led to a total breakdown in quality control on the second and third shifts.

New forecast =

New forecast=Last period's forecast+ α (Last period's actual demand − Last period's forecast) a=smoothing constant

Regardless of the system that firms use, each company faces several realities...

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 underpredictions for each of the six attractions.

focus forecasting is based on two principles:

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

Weighted moving average =

Sum of ((Weight for period n)(Demand in period in)) / Sum of weights

Forecasting

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. It may be a subjective or an intuitive prediction (e.g., "this is a great new product and will sell 20% more than the old one"). It may be based on demand-driven data, such as customer plans to purchase, and projecting them into the future. Or the forecast may involve a combination of these, that is, a mathematical model adjusted by a manager's good judgment.

Mean squared error

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

The forecast is the only...

The forecast is the only estimate of demand until actual demand becomes known.

collaborative planning, forecasting, and replenishment (CPFR)

They combine the intelligence of multiple supply-chain partners. The goal of CPFR is to create significantly more accurate information that can power the supply chain to greater sales and profits.

Mean Absolute Percent Error (MAPE)

This is computed as the average of the absolute difference between the forecasted and actual values, expressed as a percentage of the actual values. That is, if we have forecasted and actual values for n periods, the MAPE is calculated as:

Overview of Quantitative Methods

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. If we are predicting sales of lawn mowers, we use the past sales for lawn mowers to make the forecasts. Associative models, such as linear regression, 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.

unlike time series forecasting...

Unlike time-series forecasting, associative forecasting models usually consider several variables that are related to the quantity being predicted.

when a detectable trend or pattern is present...

When a detectable trend or pattern is present, weights can be used to place more emphasis on recent values.

How does forecasting impact Capacity?

When capacity is inadequate, the resulting shortages can lead to loss of customers and market share. This is exactly what happened to Nabisco when it underestimated the huge demand for its Snackwell Devil's Food Cookies. Even with production lines working overtime, Nabisco could not keep up with demand, and it lost customers. Boeing faces this problem with its 737 MAX, where demand continues to exceed forecasts, and hence capacity. On the other hand, when excess capacity exists, costs can skyrocket.

bias

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

Mean Absolute Deviation (MAD)

a measure of the overall forecast error for a model This value is 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):

tracking signal

a measurement of how well a forecast is predicting actual values

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

Adaptive smoothing

an approach to exponential smoothing 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

focus forecasting

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

Qualitative forecasts

incorporate such factors as the decision maker's intuition, emotions, personal experiences, and value system in reaching a forecast. Some firms use one approach and some use the other. In practice, a combination of the two is usually most effective.

Seasonal variations

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

Overview of Qualitative Methods

ry 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. Bristol-Myers Squibb Company, for example, uses 220 well-known research scientists as its jury of executive opinion to get a grasp on future trends in the world of medical research. 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. The state of Alaska, for example, has used the Delphi method to develop its long-range economic forecast. A large part of the state's budget is derived from the million-plus barrels of oil pumped daily through a pipeline at Prudhoe Bay. The large Delphi panel of experts had to represent all groups and opinions in the state and all geographic areas. 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. 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. 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.

Moving average =

sum of demand in previous n periods/n

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.

Moving-Average

uses a number of historical actual data values to generate a forecast. Moving averages are useful if we can assume that market demands will stay fairly steady over time.