Ops Mgmt 3

Front 1

Forecast

Back 1

A statement about the future value of a variable of interest. Forecasts are made with reference to a specific time horizon.This is important to operations management because the whole point is to match supply to demand.

Front 2

What are the two uses for forecasts?

Back 2

1. They help managers plan the system and the other is to help them plan the use of the system.

Front 3

Certain features are common to all forecasts

Back 3

1. Forecasting techniques generally assume that the same underlying causal system that existed in the past will continue to exist in the future.
2. Forecasts are not perfect' actual results usually differ from predicted values. Allowances should be made for forecast errors.
3. Forecasts for groups of items tend to be more accurate than forecasts for individual items because forecasting errors among items in a group usually have a cancelling out effect.
4. Forecast accuracy decreases as the time period covered by the forecast--the time horizon increases. Short range forecasts must contend with fewer uncertainties than longer range forecasts, so they tend to be more accurate.

Front 4

A properly prepared forecast should fulfill certain requirements:

Back 4

1. The forecast should be timely
2. The forecast should be accurate, and the degree of accuracy should be stated..
3. The forecast should be reliable; it should work consistently.
4. The forecast should be expressed in meaningful units such as dollars ect.
5. the forecast should be expressed in writing. This will not guarantee that all concerned are using the same information, but it will increase the liklihood of this.
6. The forecasting technique should be simple to understand and use. These models enjoy widespread popularity becauseusers are more comfortable working with them.
7. The forecast should be cost effective: benefits should outweigh the cost.

Front 5

Steps in the forecasting process

Back 5

1. Determine the purpose of the forecast. How will it be used and when will bit be needed? This step provides an indication of the level of detail required.
2. Establish a time horizon.
3. Obtain, clean, and analyze appropriate data. The data may need to be cleaned to get rid of outliers and obviously incorrect data before analysis.
4. Select a forecasting technique.
5. Make the forecast.
6. Monitor the forecast.

Front 6

Forecast accuracy

Back 6

Because random variation is always present, there will always be some residual error, even if all other factors have been accounted for. Decision makers will want to include accuracy as a factor when choosing among different techniques, along with cost. Accurate forecasts are necessary for the success of daily activities of every business organization.

Front 7

Error

Back 7

The difference between the actual value and the value that we predicted for a given period.

Front 8

Error=

Back 8

actual-forecast

Front 9

Mean Absolute Deviation

Back 9

MAD the average absolute forecast error.

Front 10

Mean squared error

Back 10

The average of squared forecast averages

Front 11

What do we use MAD, MSE, and MAPE for?

Back 11

One use is to compare accuracy of alternative forecasting methods. Another use is to track error performance over time to decide if attention is needed. Is error performance getting better or worse, or is it staying about the same. MAPE is used to put large errors in perspective

Front 12

Mean absolute percent error

Back 12

MAPE The average absolute percent error.

Front 13

Qualitative methods

Back 13

consist mainly of subjective inputs. These permit inclusion of soft information such as hurman factors, personal opinions, hunches in the forecasting process.

Front 14

Judgemental forecasts

Back 14

rely on analysis of subjective inputs obtained from various sources such as consumer surveys, the sales staff, managers, and executive, and panels of experts. Quite frequently these sources provide insights that are not otherwise available.

Front 15

Time series forecasts

Back 15

Simply attempt to project past experience into the future. These techniques use historical data with the assumption that the future will be like the past.

Front 16

Associative models

Back 16

Use equations that consist of one or more explanatory variables that can be used to predict demand.

Front 17

Executive opinions

Back 17

A small group of upper level managers meet and collectively develop a forecast. The advantage of bringing together the considerable knowledge and talents of various managers. There is the risk that the view of one person will prevail.

Front 18

Salesforce Opinions

Back 18

Members of the slaes staff or the customer service staff are often good sources of information because of their direct contact with consumers. Drawbacks is that staff members may be unable to distinguish between what customers would LIKE to do and what they WILL actually do. They may be overly influenced by recent experience.

Front 19

Consumer surveys

Back 19

Because it is the consumers who ultimately determined demand, it seems natural to solicit input from them. Therefore, organizations seeking consumer input usually resort to consumer surveys which enable them to sample consumer opinions. The obvious advantage of consumer surveys is that they can tap information that might not be available elsewhere. On the other hand, a considerable amount of knowledge and skill is required to construct a survey, administer it, and correctly interpret the results for valid information.

Front 20

Delphi method

Back 20

An iterative process in which managers and staf complete a series of questionnaires, each developed from the previous one, to achieve a consensus forecast. Responses are kept anonymous which tends to encourage honest responses and reduces the risk that one person's opinion will prevail. Each questionaire is developed using information extracted from the previous one, thus enlarging the scope of information on which participants can base their judgements. The Delphi method is useful for assessing changes in technology. The goal mya be to predict when video telephones may be installed in at least 50% of homes. For the most part they are long term, single time forecasts, which usually have very little hard information to go by or data that are costly to obtian. Judgents or experts or others who possess sufficient knowledge to make predictions are used.

Front 21

time series

Back 21

A time ordered sequence of observations taken at regular intervals. Made on the assumptions that future values of the series can be estimated from past values.

Front 22

Trend

Back 22

A long term upward or downward movement in data.

Front 23

Seasonality

Back 23

Short-term regular variations related to the calendar or time of day

Front 24

cycle

Back 24

Wavelike variations lasting more than one year

Front 25

Irregular variation

Back 25

Caused by unusual circumstances, not reflective of typical behavior.

Front 26

random variations

Back 26

Residual variations after all other behaviors are accounted for.

Front 27

Naive forecast

Back 27

A forecast for any period that equals the previous period's actual value. If demand for a product last week was 20 cases with seasonal variations, the forecast for this season is likely to equal the value of the series last season. For data with trend, the forecast is equal to the last value of the series plus or minus the difference between the last two values of the series. Consider the advantages; it has virvtually no cost, it is quick and easy to prepare because data analysis is nonexistent, and it is easily understandable. The main objection to this method is its inability to provide highly accurate forecsts.

Front 28

Averaging techniques

Back 28

Averaging techniques smooth variations in the data. Ideally it would be desirable to completely remove any randomness from the data and leave only real variations such as changes in the demand. As a practical matter, however, it is usually impossible to distinguish between these two kinds of variatons.

Front 29

What are the three averaging techniques

Back 29

1. Moving average
2. Weighted moving average
3. Exponential smoothing

Front 30

Moving average

Back 30

technique that averages a number of recent actual values, updated as new values become available. Consequently the forecast moves by reflecting only the most recent values. If responsiveness is important, a moving average with relatively few data points should be used. This will permit quick adjustment to a step change in the data but will also cause the forecast to be somewhat responsive even to random variations. The advantages are tat it's easy to compute and easy to understand. A possible disadvantage is that all values in the average are weighted equally.

Front 31

Weighted average

Back 31

More recent values in a series are given more weight in computing a forecast.

Front 32

Exponential smoothing

Back 32

A weighted averaging method based on previous forecast plus a percentage of the forecast error. The closer the value is to zero the slower the forecast will be to adjust to forecast errors. Conversely, the closer the value of alpha is to 1.00 the greater the responsiveness and the less the smoothing. Selecting a smoothing constant is basically a matter of judgment or trial and error using forecast errors to guide the decision. Commonly used values range from .05 to .5. Exponential smoothing is one of the most widely used techniques in forecasting, partly because of the ease with which the weighting scheme can be altered--simply by changing the value of alpha.

Front 33

focus forecasting

Back 33

Some companies use forecasts based on a "best current performance" basis. This approach, called focus forecasting involves the use of several forecasting methods all being applied to the last few months of historical data after any irregular variations have been removed. The method that has the highest accuracy is then used to make the forecast for th next month.

Front 34

Diffusion models

Back 34

When new products or services are introduced, historical data are not generally available. Instead, predictions are based on rates of product adoption and usage spread from other established products, using mathematical diffusion models. These models take into account such factors as market potential, attention from mass media, and word of mouth.

Front 35

Trend-adjusted exponential smoothing

Back 35

variation of exponential smoothing used when a time series exhibits a linear trend.

Front 36

Seasonal variations

Back 36

regularly repeating movements in series values that can be tied to recurring events. The term seasonal variation is also applied to daily, weekly, monthly, and other regularly recurring patterns in data. For example, rush hour traffic occurs twice a day--incoing in the morning and outgoing in the later afternoon.

Front 37

Two different models of seasonality

Back 37

additive and multiplicative
additive--seasonality is expressed as a quantity (i.e. 20 units) which is added or subtracted from the series average in order to incorporate seasonality.
Multiplicative model-seasonality is expressed as a percentage of the average or trend amount (i.e. 1.10) which is then used to multiply the value of a series to incorporate seasonality. Businesses use the multiplicative model much more widely than the additive model. We will focus on multiplicative model.

Front 38

Seasonal relative

Back 38

Percentage of average or trend

Front 39

To deseasonalize data

Back 39

is to remove the seasonal component from the data in order to get a clearer picture of the nonseasonal (i.e. trend) components. Deseasonalizing data is accomplished by dividing each data point by its corresponding seasonal relative.

Front 40

Incorporating seasonality in a forecast is useful when demand

Back 40

has both trend (or average) and seasonal components. Incorporating seasonality can be accomplished in this way:
1. obtain trend estimates for desired periods using a trend equation.
2. Add seasonality to the trend estimates by multiplying these trend estimates by the corresonding seasonal relative.

Front 41

Centered moving average

Back 41

A moving average positioned at the center of the data that were used to compute it.

Front 42

predictor variables

Back 42

Variables that can be used to predict values of the variables of interest

Front 43

regression

Back 43

technique for fitting a line to a set of points

Front 44

last squares line

Back 44

Minimizes the sum of the squared vertical deviations around the lines