Operations Management Chapter 4 Flashcards

1
Q

Forecasting

A

The art and science of predicting future events

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2
Q

How is a forecast classified

A

by the future time horizon it covers

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3
Q

Short-range forecast

A

Up to a year, but usually less than 3 months. Used for planning purchasing, job scheduling, workforce levels, job assignments and production levels

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4
Q

Medium-range forecast

A

3 months to 3 years. Used for sales planning, production planning and budgeting, cash budgeting and analysis of various operating plans

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5
Q

Long-range forecast

A

3 years +, used for planning new products, capital expenditures, facility locations or expansions, and research and development

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6
Q

How does short-range forecasts differ from medium and long-range forecasts

A

1) Medium and long-range deal with more comprehensive issues
2) Short-range employs different methodologies
3) Short-range tends to be more accurate

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7
Q

Product Life Cycle

A

1) Introduction
2) Growth
3) Maturity
4) Decline

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8
Q

Three major types of forecasts

A

1) Economic
2) Technological
3) Demand

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9
Q

Economic Forecast

A

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

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10
Q

Technological Forecast

A

Concerned with the rates of technological progress

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11
Q

Demand Forecast

A

Projections of demand for a company’s products or services. Also called sales forecasts, drive a company’s production, capacity, and scheduling systems and serve as inputs to financial, marketing and personnel planning

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12
Q

7 Steps of Forecasting

A

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

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13
Q

Two general approaches to forecasting

A

1) Quantitative

2) Qualitative

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14
Q

Quantitative Forecasts

A

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

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15
Q

Qualitative Forecasts

A

Incorporate such factors as the decision makers intuition, emotions, personal experiences, and value system in reaching a forecast

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16
Q

4 Major Qualitative Techniques

A

1) Jury of Executive Opinion - uses the opinion of a small group of high-level managers to form a group estimate of demand
2) Delphi Method - Using a group process that allows experts to make forecasts
3) Sales Force Composite - Based on salespersons’ estimates of expected sales
4) Consumer Market Survey - Solicits input from customers or potential customers regarding future purchasing plans

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17
Q

5 Quantitative Forecasting Methods

A
I. Time-Series Models
   A. Naive approach
   B. Moving averages
   C. Exponential Smoothing
   D. Trend projection
II. Associative Model
   A. Linear regression
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18
Q

Time-series Models

A

Uses past data points to make a forecast

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19
Q

Naive Approach

A

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

20
Q

Moving Averages

A

Average of the most recent “N” periods to forecast the next

21
Q

Exponential Smoothing

A

Weighted moving average technique where data points are weighted by an exponential function

22
Q

Smoothing Constant

A

The weighting factor used, number between 0 and 1

23
Q

Trend Projection

A

Fits a trend line to a series of historical data points and then projects the line into the future

24
Q

Time-series models has 4 components

A

1) Trend
2) Seasonality
3) Cycles
4) Random Variation

25
Trend
Gradual upward or downward movement of data over time
26
Seasonality
A data pattern that repeats itself after a period
27
Cycles
Patterns that appear every several years
28
Random Variation
"blips" caused by chance and unusual situations
29
Forecast Error
Actual Demand - Forecast Value
30
MAD
Mean absolute deviation - Measure of the overall forecast error for a model
31
MSE
Mean Squared Error - Average of the squared differences between forecast and observed values
32
MAPE
Mean absolute percent error - this is the easiest measure to interpret
33
Seasonal variations
Regular upward or downward movements in a time series that tie to recurring events
34
Associative Models
Incorporate several variables or factors that might influence the quantity being forecast
35
Linear-Regression Analysis
A strait-line mathematical model to describe the functional relationships between independent and dependent variables
36
Standard Error of the Estimate
Also called Standard Deviation of the Regression - Measures the error from the dependent variable to the regression line rather than to the mean
37
Coefficient of the Correlation
A measure of the strength of the relationship between two variables
38
Coefficient of the Determination
A measure of the amount of variation in the dependent variable about it's mean that is explained by the regression equation
39
Multiple regression analysis
An associative forecasting method with more than one independent variables
40
Tracking signal
Measurement of how well a forecast is predicting actual values Cumulative Error / MAD
41
Positive Tracking Signals
Demand is greater than forecast
42
Negative Tracking Signals
Demand is less than forecast
43
Bias
A forecast that is consistently higher or consistently lower than actual values of a time series
44
Adaptive Smoothing
Approach to exponential smoothing forecasting in which the smoothing constant is automatically changed to keep errors to a minimum
45
Focus Forecasting
Tries a variety of computer models and selects the best one for a particular application
46
2 Principles of Focus Forecasting
1) Sophisticated forecasting models are not always better | 2) There is no single technique that should be used for all products or services