Chapter 3 - Demand Forecasting

Question 1

Demand forecast

Answer 1

The estimate of expected demand for a specified period of time in the future.

Question 2

What are the three types of uses for demand forecasts in operations?

Answer 2

1) to help managers design the system (long-term plans)
2) To help them plan the medium-term use of the system
3) To schedule the short-term use of the system.

Question 3

What are the long-term plans when designing the system?

Answer 3

• Which goods and services to offer
  *Which capacities, facilities and equipment to have
  *Where to locate

Question 4

What are the medium-term plans when designing the system?

Answer 4

*Planning overall inventory and workforce levels
*Planning production at the aggregate product family level

Question 5

What are the short-term plans when designing the system

Answer 5

*Scheduling of production
*Purchasing of parts and raw materials,
*Staff scheduling

Question 6

What are the two important aspects of demand forecasts?

Answer 6

1) The expected level of demand (forecast)
2) the degree of accuracy that can be assigned to a forecast

Question 7

Forecasting horizon

Answer 7

The time period we are forecasting for

Question 8

what are the elements of a good forecast

Answer 8

1) Timely
2) Accurate
3) Reliable
4) meaningful units
5) in writing
6) simple to understand and use
7) cost-effective

Question 9

What are the 6 steps to the forecasting process?

Answer 9

1) Determine the purpose of the forecast
2) establish a forecasting horizon
3) gather and analyze relevant historical data
4) select a forecasting technique
5) prepare the forecast
6) monitor the forecast

Question 10

Judgmental methods

Answer 10

Use nonquantitative analysis of historical data and/or analysis of subjective inputs from consumers, sales staff, managers, executives, similar products, and experts to help develop a forecast

Question 11

Time series models

Answer 11

identify specific patterns in the data and project or extrapolate those patterns into the future, without trying to identify causes of the patterns

Question 12

Associative models

Answer 12

use equations that consist of one or more explanatory variables that can be used to predict future demand for the variable of interest.

Question 13

Delphi method

Answer 13

Involves circulating a series of questionnaires among experts. Responses are anonymous and the new questionnaire is developed using the information extracted from the previous one.

Question 14

Level (Average)

Answer 14

or constant, refers to a horizontal (flat) pattern of time series. In this setting, the data do not show any particular pattern over time and are, generally, constant.

Question 15

Trend

Answer 15

refers to a persistent upward or downward movement in the data. Population growth, increasing incomes, and cultural changes often account for each movements.

Question 16

Seasonality

Answer 16

refers to regular repeating wavelike variations generally related to factors such as the calendar, weather, or recurring events. For example, sales of ice cream are higher in the summer. Restaurants, supermarkets and theatres experience weekly and even daily “seasonal” variations.

Question 17

Cycles

Answer 17

are wavelike variations lasting more than one year. These are often related to a variety of economic, political, and even agricultural conditions, such as supply of cattle.

Question 18

Irregular variations

Answer 18

Are due to unusual one-time explainable circumstances not reflective of typical behavior, such as severe weather conditions, strikes or sales promotions. They do not reflect typical behavior, and whenever possible should be identified and removed from the data.

Question 19

Random variations

Answer 19

are residual variations that remain after all other behaviors have been accounted for (also called noise). This randomness arises from the combined influence of many perhaps a great many relatively unimportant factors and it cannot be reliably predicted

Question 20

Naive Method

Answer 20

the naive method can be used with a stable series (level with random variations),a simple but widely used approach to forecasting.

Question 21

Averaging method

Answer 21

Averaging techniques smooth fluctuations in a time series because the individual highs and lows in the data offset each other when they are combined into an average.

Question 22

What are the three techniques for averaging?

Answer 22

1) Moving Average
2) Weighted moving average
3) Exponential smoothing

Question 23

Moving average

Answer 23

technique tries to overcome the issue of not having the random variations smoothed out, by expanding the amount of historical data the forecast is based on.

Question 24

Weighted moving average

Answer 24

similar to a moving average, except that i tallows for the assignment of different weights for different periods included in the forecasting analysis.

Question 25

Exponential smoothing

Answer 25

a sophisticated weighted averaging method, where the forecast for each period is based on the forecast for the previous period plus a percentage of the forecast error.

Question 26

trend-adjusted exponential smoothing

Answer 26

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

Question 27

what are the two different models of seasonality and what is the differences between the two?

Answer 27

1) additive - seasonality is expressed as a quantity (e.g., 20 units), which is added to or subtracted from the series average in order to incorporate seasonality. 2) Multiplicative model - seasonality is expressed as a proportion of the average (or trend) amount which is then multiplied by the average of the series.

Question 28

what are the steps to the time series decomposition?

Answer 28

1) compute the seasonal relatives 2) deseasonalize the demand data 3) Fit a model to the deseasonalized demand data 4) forecast using this model (To obtain the deseasonalized forecasts) 5) Reasonalize the deseasonalized forecasts.

Question 29

least square line

Answer 29

minimizes the sum of the squared deviations around the line.

Question 30

Correlation Coefficient

Answer 30

measures the strength, as well as the direction, of the relationship between two variables. A positive correlation coefficient indicates that the two variables move in the same direction a negative correlation coefficient means the two variables move in opposite directions.

Question 31

forecast error

Answer 31

the difference between the value that actually occurs and the value that was forecasted for a given time period.

Question 32

what are the three alternative forecast-error summaires?

Answer 32

1) Mean absolute deviation (MAD) 2) Mean Squared error (MSE) 3) Mean absolute percent error (MAPE)

Question 33

Bias

Answer 33

The sum of forecast errors Positive bias - forecasts frequently underestimate the actual values Negative bias - forecasts frequently overestimate the actual values

Question 34

What are some examples of forecast errors?

Answer 34

1) the omission of an important variable 2) a change or shift in the variable that the model cannot deal with 3) The appearance of a new variable. 4) Irregular variations from severe weather or other natural phenomena, temporary shortages or breakdowns, catastrophes, or similar events. 5) random variations randomness is the inherent variation that remains in the data after all causes of variation have been accounted for. There are always random variations.

Question 35

what are the two most important factors to consider when forecasting?

Answer 35

1) cost 2) Accuracy