Chap 12

Question 1

demand planning

Answer 1

the process of forecasting and managing customer demands to create a pattern of demand that meets the firm’s operational and financial goals

Question 2

demand forecasting

Answer 2

decision process in which managers use data to predict demand patterns

Question 3

demand management

Answer 3

practice approach to influence patterns of demand using pricing, advertising, merchandising, etc

Question 4

By doing a good job of demand planning..

Answer 4

operation managers can more effectively plan for the amount of productive capacity and other resources their bussiness needs

Question 5

why is demand planning important for operations management

Answer 5

-Costs of making forecasts that are too high include money lost in holding inventory that is never sold, lost capacity that is spent making products that no one wants to buy, lost wages spent paying workers who are not needed, and so on.
-Cost of making forecasts that are too low include lost sales, overused capacity, overworked employees, and lower product availability for customers

Question 6

information a forecasting process integrates

Answer 6

-Past demands,
-Past forecasts and their associated errors,
-Business and economic metrics,
-The judgments of experts, and
-Demand management plans that specify pricing strategies and promotional plans.

Question 7

decisions involved in long term strategic planning

Answer 7

-Find new sources of supply
-Build or sell a plant
-Contract for transportation services
-Open or close new service location

Question 8

decisions involved in intermediate/tactical planning

Answer 8

-Aggregate production plans
-Employee hiring and firing
-Planned overtime work
-Subcontracting
-New product launches

Question 9

decisions in short term/operational materials

Answer 9

-Daily production schedule
-Daily work schedule
-Purchase orders

Question 10

4 types of patterns in demand

Answer 10

Stable (no trend)
Seasonal (Cycle)
Trend (Probably linear)
Step Change

Question 11

Stable pattern

Answer 11

a consistent horizontal stream of demands. Mature consumer products, for example, shampoo or milk, often exhibit this type of pattern.

Question 12

Seasonality and Cycles

Answer 12

regular patterns of repeating highs and lows. Seasonality may be daily, weekly, monthly, or even longer

Question 13

trend

Answer 13

general sloping tendency of demand, either upward or downward, in a linear or nonlinear fashion. New products in the growth phase of the life cycle typically exhibit an upward, nonlinear trend.

Question 14

shift or step change

Answer 14

emand is a one-time change, usually due to some external influence on demand such as a major product promotional campaign, or a sudden economic shock.

Question 15

autocorrelation

Answer 15

relationship of current demand with past demand. If values of demand at any given time are highly correlated with demand values from the recent past, then we say that the demand is highly autocorrelated.

Question 16

forecast error

Answer 16

unexplained” component of demand that seems to be random in nature.

Question 17

Primary goal in designing a forecasting process

Answer 17

to generate forecasts that are usable, timely, and accurate.

Question 18

5 Steps to help managers in forecasting achieve their goals

Answer 18

1. Identify the users and decision-making processes that the forecast will support.
2. Identify likely sources of the best data inputs
3. Select forecasting techniques that will most effectively transform data into timely, accurate forecasts over the appropriate planning horizon.
4. Document and apply the proposed technique to the data gathered for the appropriate business process
5. Monitor the performance of the forecasting process for continuous improvement.

Question 19

the forecasting process should address which users needs

Answer 19

time horizon
level of detail
accuracy vs cost
fit with existing business processes

Question 20

big data

Answer 20

Large data sets generated by technologies such as social media and the Internet of Things (IoT). Big data are often paired with predictive analytics or other similar analytical procedures.

Question 21

judgement based forecasts are built upong

Answer 21

the estimates and opinions of people, most often experts who have related sales or operational experience

Question 22

grassroots forecasting

Answer 22

technique that seeks inputs from people who are in close contact with customers and products.

Question 23

executive judgement

Answer 23

Forecasting techniques that use input from high-level, experienced managers.

Question 24

historical analogy

Answer 24

data and experience from similar products to forecast the demand for a new product

Question 25

marketing research

Answer 25

forecasts on the purchasing patterns and attitudes of current or potential customers.

Question 26

Delphi method

Answer 26

forecasts by asking a panel of experts to individually respond to a series of questions

Question 27

four methods of statistical model based forecasting

Answer 27

time series analysis casual studies simulation models artificial intelligence

Question 28

time series analyses

Answer 28

extrapolate forecasts from past demand data

Question 29

casual studies

Answer 29

casual relationships between leading variables and forecasted variables

Question 30

simulation models

Answer 30

represent past phenomena in mathematical relationships and then evaluate data to project future outcomes.

Question 31

Artificial intelligence,

Answer 31

in which a “smart” computer program “learns” from a combination of causal and simulation analyses using a wide array of data.

Question 32

time series analysis models

Answer 32

compute forecasts using historical data arranged in the order of occurrence.

Question 33

naive model

Answer 33

simply assumes that tomorrow’s demand will be the same as today’s.

Question 34

moving average

Answer 34

forecasting model computes a forecast as the average of demands over a number of immediate past periods

Question 35

weighted moving average

Answer 35

An adjustment to the moving average model that is sometimes used for stable demand patterns

Question 36

exponential smoothing

Answer 36

Another time series model used for stable demand patterns assigns weights to a moving average calculation in a systematic way;

Question 37

smoothing coefficient

Answer 37

A moving average approach that applies exponentially decreasing weights to each demand that occurred farther back in time.

Question 38

forecasting errors can be examined to determine which two primary aspects of forecast performance over time:

Answer 38

forecast accuracy forecast bias

Question 39

forecast accuracy

Answer 39

how closely the forecast aligns with the observations over time. Every error, whether the forecast was too high or too low, reduces accuracy

Question 40

forecast bias

Answer 40

the other hand, is simply the average error. Forecast bias indicates the tendency of a forecasting technique to continually overpredict or underpredict demand.

Question 41

A positive forecast bias indicates: a negative bias indicates:

Answer 41

that over time forecasts tend to be too low that forecasts tend to be too high.

Question 42

Mean percent errors

Question 43

mean absolute deviation

Answer 43

The average size of forecast errors, irrespective of their directions. Also called mean absolute error.

Question 44

mean absolute percentage error

Answer 44

The MAD represented as a percentage of demand.

Question 45

mean squared error

Answer 45

A more sensitive measure of forecast errors that approximates the error variance.

Question 46

root mean squared error

Answer 46

Gives an approximation of the forecast error standard deviation.

Question 47

tracking signal

Answer 47

The ratio of a running total of forecast error to MAD that indicates when the pattern of forecast error is changing significantly.In lieu of these rather sophisticated tests, managers often opt for a simpler metric

Question 48

adaptive forecasting

Answer 48

the smoothing coefficients in exponential smoothing models are automatically adjusted as a function of the tracking signal (a larger tracking signal creates a larger smoothing coefficient).

Question 49

rules in situational drivers of forecast accuracy to give an indication of how situational characteristics tend to affect forecast accuracy:

Answer 49

1. Short-term forecasts are usually more accurate than long-term forecasts. 2. Forecasts of aggregated demand are usually more accurate than forecasts of demand at detailed levels. 3. Forecasts developed using multiple information sources are usually more accurate than forecasts developed from a single source.