Brainscape's Knowledge GenomeTM

Browse over 1 million classes created by top students, professors, publishers, and experts.


See full index

Operations and Supply Chain Management > CH 3: Demand Forecasting > Flashcards

CH 3: Demand Forecasting Flashcards

1
Q

features common to all forecasts

A

generally assume that the same underlying casual system that existed in the past will continue to exist in the future, rarely perfect because of randomness, tend to be more accurate for groups of items versus individual items, accuracy decreases as time horizon increases

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
2
Q

forecasting

A

basic inputs for many kinds of decisions in organizations

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
3
Q

demand forecasting

A

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

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
4
Q

uses for demand forecasts

A

to help managers design system (long-term), help plan medium-term use of system, to schedule short-term use of system

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
5
Q

elements of a good forecast

A

timely, accurate, reliable, meaningful units, in writing, simple to understand and use, cost-effective

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
6
Q

steps in forecasting model

A
  1. determine purpose
  2. establish forecasting horizon
  3. gather and analyze relevant historical data
  4. select forecasting technique
  5. prepare forecast
  6. monitor forecast
How well did you know this?
1
Not at all
2
3
4
5
Perfectly
7
Q

two types of forecasting

A

quantitative
qualitative

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
8
Q

qualitative approach

A

non quantitative analysis of subjective inputs, considers soft information such as human factors, experience, gut instinct

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
9
Q

quantitative approach

A

time series models extends historical patterns of numerical data, associative models create equations with explanatory variables to predict the future

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
10
Q

qualitative methods

A

executive opinions, sales force opinions, consumer surveys, historical analogies, expert opinions

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
11
Q

executive opinions

A

pool opinions of high level executives, long term strategic or new product development

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
12
Q

sales force opinions

A

based on direct customer contact

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
13
Q

consumer surveys

A

questionnaires or focus groups

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
14
Q

historical analogies

A

use demand for a similar product

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
15
Q

expert opinions

A

delphi method: iterative questionnaires circulated until consensus is reached, technological forecasting

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
16
Q

what are forecasters looking for in data

A

patterns in historic data and random variation in data

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
17
Q

historic patterns

A

level, trend, seasonality, cycle

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
18
Q

level pattern

A

data fluctuates around constant mean for long period of time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
19
Q

trend pattern

A

data exhibits an increase or decrease pattern over time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
20
Q

seasonal pattern

A

variable is influenced by seasonal factor

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
21
Q

cycle pattern

A

recessions, inflations, life cycle of product

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
22
Q

time series

A

time ordered sequence of observations take at regular intervals of time

How well did you know this?
1
Not at all
2
3
4
5
Perfectly
23
Q

time series models

A

naive method, averaging method, trend models, techniques for seasonality

24
Q

naive forecasts

A

simple to use, low cost, low accuracy

methods: stable series, seasonal variations, trend

25
Q

naive forecast for stable series

A

last data point becomes the naive forecast for the next period

Ft+1 = At

26
Q

naive forecast for seasonal variations

A

naive forecast for this season is equal to the value of the series last season

27
Q

naive forecast for trend

A

naive forecast is equal to the last value of the series plus or minus the difference between the last two values of the series

Ft+1 = At +- change

28
Q

moving average

A

uses number of recent actual data values and uses that average to develop forecast

Ft = total of demands in n periods / n

29
Q

weighted moving average

A

allows for the assignment of different weights for different periods

Ft = total of WxD / total of W

30
Q

exponential smoothing

A

forecast for each period is based on the forecast for the previous period plus a percentage of the forecast error

Ft = Ft-1 + a(At-1 - Ft-1)

31
Q

choosing a smoothing constant (a)

A

a is between 0 and 1
closer to 0: slower the forecast will adjust to forecast error (greater the smoothing)
closer the responsiveness to forecast error (less smoothing)
when demand is fairly stable, use a lower a (smooths out random fluctuations)
when demand is increasing or decreasing use a higher a (more responsive to real changes)

32
Q

linear trend equation

A

simple regression model

Ft = a + bt

y = mx + b

33
Q

non linear trend

A

exponential trendline

34
Q

trend adjusted exponential smoothing

A

use when a time series exhibits a linear pattern

TAFt+1 = St + T1

St = TAFt + a(At - TAFt)
Tt = Tt-1 + B(St - St-1 - Tt-1)

St = smoothed time series at the end of period t
Tt = smoothed trend at the end of period t

35
Q

seasonal variations

A

are regularly repeating wavelike movements in series values that can be tied to recurring events, weather, or a calendar

36
Q

seasonal relative

A

equals proportion of average or trend for a season in the multiplicative model

37
Q

deseasonalize

A

remove seasonal component to more clearly see the other components, divide by seasonal relative

38
Q

reseasonalize

A

adjust the forecast for seasonal component, multiply by seasonal relatives

39
Q

steps to forecasting seasonal demand (time series decomposition)

A
  1. compute the seasonal relatives
  2. deseasonalize the demand data
  3. fit a model to the deseasonalized demand data
  4. forecast using this model
  5. reseasonalize the deseasonalized forecasts
40
Q

calculating the seasonal relatives

A
  1. centred moving average (CMA)
  2. annual average method
41
Q

Centred moving average

A

similar to moving average forecast, values are not projected as forecasts but positioned in the middle of the set periods and used to compute the moving average

42
Q

annual average method

A
  1. for each year compute its total annual demand and average seasonal demand
  2. for each year, for each season, compute the ratio of the actual demand relative to average seasonal demand
  3. for each season, average the ratios across years to get seasonal relatives
  4. fit a linear trend to the total amount demand series
  5. extend the model into the future to get next years total annual demand
  6. divide forecast of next years total annual demand by 4 to get the forecast of next years average seasonal demand
  7. for each season, multiply the forecast of next years average seasonal demand by the seasons seasonal relative
43
Q

associative techniques

A

rely on identification of related variables that can be used to predict values of the variable of interest

44
Q

simple linear regression

A

obtain an equation of a straight line that minimizes the sum of squared deviations of data points from the line

y = a+ bx

45
Q

correlation coefficient (r)

A

measure of strength of relationship between two variables

-1: move together in same direction
1: move together in opposite direction

46
Q

accuracy and control of forecasts

A

vital aspects of forecasting, degree of correctness, necessary for daily activities

47
Q

forecast error

A

actual value - forecast value

+ : forecast too low
- : forecast too high

48
Q

three measure of forecasts

A

mean absolute deviation (MAD)
mean squared error (MSE)
mean absolute percent error (MAPE)

49
Q

MAD equation

A

sum of |actual - forecast| / n

50
Q

MSE equation

A

sum of (actual - forecast) ^2 / n

51
Q

MAPE equation

A

sum of (|actual - forecast| / actual) / n x 100

52
Q

control chart

A

visual tool for monitoring forecast errors, used to detect non-randomness in errors

upper control and lower control limits

53
Q

control limits

A

= 0 +- 2s

s = square root of MSE

54
Q

tracking signal

A

used to control the forecasting process

sum of (actual - forecast) / MAD

55
Q

choosing a forecasting method

A

two most important factors: cost, accuracy
other factors: availability of historical data, forecasting horizon, pattern of data

56
Q

why is forecasting important for the supply chain

A

inaccurate forecasts can lead to shortages and excesses throughout the supply chain, shortages of materials can lead to missed deliveries, work disruption, and poor customer service

Operations and Supply Chain Management (7 decks)

Brainscape helps you reach your goals faster, through stronger study habits.
© 2024 Bold Learning Solutions. Terms and Conditions