Qm - Module 13 - The Context Of Forecasting

Front 1

List types of forecasting techniques

Back 1

1. Qualitative
2. Causal modeling
3. Time series methods

Front 2

Describe qualitative methods

Back 2

based on judgement rather than records of past data
inferior to quantitative methods

Front 3

Describe Causal Modeling

Back 3

means the variable to be forecast is related statistically to one or more which are thought to cause changes in it

Front 4

Time series methods

Back 4

predict future values of a variable solely from historical values of itself (with regression analysis)

Front 5

When are time series methods of forecasting best?

Back 5

1. Conditions are stable and will continue
2. Short-term forecasts are required
3. Base forecast is needed on which can be built changes in future conditions

Front 6

Which forecasting method is cheapest and easiest to maintain?

Back 6

Time series

Front 7

What is the time horizon for forecasts?

Back 7

Short term - up to a year
Medium term - 1 to 5 years
Long term - more than 5 years

Front 8

Which methods are used for which time frames?

Back 8

Short-term - time series
Medium-term - causal modeling
Long-term - qualitative techniques

Front 9

Describe qualitative forecasting techniques

Back 9

combination of experience, judgement, intuition. Use Delphi technique.

Front 10

What are the two motives behind qualitative forecasting?

Back 10

1. a lack of adequate data
2. factors affecting the forecast may be better handled qualitatively

Front 11

Common qualitative methods

Back 11

Visionary forecasting - 1 person
Panel consensus - generally inaccurate
Brainstorming - can be useful - to panel type
Market Research - accurate but expensive
Delphi - no communication between
Scenario writing -
Cross-impact matrices

Front 12

Common Qualitative methods (cont)

Back 12

1. Analogies - when no data - a second variable who history is completely known is used
2. Catastrophe theory - like contingency planning
3. Relevance trees - starts in future and what must happen today to make future happen (normative)

Front 13

Procedure for building relevance tree

Back 13

1. Draw the relevance tree
2. Establish criteria
3. Weight the criteria
4. Weight sub-criteria (elements) - equals to 1 for each criteria
5. Calculate PRN - partial relevance numbers PRN = Criterion wt. x element wt.
6. Calculate LRN - local relevance number for each element (sum of PRNs for that element)
7. Calculate CRNs - Cum. relevance numbers for each element (CRN = sum of LRNs for each associated element
5.