Foreasting

Question 1

what is forecasting

Answer 1

the art and science of predicting future events.

Question 2

what are the forecasting time horizons.

Answer 2

1. Short-range forecast
   * Up to 1 year, generally less than 3 months
   * Purchasing, job scheduling, workforce levels, job assignments,
   production levels
2. Medium-range forecast
   * 3 months to 3 years
   * Sales and production planning, budgeting, cash budgeting, analysis of operation plans.
3. Long-range forecast
   * 3+ years
   * New product planning, facility location, capital expenditures, research
   and development

Question 3

what are the differences in the horizons?

Answer 3

1. Medium/long range forecasts deal with more comprehensive issues and
   support management decisions regarding planning and products, plants
   and processes
2. Short-term forecasting usually employs different methodologies than
   longer-term forecasting
3. Short-term forecasts tend to be more accurate than longer-term
   forecasts

Question 4

types of forecasting

Answer 4

1. Economic forecasts
   * Address business cycle – inflation rate, money supply, housing
   starts, etc.
2. Technological forecasts
   * Predict rate of technological progress
   * Impacts development of new products
3. Demand forecasts
   * Predict sales of existing products and services

Question 5

strategic importance of forecasting

Answer 5

Critically important in all aspects of a business: The forecast is the only estimate of
demand until actual demand becomes known. Forecasts of demand therefore drive
decisions in many areas.
* Supply Chain Management – Good supplier relations, advantages in
product innovation, cost and speed to market
* Human Resources – Hiring, training, laying off workers
* Capacity – Capacity shortages can result in undependable delivery,
loss of customers, loss of market share

Question 6

7 steps in forecasting?

Answer 6

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

Question 7

realities in forecasting

Answer 7

• Forecasts are seldom perfect; unpredictable outside factors may
  impact the forecast
• Most techniques assume an underlying stability in the system
• Product family and aggregated forecasts are more accurate
  than individual product forecasts

Question 8

what are the two forecasting approaches and when are they used

Answer 8

qualitative methods are used when the situation is vague and there is little data.
-new products
-new technology
involves intuition and experience.

qualitative methods are used when the situation is more stable and there is historical data.
- existing products
- current technology
it involves mathematical techniques

combination of the two is the most effective

Question 9

what are the 4 qualitative methods

Answer 9

1. Jury of executive opinion
   * Pool opinions of high-level experts, sometimes augmented by statistical
   models
2. Delphi method
   * Panel of experts (decision makers, employees/staff and
   respondents/customers) queried iteratively
3. Sales force composite
   * Estimates from individual salespersons are reviewed for reasonableness,
   then aggregated
4. Market Survey
   * Ask the customer

Question 10

explain the jury of executive opinion

Answer 10

• Involves small group of high-level experts and managers
• Group estimates demand by working together
• Combines managerial experience with statistical models
• Relatively quick
• ‘Group-think’ disadvantage

Question 11

Delphi method?

Answer 11

• Iterative group process, continues
  until consensus is reached
• Three types of participants
• Decision makers - experts who do the forecast
• Staff - they prepare collect distribute and summarize a series of questionnaires
• Respondents - group of people located in varied places who respond.

Question 12

Sales force composite

Answer 12

• Each salesperson projects his or her sales
  *reviewed to see if realistic
• Combined at district and national levels to reach overall forecast
• Sales reps know customers’ wants
• May be overly optimistic

Question 13

Market survey

Answer 13

• Ask customers about purchasing plans
• Useful for demand and product design and planning
• What consumers say and what they actually do may be different
• May be overly optimistic

Question 14

what are the quantitative methods and what models are they grouped into

Answer 14

time-series model
-naïve approach
-moving averages
-exponential smoothing
-trend projection

associative model
-linear regression

Question 15

time series models

Answer 15

work on the assumption that the future is a function of the past. use past data to predict the future.

Question 16

associative models

Answer 16

such as linear regression use variables that might influence the the variable being forecasted.

Question 17

time series forecasting

Answer 17

• Set of evenly spaced numerical data points. (monthly, daily, weekly, etc.) eg. weekly sales.
• Obtained by observing response variable at regular time
  periods
• Forecast based only on past values, no other variables important
• Assumes that factors influencing past and present will continue
  influence in future

Question 18

time series components

Answer 18

trend - gradual upward or downward movement
cyclical - patterns that occur every several years. ties to the business cycle.
seasonal - repeats itself over a series of set time. there are 6 patterns.
random - erratic, unsystematic.

Question 19

Naive approach

Answer 19

• Assumes demand in next period is the same as
  demand in most recent period
• e.g., If January sales were 68, then February
  sales will be 68
• Sometimes cost effective and efficient
• Can be good starting point

Question 20

Moving averages

Answer 20

• MA is a series of arithmetic means
• Used if little or no trend
• Used often for smoothing
• Provides overall impression of data over time

Question 21

weighted moving averages

Answer 21

• Used when some trend might be present
• Older data usually less important
• Weights based on
  experience and
  intuition

Question 22

Potential problems with moving averages

Answer 22

1. Increasing n(number of periods averaged) smooths the forecast
   but makes it less sensitive to changes
2. Does not forecast trends well
3. Requires extensive historical data
4. Form of weighted moving average
   * Weights decline exponentially
   * Most recent data weighted most
5. Requires smoothing constant (α)
   * Ranges from 0 to 1
   * Subjectively chosen
6. Involves little record keeping of past data

Question 23

Focus forecasting

Answer 23

• Developed at American Hardware Supply, based on two
  principles:
  1. Sophisticated forecasting models are not always better
  than simple ones
  2. There is no single technique that should be used for all
  products or services
• Uses historical data to test multiple forecasting models for
  individual items
• Forecasting model with the lowest error used to forecast the
  next demand

Question 24

challenges presented with forecasting in the service sector

Answer 24

• Special need for short-term records
• Needs differ greatly with function of industry and product
• Holidays and other calendar events
• Unusual events