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25 Cards in this Set

  • Front
  • Back
What is forecasting
Process of predicting a future event
underlying basis of all business decisions(production, inventory, personnel, facilities)
Types of Forecasts by Time Horizon
Short Range Forecast: up tp 1 year, less than 3 months
Job scheduling, worker assignments
Medium Range: 3 months to 3 years
Sales and production planning, budgeting
Long Range: 3+ years,
new product planning, facility location
Short vs Medium vs Long term forecasting
Medium/long: forecasts deal with more comprehensive issues and support management decisions regarding planning and products, plants and processes
Short Term: forecasting usually employs different methodologies than longer term forecasting
Short Term: forecasts tend to be more accurate then longer term forecasts
Influence of Product lifestyle
Introduction, growth, maturity, decline
Stages of intro and growth require longer forecasts than maturity and decline
Forecasts useful in projecting: staffing levels, inventory levels, factor capacity
Strategy in Product Life Cycle: Introduction
Introduction: Best period to increase market share, R&D product engineering critical- develop design of product, short production runs
high production costs
limited models
attention to quality
PLC: Growth
Practical to change price or qualtiy image, strengthen niche
forecasting critical
product and process reliability
competitive product improvements and options
increase capacity
shift toward product focused
enhance distribution
PLC: Maturity
Poor time to change image, price, quality
Competitive costs become critical
defend market position
Standardization
Less rapid production
Optimum capacity
increasing stability of process
long term production runs
Product improvement and cost cutting
PLC: Decline
Cost control critical
Little product differentiation
Cost minimization
Over capacity in the industry
Prune line to elimiate items not returning good margin
Reduce capacity
Types of Forecasts:
Economic Forecasts: adress business cycle eg. inflation rate, money supply
Technological Forecasts: predict rate of tech progress
Predict acceptance of new product
Demand Forecast: predict sales of existing product
Seven Steps in Forecasting
Determine the use of forecast
select items to be forecasted
determine time horizon of forecast
select forecast model
gather data
make forecast
validate and implement results
Forecasting Approaches
Qualitative Method:
Used when situation is vague and little data exists(new product, new tech)
Involves intuition experience( eg forecasting sales on internet)
Quantitative Methods:
Used when situation is "stable" and historical data exists( existing products/ current tech)
Involves mathematical techniques(forecasting sales of color tv's)
Overview of Qualitative Methods
Jury of executive opinion: pool opinions of high level execs, sometimes augment w/ stat models
Delphi Method: Panel of experts, queried
Sales Force Composite: estimates from individual salespersons are reviewed for reasonableness, then aggregated
Consumer Market Survey: ask the consumer
Delphi Method
Iterative group process
3 types of ppl, (decision makers, staff, respondents)
Reduces "group think"
What is a Time Series?
Set of evenly spaced numerical data( obtained by observing responsive variable at regular time periods
Forecast based only on past values( assumes factors influencing past and present will continue to influence future)
Time Series Components
Trend
Cyclical
Seasonal
Random
Trend Component
Persisten, overall upward or down pattern
due to population, tech
several years duration
Seasonal Component
Regular pattern of up/down fluctuations
Due to weather, customs
Occurs within 1 year
Cyclical Component
Repeating up/downs
due to interactions of factors influencing economy
usually 2-10 years in duration
Random Component
Erratic, unsystematic
Due to random variation or unforseen events(tornado, earthquake)
Short duration, non repeating
Naive Approach
Assumes demand in next period= demand in most recent period
Sometimes cost effective and efficient
Moving Average Method
MA is a series of arithmetic means
used if little or no trend
used for smoothing
equaiton= sum(demand in previous n periods)/n
Weighted Moving Average Method
Used when trend is present
weights based on intuition
equation:
sum(weight for period n)(demand in n period)/ sum of wieghts
Disadvantages Moving Averages
Increasing n makes forecast less sensitive to changes
Do not forecast trends well
Require much historical data
Exponential Smoothing Method
form of weighted moving average
weights decline exponentialy
requires smoothing constant: ranges from 0 to 1 ; subjectively chosen
Involves little record keeping of past data
Choosing smoothing constant
seek to minimize mean absolute deviation
forecast error=demand-forecast