OM - GB335 - Chapter 4 Flashcards
Forecasting
Process of predicting a future event, and is the underlying basis of all business decisions
Forecasting Time Horizons
- Short-Range
- Medium-Range
- Long-Range
is influenced by the product life cycle. Into and growth require longer forecasts than maturity and decline.
Types of Forecasts
- Economic
- Technological
- Demand
Stategic Importance of Forecasting
- Human Resources
- Capacity
- Supply-Chain Management
7 Steps in Forecasting
- Determine the use of the forecast
- select the items to be forecasted
- Determine the time horizon of the forecast
- select the forecasting model(s)
- gather the data
- Make the forecast
- Validate and implement results
Forecasting Approaches: Quantitative
Used when situation is stable and historical data exist, and involves mathematical techniques
Forecasting Approaches: Qualitative
Used when situation is vague and little data exists, and involves intuition, experience
Forecasting Approaches: Qualitative Methods
- Jury of executive Opinion (pool opinions of high-level experts)
- Delphi Method (panel of experts, queried iteratively)
- Sales force composite
- Consumer Market Survey
Forecasting Approaches: Quantitative Methods
Time Series Models 1. Naive Approach 2. Moving Averages 3. Exponential Smoothing 4. Trend Projection Associative Model 5. Linear Regression
Naive Approach
Assumes demand in the next period is the same as demand in most recent period
can be cost-effective, and can be a good starting point
Moving Average Method
is a series of arithmetic means, used often for smoothing and provides an overall impression of data over time
Exponential Smoothing
a form of weighted moving average, qhich requires smoothing constant and involves little record keeping of past data
Trend Projections
Fitting a trend line to historical data points to project into the medium to long-range
Time Series Forecasting
set of evenly spaced numerical data obtained by observing response variable at regular time periods
forecast based on only past values, no other variables important, assumes that factors influencing past and present will continue influence in future
Time Series Components
- Trend
- Cyclical
- Seasonal
- Random
Time Series Components: Trend
- persistent, overall upward or downward pattern
- changes due to population, technology, etc.
- Typically several years duration
Time Series Components: seasonal
- Regular pattern of up and down fluctuations
- Due to weather, customs, etc.
- Occurs within a single year
Time Series Components: Cyclical
- Repeating up and down movements
- Affected by business cycle, political, and economic factors
- Multiple years duration
- Often causal or associative relationships
Time Series Components: Random
Erratic, unsystematic, residual fluctuations
- due to random variation or unforeseen events
- short duration and nonrepeating
Associative Forecasting
used when changes in one or more independent variables can be used to predict the changes in the dependent variable
Adaptive Forecasting
its possible to use the computer to continually monitor forecast error and adjust the values used in exponential soothing to continually minimize forecast error ( adaptive smoothing )
Focus Forecasting
Based on two principles:
- sophisticated forecasting models are not always better than simple ones
- there is no single technique that should be used for all products or services
- this approach uses historical data to test multiple forecasting models for individual items
- The forecasting model with the lowest error is then used to forecast the next demand
Forecasting in the Service Sector
Presents unusual challenges
- special need for short-term records
- needs differ greatly as function of industry and product
- Holidays and other calendar events
- unusual events
