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Chapter 9 SCM 304
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Terms in this set (20)
A. Forecast
An estimate of the future level of some variable.
a. Forecasting is used to determine
i. Long Term capacity needs
ii. Yearly business plans
iii. Shorter-term operations & supply chain activities
b. Forecast Types
i. Demand -
Overall market demand
Firm-Level demand
ii. Supply-
Number of current producers and suppliers
Projected aggregate supply levels
Technological and political trends
iii. Price-Forecast prices for key materials and services
c. Laws of forecasting
i. Law 1-Forecasts Are Almost Always Wrong (But They Are Still Useful).
ii. Law 2-Forecasts for the Near Term Tend To Be More Accurate.
iii. Law 3- Forecasts for Groups of Products or Services Tend to Be More Accurate.
iv. Law 4- Forecasts Are No Substitute For Calculated Values.
B. Selecting a Forecasting Method
...
a. Qualitative forecasting techniques
Forecasting techniques based on intuition or informed opinion.
i. Market surveys- structured questions submitted to potential customers, expensive, time consuming
ii. Panel Consensus forecasting-Panel of experts - group discussions, expensive, time consuming, pretty actuate
iii. Delphi Method-Individual expert - group discussions, expensive, time consuming, pretty actuate
iv. Life-cycle analogy method-method - Introduction, growth maturity cycle. Effective if replacing a previous product or service with the same market.
v. Build-up forecasts- sum of individual forecasts from area representatives.
b. Quantitative forecasting models
Forecasting models that use measurable or historical data to generate forecasts.Forecasting models that use measurable or historical data to generate forecasts.
i. Time Series forecasting models
A quantitative forecasting model that uses a time series to develop forecasts.
1. Demand patterns
a. Randomness-Unpredictable movement from one time period to the next.
b. Trend-.Long-term movement up or down in a time series.
c. Seasonality-A repeated pattern of spikes or drops in a time series associated with certain times of the year.
ii. Last Period model
The simplest time series model which uses demand for the current period as a forecast for the next period.
iii. Moving Average Model
A time series forecasting model that derives a forecast by taking an average of recent demand values.
iv. Weighted Moving Average Model
A form of the moving average model that allows the actual weights applied to past observations to differ.
v. Exponential Smoothing Model
A form of the moving average model in which the forecast for the next period is calculated as the weighted average of the current period's actual value and forecast
vi. Adjusted Exponential Smoothing Model
An expanded version of the exponential smoothing model that includes a trend adjustment factor.
vii. Linear Regression
A statistical techniques that expresses a forecast variable as a linear function of some independent variable
viii. Seasonality
Repeated patterns or drops in a time series associated with certain times of the year.
ix. Causal Forecasting Models
A class of quantitative forecasting models in which the forecast is modeled as a function of something other than time.
c. Measures of Forecast Accuracy
1. Forecast error for period
2. Mean forecast error (MFE)
3. Mean absolute deviation (MAD)
4. Mean absolute percentage error (MAPE)
5. Tracking Signal
C. Computer Based Forecasting
based forecasting packages are used to develop, evaluate and change forecasting models as needed.
D. CPFR - Collaborative Planning, Forecasting and Replenishment
A set of business processes, backed up by information technology, in which supply chain partners agree to mutual business objectives and measures, develop joint sales and operational plans, and collaborate to generate and update sales forecasts and replenishment plans.
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