Get Started. It's Free
or sign up with your email address

1. Quantitative Methods

1.1. Quantitative forecasting models - Use mathematical techniques - Based on historical data - Can include causal variables - Less accurate as the forecast’s time horizon increases

1.1.1. Time Series Forecasting Models Assumption: The future is an extension of the past  Historical data can be used to predict future demand. Naïve Forecast the estimate for the next period is equal to the actual demand for the immediate past period. Ft+1 = At Where Pros Cons Simple Moving Average Forecast uses historical data to calculate a moving average and works well when the demand is fairly stable over time. Actual demand for period t +1 equal to Sum of actual demand from t to t-n+1 devided by n( number of periods used to calculate moving average) t>n Pros Cons Weighted Moving Average Forecast An n-period weighted moving average forecast is the weighted moving average of the n-period observations, using unequal weights. Formular Pros & cons Exponential Smoothing Forecast the forecast for the next period’s demand is the current period’s forecast adjusted by a fraction of the difference between the current period’s actual demand and forecast. Ft+1= Ft + a(AF) = aA + (1-a)Ft) When: Ft=forecast for period t A = actual demand for period t a = smoothing constant (0 ≤ a ≤1) Pros Cons Linear Trend Forecast using simple linear regression (trend line) to fit a line to a series of data occurring over time. Formula: The trend line equation Ŷ = bo + b₁x Ŷ= forecast or dependent variable x = time variable, also independent variable values b₁ = n Σ(xy)-Σx Σy, by = Σy-b₁ Ex y dependent variable values n = No. of observations Pros Cons

2. Forecast Accuracy

2.1. Forecast Error

2.1.1. is the different between the actual quantity and the forcast

2.1.2. et = At - Ft Cause-and-Effect Forecasting Models Assumption: One or more factors (independent variables) are related to demand  Can be used to predict future demand. Simple Linear Regression Forecast Multiple Regression Forecast

2.1.3. Where At: actual demand for period t et: forcast error of period t Ft: forcast for period t

2.2. Measuare

2.2.1. MAD( mean absoltedeviation)

2.2.2. MAPE( Mean absolute percentage error)

2.2.3. MSE( Mean square error)

3. Qualitative Methods

3.1. Qualitative Forcasting Models

3.1.1. Jury of Executive Opinion A meeting of SME to forcast the market Apply for long range planning & new product introductions, general demand forcasting Pros: Knowledgable and exprienced => Forecast valuable Cons: value & realibity of outcome can be disminished

3.1.2. Delphi Method From Round 1(expert respond) => Round 2(expert respond) => Round n( expert respond)=> Final round( reac consensus) Apply for: H-Risk technology forcasting, large, expensive project, major new product introduction Pros:Group members do not physically meet  avoid the scenario where one or a few experts could dominate a discussion Cons: time- consuming and very expensive

3.1.3. Sales Force Composite Based on the knowledge of sales team about the market and estimates of customer needs. Apply for: all kind of projects Pros: The forecast tends to be reliable because salespeople are close to customers. Cons: Individual biases could negatively impact the effectiveness of this approach.

3.1.4. Customer Surveys 1. Design a forecasting questionnaire. 2. Choose the target population. 3. Carry out the survey through telephone, mail, Internet, or personal interviews. 4. Collect and analyze data. 5. Make forecasts from the results.