proje
1)It is applied to predict backhoe loader sales number.
Make a guess about how much backhoe loader will be sold, provides us to assist managing inventories specifically, to be gained a vision for marketing department and sales engineers, to be planned a strategy by Managers.
2) The quarters has been assigned time periods. Each quarters corresponds to different months
3) In order to implement seasonal exponential smoothing to this data, we have to follow this steps.
4) In this step, in order to begin algorithm, the initial values for the smoothed series (Lt ), the trend (bt), seasonal estimate (St) must be set.
In determination of initial values, the method we will use is to develop a regression equation. We using Excel Data Analysis Add-Ons. seasonal index set to one.
5) As you can see in here, Table -3 is the result of data initilazition.
6) The weights of constants are selected randomly.
7) After selection of weights, we implement formulas to the data with excel format.
Algorithm is working!
8) Before to minimize forecast error which is MSE , we have to calculate it even if not optimize.
9) The weights α, β and γ can be selected subjectively or generated by minimizing a measure of forecast error.We chose the second one, this smoothing constants of winter’s method are optimized using MS Excel Solver.
10) Algorithm is ready now to forecast sales number for next 5 quarter.
Lets see big picture of this forecast.
11) It couldn’t say perfect forecasting bacuse of too much variation. When a pattern catches, just then,, we met unexpected sales number.
I have admitted that this algorthim don’t fit exactly with our sales. At least, gives us a chance to make a guess.
Make a guess about how much backhoe loader will be sold, provides us to assist managing inventories specifically, to be gained a vision for marketing department and sales engineers, to be planned a strategy by Managers.
2) The quarters has been assigned time periods. Each quarters corresponds to different months
3) In order to implement seasonal exponential smoothing to this data, we have to follow this steps.
4) In this step, in order to begin algorithm, the initial values for the smoothed series (Lt ), the trend (bt), seasonal estimate (St) must be set.
In determination of initial values, the method we will use is to develop a regression equation. We using Excel Data Analysis Add-Ons. seasonal index set to one.
5) As you can see in here, Table -3 is the result of data initilazition.
6) The weights of constants are selected randomly.
7) After selection of weights, we implement formulas to the data with excel format.
Algorithm is working!
8) Before to minimize forecast error which is MSE , we have to calculate it even if not optimize.
9) The weights α, β and γ can be selected subjectively or generated by minimizing a measure of forecast error.We chose the second one, this smoothing constants of winter’s method are optimized using MS Excel Solver.
10) Algorithm is ready now to forecast sales number for next 5 quarter.
Lets see big picture of this forecast.
11) It couldn’t say perfect forecasting bacuse of too much variation. When a pattern catches, just then,, we met unexpected sales number.
I have admitted that this algorthim don’t fit exactly with our sales. At least, gives us a chance to make a guess.