demand planning

Demand planningForecasting NeedThe process of forecasting need (or demand planning) is a means to improve the general effectiveness of an organization. In particular, forecasting need is designed to allow an organization to: · Schedule resources (such as production facilities, means of transportation, available capital, and personnel) more efficiently. · Acquire resources (such as raw materials, machines, and employees) in a timely fashion. · Determine resource requirements in advance. CannibalismWhen you enter an event on a forecast part (see Forecast Graph) you can do a cannibal analyzes on how this event is expected to affect other parts. For example if you are promoting part A and this promotion is expected to lead to an increase of 35% in the sale of part A. Then the question how will this affect the sales of part B which is similar to part A. This cannibal analyze tool is meant to give you a suggestion in how the sale of B is likely to be effected.When you select the Cannibalism check the system will show you a dialog where you are asked to enter which parts in the current flow you want to check for cannibalism. I this dialog you can select if you only want to run the cannibal check on parts belonging to the same group as the cannibal part (the cannibal part is the part that has the campaign/event planned; you must select this part when selecting cannibal check form the menu). You can only select one group (1.6), you can not select multiple groups. E.g. it is possible to check for cannibalism on all parts belonging to the same part product family as the cannibal part. It is not possible to check for cannibalism on parts that belong to the same part product family and that have the same ABC class as the cannibal part. You can only select one group. It is possible to do a cannibal check of all parts in a flow.  You can only do a cannibalism check on parts that are in the same flow. The result of the cannibal flow is displayed as an event in the same period as the events in the cannibal part. The events on the checked parts can be both negative and positive, big or large depending on the found correlation with the cannibal part.There are also two fields in detail view associated with the cannibal check. · The cannibal part which shows the part no that are the current parts cannibal part. The cannibal part is the part that have the original planned campaign activity. · Cannibal correlation is the correlation number that sates how strongly the parts historical sales are with the historical sales of the cannibal part. If a part has already an event value defined in one of the cannibal campaign period, then that part gets no cannibal effect calculated in that period. This means that you can only run a cannibal run for one part per period within the same group selection. Note that the detail view field cannibal correlation is set always even if the part checked has not got an cannibal event adjustment (due to previous event value in that period). The cannibal part field is only set if the part has got an cannibal event adjustment. This allows the user to see which parts that have got an cannibal adjustment and at the same time check the correlation of the parts that don't get cannibal adjustments.After a cannibal check is preformed all parts belonging to the cannibal run will be sorted according to the cannibal correlation factor at the top of the list, the cannibal part will be at the top. The most effected part will be second and so on.Note that the cannibal part has to have at least half a year of historical demand to run the cannibal check.The cannibal equationWe use the regression formula to calculate the cannibalism effectWhere x is the cannibal part and F is the part checked for cannibalism. When A is negative the parts are negative correlated this means that when you sell 1 more of part x (the cannibal part), then the sales of part F is decreased by A units.The cannibal correlation is calculated based on the correlation formula.Forecast ModelsGeneralThe following eleven forecast models are available: 1. Manual forecast (based upon yearly demand)2. Simple moving average3. Exponential smoothing with level4. Exponential smoothing with level and trend5. Naive6. Adaptive exponential smoothing with level7. Regression line (least squares)8. Best fit (finds the best of the seven forecast models in a known historical period)9. Browns smoothing with level and trend10. Croston's intermittent11. Multiple regression12. BayesianAll these forecast models except naive and multiple regression can be combined with periodic seasonal indexes. Multiplying the basic forecast calculated for a period with the current index value for the same period results in the final forecast. Seasonal indexes are used whenever there is seasonal fluctuation in demand. These indexes are explained in detail in Season Indexes.IntroductionNotationYExpected yearl