| CIN Formulae-COPYING REQUIREMENTS | | 2008-04-08 07:45:04 | | OPYING REQUIREMENTSFormulaWhere is itWhen is it calledWhat does it do310Copy delivery to Billing in copy rules for required item categoriesCreation of billingFactory – Ensure that J1II is done after JEX before F2· Check for chapter-id and only then proceed· If proforma document continue.· If invoice then get proforma document· Check if the document type is the CIN reference· Check for excise invoice for the proforma billingDepot – Ensure that J1IJ is done before F2Check in RG23D for the delivery document. Proceed only if it exists311Copy delivery to Billing in copy rules for required item categoriesCreation of billingRelevant only for factory- Ensures that GI is done before – JEX (Proforma billing). Stops multiple JEX from being created· Check if PGI is done and give error· Check in VBFA whether proforma documents already exist and give error(Proforma document- doc type from CIN customizing)CIN Formulae-DATA TRANSFERCIN Formulae-COPYING REQUIREMENTSSAP SD CIN Configurable | | By: Free Download SAP Sales And Distribution(SD) Books | | |
|
| CIN Formulae-DATA TRANSFER | | 2008-04-08 07:44:47 | | DATA TRANSFERFormulaWhere is itWhen is it calledWhat does it do351Copy delivery to Billing in data transfer routine for required item categoriesCreation of billing· Get regid for the delivering plant· Get no of items from regid customizing· Get the chapter-id for the material· Enhance ‘ZUK’ structure for splitting with 2 and 3 aboveCIN Formulae-DATA TRANSFERCIN Formulae-COPYING REQUIREMENTSSAP SD CIN Configurable MessagesCIN Transaction Data J_1IEXCHDRCIN Transaction Data J_1IEXCDTLCIN Transaction Data J_1IGRXREFCIN Transaction Data J_1IGRXSUBCIN Transaction Data J_1IPART1CIN Transaction Data J_1IPART2CIN Transaction Data J_1IRG1CIN Transaction Data J_1IRGSUMCIN Transaction Data J_1IRG23DFree SAP CIN Transaction Data J_1ITDSFree SAP CIN-SecurityCIN FI - Authorization objectsSAP CIN-Roles-Master Roles | | By: Free Download SAP Sales And Distribution(SD) Books | | |
|
| Forecast Formulae Overview | | 2008-03-31 08:46:44 | | In this section, a description is given of the formulae which form the basis of the forecast. The following formulae are displayed in detail:formula for the forecast models formula for the evaluation of the forecast formula for calculating the reorder level and safety stockFormula for Calculating the Reorder Level Forecast...Formula for Calculating the Safety Stock Forecast ...Formula for Evaluating the ForecastModel: Second-Order Exponential Smoothing Forecast...General Formula for First-Order Exponential Smooth...Model: Constant Forecast FormulaeModel: First-Order Exponential Smoothing Forecast ...Model: Weighted Moving Average Forecast FormulaeModel: Moving Average Forecast FormulaeForecast Formulae Overview | | By: Free Download SAP MM Books And Interview Questions | | |
|
|
|
| Model: Moving Average Forecast Formulae | | 2008-03-31 08:46:27 | | This model is used to exclude irregularities in the time series pattern. In order to this, the average of the n last time series values is calculated. The average can always be calculated from n values according to formula (1). Therefore, the new average is calculated from the previous average and the current consumption value weighted with 1/n, minus the oldest consumption value weighted with 1/n.It is only worth using this procedure for time series which are constant, that is, for time series with no trend-like or season-like patterns. As all historical data is equally weighted with the factor 1/n, it takes precisely n periods until the forecast can adapt to a possible level change.Formula for Calculating the Reorder Level Forecast...Formula for Calculating the Safety Stock Forecast ...Formula for Evaluating the ForecastModel: Second-Order Exponential Smoothing Forecast...General Formula for First-Order Exponential Smooth...Model: Constant Forecast FormulaeModel: First-Order Expon | | By: Free Download SAP MM Books And Interview Questions | | |
|
| Model: Weighted Moving Average Forecast Formulae | | 2008-03-31 08:46:07 | | You achieve better results than those received from the moving average model by introducing weighting factors for each past value. This means that every past value is weighted with the factor R. The sum of the weighting factors is 1 (see formulae (3) and (4) below). If the time series to be forecast contains trend-like variations you will receive better results by using the weighted moving average model rather than the moving average model. The reason for this is that more weight is given to more recent data when determining the average than to older data, that is, if you selected appropriate weighting factors. Therefore, the system will be able to react more quickly to a change in level.This model, however, depends strongly on your choice of weighting factors. If the time series pattern changes, you must also adapt the weighting factors.Formula for Calculating the Reorder Level Forecast...Formula for Calculating the Safety Stock Forecast ...Formula for Evaluating the ForecastModel: S | | By: Free Download SAP MM Books And Interview Questions | | |
|
| Model: First-Order Exponential Smoothing Forecast Formulae | | 2008-03-31 08:45:41 | | The ideas behind this model are:The older the time series values, the less important they become for the calculation of the forecast. The present forecast error is taken into account for the following forecasts.Formula for Calculating the Reorder Level Forecast...Formula for Calculating the Safety Stock Forecast ...Formula for Evaluating the ForecastModel: Second-Order Exponential Smoothing Forecast...General Formula for First-Order Exponential Smooth...Model: Constant Forecast FormulaeModel: First-Order Exponential Smoothing Forecast ...Model: Weighted Moving Average Forecast FormulaeModel: Moving Average Forecast FormulaeForecast Formulae Overview | | By: Free Download SAP MM Books And Interview Questions | | |
|
|
|
| Model: Constant Forecast Formulae | | 2008-03-31 08:45:13 | | From these two points mentioned above, the constant model of exponential smoothing can be derived (see formula (5)). In this case, the formula is used for calculating the basic value. A straight forward derivation produces the basic formula for exponential smoothing (see formula (6)). To determine the forecast value, you only require the preceding forecast value, the last past consumption value and the so-called smoothing factor, alpha. This smoothing factor is responsible for weighting the most recent consumption values more than the past values so that they have a stronger influence on the forecast.How quickly the forecast should react to a change in consumption pattern depends on your choice of smoothing factor. If you select 0 for alpha then the new average will be equal to the old one. In this case, the basic value calculated previously remains, that is, the forecast does not react to current consumption data. If you select 1 for the alpha value, the the new average will equal | | By: Free Download SAP MM Books And Interview Questions | | |
|
| General Formula for First-Order Exponential Smoothing Forecast Formulae | | 2008-03-31 08:44:52 | | Using the basic formula derived above (6), the general formula for first-order exponential smoothing (7) is determined by taking both trend and seasonal variations into account. Here, the basic value, the trend value and the seasonal index are calculated as displayed in formulae (8) - (10). Below is a legend for the formulae. Formula for Calculating the Reorder Level Forecast...Formula for Calculating the Safety Stock Forecast ...Formula for Evaluating the ForecastModel: Second-Order Exponential Smoothing Forecast...General Formula for First-Order Exponential Smooth...Model: Constant Forecast FormulaeModel: First-Order Exponential Smoothing Forecast ...Model: Weighted Moving Average Forecast FormulaeModel: Moving Average Forecast FormulaeForecast Formulae Overview | | By: Free Download SAP MM Books And Interview Questions | | |
|
| Model: Second-Order Exponential Smoothing Forecast Formulae | | 2008-03-31 08:44:35 | | If, over several periods, a time series shows a change of the average value which corresponds to the trend model, the forecast values always lag behind the actual values by one or several periods in the first-order exponential smoothing procedure. You can achieve a more efficient adjustment of the forecast to the actual consumption values pattern by using the second-order exponential smoothing procedure.The second-order exponential smoothing model is based on a linear trend and consists of two equations (see formulae (11) below). The first equation corresponds to that of first-order exponential smoothing except for the indices in brackets. In the second equation, the values calculated in the first equation are used in the second equation as initial values and are smoothed again. Formula for Calculating the Reorder Level Forecast...Formula for Calculating the Safety Stock Forecast ...Formula for Evaluating the ForecastModel: Second-Order Exponential Smoothing Forecast...General Formula | | By: Free Download SAP MM Books And Interview Questions | | |
|
| Formula for Calculating the Safety Stock Forecast Formulae | | 2008-03-31 08:43:45 | | The safety stock depends on the service level that you specified in the MRP II view of the material master record and on the accuracy of the forecast. The more accurate the forecast, the smaller your safety stock can be. The following figure shows that, without safety stock, customer demand can be satisfied by 50%. It also shows that it is almost impossible to satisfy customer demand 100% of the time. Factor R describes the relationship between forecast accuracy and service level (SL).If replenishment lead time is greater than the forecast period by factor W, then the mean absolute deviation (MAD) is recalculated for this period (formula 17). The MAD is a parameter of forecast accuracy. Otherwise, see formula 18.Safety Stock (SS) Formulas If the material is produced in-house, the delivery time is: opening period + in-house production time + goods receipt processing time. It is expressed in workdays. The forecast period is taken from the material master record and is also expressed in w | | By: Free Download SAP MM Books And Interview Questions | | |
|
| Formula for Calculating the Reorder Level Forecast Formulae | | 2008-03-31 08:43:24 | | The reorder level is defined as the sum of the safety stock plus the requirement forecast within the replenishment lead time (see formula (17)).A forecast was carried out on a monthly basis. A month has 30 days in the case of external procurement. Safety stock 100 Forecast 1st subsequent period 200 2nd subsequent periode 00 3rd subsequent period 400 Replenishment lead time 40 days 0/30 30/30 + 10/30 (an entire monthly requirement + a part of the the following month)Reorder level = 100 + 30/30 * 200 + 10/30 * 300 = 400Formula for Calculating the Reorder Level Forecast...Formula for Calculating the Safety Stock Forecast ...Formula for Evaluating the ForecastModel: Second-Order Exponential Smoothing Forecast...General Formula for First-Order Exponential Smooth...Model: Constant Forecast FormulaeModel: First-Order Exponential Smoothing Forecast ...Model: Weighted Moving Average Forecast FormulaeModel: Moving Average Forecast | | By: Free Download SAP MM Books And Interview Questions | | |
|
| Common Formulae: Part Two | | 2007-04-25 14:58:00 | | I saw this company featured on PBS last night in a program on renewable energy. I'm sure there are similar companies, but this one was singled out as being effective.It got me thinking whether there is a formula that would fit this aspect of Long Island's health into the Long Island 2.0 concept (among other concepts we've explored).Maybe in part the formula can be constructed as follows:If there are x number of government-education-other public buildings and there is y square feet of available space for solar panels then Long Island public facilities can generate z amount of power at bb rate minus cost of installation/cost of power and or federal or state subsidies (if any).Of course the PBS program focused on how Sun Edison worked with local business to lessen the impact of peak power needs, so this partial formula could be applied there as well.Here is LIPA's current solar energy program. I don't know how they determine the upside of solar or if a program like Sun Edison's is p | | By: Long Island Idea Factory | | |
|
| Common Formulae: Part One | | 2007-04-17 17:21:00 | | If we are successful in coordinating data and creating some sort of "common language" (as described in previous posts), in some way on Long Island can we construct a series of formulae (however ultimately defined) which will aid us in our analysis and ultimately in our decision-making process?I am using formulae in the broadest sense of the word here and only as a concept (since I make no claims on any expertise in this area!).For example to determine the number of housing units linked to job growth needed on Long Island maybe a partial formula could include:If reasonable workforce housing should cost between $ and $$$ and if X acres of land that can be developed is available on Long Island and Y number of people current reside on Long Island and Z number of people is the sustainable population limit of Long Island and there is XX number of land parcels that can be developed for business development on Long Island and Long Island Business employs YY number of people and there is sustai | | By: Long Island Idea Factory | | |
|
