private void initLearningMetrics() {
if (learningMetricsInitialized) {
return;
}
double defaultWeight = SystemDao.getDefaultWeight();
double lastWeekLift = getDemandUplift(SystemDao.getReviewCycleStartDate());
Sales beginOfRunCycleSales = getSales(SystemDao.getReviewCycleStartDate());
double beginOfRunCycleRcAvgSales = 0;
if (beginOfRunCycleSales != null) {
beginOfRunCycleRcAvgSales = beginOfRunCycleSales.getRcAvgSales();
}
rcAvgSales =
getWeightedWeight1(learningWeekCounter, defaultWeight) * epSales / lastWeekLift
+ (1 - getWeightedWeight1(learningWeekCounter, defaultWeight))
* beginOfRunCycleRcAvgSales;
// Tim's documentation states:
// Its important sales metrics are initialized to AVG_WEEKLY_SALES based on the initialization
// logic.
// This estimates initial values based on different combinations of the PLs product and location
// hierarchies dependent on the specific situation for that PL
rcAvgSalesActual = rcAvgSales;
rcAvgDemand = rcAvgSales;
rcOldAvgDemand = rcAvgSales;
rcAvgDemandActual = rcAvgSales;
epAvgInv = rcAvgSales;
rcWass2 = Math.pow(rcAvgSales, 2);
learningMetricsInitialized = true;
}
// Hook: MSSalesOutlierCheckHook.java
public void performOutlierProcessing(String locId) {
// only apply constraint if we are not in a seasonal period
System.out.println(
"Starting the Outlier/Unreported Sales Calculation for product/location: " + locId);
int specialPuchaseOrderWassMult = SystemDao.getSpecialPurchaseOrderWassMultiplier();
int specialPuchaseOrderSizeMult = SystemDao.getSpecialPurchaseOrderSizeMultipler();
double rcWass2 = Math.pow(rcAvgSales, 2);
Double maxValue =
Math.max(
rcAvgSales + specialPuchaseOrderSizeMult * Math.sqrt(Math.max(0, rcWass2)),
specialPuchaseOrderWassMult * innerPackQty);
if (epSalesActual > maxValue && eventSeasonalIndicator == false) {
epSales = maxValue;
} else {
epSales = epSalesActual;
}
}