Special Expressions and Procedures Used in APCS Configuration

AttributesPickListExpr - Attribute Picklist Expression

This procedure is used to determine a picklist of values from a base string measure along the positions of the specified hierarchy. It also takes in a Filter Boolean as input to control which intersections are to be used to pick data for the picklist.

Syntax:

OUTMEAS <- AttributesPickListExpr(FILTERMEAS, INPUTMEAS, HIER, OUTFORMAT)

Example:

ADHDLikePoCT <- AttributesPickListExpr(ADHDLikePoCB,ADHDLikePoCL, "LOC", "0")

Expression Parameters:

The following table shows the parameter details for the expression.

Parameter Name	Parameter Type	Data Type	Description
OUTMEAS	Output	String	Output Picklist Measure which will be a string value.
INPUTMEAS	Input	String	Input String Measure containing the picklist values stored along a hierarchy.
FILTERMEAS	Input	Boolean	Boolean measure with the same intersection of OUTMEAS. It should be set to true for the intersections picklist that needs to be created.
HIER	Input	String	Hierarchy Name specified as a String constant along which the picklist values need to be used. This hierarchy should be present in INPUTMEAS.
OUTFORMAT	Input	String	Output format with valid values "0" or "1". Using "0" will create a picklist using position names as picklist internal names and labels as attribute values. "1" will create a picklist using position values as name and labels for the picklist.

BopEopCalcExpr - Inventory Roll Calculation Expression

All planning applications typically need to create some batch rules to do inventory roll operations for all their actualized positions. Inventory roll calculations involve Beginning Inventory (BOP) and Ending Inventory (EOP) metrics, where BOP is calculated as lag of EOP (previous periods EOP) and EOP will be calculated from that period BOP plus additional metrics (Receipts, Sales, and so on). RPAS supports the use of the lag function to calculate BOP and creation of a separate expression to calculate EOP.

They normally run as cyclic rules in a batch rule group. Problem is this dual cyclic expression is highly performance intensive if it needs to roll inventory at the item/sku/store level. This is an optimized procedure to do inventory rollup calculations as a single expression instead of using them as cyclic expressions. This procedure can be used only in batch mode and should not be used in workbook calculation cycle rule groups.

Syntax:

EOP:<MEAS>,BOP:<MEAS> <- BopEopCalcExpr(TODAYINDEX:<MEAS>, INCEOP:<MEAS>, ACTEOP:<MEAS>, TSMASK:<MEAS>, INITIALBOP:<MEAS>)

Example:

EOP:ISWPEOPU,BOP:ISWPBOPU <- BopEopCalcExpr(TODAYINDEX:DRDVUnElapI, INCEOP:ISDVEOPU, ACTEOP:DRDVEOPU, TSMASK:DRDVEOPB, INITIALBOP:DRDVBOSU)

The above expression is equivalent to running the following two expressions in batch as a cyclic rule group:

ISWPBOPU = if (DRDVEOPB, if (current == first, DRDVBOSU, lag(ISWPEOPU)), ignore)

ISWPEOPU = if (DRDVEOPB, if (DRDVUnElapB, ISWPBOPU + ISDVEOPU, DRTYEOPU), ignore)

Expression Parameters:

The following table shows the parameter details for the expression.

Parameter Name	Parameter Type	Data Type	Description
EOP	Output	Real	Calculated Ending Inventory value measure.
BOP	Output	Real	Beginning Inventory value measure, same intersection as EOPMEAS.
ACTEOP	Input	Real	Actual Ending Inventory for Elapsed Periods. Same Intersection as BOP and EOP.
INCEOP	Input	Real	Incremental Inventory value for UnElapsed Periods. Same intersection as BOP and EOP.
TODAYINDEX	Input	Integer	Index of Current RPAS Today period. Periods above this will be copied with actual EOP, periods after this will be calculated with BOP and Incremental EOP. Base Intersection higher or equal to BOP/EOP but should not have the calendar dimension.
TSMASK	Input	Boolean	Intersections to which inventory roll calculations need to be performed. Same intersection as BOP and EOP but without calendar dimension.
INITIALBOP	Input	Real	First Period Beginning Inventory value measure. Same Intersection of BOP and EOP but without the calendar dimension.

ClusterExpr - Cluster Expression

This procedure is used to define a dynamic cluster based on input parameters.

Syntax:

POINTMEMBERSHIP: <MEAS>, POINTMEMBERSHIPSTR: <MEAS>, CENTROID: <MEAS> <- ClusterExpr(MEASURE: <MEAS>, METHOD: <STRING>, NUMCLUSTERS: <MEAS>, CLUSTERHIER: <HIER>, BYGROUPDIMS: <DIMENSION>)

Example:

POINTMEMBERSHIP: SCDVBangGrpID, POINTMEMBERSHIPSTR: SCDVBangGrpT, CENTROID: SCDVBangGrpX <- ClusterExpr(MEASURE: SCDVCmbIdxV, METHOD: "BANG", NUMCLUSTERS: SCHDMaxPrfGrpU, CLUSTERHIER: "LOC", BYGROUPDIMS: "CHNL")

Expression Parameters:

The following table shows the parameter details for the expression.

Parameter Name	Parameter Type	Data Type	Description
POINTMEMBERSHIP	Output	Integer	Output Cluster Group Id at Location level.
POINTMEMBERSHIPSTR	Output	String	Output Cluster Group String at Location level.
CENTROID	Output	Real	Output Centroid value at Aggregate level.
MEASURE	Input	Real	Input Score measure at Location level.
METHOD	Input	String	String constant with valid value, "BANG" for Bang Algorithm to use.
NUMCLUSTERS	Input	Integer	Number of Clusters to Create at Aggregate of Location level.
CLUSTERHIER	Input	String	String constant with hierarchy name of Location hierarchy for which cluster is created.
BYGROUPDIMS	Input	String	String constant of dimension name in Location hierarchy at Aggregate level.

CMNewItemSimilarityV2Expr - Item Similarity Expression

This procedure is a wrapper procedure used for calling AI Foundation functions which calculate the Item Similarity Scores based on Product Attributes and Product Attribute Weights. This similarity score can be internally calculated and used in Demand Transference procedures as input to determine the Demand Transference across similar items. It is also used in Assortment Improvement Procedures using Demand Transference. This procedure is not currently used in the current AP template version but examples are kept for customers using the non-template version.

Syntax:

SIM_OUT <- CMNewItemSimilarityV2Expr(SIM_IN, PROD_ATTR, ATTR_WGT, DT_MODE, FUNC_FIT, SAME_ITEM_MAP, VALID_ATTR, SIM_MODE)

Example:

BWHDSimOutVp <- CMNewItemSimilarityV2Expr(BWHDSimInV, ADDVPrdAttT, BWHDAttrWgtV, DRDVDTModeX, BWHDFuncFitB, DRDVSkupMapB, BWHDPrdAttB, DRDVSimModeV)

Expression Parameters:

The following table shows the parameter details for the expression.

Parameter Name	Parameter Type	Data Type	Description
SIM_OUT	Output	Real	Similarity score across LHS and RHS Items at Item/Cluster/RHS Item level.
SIM_IN	Input	Real	It is needed only if a different Similarity Mode other than 0 is used. For default Similarity Mode 0, this can be initialized to 0 which calculates similarity for all items.
PROD_ATTR	Input	String	Product Attribute values at Item/Product Attribute level.
ATTR_WGT	Input	Real	Attribute Weight at Item/Attribute level.
DT_MODE	Input	Integer	Demand Transference Mode to calculate the similarity scores, value of 4 is for calculating Bi-Directional Similarity Score, 3 for One Directional Score.
FUNC_FIT	Input	Boolean	Functional Fit Boolean at Sub-Class/Product Attribute level.
SAME_ITEM_MAP	Input	Boolean	Same Item mapping across LHS-RHS Items at LHS-RHS Item level.
VALID_ATTR	Input	Boolean	Product Attribute Eligibility at Sub-Class/Attribute level.
SIM_MODE	Input	Real	Similarity mode at sub-class level with default value as 0 for similarity values calculating for all items.

CMDemandSignTranV2Expr - Demand Transference Expression

This procedure is a wrapper procedure used for calling AI Foundation functions which in turn calculate the Demand Transferences and Substitutable Sales Units. It also calculates the significant similar items and substitutable sales percentage against each similar item based on the similarity scores. It internally calculates the similarity scores using the attribute inputs and uses it to calculate the Demand transference outputs. This procedure is not currently used in the current AP template version but examples are kept for customers using the non-template version.

Syntax:

DT_SIG_SKU_COUNT, DT_SIG_SUB_PCT, DT_SALES, DT_SUB_SALES <- CMDemandSignTranV2Expr(DT_MASK, SIM_OUT, ITEM_KAD, INPUT_SALES, SAME_ITEM_MAP, ASSORT_ELASTICITY, DT_CUTOFF, DT_GUARD, PROD_ATTR, ATTR_WGT, SIM_MODE, FUNC_FIT, VALID_ATTR)

Example:

BWHDDTSigSkuV, BWHDDTSigSkuVp, BWHDDTSlsU, BWHDDTSubSlsU <- CMDemandSignTranV2Expr(BWHDRunMaskB, BWH1SimOutVp, BWHDDTKADX, BWHDDTROSU, DRDVSkupMapB, BWHDAssrtElasV, DRDVDTCutOffVp, DRDVDTGuardB, ADDVPrdAttT, BWHDAttrWgtV, DRDVSimModeV, BWHDFuncFitB, BWHDPrdAttB)

Expression Parameters:

The following table shows the parameter details for the expression.

Parameter Name	Parameter Type	Data Type	Description
DT_SIG_SKU_COUNT	Output	Integer	Significant Substitutable Items count at Item/Cluster level.
DT_SIG_SUB_PCT	Output	Real	Significant Sku Substitutable Percentage at Item/Cluster/RHS Item level.
DT_SALES	Output	Real	Demand Transference ROS/Sales at Item/Cluster level (At same level of input ROS).
DT_SUB_SALES	Output	Real	Substitutable ROS/Sales at Item/Cluster level.
DT_MASK	Input	Boolean	Mask at Sub-class/Cluster level for which DT needs to be calculated.
SIM_OUT	Input	Real	It is needed only if different Similarity Mode other than 0 is used. For default Similarity Mode 0, this can be initialized to 0 procedure internally calculates similarity for all items.
ITEM_KAD	Input	Integer	Item Keep/Add/Drop Status with valid values as 1 for Add, 2 for Keep, and 3 for Drop Status of Items at Item/Cluster level.
INPUT_SALES	Input	Real	Initial Forecasted/Historical Sales or Rate of Sales at Item/Cluster level.
SAME_ITEM_MAP	Input	Boolean	Same Item mapping across LHS-RHS Items at LHS-RHS Item level.
ASSORT_ELASTICITY	Input	Real	Assortment Elasticity at Sub-Class/Cluster level.
DT_CUTOFF	Input	Real	DT Cut-Off % at Sub-Class/Cluster level.
DT_GUARD	Input	Real	DT Guard Boolean at Sub-Class/Cluster level.
PROD_ATTR	Input	String	Product Attribute values at Item/Product Attribute level.
ATTR_WGT	Input	Real	Attribute Weight at Item/Attribute level.
SIM_MODE	Input	Real	Similarity mode at sub-class level with default value as 0 for similarity values calculating for all items.
FUNC_FIT	Input	Boolean	Functional Fit Boolean at Sub-Class/Product Attribute level.
VALID_ATTR	Input	Boolean	Product Attribute Eligibility at Sub-Class/Attribute level.

CMAssortImprovementV2Expr - Assortment Improvement using DT Expression

This procedure is a wrapper procedure used for calling AI Foundation functions which in turn use the Demand Transferences and suggest the Improved Assortments after applying Demand Transferences based on the input assortment improvement parameters. This procedure is not currently used in the current AP template version but examples are kept for customers using the non-template version.

Syntax:

OPT_ASSORT, OPT_SALES <- CMAssortImprovementV2Expr(DT_MASK, INIT_SALES, SAME_ITEM_MAP, SIM_OUT, NEW_ITEM, INIT_ASSORT, IMPROVE_METRIC, ELIGIBLE_ITEM, IMPROVE_COUNT, MIN_TO_KEEP, OPT_MODE, ASSORT_ELASTICITY, ITEM_MANDATORY, OPT_TARGET, PROD_ATTR, ATTR_WGT, SIM_MODE, FUNC_FIT, VALID_ATTR)

Example:

BWHDDTImprAsrtB, BWHDDTImprAsrtU <- CMAssortImprovementV2Expr(BWHDRunMaskB, BWHDDTInitAsrtU, DRDVSkupMapB, BWH1SimOutVp, BWHDDTNewB, BWHDDTInitAsrtB, BWHDDTMetricImprV, BWHDDTEligB, BWHDDTImprAsrtV, BWHDMinKeepVp, BWHDDTOptModeV, BWHDAssrtElasV, BWHDDTMandB, BWHDDTOptTgtV, ADDVPrdAttT, BWHDAttrWgtV, DRDVSimModeV, BWHDFuncFitB, BWHDPrdAttB)

Expression Parameters:

The following table shows the parameter details for the expression.

Parameter Name	Parameter Type	Data Type	Description
OPT_ASSORT	Output	Boolean	Optimized Assortment after adding/removing items based on input parameters at Item/Cluster level.
OPT_SALES	Output	Real	Optimized Sales/ROS by calculating Improved Assortment after applying Demand Transference at Item/Cluster level.
DT_MASK	Input	Boolean	Mask at Sub-class/Cluster level for which Optimized Assortment needs to be calculated.
INIT_SALES	Input	Real	Initial Forecasted or Historical Sales or ROS at Item/Cluster level for all eligible items. It needs Sales/ROS for both historical and new items.
SAME_ITEM_MAP	Input	Boolean	Same Item mapping across LHS-RHS Items at LHS-RHS Item level.
SIM_OUT	Input	Real	It is needed only if different Similarity Mode other than 0 is used. For default Similarity Mode 0, this can be initialized to 0 procedure internally calculates similarity for all items.
NEW_ITEM	Input	Boolean	New Items within the Assortment at Item/Cluster level.
INIT_ASSORT	Input	Boolean	Initial Planned Assortment before running Assortment Improvement at Item/Cluster level.
IMPROVE_METRIC	Input	Real	Metric to Improve, 1.0 for Sales Units (at same level of input ROS/Sales). For Sales Retail or Gross Margin, multiply it with Sales Price or Price Margin at Item/Cluster level.
ELIGIBLE_ITEM	Input	Boolean	All Eligible Items that can be included into the assortment for improvement at Item/Cluster level.
IMPROVE_COUNT	Input	Integer	Number of Items to be added, removed, or swapped from the assortment at Sub-Class/Cluster level.
MIN_TO_KEEP	Input	Real	Minimum Percentage of Initial Assortment to Keep during Assortment Improvement at Sub-Class/Cluster level.
OPT_MODE	Input	Integer	Optimization Mode with values 1 for Add with Swap, 2 for Drop with Swap, 3 for Swap, 4 for Add, and 5 for Remove at Sub-Class/Cluster level.
ASSORT_ELASTICITY	Input	Real	Assortment Elasticity at Sub-Class/Cluster level.
ITEM_MANDATORY	Input	Boolean	Mandatory Items to Keep after running Assortment Improvement at Item/Cluster level.
OPT_TARGET	Input	Real	Target to Achieve by Swapping assortments at Sub-Class/Cluster level.
PROD_ATTR	Input	String	Product Attribute values at Item/Product Attribute level.
ATTR_WGT	Input	Real	Attribute Weight at Item/Attribute level.
SIM_MODE	Input	Real	Similarity mode at sub-class level with default value as 0 for similarity values calculating for all items.
FUNC_FIT	Input	Boolean	Functional Fit Boolean at Sub-Class/Product Attribute level.
VALID_ATTR	Input	Boolean	Product Attribute Eligibility at Sub-Class/Attribute level.