| Oracle® Retail Demand Forecasting Cloud Service Implementation Guide Release 19.0 for Windows F24923-16 |
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| Oracle® Retail Demand Forecasting Cloud Service Implementation Guide Release 19.0 for Windows F24923-16 |
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The following information must be considered before configuring Demand Forecasting Cloud Service:
Before implementing RDF Cloud Service, an implementer should first answer the following questions:
1) Is my forecasted item Long Lifecycle (LLC) or Short Lifecycle (SLC)?
2) Are there any promotions that impact my forecast? If yes, how can I define the promotions?
3) What is the purpose of my forecast? To drive replenishment, allocation, or others?
4) Based on the purpose of my forecasting, which level should the forecast be generated on (sku/stor/week)? How many escalation levels are needed for the forecasting? Which level should the forecast be exported to?
5) What data is available to use for forecasting: rsal, psal, csal, Promotions, or Price?
6) What kind of preprocessing is needed: Outage, Outlier, Depromote, or Deseasonalize Smooth? Configuration details can be found in Preprocessing Configuration Process.
7) How do I want to handle New Items? Is there any product attribute information?
8) Do I want to integrate RDF Cloud Service with other Applications?
9) How do I want to partition the RDF Cloud Service domain?
10) If I want to use grouping in my escalation levels or pooling levels, how do I group my item/stores?
11) Do I have a foundation system to provide foundation (hierarchy) data?
12) Do I need to generate daily forecast, and/or both weekly and daily forecasts?
13) Do I have a foundation system to provide foundation (hierarchy) data?
Depending on the answers to the previous questions, the implementer can use the RDF Cloud Service plug-in to generate RDF Cloud Service configurations. For details about how to generate RDF Cloud Service configuration, refer to Chapter 3, "RDF Configuration". The generated RDF Cloud Service configuration can be customized to satisfy client specific requirement. For details about how to customize RDF Cloud Service configuration, refer to Chapter 4, "RDF Cloud Service Extensibility".
There are four type of hierarchies in RDF Cloud Service:
Standard RPAS hierarchy, user provide the hierarchy loading files.
This is the foundation data to build any RPAS solution. Demand Forecasting Cloud Service requires the standard three hierarchy files, Calendar, Product, and Location. Also, additional sets of hierarchy files specific to different solutions are needed.
For information on the hierarchy files, see the following sections:
|
Note: All of the following hierarchy files need to be provided. If the Group and Season Code hierarchy files are not available at the time of implementation, either the GA file or dummy positions need to be provided. |
File name: clnd.csv.dat
File format: comma-separated values file
The following table describes the fields in this file.
| Name | Label | Hierarchy Type | Parent |
|---|---|---|---|
| DAY | Day | Main | None |
| WEEK | Week | Main | DAY |
| MNTH | Month | Main | WEEK |
| QRTR | Quarter | Main | MNTH |
| HALF | Half | Main | QRTR |
| YEAR | Year | Main | HALF |
| DOW | DAY OF WEEK | Alternate | DAY |
| WOYR | Week of Year | Alternate | WEEK |
| STDB | STD/BTA | UDA | WEEK |
Example:
20170101,1/1/2017,W01_2017,1/6/2017,JAN_2017,"January, FY 2017",Q1_2017,"Quarter 1, FY 2017",S1_2017,"Season 1, FY 2017",A2017,FY2017,SUN,Sunday,WY01,Week 01 20170102,1/2/2017,W01_2017,1/6/2017,JAN_2017,"January, FY 2017",Q1_2017,"Quarter 1, FY 2017",S1_2017,"Season 1, FY 2017",A2017,FY2017,MON,Monday,WY01,Week 01 20170103,1/3/2017,W01_2017,1/6/2017,JAN_2017,"January, FY 2017",Q1_2017,"Quarter 1, FY 2017",S1_2017,"Season 1, FY 2017",A2017,FY2017,TUE,Tuesday,WY01,Week 01 20170104,1/4/2017,W01_2017,1/6/2017,JAN_2017,"January, FY 2017",Q1_2017,"Quarter 1, FY 2017",S1_2017,"Season 1, FY 2017",A2017,FY2017,WED,Wednesday,WY01,Week 01 20170105,1/5/2017,W01_2017,1/6/2017,JAN_2017,"January, FY 2017",Q1_2017,"Quarter 1, FY 2017",S1_2017,"Season 1, FY 2017",A2017,FY2017,THR,Thursday,WY01,Week 01 20170106,1/6/2017,W01_2017,1/6/2017,JAN_2017,"January, FY 2017",Q1_2017,"Quarter 1, FY 2017",S1_2017,"Season 1, FY 2017",A2017,FY2017,FRI,Friday,WY01,Week 01 20170107,1/7/2017,W02_2017,1/13/2017,JAN_2017,"January, FY 2017",Q1_2017,"Quarter 1, FY 2017",S1_2017,"Season 1, FY 2017",A2017,FY2017,SAT,Saturday,WY02,Week 02
File name: prod.csv.dat
File format: comma-separated values file
The following table describes the fields in this file.
| Name | Label | Hierarchy Type | Parent |
|---|---|---|---|
| SKU | Item | Main | None |
| SKUP | Style/Color | Main | SKU |
| SKUG | Style | Main | SKUP |
| SCLS | Sub-Category | Main | SKUG |
| CLSS | Category | Main | SCLS |
| DEPT | Department | Main | CLSS |
| PGRP | Group | Main | DEPT |
| DVSN | Division | Main | PGRP |
| CMPP | Company | Main | DVSN |
| VNDR | Vendor | ALT | SKU |
| PAT1 | Prod Attribute 1 | UDA | SKU |
| PAT2 | Prod Attribute 2 | UDA | SKU |
| STA1 | Style UDA 1 | UDA | SKUG |
Example:
10000010,10000010Leather Loafer - Black 6 B,10000010,10000010Leather Loafer - Black 6 B,10000009,10000009Leather Loafer,122,122Loafer,1312,1312Casual*,1310,1310Footwear Women's*,1300,Group 1,1,Long Life Cycle Items,1,All Product,1000,Supplier 1 10000011,10000011Leather Loafer - Black 6.5 B,10000011,10000011Leather Loafer - Black 6.5 B,10000009,10000009Leather Loafer,122,122Loafer,1312,1312Casual*,1310,1310Footwear Women's*,1300,Group 1,1,Long Life Cycle Items,1,All Product,1000,Supplier 1 10000012,10000012Leather Loafer - Black 7 B,10000012,10000012Leather Loafer - Black 7 B,10000009,10000009Leather Loafer,122,122Loafer,1312,1312Casual*,1310,1310Footwear Women's*,1300,Group 1,1,Long Life Cycle Items,1,All Product,1000,Supplier 1 10000013,10000013Leather Loafer - Black 7.5 B,10000013,10000013Leather Loafer - Black 7.5 B,10000009,10000009Leather Loafer,122,122Loafer,1312,1312Casual*,1310,1310Footwear Women's*,1300,Group 1,1,Long Life Cycle Items,1,All Product,1000,Supplier 1
File name: loc.csv.dat
File format: comma-separated values file
The following table describes the fields in this file.
| Name | Label | Hierarchy Type | Parent |
|---|---|---|---|
| STOR | Location | Main | None |
| DSTR | District | Main | STOR |
| REGN | Region | Main | DSTR |
| CHNL | Channel | Main | REGN |
| CHAN | Chain | Main | CHNL |
| COMP | Company | Main | CHAN |
| SFMT | Store Format | Alternate | STOR |
| STCL | Store Class | Alternate | STOR |
| SAT1 | Store Attribute 1 | UDA | SAT1 |
| SAT2 | Store Attribute 2 | UDA | SAT2 |
Example:
1000,New York City,1000,US,1000,North America,1000,The Americas,1000,Bricks & Mortar,100,JCB Trading Company,4,4,A,A 1010,Boston,1000,US,1000,North America,1000,The Americas,1000,Bricks & Mortar,100,JCB Trading Company,5,5,A,A 1020,San Francisco,1000,US,1000,North America,1000,The Americas,1000,Bricks & Mortar,100,JCB Trading Company,5,5,A,A 1030,Seattle,1000,US,1000,North America,1000,The Americas,1000,Bricks & Mortar,100,JCB Trading Company,4,4,A,A
The group hierarchy is an internal application-specific hierarchy to divide item/stores into different grouping to use during parameter estimation and forecasting.You can customize this hierarchy during implementation and use the GA dataset hierarchy as a reference. Users can add or change how many groups are allowed in the domain through modifying the group hierarchy data file.
File name: grph.csv.dat
File format: comma-separated values file
The following table describes the fields in this file.
| Name | Description |
|---|---|
| GRPD | This is the grouping to use during estimation and forecast. |
Example:
111,Time Series Group 111 112,Time Series Group 112 113,Time Series Group 113 114,Time Series Group 114 115,Time Series Group 115
Short Lifecycle items that start selling around the same time and have a similar seasonality curve can be grouped together and assigned a Season code. Each season code represents one or several weeks within a range of seasonal length. Refer to the section, "Season Code Setup" in the Oracle Retail Demand Forecasting Cloud Service User Guide.
You can customize this hierarchy during implementation and use the GA dataset hierarchy as a reference.
Users can change how many season codes are allowed in the domain by modifying the season code hierarchy data file. The definition of each season code can be done through four measures:
| Name | Description |
|---|---|
| seabegin_SF_ | seabgein_SF_ defines the beginning of the sales start range. Its value should be a position name of the woyr dimension |
| seaend_SF_ | seaend_SF_ defines the end of the sales start range.Its value should be a position name of the woyr dimension. |
| sealenmin_SF_ | sealenmin_SF_ defines the minimum of the season length. Its value should be an integer. |
| sealenmax_SF | sealenmax_SF_ defines the maximum of the season length. Its value should be an integer. |
File name: seac.csv.dat
File format: comma-separated values file
The following table describes the fields in this file.
| Name | Description |
|---|---|
| code | This is the season code grouping to use during SLC forecasting. |
Example:
001,Season code 001 002,Season code 002 003,Season code 003 004,Season code 004
The product attributes hierarchy represents attributes associated with products. These attributes are used to group products within categories. This grouping is what consumer decision trees are built on and are used when showing dynamic rollups at item level.
This hierarchy is intended to capture all product attributes for all product types. The attributes are then assigned to individual products. This assignment is used when processing the dynamic rollups.
This hierarchy is intended to be customized for the individual retailer's needs.
| Name | Label | Hierarchy Type | Aggs |
|---|---|---|---|
| PATV | Prod Attribute Value | Main | None |
| PATT | Prod Attribute | Main | PATV |
File name: patr.csv.dat
File format: comma-separated values file
The following table describes the fields in this file.
| Field | Description |
|---|---|
| Prod Attribute Value | The various values that an attribute might have. For example, the package type attribute might take the values bag, box, or convenience. |
| Prod Attribute | The name of a product attribute, such as brand, family type, flavor, grain, package type, size, or temperature. |
Example:
patv,patv_label,patt,patt_label roast~100_columbian,100% Columbian,roast,Roast formatsize~12_ct,12 CT,formatsize,FormatSize formatsize~12_oz,12 oz,formatsize,FormatSize formatsize~30_oz,30 oz,formatsize,FormatSize formatsize~48_ct,48 CT,formatsize,FormatSize subsegment~bag,Bag,subsegment,SubSegment roast~breakfast,Breakfast,roast,Roast subsegment~can,Can,subsegment,SubSegment
These internal RDF Cloud Service hierarchy loading files are static. The GA RDF Cloud Service package contains the hierarchy loading files
These internal RDF Cloud Service hierarchy loading files are dynamic. The RDF Cloud Service plug-in generates their hierarchy loading files based on the RDF Cloud Service configuration.
The PROR and LOCR internal hierarchies are mirrored hierarchies of the PROD and LOC hierarchies. They are also referred as PROD RHS and LOC RHS. In the RPAS Cloud Edition versions 19.0 and later, PROR and LOCR are considered as virtual hierarchies. Refer to the Oracle Retail Predictive Application Server Cloud Edition Configuration Tools User Guide for information on Virtual Hierarchies.
Since these hierarchies are virtual, you do not have to load the hierarchy files. All of the other operations remain the same. You can register measures on PROR and LOCR and include them in workbooks.
Notes about these virtual hierarchies:
PROR and LOCR hierarchies have been marked as virtual in the GA configuration.
We cannot define security dimension on a virtual hierarchy or make them translatable.
Virtual hierarchies cannot have user defined dimensions.
If a retailer is upgrading from a pre-19.0 RDF version, then RDF will automatically mark them as virtual and conform to the virtual hierarchy requirements.
A detailed data set is required to use the capabilities of RDF Cloud Service to its fullest. Some of the data required is relatively easy to obtain, for example, information about sales. To simplify the data integration, all measure files are configured to be loaded as one measure per file. Each measure's data must be present in a separate file and the file name must be the same as the measure name with the .csv.ovr extension. All files must be in csv format. During the initial domain build, all data files marked as required are needed with historical data to build the domain.
Because many RPAS measure names and intersections are dynamically generated by RDF Cloud Service plug-in. Tokens will be used to represent the RDF Cloud Service level names. The labeled intersection were also listed for measure intersection
Table 2-1 lists the Tokens.
Table 2-1 Token Names
| Token Name | Description |
|---|---|
|
_SF_ |
Short Life Cycle Final Level Name, such as ”01” |
|
_CF_ |
Long Life Cycle Final Level Name, such as ”07” |
|
_SFS_ |
Short Life Cycle Escalation Level Name, such as ”01”, ”02” … or ”06” |
|
_CFS_ |
Long Life Cycle Escalation Level Name, such as ”07”, ”08” … or ”09” |
|
_CFP_ |
Long Life Cycle Pooling Level Name, such as ”07”, ”10” … or ”12” |
|
_SSG_ |
Short Life Cycle escalation level with Grouping dimension |
|
_CSG_ |
Long Life Cycle escalation level with Grouping dimension |
|
_CPG_ |
Long Life Cycle pooling level with Grouping dimension |
|
_XP_ |
Preprocessing path name such as ”P01”, ”P02” |
|
_BF_ |
Baseline only LLC final level name such as ”07” Note: Most measures ending in CF, CFS, or CSG will have the same measure for baseline only levels. The only exception is for the causal related measures. Causal related measures do not exist for baseline only levels |
|
#SLC_data_L_# |
SLC final level data intersection = SLC final level intersection – clnd dim + data's clnd dim generated by plug-in based on user specified plug-in input parameters |
|
#SLC_lvl_L_# |
SLC final level timeseries intersection = SLC final level intersection – clnd dim generated by plug-in based on user specified plug-in input parameters |
|
#LLC_frcst_L_# |
LLC final level intersection generated by plug-in based on user specified plug-in input parameters |
|
#LLC_frcstTS_L_# |
LLC final level timeseries intersection = LLC final level intersection – clnd dim generated by plug-in based on user specified plug-in input parameters |
|
#SLC_seascurve_L_# |
SLC level intersection + code generated by plug-in based on user specified plug-in input parameters |
|
#LLC_seascurve_L_# |
LLC level intersection – clnddim + woyr generated by plug-in based on user specified plug-in input parameters |
|
#LLC_peff_L_# |
LLC promo effect intersection = LLC level intersection –clnd dim + LPRM generated by plug-in based on user specified plug-in input parameters |
|
#SLS_INTX# |
Sales History intersection. This labeled intersection is user defined |
|
#SLSNC_INTX# |
Sales History intersection -clnd dim This labeled intersection is user defined |
|
#SLC_LVL# |
SLC Dashboard final level intersection - clnd dim. |
|
#NIT_ATT_WGT# |
Attribute weight intersection, generated by plug-in based on user specified plug-in input parameters |
|
#NIT_SKU_ATT# |
Product attribute intersection, generated by plug-in based on user specified plug-in input parameters |
|
#NIT_SKUSTR_INTX# |
New Item assignment intersection, generated by plug-in based on user specified plug-in input parameters |
|
#PRESLS_INTX# |
LLC Preprocessing data source input intersection |
Table 2-2 lists the measure names and descriptions. The measure field descriptions include:
Module Used
This field explains which solution is using the file. The possible values can be: All, Preprocess, New Item, SLC (Short Life Cycle), LLC (Long Life Cycle), Promote
Required or Optional Required
This field means the data is necessary. Optional means that during data load and, if not loaded, certain functionality which uses those measures cannot be used. All administration measures are marked as Optional for data load, since those can be directly set in the Admin workbooks as well.
Load Frequency
This specifies the suggested frequency for the data load. It uses the following values:
W - Weekly
A - Anytime as needed or when the values change in source system; it can be weekly, monthly, quarterly, or yearly
Data Source
This specifies the typical data source to get that measure data:
RI - Oracle Retail Insights or equivalent Data Warehouse solutions
Admin - Data can be set by Administrator based on customer data referencing sample data in GA domain.
MFP, IPCS - Oracle Retail Planning Cloud Service or equivalent. Can be readily loaded from RMS or derived from data loaded from RMS.
ORASE - Oracle Retail Advanced Science. Those are the derived measure files extracted from ORASE integration files.
RMS - Oracle Retail Merchandising System or equivalent. Can be readily loaded from RMS or derived from data loaded from RMS.
3P - Third-party data aggregator such as Nielsen or Symphony IRI.
Load Intersection
Most of the time, the load intersection of the measure is the same as the base intersection of the measure. When the field is empty, the load intersection is the same as base intersection.
Table 2-2 RPAS Measure Names and Intersections
| Measure Name | Measure Description | Base Intersection | Measure Type | Module Used | Required or Optional | Load Frequency | Data Source | Load Intersection |
|---|---|---|---|---|---|---|---|---|
|
rsal |
Regular sales |
#SLS_INTX# |
Real |
all |
Required |
W |
RMS/RI |
#DAYSLS_INTX# |
|
psal |
Promotion Sales |
#SLS_INTX# |
Real |
all |
Required |
W |
RMS/RI |
#DAYSLS_INTX# |
|
csal |
Clearance Sales |
#SLS_INTX# |
Real |
all |
Required |
W |
RMS/RI |
#DAYSLS_INTX# |
|
osal |
Other Sales |
#SLS_INTX# |
Real |
all |
Optional |
W |
#DAYSLS_INTX# |
|
|
flagslc |
Short Life Cycle Item Indicator |
#slc_lvl# |
Boolean |
SLC |
Required |
W |
||
|
ldactivefcstitem |
Active Forecast Item Indicator |
#SLSNC_INTX# |
Boolean |
all |
Optional |
W |
||
|
PreOosInd |
Loaded OutLier Indicator |
#PRESLS_INTX# |
Boolean |
LLC Predemand |
Optional |
W |
||
|
PeOutInd |
Loaded Outage Indicator |
#PRESLS_INTX# |
Boolean |
LLC Predemand |
Optional |
W |
||
|
ldPrePpiInd |
Loaded Promotion Indicator |
#PRESLS_INTX# |
Boolean |
LLC Predemand |
Optional |
W |
||
|
prdattT |
Product Attribute |
#NIT_SKU_ATT# |
String |
Newitem |
Optional |
W |
RMS/RI |
|
|
nitdattwgt |
Attribute Weight |
#NIT_ATT_WGT# |
Real |
Newitem |
Optional |
W |
||
|
nitfcststovr |
New item forecast start date |
#NIT_SKUSTR_INTX# |
Date |
LLC Newitem |
Optional |
W |
||
|
nisros |
New Item Base Rate of Sales |
#NIT_SKUSTR_INTX# |
Real |
LLC Newitem |
Optional |
W |
||
|
likeitemexmask |
Like Item Exclusion Mask |
#NIT_SKUSTR_INTX# |
Boolean |
LLC New Item |
Optional |
W |
||
|
regprc_SF_ |
Regular Price |
#slc_lvl_L_# |
Real |
SLC |
Require |
W |
||
|
slsprc_SF_ |
Sales Price |
#slc_data_L_# |
Real |
SLC |
Required |
W |
||
|
mdind_SF_ |
Markdown Indicator |
#slc_data_L_# |
Boolean |
SLC |
Required |
W |
||
|
basedmd_SF_ |
User Provided Base Rate of Sales |
#slc_lvl_L_# |
Real |
SLC |
Optional |
W |
||
|
slcplanstdt_SF_ |
Item Planned Start date |
#slc_lvl_L_# |
Date |
SLC |
Required |
W |
||
|
slcplanenddt_SF |
Item Planned end Date |
#slc_lvl_L_# |
Date |
SLC |
Required |
W |
||
|
pvar_SLCP_ |
Promotion for Short LifeCycle User |
provided during configuration time |
Boolean |
SLC |
Optional |
W |
||
|
promoaggprof_SF_ |
Promotion Aggregation profile for SLC |
User provided during configuration time (Baseline Spread Prof Intx) |
Real |
SLC |
Optional |
W |
||
|
pvar_LLCP_ |
Promotion for Long Life Cycle |
User provided during configuration time |
Boolean/Real |
LLC causal |
Optional |
W |
||
|
bayplan_CF_ |
Bayesian Plan |
#llc_frcst_L_# |
Real |
LLC |
Optional |
W |
||
|
promoaggprof_CF_ |
Promotion Aggregation profile for LLC |
User provided during configuration time (Promo Aggprof Intx) |
Real |
LLC |
Optional |
W/A |
||
|
basespreadprof_CF_ |
Baseline spreading profile for LLC |
User provided during configuration time (Baseline Spread Prof Intx) |
Real |
LLC |
Optional |
W/A |
||
|
week53indicator_CF_ |
Week53 Indicator |
User provided during configuration time |
Boolean |
LLC |
Optional |
W/A |
||
|
The following measures can be edited in RDF Cloud Service workbooks. They can also be loaded if a data file is provided. |
||||||||
|
grpassignPos_SSG_ |
TimeSeries Grouping membership for SLC.It shall contain group dimension position names. |
#slc_lvl_L_# |
String |
SLC |
Optional |
W/A |
||
|
grpAssignPos_CSG_ |
TimeSeries Grouping membership for LLC. It shall contain group dimension position names. |
#llc_frcstTS_L_# |
String |
LLC |
Optional |
W/A |
||
|
seascureovr_SFS_ |
User provided SLC seasonal Curve |
#slc_seascurve_L_# |
Real |
SLC |
Optional |
A |
||
|
seabegin_SF_ |
Season code start. The measure shall contain the position name of WOY dimension (such as WY01). It specify the beginning of item on sale date range |
User provided during configuration time (season code intx) |
String |
SLC |
Optional |
A |
||
|
seaend_SF_ |
Season code end. The measure shall contain the position name of WOY dimension (such as WY04). It specify the ending of item on sale date range |
User provided during configuration time (season code intx) |
String |
SLC |
Optional |
A |
||
|
sealenmin_SF_ |
Season length min. It specify the minimum seasonal length of items in a season code. |
User provided during configuration time (season code intx) |
Integer |
SLC |
Optional |
A |
||
|
seaslenmax_SF_ |
Season length max . It specify the maximum seasonal length of items in a season code. |
User provided during configuration time (season code intx) |
Integer |
SLC |
Optional |
A |
||
|
defescpath_SF_ |
Default Escalation Path |
Elvl+User provided during configuration time (Escalation Path intx) |
Integer |
SLC |
Optional |
A |
||
|
elasovr_SF_ |
User Provided Elasticity |
#slc_lvl_L_# |
Real |
SLC |
Optional |
A |
||
|
glescpath_SF_ |
Global Escalation Path |
Elvl |
Integer |
SLC |
Optional |
A |
||
|
grpAssignPos_CPG_ |
TimeSeries Grouping membership for LLC Causal Pooling |
#llc_frcstTS_L_# |
String |
LLC |
Optional |
A |
||
|
usrllccurve_CFS_ |
User Provided LLC Season Curve |
#llc_seascurve_L_# |
Real |
LLC |
Optional |
A |
||
|
week53indicator_CF_ |
Week53 Indicator |
User provided during configuration time (week53 flag intx) |
Boolean |
LLC |
Optional |
A |
||
|
prmovreff_CFP_ |
Promotion Effects Override |
#llc_peff_L_# |
Real |
LLC |
Optional |
A |
||
|
defescpath_CF_ |
Default Escalation Path |
Elvl+User provided during configuration time (Escalation Path intx) |
Integer |
LLC |
Optional |
A |
||
|
glescpath_CF_ |
Global Escalation Path |
Elvl |
Integer |
LLC |
Optional |
A |
||
|
defpoolesc_CF_ |
Default Pool Escalation |
Elvl+User provided during configuration time (Escalation Path intx) |
Integer |
LLC |
Optional |
A |
||
|
glpoolesc_CF_ |
Global Pool Escalation |
Elvl |
Integer |
LLC |
Optional |
A |
||
|
The following measures' data file were generated by RDF Cloud Service plug-in and loaded at domain build/patch time |
||||||||
|
promoefftype_CF_ |
Promotion Model Type for LLC |
LPRM |
Integer |
LLC |
Required |
|||
|
lprmefftyplist |
LLC Promotion Model Type PickList |
LPRM |
String |
LLC |
Required |
|||
|
enabledpromo_SF |
Enable SLC Promotions |
SPRM |
Boolean |
LLC |
Optional |
|||
|
promoefftype_SF_ |
Promotion Model Type for SLC |
SPRM |
Int |
LLC |
Optional |
|||
|
esclist_SF_ |
SLC Escalation Level Picklist |
scalar |
String |
LLC |
Required |
|||
|
esclist_CF_ |
LLC Escalation Level picklist |
scalar |
String |
LLC |
Required |
|||
|
poollist_CF_ |
LLC Pooling Level picklist |
scalar |
String |
LLC |
Required |
|||
|
wblvlrange |
Dashboard level range |
ELVL |
Boolean |
Dashboard |
Required |
|||
|
wblvllblmap |
Dashboard Level label |
ELVL |
String |
Dashboard |
Required |
|||
|
flvlint |
Forecast Level Intersection |
ELVL |
String |
All |
Required |
|||
|
bslpqbfs_BF_ |
Baseline Position Query |
ELVL |
Boolean |
LLC |
Required |
|||
|
cslpqcfs_CF_ |
Causal Position Query |
ELVL |
Boolean |
LLC |
Required |
|||
|
cslpqcfp_CF_ |
Causal Position Query |
ELVL |
Boolean |
LLC |
Required |
|||
|
cslpqcp_CF_ |
Causal Position Query |
ELVL |
Boolean |
LLC |
Required |
|||
|
slcpqsfs_SF_ |
SLC Level Position Query |
ELVL |
Boolean |
LLC |
Required |
|||
|
ppsDataSrc_XP_ |
Preprocessing Input Data Source |
Scalar |
String |
LLC |
Required |
|||
|
ppsOutput_XP_ |
Preprocessing Output Data Source |
Scalar |
String |
LLC |
Required |
|||
|
ppsMethod_XP_ |
Preprocessing Methods |
RUND |
Integer |
LLC |
Required |
|||
|
ppsRunLabel_XP_ |
Preprocessing Run Label |
RUND |
String |
LLC |
Required |
|||
|
ppsFirstAux_XP_ |
Preprocessing Run Parameter 1 |
RUND |
String |
LLC |
Required |
|||
|
ppsSecAux_XP_ |
Preprocessing Run Parameter 2 |
RUND |
String |
LLC |
Required |
|||
|
ppsRunOrder_XP |
Preprocessing Run Order |
RUND |
String |
LLC |
Required |
|||
|
ppsRunPreB_XP_ |
Run Preprocessing Flag |
RUND |
Boolean |
LLC |
Required |
|||
|
ppsStdESAdjust_XP_ |
Preprocessing Adjustment Mode Flag |
RUND |
Boolean |
LLC |
Required |
|||
It is recommended that you have at least two full years of historical data for long life cycle forecasting and one full year of historical data for short life cycle forecasting.
Data is loaded into RDF Cloud Service using the Online Administration Tools, which in turn use standard RPAS utilities. For more information on loading and extracting data using Online Administration Tools, see the Oracle Retail Demand Forecasting Cloud Service Administration Guide
RDF Cloud Service is pre-configured to support the display of images for items and product attributes in the Forecast Review and New Item workbooks. Table 2-3 lists the dimension attribute measures used to load images.
Table 2-3 Labeled Intersections
| Measure | Hierarchy | Dimension |
|---|---|---|
|
skuimage |
PROD |
sku |
|
skupimage |
PROD |
skup |
|
skugimage |
PROD |
skug |
|
skurimage |
PROR |
skur |
|
skprimage |
PROR |
skpr |
|
skgrimage |
PROR |
skgr |
|
patvimage |
PATR |
patv |
|
pattimage |
PATR |
patt |
The Content Server exposes the client's image files placed into a particular directory as HTTP URLs. The images must be defined in the load file in an xml format. The images are available at:
http://{content server url}/imgfetch/{sub directory if defined}
The first field represents the SKU ID followed by the required image location. At a minimum, a thumb size image file must be loaded to show in the pivot table. However, both the thumb and full size images can be loaded.
10000010,"<image id=""main"" label=""Front View"">\<url size=""thumb"">http://msp00alq.us.oracle.com:9001/contentserver/imgfetch/sku_10000010_main_thumb.jpg</url></image>"
10000010,"<image id=""main"" label=""Front View"">\ <url size=""thumb"">http://msp00alq.us.oracle.com:9001/contentserver/imgfetch/sku_10000010_main_thumb.jpg</url> <url size=""full"">http://msp00alq.us.oracle.com:9001/contentserver/imgfetch/ sku_10000010_main_full.jpg</url></image>"
RDF Cloud Service supports the flat file integration of hierarchy and data files from source systems.
Retailers must extract and provide the hierarchy files and data files from their respective source systems as flat files in the required format and upload them to the Oracle Cloud SFTP server ($FTP_INCOMING). The automated process send those files over to the RPAS DB Server and from there the files can be accessed by batch process triggered using the Online Administration Tools. In the same way, exported files in CSV format from the solution are pushed back to the Oracle Cloud SFTP server and from there retailers can download the extracted files.
RDF Cloud Service supports integration with Oracle Retail Merchandising Foundation Cloud Service (RMF CS). If a retailer has RMF CS as the source system for transactional data, they can readily integrate to get foundation hierarchy data and transactional data from RMF Cloud Service. Refer to Appendix B, "Appendix: RDF Cloud Service integration with RMF Cloud Service."
RDF Cloud Service can also integrate with other applications using Planning Data Source (PDS) and Bulk Data Integration (BDI). Refer to Appendix C, "Appendix: RDF Cloud Service Integration with PDS and BDI.".
RDF Cloud Service has the capability to calculate the Demand Transference (DT) Effects and apply it to the forecast if DT is enabled. It needs the Demand Transference (DT) Multiplier as input to calculate these effects. RDF Cloud Service integrates with ORASE to get the DT multipliers. It can also receive the size profiles from ORASE which can be used to spread the Short Life cycle forecast from SKUP to SKU.Refer to Appendix E for RDF Cloud Service integration with ORASE / RI.
To define workbook template security, the system administrator grants individual users, or user groups, access to specific workbook templates. Granting access to workbook templates provides users the ability to create, modify, save, and commit workbooks for the assigned workbook templates. Users are typically assigned to groups based on their user application (or solution) role. Users in the same group can be given access to workbook templates that belong to that group alone. Users can be assigned to more than one group and granted workbook template access without belonging to the user group that typically uses a specific workbook template. Workbook access is either denied, read-only, or full access. Read-only access allows a user to create a workbook for the template, but the user cannot edit any values or commit the workbook. The read-only workbook can be refreshed.
For more information on security, see the Oracle Retail Predictive Application Server Cloud Edition Administration Guide. For more information on data security in a cloud environment, see the Hosting Policy documents for the cloud solution.
Internationalization is the process of creating software that can be translated more easily. Changes to the code are not specific to any particular market.
Oracle Retail applications have been internationalized to support multiple languages.
The RPAS platform supports associated solution extensions and solution templates.
A solution extension includes a collection of code and generally available configurations. Typically, solution extensions are implemented by a retailer with minimal configuration.
A solution template does not include code. A solution template is most typically implemented as a retailer configuration.
Oracle Retail releases the translations of the RPAS server and client, as well as strings from the solution extensions.
Translations of the solution templates are released. All templates have the ability to support multi-byte characters.
For more information on internationalization, see the Oracle Retail Predictive Application Server Cloud Edition Administration Guide.
Translations are available for RDF Cloud Service for the following languages:
Chinese (Simplified)
Chinese (Traditional)
Croatian
Dutch
French
German
Greek
Hungarian
Italian
Japanese
Korean
Polish
Portuguese (Brazilian)
Russian
Spanish
Swedish
Turkish
|
Note: For information about adding languages for the first time or for translation information in general, see the Oracle Retail Predictive Application Server Cloud Edition Administration. |
