1 Introduction

Category Management practice in the retail industry is a key business process which determines the success of a retailer's business. Category Management involves managing individual product categories as independent strategic business units (SBU), in a systematic and disciplined way, with each product category playing a specific role in the retailer's goal to achieve larger, established business objectives. It determines the variety, nature, and presentation of merchandise and the retailer's offers, which define a customer's in-store, in-channel experience, which holds the utmost importance in determining the retailer's success.

Conceptually, Category Management practice consists of the following actions:

1. Understand and assess the retailer's business:

   • Market-place analysis: Market analysis involving the assessment of consumer demographics, psychographics, market structure, and key market forces to gauge the business potential of individual product categories and business as a whole.

   • Product and consumer analysis: Category level and cross-category performance analysis with consumer segment level insights at various points to understand the intricacies of the retailer's business, identify the target consumer segments contributing to the retailer's business, and shape the category plans and assortment plans accordingly.

   • Competitor analysis: Assessment of the retailer's standing in the market in comparison to the competition.

2. Review the retailer's macro-strategy and assign roles, strategies, and tactics to product categories across the retail chain.

3. Create sales, gross profit, and inventory plans for national brands and private labels. Determine pricing, promotion, and supplier strategies for product categories in the form of category plans.

4. Allocate optimal space at the macro (department/category/sub-category level) and micro-levels (SKU level) per the retailer's macro-strategy.

5. Create assortment plans that define the product-mix and business targets for product categories.

Oracle Retail Category Management (RCM) supports the development of category plans, macro space optimization, assortment plans, and space optimization of assortments. It broadly follows the traditional eight-step Category Management business process, with the inclusion of the consumer segment perspective across various points in the process flow. Most importantly, the application provides a structured, measured set of activities designed to achieve specific business objectives:

• The Category Planning task enables the retailer to create high-level qualitative and quantitative business plans at the trading area level.

• The Macro Space Optimization tasks facilitate the creation of macro-space plans for optimal space productivity.

• The Assortment Planning tasks facilitate the creation of SKU-level assortment plans at the cluster and store level across the retail chain.

The RCM application consists of the following tasks:

• Category Planning: Used to analyze a retailer's business across product categories and within a category, from a market, competition, and consumer perspective. Category Planning is used to set business targets and assign roles, strategies, and tactics for individual product categories at the trading area level.

• Macro Space Optimization @Dept: Used to allocate optimal space to different departments based on the historical relationship between space and profit. This helps determine the macro-space plan at the department level, specifications for store layouts, shelves, and fixtures, and merchandising techniques, thereby, facilitating efficient utilization of the available space by the retailer.

• Macro Space Optimization @Sub-Category: Used to allocate optimal space to different sub-categories, under a department, to efficiently use the available space at this level. This helps in determining the planogram design and merchandising method at the sub-category level.

• Assortment Planning Analysis: Used to analyze an assortment's historic performance from a market, competition, and consumer perspective at an item level. It also provides an insight into the trends, market composition, and market structure from a competition and consumer segment perspective. It is used to review roles, strategies, and tactics from the Category Plan and targets from both the Category Plan and Merchandise Financial Plan. Visibility to roles, strategies, tactics, and financial targets in assortment planning ensures that SKU/item level assortments align back to the overall category-level objectives.

• Assortment Planning @ Cluster: Used to create, analyze, adjust, and finalize assortment plans at the cluster level. Assortment plans are created using Item Priority Index (IPI) and Market Coverage based methods. The functionality of Demand Transference, Assortment Improvement, and Incremental Curve are used to fine tune and optimize draft assortments. Integration with Assortment and Space Optimization (ASO), an Oracle Retail Advanced Science (ORASE) solution, is utilized align assortments to the space available for optimal space productivity.

• Assortment Planning @ Store: Used to create, adjust, review, and approve custom assortment plans at the store level utilizing IPI-based assortments, Demand Transference, and ASO.

Following are the key highlights of the application:

• Provides a platform to analyze consumer and market insights available in the form of syndicated data, provided by third parties, such as, Nielsen and IRI. Consumer segment level insights for the retailer's business can be also be sourced from the retailer's loyalty program data.

• Analysis of market structure by viewing the consumer segments among the shoppers and evaluating the business opportunity in a trading area.

• Performance analysis of individual product categories, based on various retail business parameters, as compared to the market in general and the competition in particular.

• Creation of category plans with business targets in terms of different sales, gross profit, inventory parameters with role assignments to individual product categories, and a blueprint for strategic and tactical action within a category and across categories.

• Macro Space Optimization, at the department and sub-category level, to allocate optimum space to different product categories and improve space productivity.

• Integration with ORASE to source optimized store clusters (assortment clusters), Consumer Decision Trees (CDTs), product attributes, and demand transference insights. CDTs can also be sourced from an external source, such as a supplier or third-party vendor. Consumer segmentation, clustering of stores, and CDTs are utilized to tailor assortments to specific markets across geographies and channels.

• System-recommended target-focused assortments can be generated using IPI and Market Coverage-based approaches through user-defined constraints.

• Creation of assortment plans, at the cluster and store level, in alignment with Category Plans and Merchandise Financial Plans created through user-defined target-constrained assortment generation.

• Demand Transference, based on advanced science insights, to manually fine tune and refine assortment plans by reviewing demand shifts between SKUs with changes in assortment.

• The functionality of Incremental Curves, to arrive at an appropriate assortment range for product categories by identifying the point at which the cannibalization of sales between products in an assortment begins.

• Assortment Improvement, based on the concept of Demand Transference, automates the refinement of assortment plan parameters such as sales retail, sales units, or gross profit based on user-defined constraints.

• Space optimization of assortments through the integration with ASO to generate actionable and optimal assortments per the available space at the stores.

• Publication of final assortment plans for execution and implementation.

Note that the previously mentioned activities, part of the Category Management application, are performed at different levels of the product and location hierarchies. This facilitates customized Category Management planning across the retail chain.

Note: Product Category or Merchandise Category refers to Class in Oracle Retail Merchandising System (RMS) terminology. Similarly, Sub-Category refers to Sub-Class.

Category Management Activities

The following Category Management activities and tasks are described in this guide:

• Category Management Planning activity:

  • Category Planning

    For more information, see Chapter 4.

  • Macro Space Optimization @ Dept

    For more information, see Chapter 5.

  • Macro Space Optimization @ Sub-Category

    For more information, see Chapter 6.

  • Assortment Planning Analysis

    For more information see Chapter 7.

  • Assortment Planning @ Cluster task

    For more information, see Chapter 8.

  • Assortment Planning @ Store

    For more information, see Chapter 9.

• Category Management Administration activity:

  • Category Management Administration

    For more information, see Chapter 3.

  • CDT Editor

    For more information, see Chapter 2.

Solution Task Flows

Figure 1-1 illustrates the task flow for the Category Planning task. The Category Planning task facilitates the creation of a category business plan at the sub-category level along with roles, strategies, and tactics assignment to product categories. It also presents various views for consumer, market, and competitor analysis.

Figure 1-1 Category Planning Task Flow

Figure 1-2 illustrates the task flow for the Macro Space Optimization @Dept task. This task presents category scorecard review, setup and review of optimization results, and approval of MSO results at the department level.

Figure 1-2 Macro Space Optimization @Dept Task Flow

Figure 1-3 illustrates the task flow for the Macro Space Optimization @Sub-Category task. This task presents category scorecard review, setup and review of optimization results, and approval of MSO results at the POG sub-category level.

Figure 1-3 Macro Space Optimization @Sub-Category Task Flow

Figure 1-4 illustrates the task flow for the Assortment Planning Analysis task. This task presents performance, market, and consumer analysis at the item level. It also presents roles, strategies, tactics, and targets from the category plan and targets from the merchandise financial plan for the assortment planning to begin.

Figure 1-4 Assortment Planning Analysis Task Flow

Figure 1-5 illustrates the task flow for the Assortment Planning @ Cluster task. This task begins with assortment setup for the assortment planning involving seeding of working plans, setting assortment eligible and mandatory items, and then defining the assortment constraints to generate system-recommended assortments using:

1. IPI-based assortment method

2. Market Coverage-based assortment method

Figure 1-5 Assortment Planning@ Cluster Task Flow

Figure 1-6 illustrates the task flow for the Assortment Planning @ Store task. Store level assortment plans can be created using the IPI-based assortment generation method and space optimized assortments derived under this task.

Figure 1-6 Assortment Planning @ Store Task Flow

Category Management Process Flow

Figure 1-7 depicts the overall process flow in Category Management and data exchange with other applications. Broadly the process flow consists of the following steps:

1. Import MFP Targets in the Category Planning and Assortment Planning modules.

2. Create and approve Category Plans and share them with Macro Space Optimization and Assortment Planning.

3. Import current space and adjacency information from a space management system.

4. Perform Macro Space Optimization.

5. Share the Macro Space Optimization output with a space management system and Assortment Planning.

6. Import clusters, product attributes, CDTs, Demand Transference (DT), and Assortment Improvement intelligence from ORASE.

7. Create Assortment Plans.

8. Export Assortment Plans to Assortment and Space Optimization.

9. Import space optimized assortments from Assortment and Space Optimization. Review, accept, approve, and finalize the assortment plans.

Figure 1-7 Category Management Process Flow

Space profit tables form the basis of MSO and are maintained by an administrator. These tables are used to store a relationship between allocated space and the respective gross profit returns at the retailer based on historic data. Space profit tables are also referred to as space profit elasticity curves, which are derived using Advanced Science algorithms.

Macro Space Optimization can be conducted at the store level, cluster level, or trading area level. It uses an algorithm, which runs at the store level, to recommend optimum space to a department and sub-category. At the cluster and trading area levels, the recommendations are presented by aggregating data from the store level.

Advanced Science in Category Management

This section covers RCM's integration with Oracle Retail Advanced Science Engine (ORASE). RCM is integrated with the ORASE and sources key information, which is used in the Category Management process flow. ORASE is also referred to Advanced Science or Science.

ORASE is the umbrella name for all the Advanced Science solutions. In Category Management, it is used for the following functionality:

• Used to generate assortment clusters, CDTs, and DT science parameters. The Advanced Clustering solution is used to generate assortment clusters.

• Used for assortment space optimization.

ORASE, through its specialized set of applications, derives key insights data by deep data mining rich historic business data. Imports from ORASE provide powerful insights about the retail business, bring efficiencies into the assortment planning process, and facilitate quick and effective decision making. In effect, RCM's integration with ORASE sets the stage for advanced science-based category management practice.

Key integration points with ORASE include the following:

• Import of Assortment Cluster Data: Stores are grouped into assortment clusters or store clusters to be used to create Assortment Plans at the cluster level. A prerequisite for RCM to source assortment clusters from ORASE is that both the applications must be sharing a common location hierarchy, that is, in terms of stores to trading areas mapping. ORASE provides retailers with a flexible, user-friendly clustering solution utilizing advanced clustering methodology, where clusters can be generated based on various parameters such as seasonality, consumer segment profiles, store attributes, performance attributes, and product attributes. Other than ORASE, assortment clusters can also be sourced from the retailer's legacy system or any other external application.

• Import and Export of Product Attributes: Product attributes, or simply attributes, consist of attribute names and attribute values at the SKU/item level. RCM sources attributes from ORASE which in turn sources this information from Oracle Retail Analytics (RA). RA extracts this information from RMS through Oracle Retail Extract, Transform, and Load (RETL) procedures. This import is a prerequisite for importing CDTs from ORASE as CDTs are based on product attributes. RCM and ORASE need to be in synch in terms of product attributes. RCM provides the facility to add and modify attribute values that map to SKUs/items. There is an export facility available to communicate the changes to the Master Data Management system, such as RMS and RA, and eventually to ORASE. This is to keep both the applications in synch in terms of product attributes.

• Import of Consumer Decision Trees: CDTs are used to understand the consumer buying process and identify key product attributes that influence consumer buying decisions from a consumer segment profile perspective. CDTs can be leveraged within the Assortment Planning process as an alternate product/merchandise hierarchy to analyze the assortment to ensure the presence of key items for target consumer segments in the planned assortment, reduce product attributes duplication beyond the required number of options in the assortment, and avoid dropping of unique items that can result in lost customers.

  CDTs are generated in ORASE by utilizing customer purchase history data and doing deep data mining using advanced science methods. Customer purchase history can be internally sourced from Point-of-Sale (POS) or loyalty solutions and externally from home-scan data providers. CDTs can also be fed to RCM directly from a legacy system or application at the retailer. These CDTs could come from third-party sources such as vendors or providers of syndicated market data.

• Import of Demand Transference Data: Demand Transference is used within the assortment planning process to understand the shift in sales between items within an assortment with changes to the assortment. This enables the assortment planner to assess both the assortment as a whole and evaluate the effectiveness of each item within the assortment. ORASE mines historic customer purchase data and generates Demand Transference parameters that are leveraged interactively, through an Application Programming Interface (API), in the Assortment Planning and Optimization process.

• Export to ASO: Assortment and Space Optimization (ASO) is a specialized application of ORASE. RCM exports assortment plan and category plan data to ASO with a space optimization request. ASO optimizes the assortment to the available space at the retailer and allots a suitable number of facings and linear feet, taking into consideration the planograms (POGs) used, demand transference, and supply chain factors. There are three types of exports available:

  • An export of assortment plan and category plan data to ASO with a space optimization request.

  • An export of an updated assortment plan for an existing space optimization request.

  • An export to ASO with an update on assortment finalization for implementation.

  For detailed information on these exports, see the Oracle Retail Category Management Implementation Guide.

Data Requirements in Category Management

The Category Management application has special data requirements that factor consumer-centricity and the retailer's market standing into the Category Management practice. A lot of this data is sourced from third-party syndicated data suppliers, also referred to as external market data providers. Examples of external market data providers include AC Nielsen, Symphony IRI, Axciom, and FICO.

Some of this data is also sourced internally from other applications such as Point-of-Sale (POS), Loyalty Program applications, Analytic Applications such as Retail Analytics, and so on.

All of this data is imported into Category Management as part of the implementation process or, from time to time. Table 1-1 describes the types of data entered and used for Category Management.

Table 1-1 Types of Data

Data	Description
Market Syndicated Data	Third-party syndicated data suppliers, or External Market Data Providers, have an agreement with most retailers (including both the retailer and their competitors) competing in the market to share their POS data with them. External Market Data Providers then combine and analyze this data to provide Market Syndicated Data with rich insights to the retailer. This data reflects most retailers as a whole and the retailer's standing in the market or a trading area.
Household Panel Data	Also referred to Home-scan data. Third-party syndicated data suppliers, or External Market Data Providers, collect this data from customers in the market by registering them for scanning their purchases from the retailers and sharing it with them. External Market Data Providers use this data to identify key patterns for different consumer segments. This provides a sample representation of the market for different trading areas with rich consumer profile or consumer segment information.
Market Basket Data	Market Basket refers to customer purchase data at the retailer. Customer purchases refer to market basket or the customer's basket or basket in general. The concept of Market Basket deals with analyzing the product (SKU) mix, number of baskets over a period of time, value of the basket bought, and category/sub-category contribution's in a customer's basket. Market Basket is a focus area in IPI calculations used to derive an Assortment Plan. This type of information provides insight into the halo/cannibalization effect of items that are purchased, which items are typically found in high basket rings, and so on.
Customer and Market Loyalty Data	Customer loyalty data represents loyalty metrics associated with consumers within a particular trading area towards specific retailers or a particular retailer. Market loyalty data is more product-focused loyalty information. Brand loyalty is an example of market loyalty data which represents the percent of the category dollar volume that is satisfied by the brand among brand buyers within a particular trading area. Customer and market loyalty data is supplied to retailers by syndicated data suppliers, such as Nielsen, Symphony IRI, NPD, and so on.
Loyalty Card/Program Data	Data captured through loyalty programs for a particular retailer. This is the retailer's internal data from its Customer Relationship Management (CRM) applications. Retailers mine the data of their loyalty card holders for the purpose of understanding who their profitable customers are, what they buy, how often they buy, and which products and attributes they buy. Customer loyalty data is a useful component in Assortment Planning and the Category Management process, as retailers need to ensure that they are carrying products that appeal to their most profitable customers. This data is also referred to as frequent shopper data.

The data described in Table 1-1 is required to leverage the full functionality of the RCM application which includes Category Planning and Assortment Planning and Optimization. It is possible to leverage the Category Management solution without all of the data, but access to some key and relevant pieces of functionality would be unavailable in the absence of such data. For example, without Market Syndicated data, you cannot perform a market focused analysis of categories and assortments. For more information, see the Oracle Retail Category Management Implementation Guide.

For the list of data requirements for CDT/DT, science, and ORASE, see the Oracle Retail Advanced Science Engine Implementation Guide.

Required Data

This section lists all of the data that is required, along with its source, before a user can begin the category planning process.

The following data is required for category planning:

• Merchandise or Product hierarchies defined for each retail vertical such as grocery, hardliners, general merchandise, and so on

• Products fully defined for each product category

• Consumer and customer segments defined for each retail vertical

• Location hierarchy and trading areas clearly defined

Trading area is a new concept in Oracle Retail planning and optimization applications and is defined as (also commonly referred to as a market) a specific geographic area demarcated by a retail chain to conduct (view, plan, and manage) its business and compare it with the competitors' business. For the geographic area being demarcated to qualify as a trading area, it must be generating a threshold volume of business (sales) neither too large nor too small, but contributing to a significant chunk of the business in that region. The value of threshold volume of business is based on the retailer's discretion, for example, Atlanta, Jacksonville, Greater Mumbai area, and so on.

Trading areas are also defined on the basis on traditional geographical boundaries such as a city or town, census survey reports, governmental directions, common understanding among different retailers and market research companies in the industry, and the retailer's own business requirements. Trading area definition may differ from retailer to retailer competing in the same region, state, country, and so on. There can also be a common agreement in the industry on the trading areas definition and division among retailers and third-party market research companies collecting and providing syndicated data to the retailers for everyone's benefit.

For example, the US market is conventionally divided into 52 trading areas by market research largely following state-level demarcations.

The preceding data requirements are met by importing the following:

• Store clusters

  In the generally available version of the solution, store clusters or assortment clusters are imported from ORASE.

• Product hierarchies

• Location hierarchies

• CDTs

• Product attribute names and attribute values

  In the generally available version of the solution, this product attributes information is imported from ORASE.

• Market scan data

• Retailer POS data

• Space-related data

• Retailer loyalty data

• Market basket data

• Demand Transference input parameters from ORASE

Key Take Aways

A key take away is a free-form text field that enables the user to enter notes pertinent to a specific line-item in a view:

• The notes can be any important points the user wants to capture, such as additional information (quick insights such as supplier insights, competition insights, consumer insights, product insights, and so on) used in arriving at a decision highlighting the rationale or perhaps a point that led them to their final decision.

• Key take aways can be used for saving notes from findings derived from different analyses.

• Key take aways can be used by the owner of a step to provide notes to other users who have lower privileges.

User Roles

Users are typically assigned to specific roles based on organizational structure.

Note: Access to tasks is controlled by security permissions. If you do not have the permission to access a task, that task does not appear in your task list.

Table 1-2 shows an example list of roles and their functions and areas of responsibility:

Table 1-2 Example of Roles

Role	Application	Description
Category Manager	Category Planning	Analyzes category performance and compares with competition. Determines the Roles, Strategies, and Tactics. Responsible for meeting financial targets.
Buyer / Assortment Planner	Assortment Planning	Selects the optimal assortment for each store, taking into account sales performance, forecast, budget, and space constraints.
Merchandising Analyst	Category Planning and Assortment Planning	Analytical support for Nielsen/IRI syndicated data, product attributes validation, ORASE administration, CDT Analytics, Demand Transference, and Assortment Optimization.
Planogrammer	ORASE and POG Software	Ensures that the assortment can fit within the allocated space, and adds the art to the planogram.
Floor Planner	MSO, a space management system	Uses space targets to define department sizes.
Merchandiser	MSO, a space management system	Uses category/sub-category space targets to determine what length of planograms to place.
Administrator	NA	Application installation, admin and support, data imports, and user ID creation.

Figure 1-8 presents various business roles involved in the end-to-end category management process. Note that not all the standard user roles defined in this figure are directly working on the RCM application.

Figure 1-8 Business Roles Involved in Category Management Process

Plan and Measure Roles and Versions

The measures in the RCM GA are composed of a Role, Version, Metric, and Unit of Measure. Each component is designed to carry a particular piece of information that together indicates the purpose of the measure. Table 1-3 lists a description of each component.

Table 1-3 Description of Measure Components

Component	Description
Role	The role describes an anticipated type of user that will be using the system. Workbooks are tailored to meet the needs of these different types of users, so it also generally indicates where the measure is expected to be found in RCM.
Version	The version tells at what stage in the planning process the measure's data represents. Examples of this are this year (TY) versus last year (LY), or working plan (WP) versus original plan (OP) or target (Tgt). It can also indicate that a measure is intended to be an intermediate measure.
Metric	This component describes the nature of the information in a measure. Typical examples are sales, profit, inventory turn, and so on.
Unit of Measure	The unit of measure represents the data format, integer, decimal number, date, text, and so on.

Some of the commonly used roles are listed in Table 1-4.

Table 1-4 Category Management Roles

Role	Description	Notes
AC	Assortment Planning @ Cluster	Measures with this role are almost all found in the Assortment Planning @ Cluster workbook.
AD	Administrator	Measures with this role are mostly found in the administration workbook. They are occasionally found in other workbooks, and often are loaded with an initial set of default values.
CM	Category Planning	Measures with this role are usually found in the Category Planning workbook. Some CM measures are also shown in the Assortment Planning Analysis workbook for reference.
DR	Default Role	This role usually indicates that the measure is for raw, loaded data. This data is usually processed somehow before appearing in any other workbook. It also is used for utility purposes, for example, for the role for a measure whose only purpose is to provide white-space on a view.
CE	CDT Editor	This role indicates that the measure is associated with the CDT Editor workbook. There are relatively few of these.
AS	Assortment Planning @ Store	Measures with this role are found in the Assortment Planning @ Store workbook.
MD	Macro Space Optimization @Dept	Measures with this role are found at the Macro Space Optimization at Department level.
MS	Macro Space Optimization @Sub-Category	Measures with this role are found in at Macro Space Optimization at Sub-Category level.
SO	Assortment Space Optimization	This role is used for Assortment Space and Optimization (ASO) interface database measures stored in the Data Mart in the RPAS Hybrid Storage Architecture.

Some of the commonly used versions are listed in Table 1-5.

Table 1-5 Category Management Versions

Version	Description	Notes
CP	Current Plan	These measures contain the most recently approved assortment plan values.
LP	Last Plan	These measures contain a copy of the CP measures, time-shifted into the future by one period. It is used to give users another data set against which to compare the WP information.
OP	Original Plan	This denotes information approved in the Category Planning workbook used as target information for the Assortment Planning workbooks.
WP	Working Plan	These measures contain data that users are currently working on.
LY	Last Year	Time-shifted data used to compare the current plan with last year's values for the same period.
TY	This Year	Usually applied to loaded historical data, and rarely shown in workbooks.
Fc	Forecast	Only used for measures that store the results of a forecast.
DV	Default Value	Data for which there is not yet a defined version in the system. Often used with the default role (DR) to indicate base loaded data.
DB	Database	Usually, measures that are part of an intermediate step in a calculation.
DC	Scalar	Usually, measures that store a global value, such as labels.
LU	Lookup	Usually, measures that are part of an intermediate step in a calculation.
Mf	Merchandise Financial Planning Targets	Version used for MFP interface measures.
AI	Assortment Improvement	Version used for Assortment Improvement versions of all metrics.
AO	ASO	Version used to store level ASO results of all metrics.

Note: The version Tg (Target) is not included and will be removed in a future release. It has been replaced with OP (Original Plan).

Combined measures are widely used in presenting space optimized assortments in assortment planning aggregate plan values for different measures in assortment planning, by taking into account both cluster level and store level measure values. If, store level measure values are populated for a store location, then cluster level measure values for that measure are not considered, otherwise, cluster level measure values are used to calculate the combined measure value from that location.

Private label measures are available in assortment planning. Private label measure values are derived using the attribute value maintenance for a SKU. If a private label attribute is maintained for an item, the private label measures values are populated with the corresponding plan data. If the private label measures are directly entered or planned, they are automatically copied to the working plan measures.

The combined roles and versions of measures indicate how data flows through the system. This is referred to as version flow and is an important part of understanding how Category Management is structured.

Figure 1-9 shows how data flows through the RCM application.

Figure 1-9 Category Management Version Flow

The Category Planning workbook has WP (Working Plan), LY (Last Year), FC (Forecast), and OP (Original Plan) versions of measure. When a category plan is approved, the current WP measure data is copied to OP versions of the measures. This OP version is shared to Assortment Planning. This is accomplished by again copying the data from a CMOP role/version of the measure to a corresponding ACWP role/version. In Figure 1-9, the Tgt version is called OP in the application.

The Assortment Planning workbook has WP, LY, FC, and CP versions of measure. WP is approved to CP, and this CP version is shared to Category Planning and Assortment Planning @ Store. CM is architected so that Assortment Planning @ Cluster and Assortment Planning @ Store are a single workbook. This was done so that the sharing of version data between the two is real time, as opposed to the sharing of data to Category Planning, which requires building and refreshing of the workbooks between the two.

Assortment Planning @ Cluster and Assortment Planning @ Store share the same base sales metric (that is, ACWPSlsR and ACWPSlsU at Week/SKU/Store). The only replan you are doing at Assortment Planning @ Store is the assignment of override Core/Optional flags which will update the final Assort Sales metric at Store versus Cluster.

Finally, the Assortment Planning information AC WP and AC CP (Assortment Planning @ Cluster role) is also viewable in the Category Planning workbook. The reason AS WP and AS CP (Assortment Planning @ Store role) is not shown is that the cluster and store information is presented in measures called WP AS Combined Assort Sales and CP AS Combined Assort Sales. These measures combine both AC and AS values into AC WP/CP into measures shared to Category Planning. This is done in Assortment Planning as a normal calc rule group.

Custom Menu Options

Custom menu buttons are located above the top right corner of the content area for some workbooks. Custom menus are specific to the views under the steps available in different tasks. For example, the Seed Sales custom menu option, shown in Figure 1-10, is applicable to the Seed Sales and Review Sales views in the Set Targets and Approve Category Plan step under the Category Planning task. The WP Seed Sales flag needs to be checked in the Seed Sales view before running the custom menu option. The results of the custom menu option run then appear in the Review Sales view.

Custom menus are used run a batch or a special expression in the background that performs specific calculations, imports, exports, and so on, thereby activating a specific functionality in the application. Figure 1-10 shows an example of a custom menu option.

Figure 1-10 Example of Custom Menu Options

The Seed and Review Plan Sales and Like Item Mapping tabs (under the Assortment Planning @ Cluster task and Assortment Setup step) have two custom menu options available. Seed Sales and Seed Like Items are available for seeding sales to the new plan and seeding like item sales to new items respectively. The view is that of Seed Sales, which presents different versions of the plan including WP (Working Plan) along with LY (Last Year), and Fc (Forecast) plan measures.

Copy View Option

This option enables you to create custom views, by selecting and formatting the measures as required, and saving the custom views in the workflow.

The copy view option can be utilized by right clicking on any of the view labels at the bottom to create a copy of the existing view. The copied view can then be customized as needed. This option is useful in creating custom views per individual requirements.

Getting Started

Before using Category Management, be sure that you are familiar with how to access the application and use the Fusion Client user interface. If you need more information, see the Oracle Retail Predictive Application Server User Guide for the Fusion Client.