1 Introduction

Effective category management (also referred to as merchandising) is the cornerstone of a successful retail business because it determines the variety and presentation of merchandise. This determination defines the customer's in-store experience. Category management involves managing individual product or merchandise categories as though they were independent business units, each playing a specific role in the retailer's goal to achieve their established business objectives. Broadly, this practice facilitates the determination of the following:

• Roles, strategies, and tactics and their designation into categories and sub-categories across the location hierarchy.

• Pricing and promotion strategies for different categories and sub-categories across the location hierarchy.

• Inventory-related decisions across categories and sub-categories across the retail chain.

• The retailer's standing in the market as compared to the competition.

• Key consumer segments contributing to the retailer's business and plan management of product categories as a result.

• Merchandise-mix or product-mix (also referred to as assortments) for a merchandise category (also known as class in Oracle Retail Merchandising System (RMS) terminology) and a sub-category (also known as sub-class in RMS terminology) across the retail chain - including the cluster and store level across the location hierarchy.

• Space-allocation at the micro and macro-level for different categories and sub-categories at the store and cluster level.

In recent years, retailers have experienced increased difficulty in achieving desired levels of same store sales growth, gross margin, and inventory productivity. This is partly due to smaller buying staffs, shorter product life cycles, increasingly savvy and demanding customers, and cutthroat competition.

In light of these issues, retailers are looking to service their customers better, drive profitable growth, and further differentiate themselves from the competition by tailoring their product offerings to the needs of their local customers. In the past, micro-merchandising or local market assortments were extremely complex, labor intensive, and yielded marginal results.

Oracle Retail Category Management (RCM) brings in the contemporary best-practices from the retail industry as part of its functionality. RCM is based on the RPAS platform. Key differentiating factors of RCM, that facilitate decision making in the category management business practice, include the following:

• A platform to facilitate end-to-end implementation of planning and tracking of Category Management practices based on retail industry best practices.

• Assortment Planning, for store clusters and stores, sometimes referred to as Assortment Rationalization.

• Assortment clusters, commonly referred to as clusters, to group stores across the geography to create category and assortment plans.

• Consumer segment perspectives based on the market's, or trading area's, demographics and psychographic data from third-party syndicated data suppliers.

• Insight into consumer buying patterns through Household Panel Data from third-party syndicated data suppliers.

• Market and competition perspectives based on external data sourced from third-party syndicated data suppliers.

• Consumer Decision Trees to understand the consumer's buying process (consumer segment-wise) in order to align the retailer's product, pricing, and promotional offerings accordingly.

• Item Performance Index (IPI) to rank an item's and a category's performance and derive custom assortments at the cluster and store level.

• Market coverage to understand the retailer's standing in the market from a product-mix perspective and derive custom assortments.

• Demand Transference driven by advanced science to fine-tune assortments.

• Incremental Curve driven by advanced science to derive assortments.

RCM consists of the following tasks:

• Category Planning - Used for analyzing a retailer's business from a market, competition, and consumer perspective. Category Planning is used to set targets and assign roles, strategies, and tactics for individual product categories. Category Plans are created at the sub-category level.

• Assortment Planning Analysis - Used to analyze an assortment's historic performance from a market, competition, and consumer perspective.

• Assortment Planning @ Cluster - Used to create Assortment Plans at the cluster level utilizing the concepts of IPI, Market Coverage, Incremental Curve, and Demand Transference.

• Assortment Planning @ Store - Used to create Assortment Plans at the store level utilizing the concepts of IPI and Demand Transference.

• Macro Space Optimization @Dept - Used to allocate optimum space to the Planogram (POG) departments or department zones in a store.

• Macro Space Optimization @Sub-Category - Used to used to allocate optimum space to the POG sub-categories under a POG department.

The Category Planning task enables the retailer to perform higher-level category planning activities and Assortment Planning tasks that facilitate the creation of SKU/Item-level Assortment Plans at the cluster and store level.

This solution supports the development of category business plans and assortment plans. It broadly follows the traditional eight-step Category Management business process with the inclusion of the consumer dimension in a few steps to provide the following:

• Analysis of market structure in terms of target shoppers/consumers and evaluation of trading area opportunity

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

• Role assignment to individual product categories

• A blueprint for strategic and tactical action within a category and across categories

• The ability to analyze by consumer segments (sometimes called the ninth step in the Category Management business process)

• A structured, measured set of activities designed to produce specified output, that is, the development and implementation of a written category business plan

• Consumer insight, which is core to this application, brought in by utilizing external market and consumer data sourced from third-party syndicated data suppliers

Consumer segmentation and store clustering can be utilized to tailor assortments to specific markets and consumer segments by providing a profile mix of who is shopping the store and trading area. Store clusters are typically created for each product category in a trading area based upon similarity in consumers, stores, product attributes, sales profiles, and demographics so that assortments can be generated at the store cluster level. Assortments can also be generated at the store level.

Visibility into category roles, strategies, tactics, and financial objectives ensure that SKU/Item level assortments align back to overall category-level objectives.

Contents of this Guide

This implementation guide addresses the following topics:

• Implementation Considerations

• Build Scripts

• Data Flow

• Script Integration

• Configuration Considerations

• Batch Processing

• Internationalization

• Data

Key Features of Category Management

Category Management is a disciplined process for retailers and their supplier partners to treat each category as a business unit with defined strategies and tactics, leveraging multiple data sources, consumer insights and segmentations, to improve the customer experience while delivering increased sales and profits.

Category Management provides the following features:

• Packaged POV on leading edge retail business process concerning category management

• Supports consumer-centric and customer-centric category planning and assortment processes

  • Leverages consumer decision trees

• Embedded forecasting capabilities

  • Enables forward-looking insights to drive planning decisions

• Guides category roles and strategies-driven pricing and promotion tactics

Skills Needed for Implementation

The implementer needs an understanding of the following applications and technical concepts.

Applications

The implementer should understand the interface requirements of the integrated applications and data sources for the master data, demand, and inventory history. For Category Management, the implementer needs this knowledge for the following applications:

• Oracle Retail Predictive Application Server (RPAS)

• Oracle Retail Advanced Science Engine (ORASE) (optional)

Technical Concepts

The implementer should understand the following technical concepts:

• UNIX system administration, shell scripts, and job scheduling

• Performance constraints based on the retailer's infrastructure

• Technical architecture for Category Management

• Retailer's hierarchical (SKU/store/day) data

• Category Management batch processes

• Setting up an RPAS domain

• A basic understanding of RPAS configuration and how to use the RPAS Configuration Tools

• Understanding of how RPAS rule language works

• Understanding of measures and dimension constructs

• Understanding of how Fusion Client works