Modeling Autonomous Catalog for Electronic Commerce

The catalog function is an essential feature in B2C and B2B e-commerce. While catalog is primarily for end users to navigate and search for interested products, other e-commerce functions such as merchandising, order, inventory and aftermarket constantly refer to information stored in the catalog [1]. The billion-dollar mail order business was created around catalog long before e-commerce. More opportunities surface after catalog content previously created on paper is digitized. While catalog is recognized as a necessity for a successful web store, its content structure varies greatly across industries and also within each industry. Product categories, attributes, measurements, languages, and currency all contribute to the wide variations, which create a difficult dilemma for catalog designers. We have recently encountered a real business scenario that challenges traditional approaches of modeling and building e-catalog. We were commissioned to build an in-store shopping solution for branches in retail store chains. The local catalog at a branch is a synchronized copy of selected enterprise catalog content plus branch specific information, such as item location on the shelf. A key business requirement, which drives up the technical challenge, is that the in-store catalog solution needs to interoperate with the retail chains legacy enterprise catalog or its catalog software vendor of choice. This requirement reflects the business reality that decisions to pick enterprise software and branch software are usually not made simultaneously nor coordinated. As we learned that hundreds of enterprise catalog software, legacy and recent, is being used in industries such as grocery, clothing, books, office staples and home improvement, our challenge is to create a catalog model that is autonomously adapting to the content of enterprise catalog in any of the industries. A straightforward answer to the challenge is to build a mapping tool that will convert enterprise catalog content to the pre-designed in-store catalog, but this approach is highly undesirable. The difficulty lies within that it is impossible to predict the content to be stored. A simple example to illustrate the difficulty is by looking at what is stored in catalog for Home Depot, a home furnishing retailer, and by examining what is stored in catalog for Staples, an office equipment retailer. A kitchen faucet sold at Home Depot has information about its size, weight, material, color, and style. On the other hand, a fax machine sold at Staples carries attributes such as speed, resolution, and tone dialing. These attributes need to be stored …