Building a "Trusted Source"

Data synchronization issues are no small potatoes for McCain Foods, which produces nearly one-third of the world's French fries, so the company is moving to implement a "trusted source" for product information in its Canadian division as a way of...


Data synchronization issues are no small potatoes for McCain Foods, which produces nearly one-third of the world's French fries, so the company is moving to implement a "trusted source" for product information in its Canadian division as a way of improving customer service and gaining competitive advantage.

For Don Luby, product data synchronization is all about one thing: customer service. "It'll make us more agile in response to changing demands from our customers," says Luby, who is director of information technology (IT) planning and e-business at Canadian-based McCain Foods Limited.

Of course, when the McCain family founded the company in Florenceville, New Brunswick, in 1957, data synchronization probably was not much of an issue. That year, the company's 30 employees produced about 1,500 pounds of product an hour and earned sales of $152,678. Now, however, McCain's 18,000 full-time employees around the globe put out not only more than 1 million pounds of potato products every hour, but also green vegetables, desserts, pizzas, beverages, oven meals and other food products, racking up worldwide sales of $4.8 billion for the fiscal year ending last June 30.

All those products — including several dozen different French fry varieties — add up to a big serving of product attributes, everything from packaging and shipping information to nutritional, recipe and cooking information. As a global enterprise, McCain regularly receives requests for product information from other companies, its customers and regulatory agencies in countries around the world, in addition to requests to provide information to third parties, such as online trading exchanges. More recently, customers have also requested that McCain provide product data to industry-supported data repositories like ECCnet, the Electronic Commerce Council of Canada, a not-for-profit group dedicated to promoting and maintaining product data standards.

Each customer or other requesting entity typically has its own format requirements, which has made the task of responding to queries in a consistent manner all the more complicated. For instance, McCain sells those small juice boxes that parents pack in their children's lunchboxes. The juice boxes are bundled in packs of three, then four packs of three go into a store pack of 12. Six store packs make up a carton, for a total of 72. One customer might want McCain to use "one carton of grape juice" for the quantity field in the product description, while another might view the juice as 72 individual cartons, still another might see 12 cartons of six or six cartons of 12, and so on, multiplied times the couple of hundred different products that McCain offers.

The dispersion of product information throughout the company has further complicated efforts to meet customers' requests. Much of McCain's product data resides in the company's enterprise resource planning system, but a good deal of the product attribute information sits in disparate systems in the company's research and development or marketing departments in Excel spreadsheets or Access databases elsewhere in the enterprise. And that, according to Luby, is where the trouble starts. Because the data are dispersed among different systems, fulfilling an information request requires tracking down the correct set of data from the appropriate system, then ordering and formatting the information as required by the requester. That is, each request essentially requires a customized response.

At the same time, similar requests coming into sales or marketing people in different locations within the company would trigger similar efforts. "Basically, one salesman in one office might be doing it for one customer, and a hundred miles away somebody's doing basically the same thing for another customer," explains Luby. "So not only is it just manual effort, there's potential duplicate effort." And because much of the effort to meet the requests is semi-manual, the data provided are subject to errors and transposition, with incorrect data holding the potential for raising issues with customers, exchanges or industry repositories.

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