SAN ANTONIO — A group of food retailers, wholesalers and manufacturers is calling for CPG manufacturers to “measure, validate and communicate” weights and dimensions of product cases before they are shipped to retailers.
“There are a lot of companies that do not do that — and that’s a problem,” said Beckey James, electronic commerce manager for food wholesaler McLane Co., Temple, Texas, during a panel discussion on data quality at the GS1 Connect 2013 conference here June 10-13. The conference was hosted by GS1 US, Lawrenceville, N.J., which oversees bar codes and other commercial standards.
McLane is one of the members of the Data Quality Framework Initiative pilot program, formed to develop best practices for dealing with product data inaccuracies that impact supply chain efficiencies and consumer information. Other participating companies include Ahold USA, Coca-Cola, Hershey, PepsiCo, Procter & Gamble, J.M. Smucker, Wakefern Food Corp., Wegmans, 1WorldSync, Gladson, GS1 US, ItemMaster and Strategic Solutions Inc.
Inaccurate data on cases is especially significant because it prevents trucks from being filled to capacity, causes delays at weight stations, creates problems in slotting cases at warehouses and leads to purchase-order discrepancies, pointed out Christine McMaster, director of product integrity, replenishment and merchandising, Wakefern Food Corp., who also spoke at the GS1 US panel discussion.
In addition, inaccuracies in “foundational” product attributes, including GTIN, UPC, brand, net content and unit of measure, can lead to “confusion about shelf price labels” among consumers, said McMaster. This data is often channeled from manufacturers to retailers and wholesalers via the Global Data Synchronization Network (GDSN), overseen by GS1, Brussels.
Retailers and wholesalers, McMaster added, can spend between 1,300 and 7,500 labor hours annually checking and correcting data inaccuracies created by their suppliers.
To address inaccuracies in case measurements and product attributes, the pilot group urged manufacturers to adopt a five-point best-practice process. The best practices, if followed, “will help to ensure the highest levels of data accuracy,” according to a white paper from 1WorldSync, Lawrenceville, N.J. “A coordinated industry effort is needed to help make the [best practices] a standard process for manufacturers moving forward.” They include:
• Manufacturers adhere to the following foundational product attributes in internal set-up: GTIN, UPC, brand, net content and unit of measure.
• Attribute owners are identified and accountable, with written control mechanisms documenting validation procedures.
• A single group and individual are accountable for shepherding and gathering item attributes from attribute owners to ensure that control mechanisms are followed.
• All new items are measured off a stable production environment, rather than off preliminary measures.
• Production measurements are communicated internally and externally.
Product data accuracy, which the food industry has been grappling with since the dawn of data synchronization in the early 2000s, “has not grown as fast as I would like,” acknowledged James. “Hopefully this [industry standard] will help it grow faster.”
One of the key causes for inaccuracies in case measurements is that preliminary data on products, supplied many months in advance of production to retailers and wholesalers, is not updated after the final production of the product. Many manufacturers “believe R&D is accurate and can’t possibly be different from what’s actually produced,” said James. “We know that’s incorrect.” In fact, she added, “nine times out of 10 [production data] is different from R&D specs.”
Thus manufacturers must actively measure and validate case measurements after the production process, the pilot group said.
Chris Lemmond, senior director,and commercial operations, 1WorldSync, Lawrenceville, N.J., who moderated the panel discussion, asked for more industry participation in the GS1 US Data Quality Industry Work Group. The group is developing requirements for a national data quality program that would include criteria for audits and certification; it is also seeking industry consensus to deploy data-quality validation as a requirement for using the GDSN.
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