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Product data quality in supply chains: the case of Beiersdorf

Kai M. Hüner (), Andreas Schierning (), Boris Otto () and Hubert Österle ()
Additional contact information
Kai M. Hüner: Institute of Information Management
Andreas Schierning: Beiersdorf AG, Supply Chain Data Process Management
Boris Otto: Institute of Information Management
Hubert Österle: Institute of Information Management

Electronic Markets, 2011, vol. 21, issue 2, No 7, 154 pages

Abstract: Abstract A number of business requirements (e.g. compliance with regulatory and legal provisions, diffusion of global standards, supply chain integration) are forcing consumer goods manufacturers to increase their efforts to provide product data (e.g. product identifiers, dimensions) at business-to-business interfaces timely and accurately. The quality of such data is a critical success factor for efficient and effective cross-company collaboration. If compliance relevant data (e.g. dangerous goods indicators) is missing or false, consumer goods manufacturers risk being fined and see their company’s image damaged. Or if logistics data (e.g. product dimensions, gross weight) is inaccurate or provided not in time, business with key account trading partners is endangered. To be able to manage the risk of business critical data defects, companies must be able to a) identify such data defects, and b) specify and use metrics that allow to monitor the data’s quality. As scientific research on both these issues has come up with only few results so far, this case study explores the process of identifying business critical product data defects at German consumer goods manufacturing company Beiersdorf AG. Despite advanced data quality management structures such defects still occur and can result in complaints, service level impairment and avoidable costs. The case study analyzes product data use and maintenance in Beiersdorf’s ecosystem, identifies typical product data defects, and proposes a set of data quality metrics for monitoring those defects.

Keywords: Case study; Data quality; Data quality management; Data quality metrics; Product data; Supply chain management; L15; L66 (search for similar items in EconPapers)
Date: 2011
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DOI: 10.1007/s12525-011-0059-x

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