Summary: Manufacturers manage complex product master data across regions and multiple critical enterprise systems, including Product Information Management (PIM), Enterprise Resource Planning (ERP), and Product Lifecycle Management (PLM) tools. Data quality issues frequently occur within and between these systems, causing costly inefficiencies, compliance risks, and operational headaches. Manual processes to detect and resolve these issues are slow, expensive, and error-prone.

The DQC Platform automates the identification, improvement, and classification of product master data, drastically reducing errors and streamlining processes. Manufacturers using the DQC Platform discover previously hidden issues of ~4.8% (and up to 10%) of their master data entries, achieve 50x faster improvement, and see bottom-line improvements exceeding 5 million euros through improved product discoverability on sales platforms and in web shops, reduced discounts and fewer returns.

Challenges:

• Product master data scattered across multiple systems, such as PIM (e.g., STIBO Systems STEP, Akeneo, Informatica), ERP systems (e.g., SAP S/4HANA), and PLM tools (e.g., Windchill).

• Manual checking and correction of product data are highly resource-intensive, slow, and prone to human error.

• Compliance with global classification standards (ECLASS, ETIM, GS1/GPC, HS/TARIC, UNSPSC) is mandatory but difficult to maintain accurately.

• Validation and cleansing of product data are critical before syndicating to sales channels and marketplaces.

• Accelerating product onboarding and managing variants efficiently is essential for competitiveness.

• Preparation for emerging regulatory frameworks like the Digital Product Passport (DPP) is increasingly crucial.

Exhibit:

Solution:

• The DQC Platform automatically identifies data issues within each source system (STIBO Systems STEP, SAP S/4HANA, Teamcenter, Snowflake) and flags inconsistencies across systems. 

• AI-driven automation corrects data errors and completes product attributes,  

• Automated classification of products to classifying systems (e.g., ETIM). 

• Based on semantic analysis (natural language processing), an AI agent automatically assigns new and updated products to global classification schemes, facilitating seamless onboarding of new product variants. 

• Human experts retain oversight, verifying AI recommendations and ensuring accuracy and compliance. 

Impact:

• Discovered ~25,000 previously undetected product data issues (4.8% of total data entries).

• Achieved 50x faster identification, correction, and enrichment of data. The Head of product master data reports that "we have reduced manual data checks by 95%, which frees up valuable resources for strategic projects."

• Delivered €5.3 million in tangible savings through reduced discounting, enhanced product findability, improved SEO performance, and fewer product returns.

• Bonus impact: Identified and facilitated the resolution of underlying technical system bugs, adding additional value for IT teams.