How a Leading Bicycle Manufacturer Built a Global Data Foundation to Unlock Revenue Growth
A leading bicycle manufacturer produces and distributes well-known brands across multiple countries. With a decentralized structure built through years of mergers and acquisitions (M&A), the company operates across multiple European manufacturing units—each with its own systems, databases, and operational practices.
Challenges
- 400,000+ component and parts & accessories (P&A) records across siloed systems with no unified view
- Decentralized, M&A-driven structure led to a lack of standardized classification, limited pricing visibility, and no view of cross-unit inventory
- Manual data mastering would have taken about 2 years to complete
- No visibility into total supplier spend, limiting negotiating power
Outcomes
- 400,000+ SKUs mastered in 6 months—18 months faster than a manual approach that would have required 10 new hires
- Enabled cross-unit stock sharing during COVID-19 shortages, producing 1,000 additional bikes
- Unified supplier spend visibility enabled renegotiation of pricing with key suppliers
The bicycle manufacturer’s journey with Tamr began with a challenge familiar to many organizations that grow through acquisitions: a sprawling, decentralized data landscape with no shared source of truth. With manufacturing units across Europe, each running its own systems and databases, the company had accumulated hundreds of thousands of component and parts records, many of which described the same physical product under different names, codes, and supplier references.
We are a decentralized company with a lot of different systems and a lot of databases. There was a lot of common data across those databases, but no way to see it. That’s why we needed Tamr.”
The scale of the challenge was significant. The manufacturer’s components and P&A business alone included more than 400,000 SKUs, with variability that made manual reconciliation unrealistic. A single saddle, for example, might appear across systems as three distinct records: one for each of two different brands and one unbranded, all interchangeable on the production line, but invisible to any cross-group inventory view.
From Two Years to Six Months
Before turning to Tamr, the company estimated that a manual approach would take roughly two years, require parallel effort alongside daily operations, and involve hiring up to 10 additional people.
With Tamr, that timeline compressed dramatically. Tamr’s AI-native master data management (MDM) platform helped unify component data across the group by identifying similar and duplicate records and matching items that shared the same specification but carried different labels or codes. With that, human curators could confidently review and confirm low-confidence matches and build out trusted golden records for all global master data.
Within three months, we had the component data ready. Within six months, we had the P&A business done: a gain of one-and-a-half years. We could identify where components were available across the group and exchange stock between manufacturing units. In most cases, we found what we needed internally. We could solve production problems we otherwise couldn’t have solved.”
Resilience When It Mattered Most
The company launched its master data initiative just before COVID-19 disrupted global supply chains. As demand for bikes surged and component availability collapsed, the ability to see inventory across every manufacturing unit became a critical operational advantage.
As a result, the company produced approximately 1,000 bikes during the height of the pandemic that would otherwise have been impossible to complete. At an average sales price of €2,000 per bike, the immediate revenue impact was tangible.
Looking at the full picture, several years following the initial implementation, the company estimated that improved master data has enabled them to produce approximately 50,000 more bikes than would otherwise have been possible—a figure that, at a €2,000 average sales price, represented substantial incremental revenue. The actual value was higher still, accounting for the cost savings from optimized procurement and consolidated supplier negotiations.
See for yourself
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