Managing 10,000+ SKUs across 30 countries: How global brands do it
April 27, 2026Control large catalogs across systems, regions, and channels. Learn how you can build a scalable product data infrastructure.
According to McKinsey’s research on fighting portfolio complexity, 75% of company revenues come from just 13% of their product portfolios; however, many global manufacturers continue to expand their catalogs. A study from the Technical University of Denmark found that companies introduce an average of 1.7 new products for every one they retire.
If your organization operates across multiple markets and sales channels, you are likely aware of the consequences: product data becomes fragmented across systems, regional teams operate with inconsistent information, and the time to launch a new product is longer than necessary.
This article breaks down the specific problems that emerge when catalog complexity crosses a threshold, the tools companies use to manage it, and how brands operating at this scale have solved it in practice.
What happens when you’re managing large product catalogs across multiple markets
Product data stored in five different systems is described in five different ways. Regional teams fill the gaps with their own spreadsheets, compliance documentation falls out of sync across markets, and by the time a new product is ready to sell, the data needed to support it often isn’t. Here are the specific points where the breakdown typically occurs first.
1. Product data scattered across systems with no single version of truth
Most global manufacturers don’t set out to create data chaos; they accumulate it. Every new market entry, system upgrade, or acquisition adds another database, another spreadsheet, another ERP instance where product information lives separately from the rest.
Unmanaged SKU proliferation can increase a firm’s operational costs by 15-30%, and much of that cost stems from the effort required to keep disconnected data sources aligned.
Product descriptions get characterized differently across systems, images sit in separate repositories, and compliance data lives in spreadsheets that only one person on the team knows how to navigate.
The result is that your commercial teams, your engineers, and your marketing department are effectively working from different versions of the same product.
2. Mergers and acquisitions multiply the data problem
Growth through acquisition is common in global manufacturing and distribution, but it reliably produces one outcome in product data terms: you absorb another company’s systems along with its catalog.
Two ERP instances that don’t communicate, different naming conventions for the same product categories, and no shared taxonomy across the merged business make product data that looked manageable before a deal closes genuinely difficult to govern afterward.
Establishing a unified and accurate view of what the combined organization sells and to whom requires substantial groundwork before any expansion into new channels or markets can occur.
3. Selling across markets means managing multiple versions of the same product
A product sold in 15 countries often needs 15 different sets of regulatory certifications, safety documentation, and market-specific attributes, sometimes in 15 different languages. Managing that through regional spreadsheets and manual exports works at low volumes, but the workload compounds quickly as the catalog grows.
Only 20–30% of products in a typical portfolio contribute to profitability, according to the same SKU rationalization study, meaning a significant portion of your localization effort goes toward products that generate little return.
Getting market-specific requirements built into the product data structure, rather than handled manually at the point of distribution, is where most global organizations lose the most time.
4. Every channel you add creates a new set of data requirements
A dealer network, a B2B portal, a consumer website, and a print catalog don’t accept product data in the same format or at the same level of detail. Each channel has its own attribute requirements, update cadence, and consequences when information is wrong or missing.
Organizations that manage this manually, exporting product data from one system, reformatting it per channel, and pushing it out individually, find that the process breaks down as both the catalog and the channel count grow.
Errors that were occasional at a smaller scale become systemic, and the teams responsible spend an increasing share of their time correcting data rather than enriching it.
5. Manual processes turn product launches into bottlenecks
Product launch timelines at global brands are often longer than they need to be, and the delay frequently originates in product data workflows rather than in manufacturing or supply chain.
Enriching product information manually across a catalog that adds thousands of new SKUs each month, then formatting and distributing that data to multiple channels and markets, creates a bottleneck that compounds with every new product addition.
The downstream effect is that products are ready to sell before the data supporting them is ready, leaving sales teams, dealers, and digital channels waiting.
What tools should you choose for managing large catalogs?
Most organizations start with whatever is already in place, which usually means spreadsheets feeding into an ERP system, with someone manually bridging the gaps. Getting the right setup doesn’t mean replacing everything at once; it means understanding what each tool was actually built to do and where it stops being enough.
Here’s an honest breakdown of the most common options so you can figure out where your current setup is holding up and where it isn’t.
| Tool | Core job | Managed by | Reliable up to | Blind spots | Needs help from |
|---|---|---|---|---|---|
| Spreadsheets | Organizing and sharing product data manually | Anyone with access | Small catalogs, single market, one or two channels | No version control, no governance, breaks down fast with multiple contributors or markets | Everyone, because there is no system enforcing consistency |
| ERP systems | Managing operational and financial data across the business | IT and finance teams | Tracking inventory, orders, and transactional data at scale | Not built for commercial product content, channel-specific formatting, or localization | A separate system to handle content enrichment and channel publishing |
| DAM (Digital Asset Management) | Storing, organizing, and distributing digital assets like images and videos | Marketing and creative teams | Large volumes of digital assets across teams and channels | Manages files, not product attributes, relationships, or market-specific data structures | A product data system to connect assets to the right SKUs and channels |
| MDM (Master Data Management) | Governing master data across the entire business, including customers, suppliers, and products | IT and data governance teams | Enterprise-wide data consistency across multiple domains | Too broad to handle product content workflows, localization, or channel syndication specifically | Specialized tools for product enrichment and publishing |
| PIM (Product Information Management) | Centralizing, enriching, and distributing product content across markets and channels | Product, marketing, and ecommerce teams | Catalogs of any size, across any number of markets, languages, and channels | Needs ERP integration to pull in operational data and DAM integration for digital assets | ERP for product and inventory data, DAM for digital assets |
Global brands that solved large catalog management
If you recognize any of the problems above in your own organization, you’re not alone. Some of the world’s most recognized manufacturers and distributors have been in the same position, and the way they worked through it offers a practical reference point for what’s actually possible. Here’s what they did.
1. Fluidra: Product data scattered across 20+ legacy systems
Fluidra, a global leader in pool and wellness manufacturing, was manually creating every SKU in its database because its legacy system lacked integration with its ERP.
With 200,000+ SKUs spread across 20+ legacy systems and 160 branch offices, cross-functional teams had no shared data structure to work from, making collaboration across R&D, engineering, production, and marketing practically impossible. After implementing Inriver PIM, Fluidra consolidated its entire product catalog into a single, connected platform.
As their PIM Product Owner, Carolina de Rubertis, put it, the biggest achievement was the speed at which products reached market across all their websites, alongside noticeably better feedback on product catalog quality.
2. ADB Safegate: post-merger data chaos across 38 global offices
Following the merger of two major airport systems companies, ADB Safegate inherited 158,000 SKUs spread across isolated IT environments that didn’t communicate with each other. Without a shared taxonomy or a unified data structure, the risk of data chaos across 38 global offices was immediate.
Implementing Inriver PIM gave the company a single, organization-wide repository for product information, with a robust SAP integration that allows all materials and SKUs to flow in seamlessly from their ERP systems.
As Business Technology Manager Adriana Petrescu described, the flexibility and ease of use of Inriver enabled them to harmonize product information and improve efficiency across all departments.
3. Wolf Oil: selling across 100 markets in 15 languages
Wolf Oil, an independent lubricant manufacturer based in Belgium, needed to manage technical data sheets, safety documentation, and product content across 100 markets in 15 languages. Keeping that volume of market-specific compliance data accurate and consistent through manual processes was no longer viable as the company’s international footprint grew.
Integrating Inriver PIM with their ERP system and Bynder DAM gave their teams a single repository for all product and technical content, with automated syndication pushing accurate data across channels and markets simultaneously.
As Product Marketing Manager Francesca Alvino confirmed, customers and partners responded immediately to the speed and quality of data now flowing through to their website.
4. Brødrene A&O Johansen: Multi-channel data requirements across 10 webshops and print
Brødrene A&O Johansen, a leading Nordic wholesaler serving electricians and plumbers, manages 800,000 active SKUs, with 2,000 to 3,000 new SKUs added each month.
Pushing accurate product data across 10 webshops and printed catalogs simultaneously, while maintaining consistency across all of them, had become a significant operational challenge for the 50+ product assistants responsible for keeping information up to date.
After implementing Inriver PIM, the company cut product launch time from 2 to 3 days down to just 1 to 2 hours. As Head of Product Data, Lars Gronemann put it, Inriver became a foundational system that directly supports their core company strategy of getting products to market as quickly as possible.
5. Jacuzzi: Manual processes slowing product launches across 450+ retailers
Jacuzzi, a global manufacturer of hot tubs and hydrotherapy products headquartered in California, was managing 20,000+ SKUs across a dealer network of 450+ independent retailers, each with their own back-end systems and data format requirements.
Exporting and reformatting product data manually for each retailer made every product launch a time-consuming exercise, and the inconsistency in product content across channels was visibly affecting their direct-to-consumer performance.
After centralizing product data in Inriver PIM and automating syndication across channels, their DTC growth shifted from 4% to 14%.
As Senior Director of Digital and E-commerce Kyle Blades noted, PIM went from being the thing that got in the way of product launches to a critical tool that streamlines their normal processes.
Build the infrastructure your product data needs
Every company in this article reached a point where the volume and complexity of their product catalog exceeded what their existing systems could handle. The trigger was different in each case, whether a merger, a new market, or a doubling of the catalog, but the outcome was the same: product data became a bottleneck instead of a business asset.
None of them solved it overnight, but all of them started in the same place: getting their product data into a system built to handle it. See how Inriver can help you do the same.
See the Inriver PIM in action
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