Jun 15, 2026 | 10 minute read
written by Elastic Path
Summary: Manufacturing commerce has never been more important, or more achievable. Manufacturers face genuinely unique digital commerce challenges: sprawling product catalogs, complex configurations, ERP integrations, multi-geo pricing, and the ever-present threat of SKU explosion. Buyer expectations have risen sharply at the same time, and B2B buyers now demand the same speed, personalization, and ease they experience on consumer channels.
This guide covers why strong eCommerce for manufacturers matters, the platform features that make it work, practical tips for getting it right, and real examples of manufacturers who've done it well.
eCommerce for manufacturers refers to the systems, platforms, and strategies manufacturers use to sell products digitally — whether directly to end buyers (DTC), through dealer and distributor networks (B2B), or through a combination of both (B2B2C).
Manufacturing eCommerce is distinct from general B2B or retail eCommerce in several important ways. Manufacturers typically deal with:
When we talk to manufacturers about selling online, we often hear the same concern: our products are complex, our requirements are rigorous, and we're not sure a commerce solution can support our needs. These are legitimate concerns, but modern manufacturing eCommerce platforms are built precisely for this kind of complexity.
The case for manufacturing eCommerce has strengthened considerably over the past few years, driven by a fundamental shift in B2B buyer behavior.
The bar for B2B digital experiences is set by what buyers already encounter everywhere else. A manufacturer's storefront is being evaluated against Amazon, not against other industrial supplier sites. If a storefront is slow, hard to search, or short on product detail, B2B buyers will go elsewhere.
As IMI's Global Director of Digital & Marketing put it:
"Every buyer wants and deserves an exceptional online shopping experience. Just because we're a B2B seller doesn't mean we shouldn't offer D2C-level experiences."
Beyond buyer expectations, the competitive pressure is intensifying. AI answer engines like ChatGPT and Perplexity are becoming an entry point in B2B product research. Manufacturers whose catalog data is AI-ready, structured and accessible will surface in those results; those with fragmented or unstructured data may not appear at all.
Not all eCommerce platforms are built for manufacturing complexity. Here's what to look for when evaluating manufacturing eCommerce software.
A single sprocket may fit 27 different bulldozer models. A custom valve may require a dozen specification inputs before a valid SKU can be generated. Manufacturing eCommerce platforms need to support:
Buyers should be able to self-educate. If someone visits your storefront and leaves without finding the product or information they need, that's a conversion problem — and a catalog architecture problem.
B2B commerce pricing is never one-size-fits-all. Account-specific pricing, contract tiers, bulk order discounts, and loyalty pricing all need to be managed and surfaced accurately without custom development work for every customer segment. Platforms that separate pricing data from product data — rather than baking pricing into SKU definitions — handle this far more cleanly at scale.
For complex or high-value orders, buyers need a structured quoting workflow: request a quote, receive a configured price, approve, and convert to order. Manufacturing eCommerce platforms should support this natively, with rep-assisted paths for exceptions and self-service options for standard configurations.
Manufacturers run their business in their ERP. Inventory levels, pricing, order status, and customer account data all live there — and a commerce storefront that can't surface that data accurately in real time will undermine buyer trust quickly. Evaluate platforms on their integration architecture: API-first platforms with documented, stable APIs are far easier to connect to ERP systems than monolithic suites with proprietary integration layers.
Many manufacturers sell through channel partners as well as directly. A manufacturing eCommerce platform needs to support multiple selling models — B2B portals for distributors, DTC storefronts for end buyers, and co-branded or white-label experiences for dealer networks — without requiring a separate platform for each.
Enterprise B2B buyers often have complex organizational hierarchies: parent accounts with multiple subsidiaries, regional purchasing teams with different entitlements, and approval workflows that vary by order value or product category. Platforms that support shared carts, buyer impersonation, multi-org account structures, and configurable approval workflows handle this without bespoke development.
SKU explosion is one of the most underestimated risks in manufacturing eCommerce. When every product configuration generates a new SKU, a catalog of moderate complexity can spiral into tens of thousands of entries — becoming unmanageable for merchandising teams and performance-degrading for the platform.
The most effective mitigation strategies:
Separate product data from pricing data. When pricing is decoupled from the product record, you don't need a new SKU for every pricing variant. One product can carry multiple price books — account-specific, regional, promotional — without multiplying your catalog.
Use configurator logic instead of pre-generating SKUs. Rather than creating a SKU for every possible combination upfront, let configuration logic assemble valid combinations dynamically at the point of purchase.
Establish catalog governance early. SKU proliferation is far easier to prevent than to fix retroactively. Define attribute standards, naming conventions, and SKU creation policies before the catalog scales — not after it has already exploded.
Many manufacturers operate across multiple regions with meaningfully different pricing requirements — different currencies, different regulatory standards, different distributor agreements. Handling this at scale requires a platform architecture where pricing is managed as a separate, configurable data layer rather than hardcoded into each product record.
Membership programs and loyalty catalogs are equally important for manufacturers with repeat-purchase buyers. Account-specific pricing tied to purchase volume, loyalty tiers that unlock preferred rates, and personalized catalogs that surface only relevant SKUs for a given buyer — all of these drive repeat purchase rates and customer lifetime value without requiring one-off customization for every account.
IMI, a global motion control and fluid technology manufacturer operating across more than 50 countries, uses Elastic Path to support multi-geo and multi-currency pricing at scale — and doubled their gross merchandise value (GMV) as a result.
Direct-to-consumer is no longer exclusively a retail strategy. Manufacturers are increasingly launching DTC channels to own the end-customer relationship, capture first-party data, and protect margins that erode when sales run exclusively through distributors.
The business case for manufacturer DTC is straightforward:
B2B2C — where manufacturers support both direct and channel sales from a single platform — requires the kind of architectural flexibility that monolithic platforms struggle to deliver. API-first, composable commerce architectures support multiple business models within a single environment, sharing catalog, pricing, and order management infrastructure rather than duplicating it across separate systems.
AI is changing how B2B buyers find and evaluate products — and manufacturers are on the front line of that shift.
Buyers increasingly use AI answer engines to research products and compare options before reaching a storefront. For manufacturers with complex catalogs, this creates a new class of visibility problem: products that are well-documented for human browsing may be poorly structured for machine interpretation. AI systems consume structured data — clear product identities, normalized attributes, explicit relationships, accessible pricing. When that structure is missing, products become invisible to AI-driven discovery.
This isn't a distant concern. Johnstone Supply, the largest HVAC/R wholesale distributor in the US, saw this firsthand when they modernized their commerce platform to serve a mobile-first workforce of technicians. Orders placed through digital channels now contain more line items on average than counter sales — a direct result of investing in better product data and a better digital experience.
AI also changes what's possible on the merchandising side. AI-ready commerce platforms can now use catalog and customer data to automate personalization in real time — surfacing the right products, pricing, and content for each buyer without manual configuration for every segment. Manufacturers who invest in structured, clean product data today are building the foundation that makes AI-driven personalization possible.
For manufacturers sitting on large volumes of product data across legacy systems — inconsistently formatted, incomplete, or siloed — AI tools can now assist with cleaning and structuring that data, making migrations faster and ensuring catalog data is organized in a way that both merchandisers and AI discovery engines can work with effectively.
IMI operates across more than 50 countries with complex product specifications and regional pricing requirements. Using Elastic Path, they support multi-geo and multi-currency pricing at scale — delivering a fast, consistent buying experience across markets without accumulating technical debt. They doubled their GMV following the platform investment and are now applying AI selectively to improve product search across their acquisition-driven catalog.
Pella Windows & Doors connected Elastic Path to their ERP via an integration running in Amazon Web Services, enabling a lead and revenue-generating digital commerce experience that stands apart in their industry. The integration surfaces accurate inventory, pricing, and product data in real time — without the brittle custom middleware that typically creates ongoing maintenance burdens.
Johnstone Supply manages over a million SKUs across independently operated store groups, each with its own ERP and localized product assortment. Elastic Path unifies these through a central commerce layer — so contractors always see the right products and pricing for their location, while corporate teams retain visibility across the network. Digital orders now average more line items than counter sales, a direct result of the improved buying experience.
When evaluating manufacturing eCommerce platforms, these are the criteria that matter most:
eCommerce for manufacturers refers to digital commerce systems and strategies that allow manufacturers to sell products online — whether directly to end buyers, through dealer and distributor networks, or through both simultaneously. It includes the platforms, catalog architecture, pricing logic, and integrations that make complex manufacturing products sellable through digital channels.
The most common challenges are managing large, technically complex product catalogs without SKU explosion; handling account-specific and geo-specific pricing at scale; integrating commerce systems with existing ERPs; supporting both B2B and DTC selling models from a single platform; and ensuring product data is structured well enough to support AI-powered search and discovery.
Key features include complex catalog management with attribute-based filtering, product configurators, account-specific and geo-specific pricing, quote-to-cash workflows, ERP integration via stable APIs, multi-organization account structures, dealer/distributor portal support, and AI-ready product data architecture.
The most effective approach combines a product configurator — which guides buyers through valid specification combinations — with a pricing architecture that separates price books from product records. This avoids generating a new SKU for every configuration variant, keeps the catalog manageable, and surfaces accurate pricing per buyer without custom development for each account.
B2B2C refers to a selling model where manufacturers serve both business buyers (distributors, dealers, enterprise accounts) and end consumers from a single commerce environment. Rather than running separate platforms for each model, B2B2C architecture shares catalog, pricing, and order management infrastructure — with different storefronts or account experiences layered on top for each buyer type.
AI is affecting manufacturing eCommerce in two ways. On the buyer side, AI answer engines are becoming a product discovery channel — meaning manufacturers whose catalog data is structured and machine-readable will surface in AI-generated recommendations, while those with fragmented data may not. On the seller side, AI tools are automating catalog enrichment, personalization, and merchandising tasks that previously required significant manual effort.
The most effective mitigation is separating product data from pricing data, so pricing variations don't generate new SKUs. Configurator logic that assembles valid combinations dynamically — rather than pre-generating every combination as a discrete SKU — also keeps catalog size manageable. Establishing catalog governance policies early, before the catalog scales, is far easier than cleaning up SKU proliferation after the fact.
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