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RescueManufacturing & industrialCW-042-RS-MI

Assa Abloy: rebuilding manufacturing and industrial around catalogue depth.

A 5-week rescue for manufacturing and industrial, shaped around catalogue depth, catalogue governance, technical product information, connected operations and B2B, B2C, D2C and Trade buying.

Assa Abloy's operating context made continuity and recovery critical for the live platform, the teams using it and the channels depending on it.

5
Week project
Kickoff to go-live
3
Platforms
Adobe Commerce, Shopify Plus and Akeneo PIM
1
System integration
Microsoft Dynamics ERP +2 more
4
Commerce models
B2B, B2C, D2C and Trade
Read onWhat was actually wrong, what we did, and what could have gone wrong.
02
The problem

What was actually wrong.

Most commerce problems are not just front-end problems. They sit between how customers buy, how teams work, and how the systems behind the business behave.

Assa Abloy needed cleaner product data and a more maintainable catalogue so the Builders & trade business could trade with confidence.

This was not a brochure storefront. Buyers arriving for a specific item needed enough product information and context to identify it with confidence, while stock, account pricing and purchase history had to support the same buying decision.

Product information, catalogue structure and supplier feeds were likely sources of operational friction, with editorial and trading teams working around fragmented data.

The commerce layer had to sit cleanly alongside ERP, without turning every operational dependency into a launch risk.

Account-led ordering and self-serve buying had to live on the same stack without one audience compromising the other.

The work had to stabilise a live commerce surface without resetting what already worked for the business.

The platform change also depended on product information being structured, enriched and governed well enough to support the catalogue. Product data was a major workstream within the wider commerce delivery, not a separate outcome claim.

03
The risk

What happens if it isn't fixed.

When those gaps are left alone, the website becomes the place where operational problems show up. That can mean unclear data, pricing questions, repeated support queries and customers who cannot complete the job they came to do.

Unclear product, pricing or fulfilment information can create friction before an order is placed.

Most relevant to Builders & trade teams weighing similar platform decisions.

If catalogue and operational data drift, buyers can lose confidence in product information, pricing, stock and purchase history. That can delay or abandon an order, while internal teams absorb the uncertainty through manual checking and customer service.

For repeat buyers and trade accounts, uncertainty creates friction every time an order is placed again. Wrong-part risk, unclear account terms and a harder repeat-order path can frustrate buyers and move the burden back to account teams and support.

The cost of leaving this in place is rarely visible in a single quarter. It compounds across accounts, channels and renewal cycles.

04
The work

Five things, in order.

Delivery is not just a list of features. The order matters, because the wrong sequence can turn technical dependencies into business risk.

  1. 01
    Mapped the buying journey before the interface
    Started with how customers actually order here: account relationships, repeat-buy patterns and the operational context behind each purchase.
  2. 02
    Rebuilt the commerce foundation around how the business operates
    Stabilised the live commerce surface and replaced the parts that were failing without resetting customer expectations.
  3. 03
    Connected the systems that the storefront cannot work without
    The commerce layer had to sit cleanly alongside ERP without coupling the launch to every system on day one.
  4. 04
    Stabilised the product data feeding the live platform
    Product information, enrichment and catalogue structure were treated as a delivery workstream that enabled the commerce change. Technical data and downloads had to remain connected to the product context buyers used to identify the right item.
  5. 05
    Scoped the rules per audience, not per platform
    Account-led ordering and self-serve buying were shaped as distinct journeys on the same foundation.
05
Systems

Systems, one operational truth.

The customer-facing platform was one part of the operating system. The project also depended on operational data, product information, inventory and communication systems, with clear boundaries for what each one supported and what customers could rely on.
Shopify Plus
Customer-facing commerce platform
Provided the customer-facing commerce platform for catalogue, account and ordering journeys. The platform could only present information it received from the operational and product systems around it. It mattered because a mismatch could become visible through product, account, stock or order information.
Adobe Commerce (Powered by Magento)
Customer-facing commerce platform
Provided the customer-facing commerce platform for catalogue, account and ordering journeys. The platform could only present information it received from the operational and product systems around it. It mattered because a mismatch could become visible through product, account, stock or order information.
Akeneo PIM / PXM
Product information platform
Provided the product information platform used to structure, enrich and prepare catalogue data for connected channels. Product data work depended on clear boundaries between this platform, upstream data and each receiving channel. It mattered because a mismatch could become visible through product, account, stock or order information.
Multiple Commerce Sales Channels
Connected operational dependency
Supported the Multiple Commerce Sales Channels connection within the commerce operation.
Retail Syndication
Connected operational dependency
Supported the Retail Syndication connection within the commerce operation.
Microsoft Dynamics ERP
Operational business data system
Provided operational context for account, order, pricing and fulfilment data used by the commerce experience. Commerce depended on an agreed boundary between ERP-held business data and the customer-facing platform. It mattered because a mismatch could become visible through product, account, stock or order information.
06
Risk control

Where this could have gone wrong.

Difficult parts of a project need to be named early. That gives the team a shared view of the risks, the decisions needed, and the areas that cannot be left vague.
ERP dependency
Account, order, pricing and fulfilment data depended on the ERP boundary. A stale or ambiguous hand-off could surface as the wrong account context, order state or delivery expectation. How we held it: Define which operational fields the ERP owns, how commerce consumes them and how failed or delayed exchanges are identified before they affect an order.
Platform maintainability
Commerce and digital experience capabilities sat across more than one platform. Unclear boundaries could make routine change harder and allow overlapping ownership to create inconsistent behaviour. How we held it: Keep platform responsibilities explicit, document the joins and carry those decisions into support so future changes do not reopen settled architecture questions.
Account ordering
B2B and direct buying shared a platform, but account hierarchies, purchase history, documents, pricing and order paths could differ sharply by buyer. How we held it: Model account relationships and permissions explicitly, then test repeat-order and self-serve journeys against the correct customer, price and document context.
Product information
Catalogue structure, technical content and operational product data could move at different cadences. Drift risks putting incomplete, inconsistent or misleading information in front of a buyer choosing a specific item. How we held it: Separate ownership for product content, technical data, stock and price, then make each storefront dependency visible and reviewable through the product journey.
Timeline
The recorded project length set a fixed delivery context for a broad platform and integration scope. How we held it: Sequence decisions around the highest operational dependencies and flag any scope trade-off for editorial review rather than claiming an undocumented method.
Support ownership
After launch, unclear ownership across parts data, pricing imports, inventory feeds and account behaviour could make operational faults slower to understand and resolve. How we held it: Carry the system boundaries, data ownership and recovery decisions into support so the team inherits the operating model as well as the platform.
07
Outcome

Measurable, not adjectival.

The useful proof is not a bigger adjective. It is the project shape, the systems involved, the trading model supported and, where available, the numbers recorded from the work.

5
Week project
Kickoff to go-live
+80%
Team productivity and efficiency
Improvement recorded after launch
3x
System integrations
Microsoft Dynamics ERP, Multiple Commerce Sales Channels and Retail Syndication
12x
Managing complexity
Improvement recorded after launch
3x
Platforms
Adobe Commerce, Shopify Plus and Akeneo PIM
90x
IT adoption
Improvement recorded after launch
08
In their words

What the client said.

A client quote should support the case study, not carry it. The project story still needs to stay grounded in the work that was delivered.

Ultimately, iWeb has helped Assa Abloy become a PIM-minded organisation in which every team member is aware of the importance of the PIM system and where the system itself contributes to the success of our business.
Matthew Caffery, Solutions Architect & Application Analyst, Assa Abloy
09
After launch

What moved into support.

A project like this does not stop mattering at launch. The same catalogue, account, integration and trading logic has to keep working once real customers and internal teams are using it.

The project did not end when the platform went live.

Support mattered because the manufacturing and industrial still depended on parts data, pricing imports, account behaviour, inventory feeds, integrations and customer-facing information after launch.

Keeping the build decisions and system ownership visible gave the support team a clearer basis for tracing issues and maintaining the connected trading system after launch.

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