Why SKU Proliferation Hits Margins in 2026
Inventory costs stayed elevated through 2025, with the Logistics Managers’ Index reporting inventory cost readings in the high 70s while inventory levels were mixed. That cost pressure makes long-tail SKUs more expensive to carry than most teams realize. See the official monthly summaries for detail on the 2025 spikes in inventory cost components here. (the-lmi.com)
AI is moving into day-to-day supply chain tools, which lowers the effort to analyze SKU performance and customer risk at scale. Gartner expects half of supply chain management solutions to include agentic AI capabilities by 2030, a signal that decision support will keep improving in standard platforms according to Gartner’s 2025 brief. (gartner.com)
What AI Actually Does Differently
Think of AI here as disciplined pattern finding. It clusters sales histories to spot near-duplicate SKUs that only sell to one region or one distributor. It maps substitution relationships from technical services notes, claims history, and CPQ comments so you can forecast which orders will migrate cleanly if a code is retired. It scores service risk by customer and application, not in aggregate.
The models are simple enough to run on messy data. Start with gradient boosted trees or regularized regression for SKU profitability drivers and back them with interpretable rules mined from service tickets. You are not trying to predict the future perfectly. You are trying to make the next portfolio decision with less guesswork and fewer meetings that never end. If it feels a bit imperfect, that is normal in teh first cycle.
Data You Already Have That Is Decision Grade
- Twelve to twenty four months of line-level order history with customer, region, project type, and margin after rebates.
- Return reasons, field failures, and claim outcomes linked to SKU and installation context.
- Approved alternates, cross references, and “use with” rules from technical services and product management.
- Basic operations data per SKU such as changeover time, minimum run size, scrap rate, and set availability.
A Practical Eight Week Workflow
Week 1 to 2. Ingest sales, claims, and substitution notes from PIM or MDM, ERP, and ticketing. Normalize SKU lineage so supersessions roll up correctly.
Week 3 to 4. Train a baseline demand model by customer group, then run K‑means or hierarchical clustering on attributes and buyer usage to identify duplicate or low value variants. Build an account level substitution graph so you can simulate what happens if a SKU is retired.
Week 5 to 6. Score each candidate on three dimensions. Financial impact that includes true carrying cost, setup time, and scrap. Customer impact that reflects which key accounts buy it and whether a proven alternative exists. Service risk that captures claims exposure, warranty rules, and specification sensitivity.
Week 7 to 8. Review exceptions with sales and technical services. Lock decisions with a controlled rollout plan, price file updates, submittal templates, and distributor change notices. Monitor migration for ninety days and keep a fast reactivation path for any SKU that causes unplanned risk.
Guardrails That Protect Building Materials Customers
Tie rationalization to application contexts, not just part numbers. Floor coatings, fenestration, roofing membranes, and wire management components often sit behind project specifications, certifications, or color standards. Use AI to flag when the proposed substitute breaks a code requirement, accessory compatibility, or approved system warranty. This is where retrieval augmented generation and structured rules can work together. McKinsey notes that portfolio complexity often hides in component reuse and system interactions, so look across products, not in isolation see their discussion of portfolio performance with generative AI. (mckinsey.com)
Evidence You Can Show Auditors and Sales Leaders
Document how each cut candidate was evaluated. Link the SKU to its sales concentration, its nearest alternates, and a service risk score with concrete evidence. MIT researchers have shown practical approaches that combine financials with bill of material metrics to build rationalization candidates, which is a useful template for audit trails in manufacturing see this MIT study. (ctl.mit.edu)
Minimum Viable Tech Stack
You do not need a giant platform to begin. A data extract from ERP and PIM or MDM, a notebook environment, and a visualization tool can deliver the first pass. If your team already has a supply chain suite, you can likely embed clustering and propensity models directly. As mainstream tools add agentic workflows, expect more out of the box support for portfolio decisions as forecast in Gartner’s 2025 note. (gartner.com)
How To Measure Progress Without Overpromising
Track three families of metrics. Working capital released from long tail stock. Schedule stability through reduced changeovers and fewer small batch runs. Customer experience through on time and in full by account for migrated orders. Do a quarterly win or loss review on retired SKUs to see if demand truly shifted to substitutes or leaked to a competitor.
Common Pitfalls To Avoid
Killing a code that supports a high value system bundle even if it is low volume on its own. Treating all regions the same when local codes, climate, or color sets differ. Forgetting that distributors may have built their own kits around legacy SKUs. Over trusting a model without an exception path for sales and technical services to veto with evidence.
The Payoff For Construction Materials Manufacturers
Simplification rarely pleases everyone on day one. The payoff shows up in steadier schedules, lower expedite fees, and fewer quote exceptions. In a year when inventory costs are stubborn and capital is not free, a focused SKU rationalization program is one of the cleanest ways to improve resilience and service reliability. The companies that win in 2026 will retire the trivial many while protecting the vital few with data, not gut feel.
For more on how AI helps teams reason across huge SKU sets and interdependencies, see McKinsey’s guidance on using generative AI to expose hidden complexity in product portfolios here and keep an eye on the official LMI series for up to date inventory cost signals that influence carrying cost assumptions here. (mckinsey.com)
