AI + retail supply chain: what operations teams need to know now
Holiday 2025 sales are projected to climb 3.7%-4.2% from 2024, landing near $1.01-$1.02 trillion. Demand is strong, and expectations are higher: fast delivery, distinctive experiences, and packaging that feels premium. This is no longer about pushing orders through. It's about precision, speed, and consistency at scale.
SPS Commerce's approach: AI that runs on clean, standardized data
SPS Commerce is leaning into AI to improve inventory decisions, demand forecasts, and fulfillment coordination. The message is simple: AI works only as well as the data it consumes. Standardized, accurate data across partners is the backbone. SPS wants its network to be the link that lets AI workflows operate end to end.
Common language for orders, inventory, and fulfillment
To reduce friction between selling platforms and execution, SPS supports the Commerce Operations Foundation and the Order Network eXchange (onX). The goal: a shared operational language for order, inventory, and fulfillment data. For operations leaders, that means fewer one-off mappings, faster onboarding, and fewer avoidable misses.
New SPS capabilities that matter for operations
- AI-enabled fulfillment insights: Improve supplier performance, surface issues early, and make faster calls within an agile network.
- PDF-to-ERP conversion: Turn PDF orders into ERP-ready transactions to cut manual entry and reduce error rates.
- Centralized order management: Orchestrate orders across systems like SAP S/4HANA and Shopify from one place.
- Relationship Center: A shared space for retailers and suppliers to exchange specs, compliance rules, and operational expectations to speed up onboarding.
- Manufacturing Suite: Extend visibility upstream with supplier performance and quality signals as production footprints diversify.
- Supply Chain Performance Suite: Real-time dashboards with shared metrics so teams can act quickly, protect margins, and avoid strained retail relationships.
Omnichannel is the baseline-accuracy across every order type
Consumers expect speed and precision whether orders ship to home, store, or a pickup point. Suppliers now juggle multiple order types across multiple systems. The operations challenge is clear: keep data consistent, eliminate duplicate work, and close the loop between demand signals and fulfillment.
How to put this to work in your operation
- Make data standards non-negotiable: Pick a common schema for orders, inventory, and fulfillment events. Enforce it with every partner.
- Automate unstructured inputs: Use PDF-to-ERP conversion or similar capture to remove manual entry and shrink cycle time.
- Centralize order orchestration: Route by SLA, margin, and inventory position. One console, consistent rules.
- Connect suppliers with shared scorecards: Publish metrics weekly. Track fill rate, OTIF, defect rate, cancellations, and response times.
- Tighten forecast loops: Feed real-time sales and returns into AI models; recalibrate safety stock and reorder points faster.
- Plan for omnichannel exceptions: Create playbooks for partials, substitutions, split shipments, and late carrier scans.
- Adopt common language initiatives (e.g., onX): Reduce custom mapping, speed onboarding, and cut integration maintenance.
Why this matters for operations leaders
Margins get squeezed when decisions lag behind demand signals. AI can help, but only with disciplined data practices and shared visibility across partners. SPS is pushing tools that address both-automation at the edges and shared performance in the middle. If you run operations, this is a practical path to fewer exceptions and faster cash conversion.
If your team is building skills for AI-driven operations, explore focused learning paths here: AI for automation.
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