Automation Workflow for Seasonal Assortment Planning and Curation

This workflow automates the high-stakes, repetitive bottleneck of seasonal merchandising planning. It ingests historical POS data, inventory positions, and external trend signals to forecast demand and generate a data-backed seasonal assortment. The operational upside comes from compressing planning cycles from months to weeks, reducing overstock risk, and improving initial buy accuracy. Implementation requires integrating with ERP (e.g., SAP), planning systems, and a Product Information Management (PIM) platform, with orchestration logic built on frameworks like LangGraph to manage multi-agent curation.

Implementation centers on an event-driven orchestrator that sequences specialized agents for forecasting, margin analysis, and content creation. Critical controls include human-in-the-loop approval gates for final assortment, confidence scoring on recommendations, and exception routing for edge cases. The architecture must include observability for model drift and integration with systems like Salesforce Commerce Cloud for publishing. This creates a closed-loop system where sell-through performance continuously refines future seasonal models, protecting margin and reducing markdown exposure.

This workflow automates the high-labor, error-prone process of seasonal assortment planning. It ingests historical sales, trend signals, and inventory positions to generate data-driven product recommendations and full catalog assets. The operational upside comes from compressing planning cycles from weeks to days, improving forecast accuracy, and enabling rapid, localized curation. Implementation integrates with ERP (e.g., SAP), PIM, and planning systems, using orchestration frameworks like LangGraph to manage state and data flow between specialized forecasting, content generation, and compliance agents.

The architecture requires robust controls. The Orchestrator manages agent execution, exception routing, and audit trails. A mandatory human review gate validates final assortments against brand and margin targets before publishing. Observability tracks key metrics like forecast-to-sales variance and content generation latency, feeding performance data back to retrain forecasting models. Rollout is sequenced by category or region, with governance ensuring all autonomous decisions are explainable and tied to a clear business rule.

Phase 1 establishes the data foundation, integrating historical sales (ERP), real-time inventory (WMS), and external trend signals via APIs. An orchestrator, built on LangGraph, ingests this data to run initial forecasting models, identifying high-potential product themes and inventory risks. This phase delivers a working forecasting engine and a clear baseline for manual planning cycle times, setting the stage for automation.

Phase 2 introduces the agentic curation layer, where specialized agents generate product selections, descriptions, and imagery briefs based on forecasted themes. All outputs route through a mandatory human-in-the-loop review in a platform like Jira before populating the PIM. The final phase automates multi-channel syndication and integrates a feedback loop from live sales data, enabling continuous optimization. This staged approach de-risks implementation while locking in ROI from reduced planning labor and markdown avoidance.

This workflow automates the high-stakes, repetitive bottleneck of seasonal assortment planning by integrating historical sales, trend signals, and inventory data into a single orchestrated system. It replaces manual spreadsheet analysis and fragmented decision-making with a centralized agentic layer that generates curated product recommendations and draft catalog assets. The operational upside comes from compressing planning cycles from months to weeks, enabling more responsive buys and reducing end-of-season markdown exposure through better demand alignment.

Implementation requires integrating the orchestrator with core merchandising systems like SAP, Oracle Retail, or a modern PIM. The forecasting agent uses retrieval-augmented generation (RAG) against historical performance and trend databases to justify its recommendations. Governance is enforced through configurable approval gates in the review stage, where merchandisers can override agent suggestions. Rollout is sequenced by category or region, with performance monitored via a centralized dashboard tracking forecast accuracy, sell-through, and margin protection.

Effective automation hinges on ingesting high-quality, structured data from ERP (SAP, Oracle), historical sales databases, and external trend APIs. The workflow must normalize this data, resolve SKU-level conflicts, and feed a forecasting model that predicts demand by region and channel. Poor data quality or latency at this stage directly undermines forecast accuracy, leading to overstock and missed revenue opportunities, making upstream data governance a non-negotiable investment.

Integration with the Product Information Management (PIM) system is the final control point. The curated assortment, including AI-generated descriptions and metadata, must be written back to the PIM with clear versioning and audit trails. This requires a robust API layer and orchestration logic (e.g., LangGraph) to manage the sequence, handle failures, and trigger syndication to e-commerce platforms only after all validation and approval gates are passed, ensuring catalog integrity.