Retail Merchandising Planner

Part of the worlds-biggest-software-project initiative.

An AI-native, open-source platform for merchandise financial planning, range and assortment optimisation, open-to-buy management, and visual merchandising — giving retailers of all sizes tools that today only enterprise incumbents provide.

Retail Merchandising Planner is a unified planning platform for buyers, planners, and merchandise managers who need to decide what to stock, where, in what quantities, and at what price — and then track real trading performance against those plans. It replaces the fragmented combination of spreadsheets, disconnected point solutions, and expensive enterprise suites that most retail organisations struggle with today.

Why Retail Merchandising Planner?

Enterprise tools price out mid-market retailers. Blue Yonder, Oracle Retail, and RELEX are built for large enterprises with significant licensing costs, long implementation cycles, and dedicated planning teams — leaving mid-market and growing retailers without viable options.
The market is almost entirely proprietary. OPENCatman is the only open or free option identified in the landscape, and it lacks AI-driven optimisation, demand forecasting, and compliance verification. There is no credible open-source alternative that covers the full planning workflow.
Financial and assortment planning are disconnected. Most organisations still run OTB in spreadsheets and range planning in a separate system, creating version-control problems and misaligned buying decisions. A unified platform eliminates this gap.
Visual merchandising is detached from financial plans. Financial targets that cannot be mapped to actual store fixtures and planograms remain theoretical. Few tools bridge the financial plan and the physical store layout.
In-season response times are too slow. Many retailers still rely on weekly reporting cycles to detect sell-through deviations. Real-time OTB tools connected to live EPOS data can cut response times from weeks to days.

Key Features

Merchandise Financial Planning & Open-to-Buy

Top-down financial target setting by department, category, and season
Gross margin planning and sell-through targets
Open-to-buy budget control with real-time actuals vs. plan comparison
Automated alerts when sell-through deviates from plan
Scenario analysis for promotional uplifts, new store openings, and range rationalisations

Range & Assortment Planning

Attribute-based assortment analysis (colour, size, price tier)
Channel and store-cluster assortment variation
Size curve planning and option depth management for fashion and footwear
Demand forecasting integration for data-driven range decisions
Store-specific micro-optimisation based on local demand and demographics

Visual Merchandising & Planograms

Cloud-based 2D and 3D planogram design and visualisation
Multi-store planogram management with cluster support
Drag-and-drop design interface with real-time 3D preview
Mapping products to store fixtures to align financial plans with physical layouts
Sales data overlay for performance-informed product placement

Field Execution & Compliance

Mobile field execution app with photo capture
AI-powered image recognition for planogram compliance verification
Predictive stockout and shelf-gap detection
Task assignment and progress tracking for distributed field teams
Automated corrective action recommendations

Allocation & Replenishment

Stock distribution based on demand signals, size profiles, and store capacity
Automated replenishment when stock falls below threshold
Integration of demand and supply alignment across the store network

AI-Native Advantage

Current solutions are either rule-based or offer narrow AI capabilities bolted onto legacy architectures. An AI-native approach transforms the core workflow: demand forecasting models that incorporate promotional calendars, weather, trend signals, and new product introduction uplift replace static historical averages. Automated planogram generation goes beyond simple optimisation rules to incorporate local market factors, customer traffic patterns, and competitive dynamics. Compliance verification moves from binary correct/incorrect photo checks to granular confidence scoring with performance impact predictions. Natural language query interfaces democratise data access for non-technical planners who today depend on analysts for ad-hoc reporting.

Tech Stack & Deployment

Deployment modes: Cloud-hosted SaaS with self-hosted option for organisations with data sovereignty requirements
Mobile: Native mobile app for iOS/Android field execution, photo capture, and compliance workflows
Integrations: API connectivity with ERP, POS, and inventory management systems; real-time EPOS data feeds for in-season OTB management
Architecture: Cloud-native, browser-based planning interface requiring no desktop installation; role-based access control for multi-team collaboration

Market Context

The retail merchandise planning software market is served by a small number of enterprise vendors — Blue Yonder, Oracle, RELEX, Centric Software, and o9 Solutions — with significant licensing costs that effectively exclude mid-market retailers. Gartner Peer Insights and industry surveys identify strong demand for AI-driven planning tools, yet the only non-proprietary option (OPENCatman) covers only basic planogram design without forecasting or compliance capabilities. Primary buyers are merchandise planners, category managers, and retail operations leaders in fashion, footwear, grocery, and general merchandise.

Project Status

This project is in the research and specification phase.
Contributions, feedback, and domain expertise are welcome.

Contributing

We welcome contributions from developers, domain experts, and potential users. See CONTRIBUTING.md for guidelines.

Important: All contributions must be your own original work or clearly attributed open-source material with a compatible licence. Copyright infringement and licence violations will not be tolerated and will result in immediate removal of the offending contribution. If you are unsure whether a piece of code, text, or other material is safe to contribute, open an issue and ask before submitting.

Licence

Licence to be determined. See discussion for context.