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More retailers are investing in automated planogram solutions to reduce manual work, accelerate merchandising updates, and ensure consistent shelf execution across their retail chain.
As product assortments become more complex and in-store operations grow increasingly demanding, the focus has shifted. Today, the challenge is no longer simply generating planograms faster, but managing the entire visual merchandising process at scale.
How much of the merchandising expertise developed by managers over the years can be embedded into an automated system? And how much control should retailers retain over the final outcome?
Gianni CASSANO
Country Manager of Retano
“Retano Shelfplan is an advanced decision-support platform whose effectiveness depends on the quality of its configuration, the business rules it applies, and the retailer’s ability to translate merchandising expertise into the system,” explains Gianni Cassano.
“The same topics come up during almost every implementation: master data quality, merchandising rules, support for different store formats, and the level of control retailers want to maintain. Every retailer has its own merchandising priorities, operational exceptions, and execution standards. That’s why we work closely with customers throughout implementation — from system setup to user training — to ensure the platform reflects their business processes rather than forcing them to adapt.“
“Our master data isn’t perfect”
High-quality product data is the foundation of any automated planogram solution. Product dimensions, packaging orientation, assortment attributes, category hierarchies, and merchandising constraints all play a critical role in generating layouts that can be executed consistently in stores.
In reality, however, ERP master data is rarely complete or perfectly structured. Inconsistent product descriptions, inaccurate dimensions, missing attributes, and information scattered across unstructured fields are common challenges.
For this reason, every Retano Shelfplan implementation includes master data normalization and enrichment, helping retailers unlock the value of information that already exists within their enterprise systems.
Much of the data required to build an effective planogram—package size, product format, unit count, packaging type, and physical dimensions—is often embedded within product descriptions. Retano Shelfplan uses automated data extraction to identify, interpret, and structure this information, significantly reducing the time required to prepare master data while minimizing the workload for merchandising teams.
“Our stores don’t all use the same fixtures”
Few retail chains operate with completely standardized store fixtures. Even within the same banner, stores often feature different shelving systems, shelf depths, fixture types, and floor layouts.
Rather than creating a limitation, this complexity is where automation delivers the greatest value. Retano Shelfplan uses parameter-driven fixture models, standardized templates, and automated configuration import tools to model existing store environments efficiently. These fixture models are then assigned to store clusters with similar characteristics.
Instead of creating a unique planogram for every individual location, the system generates layouts for groups of comparable stores. This approach allows planograms to adapt automatically to each store’s physical environment while maintaining a consistent merchandising strategy across the retail network.
“What if the system doesn’t merchandise products the way we do?”
This is one of the most common concerns, particularly among experienced managers who have spent years building planograms manually. In practice, merchandising decisions are rarely driven by sales data alone.
Strategic SKUs may require permanent shelf presence despite modest sales. Key brands often need guaranteed visibility. Entire categories may follow merchandising principles that take precedence over short-term sales performance.
Retano Shelfplan allows retailers to convert this expertise into configurable business rules.
Retailers can establish minimum facings for strategic products regardless of sales, reserve shelf space for priority brands, or apply merchandising priorities based on broader business objectives. The same flexibility extends to category layout. Some store clusters may require vertical merchandising, others horizontal layouts, while others benefit from hybrid approaches. All of these strategies can be configured directly within the platform.
“Is the process fully automated, or can we still make manual adjustments?“
In Retano Shelfplan, planograms are generated automatically, but retailers retain complete control through configurable review and approval workflows.
Before publication, category managers and visual merchandisers can review each planogram and make manual adjustments whenever necessary.
For example, layouts can be modified to accommodate local promotions, temporary stock shortages, assortment changes, or store-specific operational requirements. If these scenarios occur regularly, they can be converted into permanent business rules that the system automatically applies during future planogram generation. This combination of automation and human oversight is what makes shelf automation effective in complex retail environments.
“The planogram is approved. What happens next?”
The next step is execution. Once approved, planograms are distributed across stores, implemented on the shelf, and verified to ensure merchandising compliance.
This is where many retailers continue to face significant operational challenges. Manual store audits, supervisor visits, and photo-based compliance checks are often inconsistent, labor-intensive, and inherently subjective. Reviewing even a single photo report can take several minutes; across hundreds or thousands of stores, the process quickly becomes difficult to scale.
Early computer vision solutions attracted considerable attention but often struggled in real-world retail environments. They typically required large numbers of images, extensive manual intervention, and performed inconsistently under changing lighting conditions, viewing angles, or packaging updates.
Retano’s implementation experience shows that merchandising excellence is achieved by combining automated planogram generation with intelligent execution monitoring. Together, Retano Shelfplan and Retano VeriShelf AI create a closed-loop merchandising process — from strategy and planogram creation to real-world shelf verification — enabling retailers to manage visual merchandising as a continuous, measurable, and scalable process across every store.
