Front-of-Store Layout Planning for POS-SCO Lane Switching

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Solution Overview

Problem

Retail store managers lack a systematic approach to optimize front-of-store layout and labor capacity planning, relying on intuition with modern hybrid terminal products, leading to suboptimal labor costs and customer satisfaction.

Innovation Solution

A machine learning model that forecasts customer traffic and recommends optimal configurations of POS and SCO terminals in real-time, adapting to changing conditions based on historical data and current transactions.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Adaptability or versatility

If hybrid terminal products are used to enable quick conversion between SCO and POS lanes, then the store's ability to respond to traffic changes is improved, but the complexity of layout planning and labor capacity management increases

Engineering Contradiction:

Improveresponse to traffic changesVSAvoidlayout planning complexity

Core Design Contradiction:

Adaptability or versatilityVSDevice complexity

Solution Approach 1:

The system enables self-service decision-making by automatically analyzing traffic patterns, terminal utilization rates, and labor capacity requirements to generate optimal layout recommendations. The machine learning model processes historical data and real-time metrics to autonomously determine when to convert between SCO and POS lanes, reducing the need for manual intervention while maintaining adaptability.

Inventive Principle:

Principle #25Self-service

Solution Approach 2:

The system implements continuous feedback loops by monitoring terminal performance metrics, customer wait times, and labor utilization in real-time. This feedback is fed into the machine learning model to continuously refine predictions and recommendations, enabling dynamic adjustment of terminal configurations based on actual performance data rather than static planning.

Inventive Principle:

Principle #23Feedback

2Ease of operation

If intuition is used to determine when to switch lanes between SCO and POS, then the decision-making process remains simple, but labor cost optimization and customer satisfaction deteriorate

Engineering Contradiction:

Improvedecision-making simplicityVSAvoidlabor cost optimization

Core Design Contradiction:

Ease of operationVSProductivity

Solution Approach 1:

The system replaces the mechanical process of manual intuition-based decision-making with an automated machine learning model. The model processes quantitative data about traffic patterns, terminal utilization, and labor requirements to generate objective recommendations, eliminating the limitations of subjective judgment while maintaining ease of operation through automated decision support.

Inventive Principle:

Principle #28Mechanics substitution (Replace mechanical system)

Solution Approach 2:

The system transforms the decision-making approach by changing from qualitative intuition to quantitative parameter analysis. The machine learning model evaluates multiple parameters including historical traffic data, current terminal utilization rates, labor capacity constraints, and predicted demand to determine optimal lane configurations, enabling data-driven optimization rather than experience-based decisions.

Inventive Principle:

Principle #35Parameter changes

3Device complexity

If the front-of-store layout is fixed with limited POS terminal lanes, then labor capacity planning is simplified, but the ability to optimize labor costs and customer experience deteriorates

Engineering Contradiction:

Improvelabor capacity planning complexityVSAvoidlabor cost optimization

Core Design Contradiction:

Device complexityVSProductivity

Solution Approach 1:

The system transforms the static, fixed layout approach into a dynamic, adaptable planning system. The machine learning model continuously predicts optimal terminal configurations based on forecasted traffic patterns and labor capacity requirements, enabling the layout to evolve over time rather than remaining fixed. This dynamic approach allows optimization of labor costs while maintaining simplicity in execution through automated recommendations.

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260065187A1System and method for planning front-of-store layout

Publication Date: 2026.03.05 NCR VOYIX CORP

AI summary

In a system and method for providing a front-of-store layout recommendation for a retail store location having a plurality of terminals, historical transactions data for the retail store location is received and stored at a remote server which identifies, for each transaction, whether the transaction was at a point of sale (POS) terminal or a self-checkout (SCO) terminal. One or more training sets of data, based on the received and stored historical transactions data, is used to generate a machine learning model that provides a recommendation of a number of terminals to be configured as POS terminals and a number of terminals to be configured as SCO terminals. Current transactions data and parameter information is provided to the machine learning model to generate a current front-of-store layout recommendation. The current front-of-store layout recommendation is provided to a user via a user interface.