Retail Terminal Labor Scheduling With Shrink-Aware ML

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Solution Overview

Problem

Existing labor planning systems for self-service and point-of-sale terminals fail to optimize store operations and profitability by overlooking the impact of shrink events, leading to suboptimal resource allocation and increased labor costs due to higher shrink losses in self-checkout lanes.

Innovation Solution

An enhanced labor capacity machine learning model (MLM) that incorporates shrink factors alongside traditional considerations such as customer traffic and labor costs, dynamically adjusting staffing recommendations based on real-time shrink data to optimize overall store margins.

Engineering Contradictions & Design Principles

VSEngineering Contradiction Analysis

1Ease of repair

If self-service terminals (SSTs) are increased to minimize labor costs, then labor costs are reduced, but shrink losses increase

Engineering Contradiction:

Improvelabor costsVSAvoidshrink losses

Core Design Contradiction:

Ease of repairVSLoss of substance

Solution Approach 1:

The system dynamically changes the parameter of staffing levels based on real-time shrink data and store conditions. Instead of static labor planning, the system adjusts staffing parameters continuously to optimize the balance between labor costs and shrink prevention, allowing retailers to respond to evolving loss patterns while maintaining cost efficiency

Inventive Principle:

Principle #35Parameter changes

Solution Approach 2:

The system implements a feedback loop where real-time shrink data from multiple sources is continuously fed back into the labor capacity machine learning model. This feedback mechanism enables the system to learn from actual shrink events and adjust staffing recommendations accordingly, preventing the same shrink losses from recurring while optimizing labor allocation

Inventive Principle:

Principle #23Feedback

2Productivity

If traditional labor planning systems focus on minimizing labor costs, then labor efficiency is improved, but overall store margins are reduced due to unaccounted shrink events

Engineering Contradiction:

Improvelabor efficiencyVSAvoidstore margins

Core Design Contradiction:

ProductivityVSLoss of energy

Solution Approach 1:

The system merges previously separate considerations of labor cost optimization and shrink prevention into a unified labor capacity machine learning model. By combining multiple data sources including traffic data, shrink data, and labor data into a single predictive model, the system simultaneously optimizes both labor efficiency and store margins through integrated decision-making

Inventive Principle:

Principle #5Merging (Combining)

Solution Approach 2:

The labor capacity machine learning model serves multiple functions: it predicts customer traffic, forecasts shrink risks, optimizes staffing levels, and provides actionable recommendations all in one system. This multi-functional approach replaces multiple separate systems while improving overall store performance through coordinated optimization of labor and loss prevention

Inventive Principle:

Principle #6Universality (Multi-functionality)

3Device complexity

If static staffing levels are maintained, then operational simplicity is preserved, but the system cannot adapt to evolving shrink patterns

Engineering Contradiction:

Improveoperational simplicityVSAvoidadaptability to shrink patterns

Core Design Contradiction:

Device complexityVSAdaptability or versatility

Solution Approach 1:

The system transitions from static staffing schedules to dynamic, real-time staffing optimization. The labor capacity machine learning model continuously processes new shrink data and traffic patterns to adjust staffing recommendations dynamically, enabling the system to adapt to evolving shrink patterns while maintaining operational simplicity through automated decision-making

Inventive Principle:

Principle #15Dynamics

Data Source

PatentUS20260065186A1Optimizing resource scheduling for terminals to mitigate shrink and labor costs

Publication Date: 2026.03.05 NCR VOYIX CORP

AI summary

A system and methods for optimizing labor scheduling in retail environments by incorporating shrink factors alongside traditional considerations are provided. A machine learning model (MLM) receives traffic data, hourly labor data, overall shrink data, and proven shrink incidents as inputs. The MLM learns the labor-shrink causality across stores, connecting staffing levels to shrink impact. The MLM outputs a recommended labor scheduling plan for both assisted and self-checkout lanes, optimized for overall store margins rather than just labor costs. This approach balances labor efficiency with shrink prevention, potentially improving store profitability. In an embodiment, an application programming interface (API) is provided for consuming recommendations from the MLM as a service to integrate insights into business intelligence dashboards, providing a comprehensive solution for retail labor management.