The race against shrinkage losses

Safeguarding the holiday cheer Fractal’s hybrid solution resolves retail shrinkage woes with 90% predictive accuracy

THE BIG PICTURE

Defending the festive joy in retail Drawing level with shrinkage and theft challenges through timely data and targeted strategies Shrinkage and theft pose persistent challenges in the retail industry, with an average annual shrink rate of 5% to 10%. Traditional monitoring methods are often inaccurate and rely on outdated semi-annual reports. Retailers now realize the importance of timely and accurate data for effective loss prevention. Swift action is hindered by the time-consuming task of manually counting every store item. Therefore, a shift from longer periods to shorter turns is crucial for resource allocation and targeted strategies to address this costly issue.

THE CHALLENGE

Crossing the holiday hurdle with minimal losses Navigating shrinkage challenges with data-driven precision Our client, a leading off-price retailer, was grappling with significant losses due to shrinkage. They needed a timely & data-driven approach to resolve this challenge. Specifically, they needed to shift from semi-annual indications to monthly decision-making regarding shortages, provide early alerts to stores and stakeholders, and accurately identify the top categories most susceptible to shrinkage across various stores and regions. Our sustained engagement has led to a deep understanding and extensive insights into internal operations, fostering expertise in off-price retail dynamics. As a result, we were well-equipped to develop tailored solutions for challenges encountered.

SOLUTION

Crafting a defence against retail mischief 1. Engineering a hybrid solution that gave the client a pole position Fractal designed a hybrid solution, incorporating Python code, Excel sheets, and Power BI reporting to automate shrinkage prediction alongside the client's traditional physical counting method. The planning, design, and development spanned three months, with an additional two months for implementation and validation. 2. Beating shrink management through data-driven predictive modeling In five months (three for development and two for implementation), we established a timely, accurate, and data-driven solution for managing shrinkage. Our four-step process focused on improving prediction accuracy and operational efficiency.

WHAT WE PROVIDED

Steps

Actions

Output/refinement

Data gathering and categorization

The team collected essential data, including scanning information, prior shrink records, and store attributes. Split data into three subsets based on shrinkage percentages for focused model training. After cleaning the data to remove nulls and other anomalies, a simple linear regression was trained on each subset, assessing model quality using metrics like p-values and directional shrinkage numbers per their business implications. Refined division-level correlations were computed between actual and predicted shrinkage, leading to adjustments in predicted shrinkage percentages. When division correlations surpassed 50%, corresponding adjustments were made to trigger an alarm. Caps were also implemented- to eliminate pessimistic or overly high seasonal predictions, resulting in more accurate and actionable final shrinkage predictions in both percentage and dollar terms. This helps in informing strategic decisions and resource allocation for efficient operational responses.

Data preparation led to subsets for specialized model training, streamlining predictions based on shrinkage categories. Model quality evaluations enhanced prediction accuracy and refined models for reliable shrinkage forecasts.

Data cleaning, model training and evaluation

Prediction refinement and adjustment

Adjusted predictions resulted in more precise alarms & refined shrinkage estimates, thus fine-tuning predictions to make them more reliable & ensuring actionable & accurate insights.

Prediction caps and operational implementation

Implementing translated refined predictions led to actionable insights for better planning and resource allocation.

THE OUTCOME

The joyful outcome of retail vigilance Immediate impact

Our client rapidly benefited in retail shrinkage management. Periodic model indications, combined with active verification through their conventional process, led to our model achieving nearly 90% predictive accuracy at the store level and 85% at the category level. This success unlocked better inventory allocation strategies and provided invaluable insights, revolutionizing their operations.

90% Predictive accuracy at the store level

85% Predictive accuracy at the category level

Sustainable gains Over time, the client gains the ability to identify high-risk categories and stores, optimizing resource allocation. This shift is anticipated to swiftly slash shrinkage by approximately 10-20% within a year.

Fractal’s businesses include Crux Intelligence (AI driven business intelligence), Eugenie.ai (AI for sustainability), Asper.ai (AI for revenue growth management) and Senseforth.ai (conversational AI for sales and customer service). Fractal incubated Qure.ai, a leading player in healthcare AI for detecting Tuberculosis and Lung cancer. Fractal currently has 4000+ employees across 16 global locations, including the United States, UK, Ukraine, India, Singapore, and Australia. Fractal has been recognized as ‘Great Workplace’ and ‘India’s Best Workplaces for Women’ in the top 100 (large) category by The Great Place to Work® Institute; featured as a leader in Customer Analytics Service Providers Wave™ 2021, Computer Vision Consultancies Wave™ 2020 & Specialized Insights Service Providers Wave™ 2020 by Forrester Research Inc., a leader in Analytics & AI Services Specialists Peak Matrix 2022 by Everest Group and recognized as an & ‘Honorable Vendor’ in 2022 Magic Quadrant ™ for data & analytics by Gartner Inc. For more information, visit fractal.ai Fractal is one of the most prominent providers of Artificial Intelligence to Fortune 500® companies. Fractal’s vision is to power every human decision in the enterprise, and bring AI, engineering, and design to help the world’s most admired companies. About Fractal

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