Workforce Management

Company Profile

Fortune 500 Retailer

Industry

Retail

Function

Demand Planning Team

Region

Executive Summary

A leading US Retailer faced challenges in consistently allocating the correct amount of retail shift hours to their stores nationwide. The results were hugely expensive overages in some situations and short staffing that necessitated expensive overtime in others. To uncover unknown insights that led to these incorrect allotments, the Retailer implemented Chat enabled analytics, powered by Aible.

Challenge

With thousands of stores nationwide, the Retailer’s weekly plans for each store’s allotted shift-hours (for all positions) frequently exceeded the plans for overages or encountered staffing shortages which resulted in overtime to meet the hours required. This resulted in:

Costly Overages and Staff Shortages: Using more than the allotted hours resulted in millions of dollars of overages per region. Being short-staffed often resulted in locations not being able to meet desired staffing levels and using costly overtime to bridge the gap.

Where to Start?: With thousands of possible parameters that are involved in this nationwide scheduling process, there was no clear way to identify where to look first for improvement.

Solution

The Retailer implemented Chat enabled analytics with Aible to quickly identify the greatest influences and anomalies behind the scheduling overages. Now regional planning managers could make better-informed decisions by asking questions like, “What is causing overtime increases in North East District 42?”.

Meaningful Starting Points: With thousands of parameters being automatically evaluated and rank-ordered by statistical significance, the Demand Planning team can easily pinpoint where to start further exploring the influences of the overages and shortages.

Access to Crucial Insights: With the Aible platform, the Retailer’s teams can securely take the powerful new insights that would normally only be used in the central office, and allow it to be tapped in by everyone. From executive management to the individual store managers and planners.

Results

The Retailer, now armed with powerful new insights to better support the store-wide scheduling process, can systematically work through the biggest influences to whether a schedule plan is met or results in an overage. For example, looking at different store types, seasonality, or geographic location have been great areas of interest to prevent future overages and understaffing. This robust new system leverages the power of LLMs to make all future scheduling decisions as informed as possible.

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Case Study

Fortune 500 Company, Customer Churn

Analyzed 2B+ rows to identify areas of customer churn and enabled a chat application that reviewed over 2.5M variable combinations to find and summarize meaningful patterns

Read Use Case