About: 

• French multinational personal care and beauty retailer

• Channels: 2600 stores across 34 countries with 430 stores in North America plus e-commerce and department stores. 

• Blue Yonder, Dematic, Bastian

“With CognitOps Align, I can run my building on my phone. It’s a game changer.”

– Drew Sharp, Operations Manager, Sephora

See how CognitOps can help you meet your SLAs and cut warehouse labor costs…

This French beauty retailer brought a modern approach to the beauty industry with its “assisted self-service” sales experience. Unlike standard retail models for cosmetics, they encourage customers to test products in retail locations before purchasing. Keeping up with its massive omni-channel distribution and the variable volume at its warehouse required better labor predictability and order insight. 

Challenges: 

• Matching labor staffing and equipment activation with order volume and order profile variability.
• Aging orders cost the operation money and impacted customer satisfaction. 
• Inconsistent throughput during non-peak days harmed operational efficiency.

The retailer was faced with several warehouse operational challenges. Staffing plans were done using tribal knowledge, Excel spreadsheets with static inputs, and monthly forecasts – outdated information that harmed operational efficiency. Supervisors managed real-time labor allocation using siloed views of each department’s current work volumes and labor rates, lacking a holistic view of the needs across the operation.

Visibility to Aged Orders was limited to the day after an order had missed its ship date, so that if it were still not shipped after that date, it was essentially invisible. The warehouse also suffered from lack of visibility to variables that impacted cycle time at the put walls, e.g. order types, work rates.

Orders with exceptions were difficult to track down, and order release decisions were made based on historical, stale data. Combined, these challenges meant wasted time and labor across the operation and bottlenecks and constraints didn’t surface until gridlock hit.

Solution: 

• Plug CognitOps Align into real-time replicated data and activate workforce optimizations and time series data for order completion.
• Time-series-based storing of order transactions data. Algorithm to alert operations when an order will exceed cycle time of a specific fulfillment step.
• Machine Learning implementation of Graphical Models to optimize decisions such as size of processing batches.

With CognitOps Align in place, this multitude of challenges can be addressed. Staffing recommendations are now made using a combination of historical and predictive work rates and daily order forecasts. Labor is more efficiently allocated based on dynamic, worker-specific work rates, available work volume and work time. Exceptions are identified in real-time based on trending work cycle times by area.

Shipping Cutoff Notification alerts communicate which order types are at risk of missing cutoff in advance and there’s visibility to aging/at risk orders by order stage. Exceptions are identified in real-time based on trending cycle times by area, granting managers more granular decision-making recommendations and leading to greater operations efficiency.

Order release recommendations take into context the current state of the facility and upcoming order profiles and types to help minimize order cycle time. Equipment recommendations are made using composition of order pool, work cycle times by area, and real-time work rates. Equipment activation and order exception identification prevents gridlock.

Results:

• 90-Day implementation at first e-commerce warehouse; 2-week implementation per additional site
• Cost Per Unit reduction of 35%
• Order Cycle Time reduction of 30%
• Upgrade costs reduced by 50%

With its industry-leading SaaS solution, CognitOps was able to quickly address the retailer’s challenges in its e-commerce warehouses. In less than 3 months, the warehouse saw vast improvements in CPU and Order Cycle Time.

The retailer also benefited from complete order-release visibility from order release to shipment and the tracking and identification of performance trends by shift over time. It was also able to reduce upgrade costs by simply adding CognitOps intelligence layer over its existing warehouse systems. Both the warehouse supervisors and its customers are much happier with the service levels they’ve achieved.