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Distribution Centers | Memphis, TN
Barrett Distribution saw an 8% increase in their Customer Satisfaction scores
Systems of Record: Ceridian Dayforce
Targets/Destinations: Snowflake
Targets/Destinations: Snowflake
The Challenge
Barrett Distribution was switching from a legacy timecard application to a modern HRIS platform, Ceridian Dayforce. Ceridian has its own time timepunch capabilities and this data needed to be replicated into Snowflake on an hourly basis, 7 days a week in order to ensure proper staffing levels were met for each work station throughout their 24+ distribution centers.
Forecasting, budgeting and actuals for staffing fed into a net margin model for each of their customers in order to track customer profitability. Centralizing the Ceridian Dayforce data into the Snowflake data cloud allowed Barrett to combine this data with other operational data to create and track customer profitability.
Hourly refreshes of data was imperative for managers to ensure the proper people-mix throughout a plant and to adapt to the ever-changing requests.
The Solution & Outcome
By partnering with DataLakeHouse.io, Barrett Distribution was able to further enhance the staffing to meet the customer requirements. This led to a 15% increase in the accuracy of customer orders and shipments. Thus, the customer satisfaction score increased by 8% which led to a 3% reduction in customer churn.
Distribution centers equipped with automated data collection systems streamline operations and maintain a high level of organization. Eliminating the need for manual paperwork in data entry, these centers operate with real-time data, freeing up their staff to focus on more value-added tasks. With reduced reliance on additional personnel, thanks to digitalization, the existing workforce has ample time to engage in more productive endeavors. This enhancement in productivity translates to increased profitability for the distribution center.
“With any HRIS system comes a great responsibility to ensure the sensitive data is only used to address proper operational business needs. DataLakeHouse.io gives us the ability to control who gets what Ceridian data and for me to know the HR controls this data access even when the data is replicated to our Snowflake data warehouse.”
Kaitlyn DaSilva
HRIS Manager, Barrett Distribution Centers
Hypergrowth and Siloed Data Challenge
With a 3-year growth of 108% we are early adopters of technology to ensure our warehouse management system is automated and is why we were one of the first to adopt IoT in each of our distribution centers. Without DataLakeHouse.io we would’ve had to build and maintain our own connectors which doesn’t make sense to the relative cost and ease of use with DataLakeHouse.io.
Barrett Distribution Center partnered with DataLakeHouse.io to solve several immediate priorities: decisions to streamline shipments, optimize staffing and measure workforce productivity, and improve several other aspects of warehouse operations.
Data Insight to Accelerate Sales
Efficiency in a distribution warehouse heavily relies on the collaborative efforts of its team. To ensure seamless operations, businesses harness the power of labor systems. These systems serve as invaluable tools for overseeing staff, from shift scheduling to performance monitoring, ensuring the optimal workforce size.
The software empowers companies to gain deep insights into their labor dynamics, providing leadership with valuable data to fine-tune strategies and elevate worker performance.
“DataLakeHouse.io accelerated our ability to start building data models on day 1 of the project using the Ceridian data. No other tool in the market was able to bring our Ceridian Dayforce data into Snowflake and to deliver the visibility we needed to make data driven staffing decisions.”
Jordan Johnsen,
Manager, Data & Analytics, Barrett Distribution Centers
Rather than more Excel workbooks, Barrett Distribution Centers used DataLakeHouse.io to build a comprehensive view to align warehouse staffing to customer shipments using DLH.io as a crucial part to their modern data stack.
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