Rehrig Pacific Company transforms supply chain analytics with Amazon QuickSight

This is a guest post written by Robert Martin, Vice President of Technology at Rehrig Pacific Company.

At Rehrig Pacific Company, we’ve been at the forefront of supply chain and environmental solutions since our founding in 1913. As a fourth-generation family-owned business, our mission has always been to celebrate our people and empower them to create innovative solutions that effectively and responsibly move goods, resources, and ideas. In my role as Vice President of Technology, I lead a team dedicated to developing cutting-edge technology solutions that drive customer value.

In this post, I share how we transformed our analytics capabilities using Amazon QuickSight, enabling us to rapidly deliver insights and enhance our customer experience.

The challenge: Keeping pace with data growth

As an industry leader, we found ourselves grappling with an ever-increasing volume of data generated by our AI and Internet of Things (IoT) devices at the edge. Our existing approach of developing dashboards in React was becoming unsustainable, with simple dashboards taking weeks or even months to create. This slow turnaround was compounded by performance issues, because our dashboards struggled to handle the sheer volume of data, resulting in unresponsive user experiences.

Our customers were clear in their feedback: they needed a more efficient way to manage and interpret the vast amounts of data we were collecting. They were looking for AI-assisted analytics to help make sense of it all, and they wanted the ability to create and customize reports without relying heavily on our development team.

Choosing QuickSight: Speed, integration, and intelligence

After evaluating several major BI tools on the market, we ultimately chose QuickSight for three primary reasons:

• As an AWS product, QuickSight seamlessly integrated with our existing AWS infrastructure
• The powerful natural language capabilities and compelling future roadmap for Amazon Q in QuickSight aligned perfectly with our vision for AI-assisted analytics
• The embedding capabilities of QuickSight allowed us to incorporate powerful analytics directly into our product

Rapid deployment and immediate impact

The speed at which we were able to deploy QuickSight was remarkable. In just one 2-week sprint, we had our first QuickSight dashboard up and running. This rapid deployment allowed us to quickly address our customers’ needs and start delivering value almost immediately.

Our main operational performance dashboard, shown in the following screenshot, aggregates edge AI results for quality control audits of product cases on pallets, comparing them to orders. This dashboard has been instrumental in identifying SKUs with high error rates and pinpointing areas of the warehouse that require attention.

We’ve also implemented drill-down capabilities, shown in the following screenshot, allowing users to dive into employee KPIs for more granular insights. This level of detail helps our customers optimize their operations and improve overall efficiency.

Seamless integration and secure access

We’ve integrated QuickSight with Amazon Relational Database Service (Amazon RDS), with plans to expand to data lake sources in the near future. This flexibility allows us to handle diverse data types and volumes efficiently.

Security is paramount in our industry, and QuickSight has allowed us to implement robust access controls. We use single sign-on (SSO) for our internal employees and use Amazon Cognito for customer users, making sure that data access is both convenient and secure.

Embedding analytics for enhanced user experience

One of the most powerful features of QuickSight has been its embedding capabilities. We’ve seamlessly integrated QuickSight dashboards into our web application, Iris, providing our customers with a unified and intuitive interface for accessing critical insights.

This embedded analytics approach has not only improved the user experience but has also become a key selling point for our solutions. It showcases the value our edge applications provide and helps drive strategic operational initiatives for our customers.

Realizing the benefits: Speed, efficiency, and customer satisfaction

The impact of QuickSight on our operations and customer relationships has been significant:

• Speed to market – We’ve reduced dashboard development time by 80%, allowing us to create dashboards in days rather than months
• Improved customer satisfaction – Dynamic, real-time dashboard data has replaced static report emails, leading to increased customer satisfaction
• Ease of use – Setting up and embedding dashboards into our application has become significantly simpler, allowing us to focus on delivering value rather than wrestling with technical complexities
• AI-assisted analytics – The integration of Amazon Q in QuickSight has allowed us to provide an AI-powered analytics experience, meeting our customers’ demands for more intelligent data interpretation

The following screenshot is an example of Amazon Q in QuickSight. The context is drilling down into loading data by day of the week and identifying what day of the week has the largest percentage of loading errors so corrective action could be taken on that day.

Looking ahead: Expanding AI capabilities

As we look to the future, we’re excited about the possibilities that QuickSight opens up for us. Our next steps include enhancing the Q&A function to be more robust and tailored to our customers’ specific questions. We’re also exploring other generative AI tools that integrate with QuickSight to create a more conversational user experience for deeper analysis and forecasting. In partnership with AWS, we were able to get early access to Amazon Q in QuickSight scenarios, a new capability that helps simplify in-depth data analysis with AI-assisted guidance.

We feel this capability will increase the effectiveness of our data analysis and enable us to grow the edge applications at scale without having to scale people. Implementing a computer vision application with deep machine learning requires a large analytical effort to maintain a high level of object detection accuracy. Many hours are spent by human analysts sifting through images, identifying errors, annotating new objects, and preparing the data to train existing and new models. This is tedious time-consuming work. Using scenarios, we anticipate a 5–10 times improvement in analyst productivity. This productivity gain will allow us to scale more customers without having to scale analysts, which will increase our bottom line.

Conclusion

QuickSight has provided us with a powerful foundation for delivering analytics solutions that meet the evolving needs of our customers in the supply chain and environmental solutions industries. Its rapid deployment capabilities, AI-assisted features, and seamless integration with our existing AWS infrastructure have allowed us to transform our approach to data analytics.

As we continue to innovate and push the boundaries of what’s possible in supply chain technology, QuickSight will play a crucial role in helping us turn data into actionable insights. We’re excited about the future and the potential to create even more value for our customers through intelligent, responsive, and deeply integrated analytics solutions.

About the Author

Robert Martin is the Vice President of Technology at Rehrig Pacific Company. Rob has 25 years building tech products in the supply chain and waste/recycling industries. He has spent the last 18 years at Rehrig Pacific Company in technology and new product development roles. He has a passion for making products smart and improving the overall experience for the workers.