SHORT Case StudY

Advanced Logistics Data Management for a Fortune 500 Food Manufacturer through R/Shiny Applications

Developed enterprise-grade applications to streamline production rate tracking and baseline adjustments, limiting human errors and enhancing user experience.
Enterprise Solutions
American multinational food manufacturing company, S&P 500
Technologies used:
Amazon S3

What business problem
did we face?

The client faced challenges with their production rate tracking and baseline adjustment processes, both of which are connected to logistics data analysis. The manual, labor-intensive procedures led to delays, errors, and inconsistencies in production data. This hindered efficient decision-making and risked overproduction or underproduction, impacting the overall supply chain performance. The lack of automated data validation, common templates, and governance created new possibilities for creating new, robust solutions to standardize and streamline these processes across various locations.

The solution we

Appsilon developed two R Shiny applications to address these challenges.

App#1 standardized the production rate templates and automated data validation, ensuring compliance with mandatory fields and providing instant feedback to users. This reduced manual errors and enhanced data integrity.

App #2 enabled planners to accurately adjust and track production targets weekly, replacing the error-prone manual processes with a streamlined, simple interface. Both applications included comprehensive documentation and training for the client's data analytics and support team to ensure a smooth transition and user adoption in the future.

The impact of our solution
and its ROI

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- Time Savings: Analysts saved up days per month previously spent on manual data correction.

- Money Saving: Commercial tools licencing cost reduction per user.

- Increased Efficiency: Automated validation processes reduced data entry errors and improved data quality.

- Enhanced User Accountability: Empowered planners with tools to manage data inputs, resulting in higher data integrity.

- Improved Data Governance: Established a consistent, enterprise-wide data management process, reducing risks of overproduction and underproduction.


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Supply Chain Analytics Manager

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