Operating one of the largest temperature-controlled facility networks globally, with over 400 locations across North America, Europe, and Asia-Pacific, our client is among the world’s largest refrigerated warehousing and logistics providers. Specializing in warehousing, transportation, and supply chain management for the food industry, the company plays a critical role in maintaining the integrity of the food supply chain.
The client has experienced rapid growth through strategic acquisitions and organic expansion, significantly increasing its capacity and service offerings. In an industry that demands high efficiency, accuracy, and reliability for the safe storage and transportation of perishable goods, it sought to leverage AI and ML-based software solutions to predict demand, optimize routes, manage inventory, and enhance decision-making processes, thereby reducing costs and improving service levels.
In its quest for global expansion, the client identified significant opportunities to enhance operational efficiency through automation and optimization and started looking for a partner capable of delivering scalable software solutions that can be rapidly developed and deployed across various locations.
With a strong history of successfully implementing advanced technology solutions, a team of skilled professionals, and proven expertise in digital transformation projects and AI and ML-based tool integration, Brillio emerged as the ideal partner for the implementation.
Brillio adopted an incremental approach, beginning with proof-of-concepts (POCs) using various tools and platforms, evaluating different features, pros, and cons before establishing GitHub Copilot as the most suitable tool for a focused pilot with well-defined success factors and measurement criteria.
To initiate the implementation, an in-depth assessment of the client’s current software development processes was conducted, identifying key areas where GitHub Copilot could deliver the most value. These areas included rapidly reducing technical debt in existing code bases, improving and optimizing overall code quality, and accelerating the development of test automation scripts to keep pace with new feature development.
Based on this assessment, Brillio developed a customized implementation plan that aligned GitHub Copilot’s capabilities with the client’s specific requirements and operational goals. This plan included a product evaluation phase covering major company development tracks.
A three-phase approach was proposed to streamline the incremental rollout plan. This included an 8-week Pilot Phase, during which the Brillio team predefined key success factors and KPIs to measure speed, efficiency gains, and code quality. This was followed by a 3-month focused adoption phase, where specific product teams were onboarded, culminating in a larger enterprise-wide adoption. The intent was to gather sufficient knowledge and learnings from the pilot and focused adoption phases to facilitate a smoother enterprise adoption.
Additionally, Brillio designed and delivered comprehensive training sessions for the client’s development teams, covering effective utilization of GitHub Copilot, from basic functionalities to advanced features.
During the initial sprints, Brillio provided on-the-job support to ensure developers were comfortable using the new tool and could maximize its benefits. This support included pair programming sessions, real-time troubleshooting, and code reviews.
Brillio collected and documented metrics at the end of each sprint, providing an objective view of GitHub Copilot’s impact on improving the development pipeline. Key metrics captured included sprint velocity, production bugs, lower environment bugs, code smells, and code complexity.
The client’s developers experienced a significant increase in coding speed, approximately 25%. Tasks that previously took hours were completed in a fraction of the time, thanks to GitHub Copilot’s intelligent suggestions and automation capabilities.
Overall productivity saw a noticeable boost. Developers could focus more on creative problem-solving and less on mundane, repetitive tasks. This shift allowed for faster feature development and quicker turnaround times on projects.
The use of GitHub Copilot helped reduce cognitive load and fatigue among developers. By offloading routine coding tasks to the AI, developers felt less stressed and more energized, improving job satisfaction and lower burnout rates.
These efficiency gains provided significant business benefits, leading to cost savings or accelerated product delivery. Additionally, the increased product reliability over the long term added further value.
