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Fast Food Chain

We made fast food even faster.

Problem

The foodservice retailer had immature cloud automation capabilities and processes that could not support a DevOps environment.

Goal

It needed to achieve additional speed and agility, automate its operation, optimize its environments, and evole its AWS environment.

Solution

We provided an assessment and remediation plan for refactoring its automation environment, best practices for build and development pipeline automation, and designed, built and delivered an AMI factory.

About the Business

This leading global foodservice retailer is the world’s largest restaurant chain with over 37,000 locations in over 100 countries. More than 80% of its restaurants worldwide are owned and operated by independent local business men and women. The restaurant and its franchisees employ 1.9 million people worldwide and provides all locations, employees and customers with technology services and experiences to reach greater economies of scale.

The Business Challenges

An early adopter of public cloud for restaurant-based solutions, the company migrated its eCommerce platform to AWS in 2013 in order to scale the solution for worlwide demand. The platform utilizes many different data sources to provide visibility about product availability, pricing, outages, local taxes, etc. for all 37,000 restaurants.  The platform also provides a consistent interface worldwide that can be adjusted for local preferences for unique customer experiences for web-based ordering systems and delivery.  With APIs that interface with mobile apps and other third-party systems like Uber Eats, the company can adjust and expand easily. It began in Asia with a web-based ordering and delivery experience for its customers and wanted to expand the service globally using the eCommerce platform. However, the platform needed to be reengineered for elasticity, scalability and cost.  The cost of building and mainaining the global data centers necessary to deploy this initiative globally was cost-prohibited for the global operations team. The elasticity and cost efficiency of the cloud appealed to the company as well as the speed to which it could roll this intiative out globally on the cloud .

After migrating the eCommerce platform, it wanted to achieve additional speed and agility, automate its operation, optimize its environments, and evole its AWS environment.  The cumbersome process for provisioning AMIs was manual and prone to human error, which could put the global platform at risk. The AMI hardening process was inconsistent with the company’s established security standards and delivery times varied across operating systems requested. The foodservice retailer wanted to mature its cloud automation capabilities and processes to better support a DevOps environment.

The 2nd Watch Solution

After evaluating several other AWS Premier Partners, the company turned to 2nd Watch for its mature methodology, cloud-native DevOps capabilities, responsiveness and experienced team to guide it on its AWS journey.

2nd Watch’s Cloud Enablement Team provided an assessment and remediation plan for refactoring its automation environment, as well as best practices for build and development pipeline automation. 2nd Watch proposed a re-architected, more streamlined solution, including a consistent Terraform standard for implementation across the organization and foundational Terraform templates and scripts, that saved the company time and money over the other firms’ solutions. Using Jenkins and Ansible, 2nd Watch designed, built and delivered an AMI Factory, applying the foodservice retailer’s security and compliance standards to a standard AWS AMI during the hardening process. The images are then tested for functionality and tagged and published in a consistent manner for all business units across the globe. In this way, AMIs built on any AWS standard operating system can be incorporated into the AMI Factory.

After its eCommerce platform proved AWS proficient for its needs, the company made the strategic decision to move all consumer-facing applications to the cloud, including mobile apps, websites, marketing, analytics and data warehousing and its administrative apps, and is assessing the migration of its internal applications as well.

The company is leveraging 2nd Watch Managed Services to gain visability into the performance and costs assoicated with each workload and for world-class operational support for its AWS environment, which encorporates Amazon EC2, S3, RedShift, EMR, ElastiCache, RDS, CloudFront, and Elastic Beanstalk.

2nd Watch has further identified over $4 mllion in Reserved Instance purchases for the foodservice retailer  to reduce monthly costs.  Additonal optimization has been identified with orphanend resoucrses, non-well architected environments, waste, etc. that the previouse cloud service provider did not identify.

The Business Benefits

In addition to increased speed-to-market and scalability, the company is now able to reconcile its AWS invoicing at a fraction of the cost and get more grainularity and accuracy in its allocation model as well as save time crunching numbers, improving the overall customer experience. Working with 2nd Watch, the company lowered its operational costs, reduced new application build time and time to complete pipeline automation by 90%, and reduced the time it spends on monthly cost allocation/reconciliation by 95%.

2nd Watch’s AMI Factory solution provided the restaurant chain a fully automated, self-service process that incorporates the company’s security and governance standards. This same process is used across multiple Operating Systems and ensures only corporate-approved AMIs are provisioned and published for application team consumption. The AMI factory has reduced delivery time of production-ready AMIs by 75%. 2nd Watch offers the AMI Factory as a fixed bid and highly scalable solution.

Overcoming the obstacle of scaling for 37,000 locations, the fast food chain can now focus on cost optimization and its future plans for the cloud, which include IoT, artificial intelligence, and machine learning capabilities to cost effectively drive future innovation.