The client is an international corporation specializing in track & trace process implementation, which ensures end-to-end supply chain monitoring and traceability.
Challenge
The Orion team developed a QA process using a manual and automated (Apium) approach to:
- Categorize erroneous data
- Identify patterns leading to process failures
- Inspect the QA environment to validate the process in production
The team also automated the bug reporting system throughout the entire product lifecycle and recommended improvements in the existing system based on bugs identified during usability tests.
By combining big data within the QA process, the end-to-end testing workflow and methodology (including reporting, metrics, and KPIs) helped the client automatically simulate every possible end-user action using Katalon Studio + Java. The Orion team also created re-usable blocks of automated tests, enabling the client to customize test cases.
Solution
The client brought in Orion to develop a digital solution. Our Subject Matter Experts (SMEs) and technical consultants hyper-collaborated with the client to assess the existing Configure to Quote (CPQ) business process, including the backend applications and infrastructure. Our team explored the option of implementing an off-the-shelf solution, but found that this would just introduce more complexity, tech debt, and custom integration. Instead, the team built a personalized platform that seamlessly integrated with the organization’s systems, streamlined the quoting process with e-commerce functionality, and provided valuable insights with analytics.
Tools used included:
- Grafana
- Superset analytical tools
- Python
- Postman
- Orchestrated SQL queries
Impact
The implemented automated QA process proactively identified gaps in the client’s business process and functionality. Within a year of implementation, errors were reduced by 70% and P1 incidents were reduced by 80%. Orion’s module-based automation scenarios covered more than 95% of track & trace E2E test cases, driving improved logistics as errors were identified in advance.
