Current businesses need ‘Redefined Best-In-Class Quality Strategy’ according to the research into trends and views of leaders. This is an ask for the next-gen ADM and testing services demands of complex systems in agile and DevOps initiatives.
- The Cultural Challenge Building and sustaining a strong culture in a hybrid working environment.
- The Organization Challenge Structuring the dispersion of organizational decision-making.
- The Collaboration Challenge Manifesting collective network view in daily actions and decisions.
- The Scalability Challenge Addressing an urgent need for new capabilities and skills like complex problem solving, critical thinking, creativity, and self-motivated learners.
- The Transformation Challenge Shifting from existing hierarchies, processes and policies to new form of power structures with the emergence of networked ecosystems.
- The Improvement Challenge Measuring improvement towards the goals.
Quality must play a wider role to meet these challenges.
Quality.NEXT is all about the hyper-automation of quality management to improve quality engineering, quality monitoring and quality outcomes as well as the impact of blending advanced technologies, like artificial intelligence (AI), machine learning (ML), and robotic process automation (RPA), on people, traditional processes, and traditional technologies. Eliminating waste and improving efficiencies is as much a principle of hyper-automated QE as it is of Lean Management. The implementation of Quality.NEXT initiatives enable quick releases and predictability, avoids the excessive cost and risk of getting it wrong!
Our service USP model of our Quality.NEXT service delivery platform that sets us apart from competition is explained below.
- Why use our service delivery platform to implement Quality.NEXT initiatives?
- How are we differently offering services to implement Quality.NEXT initiatives?
- What services do we provide to implement Quality.NEXT initiatives?
Why deploy our service delivery platform to implement Quality.NEXT initiatives?
Our 'Value-led, Analytics-driven, and Intelligent Quality.NEXT Service Delivery Platform’ brings portfolio of hyper-automated techno domain capabilities to implement Quality.NEXT initiatives. We use AI-powered, Lean-driven software quality engineering automation, process design, resources, and governance to eliminate waste and improve efficiencies. We provide quality governance led by Industry Q leaders augmented by agile credit score and technology-centric platform for software quality analytics, software quality intelligence and enterprise-wide software quality control. Our community of T-shaped Quality.NEXT engineers practice continuous learning of AI-powered, Lean QE skills with their teammates across organization-wide projects at our experimentation lab infrastructure. This enables them to implement things they learned at their project and share them with their project team for day-to-day continuous improvement efforts.
The use of our platform brings non-linear, discontinuous ‘Hyper-Automated’ QE technological leaps to new standard quality outcomes of continuous testing i.e., flattening cost and risk curve while improving predictability and release velocity.
Quality.NEXT service delivery platform reduces costs of the resource and delivery process. The risk increases the implementation of hyper-automated techno domain capabilities which leads to increase in predictability and release velocity
Below are some of the ‘AI-powered, Lean-driven QE’ case snippets that have flattened cost and risk curves while improving predictability and release velocity.
How are we differently offering services to implement Quality.NEXT initiatives?
Eliminating waste and improving efficiencies is as much a principle of AI-powered QE technologies as it is of Lean Management. Quality.NEXT maturity level enables companies that want to align their teams with new AI modalities to leverage Lean principles as they take that next evolutionary step.
We focus on flattening cost and risk curve while improving predictability and release velocity using the ‘Quality.NEXT Maturity Level’ of our ‘nSTAR: North Star Positioning Consulting Solution’ for next-gen ADM services and testing services.
The heart of our nSTAR is ‘Quantitative Scientific Approach to Operationalize Best-In-Class Quality Advantage
Please refer below URL(s) to know details about our ‘The Best-In-Class Quality Self-Assessment Questionnaire’.
Our Quality.NEXT initiatives marry Lean and AI to improve next-gen ADM services and testing services of complex systems. We have ‘Decision Hyper-automation Map’ to identify Quality.NEXT initiatives.
Coforge brings a risk-oriented framework for deciding when and how to allocate decision problems between human quality engineers and AI-powered decision makers. We have developed this framework based on the experiences that our clients, partners while implementing quality prediction systems. The framework differentiates problems along two independent dimensions: predictability and cost. Our ‘Hyper-automaton Decision Map’ shows Quality.NEXT initiatives for the various problems of next-gen ADM services and testing services, along with possible “hyper-automation frontiers” between human QE and hyper-automated QE appropriate decision problems. A hyper-automation frontier (represented by the dotted lines) is an upward sloping line that represents the existing boundary between acceptable predictability and error. A higher cost requires a higher level of predictability for hyper-automation. The convex frontier in the figure represents a more stringent hyper-automation barrier than the linear one. Changes in predictability and cost can nudge a problem in or out of the hyper-automation zone.
The table below showcases how our key Quality.NEXT initiatives are centered on the fundamental notion of flattening cost and risk curve while improving predictability and release velocity.
What quality engineering services do we provide to implement Quality.NEXT initiatives?
We provide quality engineering services through our 'Value-led, Analytics-driven, and Intelligent Quality.NEXT Service Delivery Platform We have embedded our matured QE service features in service delivery platform organizational design.
At Coforge, we provide ‘Product Quality and Risk Advisory and Consulting’ to work with our customers to co-create quality engineering transformational roadmaps. Our nSTAR is operationalised by our matured best-in-class quality assessment model and hyper-automated capability solutions to step change improvement in quality engineering.
Our 60+ solution accelerators and incubators enable continuous improvement in automation engineering, business assurance testing, enterprise and product testing, AI and ML infused testing with faster speed.
The improvements are governed by Agile credit score for measuring team maturity.
Our 50+ AI, machine learning and RPA use cases are the focused investments to bring quality intelligence and engineering efficiency in Agile and DevOps. During the last few years, we have applied AI, machine learning and RPA with lean in test automation, predicting test quality, prioritizing test cases, classifying defects, scientific risk-based test execution and end-to-end software quality governance.